From ae64a954e727ab6f6a87aa52ab68f7feb49c5fb7 Mon Sep 17 00:00:00 2001
From: Ethan Bass <ethanbass@gmail.com>
Date: Tue, 17 Sep 2024 16:04:24 -0400
Subject: [PATCH 01/17] fix: wrong time zones returned by `strptime`

---
 R/grabMzmlFunctions.R        | 3 ++-
 tests/testthat/test_chroms.R | 5 +++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/R/grabMzmlFunctions.R b/R/grabMzmlFunctions.R
index c59ff10..21eb5f6 100644
--- a/R/grabMzmlFunctions.R
+++ b/R/grabMzmlFunctions.R
@@ -283,7 +283,8 @@ grabMzmlMetadata <- function(xml_data){
   time_node <- xml2::xml_find_first(xml_data, xpath = "//d1:run")
   time_val <- xml2::xml_attr(time_node, "startTimeStamp")
   if(!is.na(time_val)){
-    time_stamp <- as.POSIXct(strptime(time_val, "%Y-%m-%dT%H:%M:%SZ"))
+    time_val <- sub("Z$", "+0000", time_val)
+    time_stamp <- as.POSIXct(strptime(time_val, "%Y-%m-%dT%H:%M:%S%z", tz="UTC"))
   } else {
     time_stamp <- as.POSIXct(NA)
   }
diff --git a/tests/testthat/test_chroms.R b/tests/testthat/test_chroms.R
index 1d79005..9ab4669 100644
--- a/tests/testthat/test_chroms.R
+++ b/tests/testthat/test_chroms.R
@@ -21,3 +21,8 @@ test_that("warning thrown when requesting chroms from mzXML", {
   expect_warning(grabMSdata(mzXML_filenames[2], grab_what = "chroms"),
                  regexp = "grab_what = 'chroms' not available for mzXML documents")
 })
+
+test_that("time stamp is correct", {
+  expect_identical(msdata_MRM_everything$metadata$timestamp,
+                   as.POSIXct("2022-08-11 12:34:56",tz="UTC"))
+})

From a0efafa6d9f9c247097791d7fddd7ec2f3729ca2 Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Tue, 17 Sep 2024 16:30:51 -0700
Subject: [PATCH 02/17] Fixing Metabolights URLs

Now that they're stored in FILES on FTP server instead of in the directory directly
---
 vignettes/Minifying-files-with-RaMS.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/Minifying-files-with-RaMS.Rmd b/vignettes/Minifying-files-with-RaMS.Rmd
index 3d62b51..aabd821 100644
--- a/vignettes/Minifying-files-with-RaMS.Rmd
+++ b/vignettes/Minifying-files-with-RaMS.Rmd
@@ -146,7 +146,7 @@ been deposited.
 
 ```{r download from MBLs, eval=FALSE}
 if(!dir.exists("vignettes/data"))dir.create("vignettes/data")
-base_url <- "ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS703/"
+base_url <- "ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS703/FILES/"
 chosen_files <- paste0(base_url, "170223_Smp_LB12HL_", c("AB", "CD", "EF"), "_pos.mzXML")
 new_names <- gsub(x=basename(chosen_files), "170223_Smp_", "")
 

From b9d16d80467052c0c344299b3179778b63c23bc7 Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Tue, 17 Sep 2024 16:31:26 -0700
Subject: [PATCH 03/17] Specifying timezone=UTC for all files

---
 R/grabMzmlFunctions.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/R/grabMzmlFunctions.R b/R/grabMzmlFunctions.R
index 21eb5f6..aa2020d 100644
--- a/R/grabMzmlFunctions.R
+++ b/R/grabMzmlFunctions.R
@@ -283,8 +283,7 @@ grabMzmlMetadata <- function(xml_data){
   time_node <- xml2::xml_find_first(xml_data, xpath = "//d1:run")
   time_val <- xml2::xml_attr(time_node, "startTimeStamp")
   if(!is.na(time_val)){
-    time_val <- sub("Z$", "+0000", time_val)
-    time_stamp <- as.POSIXct(strptime(time_val, "%Y-%m-%dT%H:%M:%S%z", tz="UTC"))
+    time_stamp <- as.POSIXct(strptime(time_val, "%Y-%m-%dT%H:%M:%SZ", tz="UTC"))
   } else {
     time_stamp <- as.POSIXct(NA)
   }

From 76f40bbf34c6c47d73c3b0aab97423005d5d3a19 Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Tue, 17 Sep 2024 16:35:11 -0700
Subject: [PATCH 04/17] Adding correct package reference

Closes #37
---
 R/RaMS-package.R    | 2 +-
 man/RaMS-package.Rd | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/RaMS-package.R b/R/RaMS-package.R
index 63975ec..50d3a17 100644
--- a/R/RaMS-package.R
+++ b/R/RaMS-package.R
@@ -12,7 +12,7 @@
 #' alias `%between%`) imported from `data.table`.
 #'
 #' See the package intro on GitHub at https://github.com/wkumler/RaMS and
-#' explore the vignettes with \code{vignette("help", package = "mypkg")}
+#' explore the vignette with \code{vignette("Intro-to-RaMS", package = "RaMS")}
 #'
 #' @keywords internal
 "_PACKAGE"
diff --git a/man/RaMS-package.Rd b/man/RaMS-package.Rd
index 774f3f9..55507eb 100644
--- a/man/RaMS-package.Rd
+++ b/man/RaMS-package.Rd
@@ -21,7 +21,7 @@ small helper functions are also included, \code{\link[=pmppm]{pmppm()}} and \cod
 alias \verb{\%between\%}) imported from \code{data.table}.
 
 See the package intro on GitHub at https://github.com/wkumler/RaMS and
-explore the vignettes with \code{vignette("help", package = "mypkg")}
+explore the vignette with \code{vignette("Intro-to-RaMS", package = "RaMS")}
 }
 \seealso{
 Useful links:

From 02d93e9265fb65139a15e771f21ea3ad20990e8c Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Tue, 17 Sep 2024 16:35:42 -0700
Subject: [PATCH 05/17] Adding %between% to imports

---
 NAMESPACE | 1 +
 1 file changed, 1 insertion(+)

diff --git a/NAMESPACE b/NAMESPACE
index 995aaec..b42f786 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -22,4 +22,5 @@ import(utils)
 import(xml2)
 importFrom(base64enc,base64decode)
 importFrom(base64enc,base64encode)
+importFrom(data.table,"%between%")
 importFrom(stats,approx)

From 1e9fe46d98d84a376d045e06e83b4b7cb9a85f0e Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Tue, 17 Sep 2024 16:36:37 -0700
Subject: [PATCH 06/17] Updating new DESCRIPTION file with versioning info

---
 DESCRIPTION | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index ced70f9..f710b56 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: RaMS
 Type: Package
 Title: R Access to Mass-Spec Data
-Version: 1.4.0
+Version: 1.4.1
 Authors@R: c(
   person(given = "William", family = "Kumler", email = "wkumler@uw.edu",
          role = c("aut", "cre", "cph")),
@@ -26,7 +26,7 @@ Imports:
     data.table,
     utils,
     stats
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Suggests: 
     testthat,
     knitr,

From 86cf1504d79eb059c0a20d2b921a2448df755cf2 Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Tue, 17 Sep 2024 17:18:28 -0700
Subject: [PATCH 07/17] Updating README after switching to new MSMS file

---
 README.md                               |  91 ++++++++++++++++++------
 man/figures/README-isoexampleplot-1.png | Bin 8238 -> 8261 bytes
 man/figures/README-plotfragdata-1.png   | Bin 2420 -> 2222 bytes
 man/figures/README-showggplot-1.png     | Bin 9552 -> 9549 bytes
 4 files changed, 70 insertions(+), 21 deletions(-)

diff --git a/README.md b/README.md
index 56fafe5..0c6d422 100644
--- a/README.md
+++ b/README.md
@@ -101,6 +101,11 @@ plot(msdata$BPC$rt, msdata$BPC$int, type = "l", ylab="Intensity")
 
 ``` r
 library(ggplot2)
+```
+
+    ## Warning: package 'ggplot2' was built under R version 4.4.1
+
+``` r
 ggplot(msdata$BPC) + geom_line(aes(x = rt, y=int, color=filename)) +
   facet_wrap(~filename, scales = "free_y", ncol = 1) +
   labs(x="Retention time (min)", y="Intensity", color="File name: ") +
@@ -214,30 +219,55 @@ For example, we may be interested in the major fragments of a specific
 molecule:
 
 ``` r
-msdata$MS2[premz%between%pmppm(118.0865) & int>mean(int)] %>%
+msdata$MS2[premz%between%pmppm(351.0817) & int>mean(int)] %>%
   plot(int~fragmz, type="h", data=., ylab="Intensity", xlab="Fragment m/z")
 ```
 
 ![](man/figures/README-plotfragdata-1.png)<!-- -->
 
-Or want to search for precursors with a specific neutral loss:
+Or want to search for precursors with a specific neutral loss above a
+certain intensity:
 
 ``` r
-msdata$MS2[, neutral_loss:=premz-fragmz] %>%
-  filter(neutral_loss%between%pmppm(60.02064, 5)) %>%
+msdata$MS2[, neutral_loss:=premz-fragmz][int>1e4] %>%
+  filter(neutral_loss%between%pmppm(126.1408, 5)) %>%
   head(3) %>% knitr::kable()
 ```
 
-|       rt |    premz |   fragmz |        int | voltage | filename         | neutral_loss |
-|---------:|---------:|---------:|-----------:|--------:|:-----------------|-------------:|
-| 4.182333 | 118.0864 | 58.06590 | 390179.500 |      35 | DDApos_2.mzML.gz |     60.02055 |
-| 4.276100 | 116.0709 | 56.05036 |   1093.988 |      35 | DDApos_2.mzML.gz |     60.02050 |
-| 4.521367 | 118.0864 | 58.06589 | 343084.000 |      35 | DDApos_2.mzML.gz |     60.02056 |
+|       rt |    premz |   fragmz |      int | voltage | filename                               | neutral_loss |
+|---------:|---------:|---------:|---------:|--------:|:---------------------------------------|-------------:|
+| 47.27750 | 351.0817 | 224.9409 | 16333.23 |      40 | Blank_129I_1L_pos_20240207-MS3.mzML.gz |     126.1408 |
+| 47.35267 | 351.0818 | 224.9410 | 27353.09 |      40 | Blank_129I_1L_pos_20240207-MS3.mzML.gz |     126.1408 |
+| 47.42767 | 351.0818 | 224.9410 | 33843.92 |      40 | Blank_129I_1L_pos_20240207-MS3.mzML.gz |     126.1408 |
+
+#### SRM/MRM data
+
+Selected/multiple reaction monitoring files don’t have data stored in
+the typical MSn format but instead encode their values as chromatograms.
+To extract data in this format, include `"chroms"` in the `grab_what`
+argument:
+
+``` r
+chromsdata <- grabMSdata(files = msdata_files[7], grab_what = "chroms", verbosity = 0)
+```
+
+which has individual reactions separated by the `chrom_type` column (and
+the associated index) with relevant target/fragment data:
+
+``` r
+knitr::kable(head(chromsdata$chroms, 3))
+```
+
+| chrom_type | chrom_index | target_mz | product_mz |       rt | int | filename         |
+|:-----------|:------------|----------:|-----------:|---------:|----:|:-----------------|
+| TIC        | 0           |        NA |         NA | 2.000000 |   0 | wk_chrom.mzML.gz |
+| TIC        | 0           |        NA |         NA | 2.048077 |   0 | wk_chrom.mzML.gz |
+| TIC        | 0           |        NA |         NA | 2.096154 |   0 | wk_chrom.mzML.gz |
 
 #### Minifying MS files
 
-As of version 1.1.0, `RaMS` also has functions that allow irrelevant
-data to be removed from the file to reduce file sizes. See the
+As of version 1.1.0, `RaMS` has functions that allow irrelevant data to
+be removed from the file to reduce file sizes. See the
 [vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Minifying-files-with-RaMS.html)
 for more details.
 
@@ -247,24 +277,43 @@ Version 1.2.0 of RaMS introduced a new file type, the “transposed mzML”
 or “tmzML” file to resolve the large memory requirement when working
 with many files. See [the
 vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Intro-to-tmzML.html)
-for more details.
+for more details, though note that I’ve largely deprecated this file
+type in favor of proper database solutions as in the [speed & size
+comparison
+vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/speed_size_comparison.html).
 
 ## File types
 
 RaMS is currently limited to the modern **mzML** data format and the
-slightly older **mzXML** format, as well as the custom [**tmzML**
-format](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Intro-to-tmzML.html)
-as of version 1.2.0. Tools to convert data from other formats are
-available through
+slightly older **mzXML** format. Tools to convert data from other
+formats are available through
 [Proteowizard’s](https://proteowizard.sourceforge.io/tools/msconvert.html)
 `msconvert` tool. Data can, however, be gzip compressed (file ending
 .gz) and this compression actually speeds up data retrieval
 significantly as well as reducing file sizes.
 
-Currently, `RaMS` handles MS<sup>1</sup> MS<sup>2</sup>, and MS<sup>3</sup>
-data. This should be easy enough to expand in the future, but right now
-I haven’t observed a demonstrated need for higher fragmentation level
-data collection.
+Currently, `RaMS` handles MS<sup>1</sup>, MS<sup>2</sup>, and
+MS<sup>3</sup> data. This should be easy enough to expand in the future,
+but right now I haven’t observed a demonstrated need for higher
+fragmentation level data collection.
+
+Additionally, note that files can be streamed from the internet directly
+if a URL is provided to `grabMSdata`, although this will usually take
+longer than reading a file from disk:
+
+``` r
+## Not run:
+# Find a file with a web browser:
+browseURL("https://www.ebi.ac.uk/metabolights/MTBLS703/files")
+
+# Copy link address by right-clicking "download" button:
+sample_url <- paste0("https://www.ebi.ac.uk/metabolights/ws/studies/MTBLS703/",
+                     "download/acefcd61-a634-4f35-9c3c-c572ade5acf3?file=",
+                     "FILES/161024_Smp_LB12HL_AB_pos.mzXML")
+
+msdata <- grabMSdata(sample_url, grab_what="everything", verbosity=2)
+msdata$metadata
+```
 
 For an analysis of how RaMS compares to other methods of MS data access
 and alternative file types, consider browsing the [speed & size
@@ -278,4 +327,4 @@ Issues page](https://github.com/wkumler/RaMS/issues).
 
 ------------------------------------------------------------------------
 
-README last built on 2023-11-29
+README last built on 2024-09-17
diff --git a/man/figures/README-isoexampleplot-1.png b/man/figures/README-isoexampleplot-1.png
index 8c1fee6950e1501c8360da7f010d861b523aaaa0..746dae82b0a232ff6ffcdbb0164bb5c8e1203b81 100644
GIT binary patch
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From 56c73df9e67255b349857a8c0ccc089a5f034d67 Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Tue, 17 Sep 2024 17:40:12 -0700
Subject: [PATCH 08/17] Switching from htmlpreview to CRAN version of vignettes

---
 README.md                           | 17 +++++++----------
 doc/Intro-to-RaMS.Rmd               |  4 ++--
 doc/Intro-to-RaMS.html              |  4 ++--
 doc/Intro-to-tmzML.Rmd              |  2 +-
 doc/Intro-to-tmzML.html             |  2 +-
 doc/speed_size_comparison.Rmd       |  2 +-
 doc/speed_size_comparison.html      |  2 +-
 vignettes/Intro-to-RaMS.Rmd         |  4 ++--
 vignettes/Intro-to-tmzML.Rmd        |  2 +-
 vignettes/speed_size_comparison.Rmd |  2 +-
 10 files changed, 19 insertions(+), 22 deletions(-)

diff --git a/README.md b/README.md
index 0c6d422..0b68283 100644
--- a/README.md
+++ b/README.md
@@ -74,7 +74,9 @@ msdata <- grabMSdata(files = msdata_files[2:4], grab_what = c("BPC", "MS1"))
 
 Some additional examples can be found below, but a more thorough
 introduction can be found in [the
-vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Intro-to-RaMS.html).
+vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Intro-to-RaMS.html)
+or by typing `vignette("Intro-to-RaMS", package = "RaMS")` in the R
+console after installation.
 
 #### BPC/TIC data:
 
@@ -101,11 +103,6 @@ plot(msdata$BPC$rt, msdata$BPC$int, type = "l", ylab="Intensity")
 
 ``` r
 library(ggplot2)
-```
-
-    ## Warning: package 'ggplot2' was built under R version 4.4.1
-
-``` r
 ggplot(msdata$BPC) + geom_line(aes(x = rt, y=int, color=filename)) +
   facet_wrap(~filename, scales = "free_y", ncol = 1) +
   labs(x="Retention time (min)", y="Intensity", color="File name: ") +
@@ -268,7 +265,7 @@ knitr::kable(head(chromsdata$chroms, 3))
 
 As of version 1.1.0, `RaMS` has functions that allow irrelevant data to
 be removed from the file to reduce file sizes. See the
-[vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Minifying-files-with-RaMS.html)
+[vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html)
 for more details.
 
 #### tmzML documents
@@ -276,11 +273,11 @@ for more details.
 Version 1.2.0 of RaMS introduced a new file type, the “transposed mzML”
 or “tmzML” file to resolve the large memory requirement when working
 with many files. See [the
-vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Intro-to-tmzML.html)
+vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Intro-to-tmzML.html)
 for more details, though note that I’ve largely deprecated this file
 type in favor of proper database solutions as in the [speed & size
 comparison
-vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/speed_size_comparison.html).
+vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html).
 
 ## File types
 
@@ -318,7 +315,7 @@ msdata$metadata
 For an analysis of how RaMS compares to other methods of MS data access
 and alternative file types, consider browsing the [speed & size
 comparison
-vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/speed_size_comparison.html).
+vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html).
 
 ## Contact
 
diff --git a/doc/Intro-to-RaMS.Rmd b/doc/Intro-to-RaMS.Rmd
index 43afd02..0d31915 100644
--- a/doc/Intro-to-RaMS.Rmd
+++ b/doc/Intro-to-RaMS.Rmd
@@ -46,7 +46,7 @@ If you have your own data, feel free to load it here. If not, there's a couple
 small example files you're welcome to use in the "extdata" folder. The first of 
 these contains DDA data from a pooled sample, while the others are individual 
 samples. I'll be using these throughout. For more details on the origin of these
-files, see the [minification vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Minifying-files-with-RaMS.html).
+files, see the [minification vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html).
 
 ```{r findfiles, message=FALSE}
 library(RaMS)
@@ -320,7 +320,7 @@ with `RaMS` and are looking to optimize speed, reduce memory requirements, or
 are otherwise interested in the details of what `RaMS` does under the hood. If
 you're just getting started, I strongly recommend applying `RaMS` to your own
 data before you read on. For a more detailed analysis of the first two sections
-on saving space and speeding things up, consider also browsing the [speed & size comparison vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/speed_size_comparison.html)
+on saving space and speeding things up, consider also browsing the [speed & size comparison vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html)
 
 ### Saving space: EICs and rtrange
 
diff --git a/doc/Intro-to-RaMS.html b/doc/Intro-to-RaMS.html
index e4c2a87..f983b2f 100644
--- a/doc/Intro-to-RaMS.html
+++ b/doc/Intro-to-RaMS.html
@@ -373,7 +373,7 @@ <h2>Basic RaMS usage</h2>
 a couple small example files you’re welcome to use in the “extdata”
 folder. The first of these contains DDA data from a pooled sample, while
 the others are individual samples. I’ll be using these throughout. For
-more details on the origin of these files, see the <a href="https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Minifying-files-with-RaMS.html">minification
+more details on the origin of these files, see the <a href="https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html">minification
 vignette</a>.</p>
 <div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(RaMS)</span>
 <span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a></span>
@@ -894,7 +894,7 @@ <h2>Advanced RaMS usage</h2>
 getting started, I strongly recommend applying <code>RaMS</code> to your
 own data before you read on. For a more detailed analysis of the first
 two sections on saving space and speeding things up, consider also
-browsing the <a href="https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/speed_size_comparison.html">speed
+browsing the <a href="https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html">speed
 &amp; size comparison vignette</a></p>
 <div id="saving-space-eics-and-rtrange" class="section level3">
 <h3>Saving space: EICs and rtrange</h3>
diff --git a/doc/Intro-to-tmzML.Rmd b/doc/Intro-to-tmzML.Rmd
index e79b9f8..143ad60 100644
--- a/doc/Intro-to-tmzML.Rmd
+++ b/doc/Intro-to-tmzML.Rmd
@@ -28,7 +28,7 @@ knitr::opts_chunk$set(
 
 This vignette deals with a feature still undergoing active development. While I
 can't promise to update it immediately with changes, I will do my best to keep
-you in the loop as this functionality evolves. Please also see the [speed & size comparison vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/speed_size_comparison.html) for more stable alternatives.
+you in the loop as this functionality evolves. Please also see the [speed & size comparison vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html) for more stable alternatives.
 
 **Table of contents:**
 
diff --git a/doc/Intro-to-tmzML.html b/doc/Intro-to-tmzML.html
index ff60389..f26b8ed 100644
--- a/doc/Intro-to-tmzML.html
+++ b/doc/Intro-to-tmzML.html
@@ -345,7 +345,7 @@ <h1 class="title toc-ignore">Introduction to the tmzML file type</h1>
 <p>This vignette deals with a feature still undergoing active
 development. While I can’t promise to update it immediately with
 changes, I will do my best to keep you in the loop as this functionality
-evolves. Please also see the <a href="https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/speed_size_comparison.html">speed
+evolves. Please also see the <a href="https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html">speed
 &amp; size comparison vignette</a> for more stable alternatives.</p>
 <p><strong>Table of contents:</strong></p>
 <ul>
diff --git a/doc/speed_size_comparison.Rmd b/doc/speed_size_comparison.Rmd
index 8c52b83..2350f2d 100644
--- a/doc/speed_size_comparison.Rmd
+++ b/doc/speed_size_comparison.Rmd
@@ -58,7 +58,7 @@ ms_files <- list.files("vignettes/figures/ssc_vignette_renders/Sample/", full.na
 
 ## Intro
 
-As noted in the [RaMS and friends vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Minifying-files-with-RaMS.html), one of the major strengths of the 'tidy' data format is how nicely it plays with other packages, allowing for the use of powerful packages that can't normally be applied to MS data. After playing with a few different ones I wanted to do some structured benchmarking to see which is the best use case for various situations.
+As noted in the [RaMS and friends vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html), one of the major strengths of the 'tidy' data format is how nicely it plays with other packages, allowing for the use of powerful packages that can't normally be applied to MS data. After playing with a few different ones I wanted to do some structured benchmarking to see which is the best use case for various situations.
 
 The general constraints I'm interested in are speed and space, with a minor consideration for simplicity afterward. My question of interest is how costly in these areas access to the MS data is, a process that can be broken down into two relevant steps - the transformation of the MS data from mz(X)ML format into a queryable format, and then the actual query itself. The query itself is relatively straightforward and can be measured in just the amount of time it takes, but the transformation has a caveat that it can be stored either in memory (and therefore must be recreated every time R is restarted) or it can be stored on disk (which is robust across restarts). So I'm interested in
 
diff --git a/doc/speed_size_comparison.html b/doc/speed_size_comparison.html
index 0d7ffd6..513bef2 100644
--- a/doc/speed_size_comparison.html
+++ b/doc/speed_size_comparison.html
@@ -394,7 +394,7 @@ <h4 class="date">2023-12-13</h4>
 </details>
 <div id="intro" class="section level2">
 <h2>Intro</h2>
-<p>As noted in the <a href="https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Minifying-files-with-RaMS.html">RaMS
+<p>As noted in the <a href="https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html">RaMS
 and friends vignette</a>, one of the major strengths of the ‘tidy’ data
 format is how nicely it plays with other packages, allowing for the use
 of powerful packages that can’t normally be applied to MS data. After
diff --git a/vignettes/Intro-to-RaMS.Rmd b/vignettes/Intro-to-RaMS.Rmd
index 43afd02..0d31915 100644
--- a/vignettes/Intro-to-RaMS.Rmd
+++ b/vignettes/Intro-to-RaMS.Rmd
@@ -46,7 +46,7 @@ If you have your own data, feel free to load it here. If not, there's a couple
 small example files you're welcome to use in the "extdata" folder. The first of 
 these contains DDA data from a pooled sample, while the others are individual 
 samples. I'll be using these throughout. For more details on the origin of these
-files, see the [minification vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Minifying-files-with-RaMS.html).
+files, see the [minification vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html).
 
 ```{r findfiles, message=FALSE}
 library(RaMS)
@@ -320,7 +320,7 @@ with `RaMS` and are looking to optimize speed, reduce memory requirements, or
 are otherwise interested in the details of what `RaMS` does under the hood. If
 you're just getting started, I strongly recommend applying `RaMS` to your own
 data before you read on. For a more detailed analysis of the first two sections
-on saving space and speeding things up, consider also browsing the [speed & size comparison vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/speed_size_comparison.html)
+on saving space and speeding things up, consider also browsing the [speed & size comparison vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html)
 
 ### Saving space: EICs and rtrange
 
diff --git a/vignettes/Intro-to-tmzML.Rmd b/vignettes/Intro-to-tmzML.Rmd
index e79b9f8..143ad60 100644
--- a/vignettes/Intro-to-tmzML.Rmd
+++ b/vignettes/Intro-to-tmzML.Rmd
@@ -28,7 +28,7 @@ knitr::opts_chunk$set(
 
 This vignette deals with a feature still undergoing active development. While I
 can't promise to update it immediately with changes, I will do my best to keep
-you in the loop as this functionality evolves. Please also see the [speed & size comparison vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/speed_size_comparison.html) for more stable alternatives.
+you in the loop as this functionality evolves. Please also see the [speed & size comparison vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html) for more stable alternatives.
 
 **Table of contents:**
 
diff --git a/vignettes/speed_size_comparison.Rmd b/vignettes/speed_size_comparison.Rmd
index 334cd43..64ac46b 100644
--- a/vignettes/speed_size_comparison.Rmd
+++ b/vignettes/speed_size_comparison.Rmd
@@ -58,7 +58,7 @@ ms_files <- list.files("vignettes/figures/ssc_vignette_renders/Sample/", full.na
 
 ## Intro
 
-As noted in the [RaMS and friends vignette](https://htmlpreview.github.io/?https://github.com/wkumler/RaMS/blob/master/doc/Minifying-files-with-RaMS.html), one of the major strengths of the 'tidy' data format is how nicely it plays with other packages, allowing for the use of powerful packages that can't normally be applied to MS data. After playing with a few different ones I wanted to do some structured benchmarking to see which is the best use case for various situations.
+As noted in the [RaMS and friends vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html), one of the major strengths of the 'tidy' data format is how nicely it plays with other packages, allowing for the use of powerful packages that can't normally be applied to MS data. After playing with a few different ones I wanted to do some structured benchmarking to see which is the best use case for various situations.
 
 The general constraints I'm interested in are speed and space, with a minor consideration for simplicity afterward. My question of interest is how costly in these areas access to the MS data is, a process that can be broken down into two relevant steps - the transformation of the MS data from mz(X)ML format into a queryable format, and then the actual query itself. The query itself is relatively straightforward and can be measured in just the amount of time it takes, but the transformation has a caveat that it can be stored either in memory (and therefore must be recreated every time R is restarted) or it can be stored on disk (which is robust across restarts). So I'm interested in
 

From 274548151f0ce869c65539f54ec3961b7548ec2a Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Tue, 17 Sep 2024 17:54:04 -0700
Subject: [PATCH 09/17] Removing doc/

Now that I have CRAN renderings of the vignettes instead
---
 doc/Intro-to-RaMS.R                           |  225 ----
 doc/Intro-to-RaMS.Rmd                         |  543 --------
 doc/Intro-to-RaMS.html                        | 1183 -----------------
 doc/Intro-to-tmzML.R                          |   69 -
 doc/Intro-to-tmzML.Rmd                        |  246 ----
 doc/Intro-to-tmzML.html                       |  584 --------
 doc/Minifying-files-with-RaMS.R               |  118 --
 doc/Minifying-files-with-RaMS.Rmd             |  230 ----
 doc/Minifying-files-with-RaMS.html            |  586 --------
 doc/RaMS-and-friends.R                        |   83 --
 doc/RaMS-and-friends.Rmd                      |  156 ---
 doc/RaMS-and-friends.html                     |  543 --------
 doc/figures/speedsizecomp.png                 |  Bin 64401 -> 0 bytes
 .../ssc_vignette_renders/make_time_gp.png     |  Bin 25596 -> 0 bytes
 .../ssc_vignette_renders/multichrom_gp.png    |  Bin 18011 -> 0 bytes
 .../ssc_vignette_renders/par_time_gp.png      |  Bin 16294 -> 0 bytes
 .../ssc_vignette_renders/query_time_gp.png    |  Bin 24073 -> 0 bytes
 .../ssc_vignette_renders/size_cons.png        |  Bin 20977 -> 0 bytes
 doc/figures/ssc_vignette_renders/sum_plot.png |  Bin 45004 -> 0 bytes
 doc/speed_size_comparison.R                   |  563 --------
 doc/speed_size_comparison.Rmd                 |  685 ----------
 doc/speed_size_comparison.html                | 1150 ----------------
 22 files changed, 6964 deletions(-)
 delete mode 100644 doc/Intro-to-RaMS.R
 delete mode 100644 doc/Intro-to-RaMS.Rmd
 delete mode 100644 doc/Intro-to-RaMS.html
 delete mode 100644 doc/Intro-to-tmzML.R
 delete mode 100644 doc/Intro-to-tmzML.Rmd
 delete mode 100644 doc/Intro-to-tmzML.html
 delete mode 100644 doc/Minifying-files-with-RaMS.R
 delete mode 100644 doc/Minifying-files-with-RaMS.Rmd
 delete mode 100644 doc/Minifying-files-with-RaMS.html
 delete mode 100644 doc/RaMS-and-friends.R
 delete mode 100644 doc/RaMS-and-friends.Rmd
 delete mode 100644 doc/RaMS-and-friends.html
 delete mode 100644 doc/figures/speedsizecomp.png
 delete mode 100644 doc/figures/ssc_vignette_renders/make_time_gp.png
 delete mode 100644 doc/figures/ssc_vignette_renders/multichrom_gp.png
 delete mode 100644 doc/figures/ssc_vignette_renders/par_time_gp.png
 delete mode 100644 doc/figures/ssc_vignette_renders/query_time_gp.png
 delete mode 100644 doc/figures/ssc_vignette_renders/size_cons.png
 delete mode 100644 doc/figures/ssc_vignette_renders/sum_plot.png
 delete mode 100644 doc/speed_size_comparison.R
 delete mode 100644 doc/speed_size_comparison.Rmd
 delete mode 100644 doc/speed_size_comparison.html

diff --git a/doc/Intro-to-RaMS.R b/doc/Intro-to-RaMS.R
deleted file mode 100644
index 2a99e0c..0000000
--- a/doc/Intro-to-RaMS.R
+++ /dev/null
@@ -1,225 +0,0 @@
-## ----include = FALSE----------------------------------------------------------
-options(rmarkdown.html_vignette.check_title = FALSE)
-options(tidyverse.quiet = TRUE)
-data.table::setDTthreads(2)
-
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>",
-  out.width = "80%",
-  fig.align = 'center',
-  fig.height = 3,
-  fig.width = 6.5
-)
-
-## ----findfiles, message=FALSE-------------------------------------------------
-library(RaMS)
-
-# Locate the file directory
-msdata_dir <- system.file("extdata", package = "RaMS")
-
-# Identify the files of interest
-data_files <- list.files(msdata_dir, pattern = "mzML", full.names = TRUE)[1:4]
-
-# Check that the files identified are the ones expected
-basename(data_files)
-
-## ----loadTIC------------------------------------------------------------------
-single_file <- data_files[2]
-
-msdata <- grabMSdata(single_file, grab_what = "TIC")
-
-knitr::kable(head(msdata$TIC, 3))
-
-## ----headerTIC----------------------------------------------------------------
-plot(msdata$TIC$rt, msdata$TIC$int, type = "l")
-
-## ----loadBPC, fig.height=3----------------------------------------------------
-msdata <- grabMSdata(single_file, grab_what = "BPC")
-
-## ----plotBPC, warning=FALSE---------------------------------------------------
-library(tidyverse)
-ggplot(msdata$BPC) + geom_line(aes(x=rt, y=int))
-
-## ----loadmultiBPC-------------------------------------------------------------
-msdata <- grabMSdata(data_files[2:4], grab_what = "BPC")
-
-ggplot(msdata$BPC) + geom_line(aes(x=rt, y=int, color=filename))
-
-## ----ggplotdemo, dpi=144------------------------------------------------------
-ggplot(msdata$BPC) + 
-  geom_polygon(aes(x=rt, y=int, color=filename), lwd=1, fill="#FFFFFF44") +
-  theme(legend.position = c(0.8, 0.7), plot.title = element_text(face="bold"),
-        axis.title = element_text(size = 15)) +
-  scale_y_continuous(labels = c(0, "250M", "500M"), breaks = c(0, 2.5e8, 5e8)) +
-  scale_colour_manual(values = c("#2596be", "#6c25be", "#bea925")) +
-  labs(x="Retention time (minutes)", y="Intensity", 
-       title = "Base peak chromatogram", color="Files:") +
-  coord_cartesian(xlim = c(7.50, 9), ylim = c(0, 5e8))
-
-## ----metadatademo-------------------------------------------------------------
-# Since the minification process strips some metadata, I use the 
-# less-minified DDA file here
-grabMSdata(files = data_files[1], grab_what = "metadata")
-
-## ----MS1demo------------------------------------------------------------------
-msdata <- grabMSdata(data_files[2:4], grab_what = "MS1")
-
-knitr::kable(head(msdata$MS1, 3))
-
-## ----adenineplot, warning=FALSE, message=FALSE--------------------------------
-library(data.table)
-
-adenine_mz <- 136.06232
-
-adenine_data <- msdata$MS1[mz%between%pmppm(adenine_mz, ppm=5)]
-
-ggplot(adenine_data) + geom_line(aes(x=rt, y=int, color=filename)) + lims(x=c(4, 9))
-
-## ----multicmpdplot------------------------------------------------------------
-mzs_of_interest <- c(adenine=136.06232, valine=118.0865, homarine=138.055503)
-
-mass_data <- imap_dfr(mzs_of_interest, function(mz_i, name){
-  cbind(msdata$MS1[mz%between%pmppm(mz_i, ppm=10)], name)
-})
-
-ggplot(mass_data) + 
-  geom_line(aes(x=rt, y=int, color=filename)) + 
-  facet_wrap(~name, ncol = 1, scales = "free_y") +
-  lims(x=c(4, 9))
-
-## ----MS2data------------------------------------------------------------------
-msdata <- grabMSdata(data_files[1], grab_what = "everything")
-knitr::kable(head(msdata$MS2, 3))
-
-## ----MS2demo------------------------------------------------------------------
-homarine_mz <- 137.047678+1.007276
-
-homarine_MS2 <- msdata$MS2[premz%between%pmppm(homarine_mz, 5)]
-homarine_MS2$int <- homarine_MS2$int/max(homarine_MS2$int)
-
-ggplot(homarine_MS2) +
-  geom_point(aes(x=fragmz, y=int)) +
-  geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0)) +
-  scale_y_continuous(breaks = c(0, .5, 1), labels = c("0%", "50%", "100%")) +
-  labs(x="Fragment m/z", y="")
-
-## ----plotly, eval=FALSE-------------------------------------------------------
-#  ## Not run to save space in the vignette:
-#  library(plotly)
-#  msdata <- grabMSdata(data_files, grab_what = c("MS1", "MS2", "BPC"))
-#  
-#  compound_MS1 <- msdata$MS1 %>%
-#    filter(mz%between%pmppm(homarine_mz, 10)) %>%
-#    filter(!str_detect(filename, "DDA"))
-#  
-#  compound_MS2 <- msdata$MS2[premz%between%pmppm(homarine_mz, 10)] %>%
-#    group_by(rt) %>%
-#    arrange(desc(int)) %>%
-#    summarise(frags=paste(
-#      paste(round(fragmz, digits = 3), round(int), sep = ": "), collapse = "\n"),
-#      .groups="drop"
-#    ) %>%
-#    mutate(int=approx(x = compound_MS1$rt, y=compound_MS1$int, xout = rt)$y)
-#  plot_ly(compound_MS1) %>%
-#    add_trace(type="scatter", mode="lines", x=~rt, y=~int, color=~filename,
-#              hoverinfo="none") %>%
-#    add_trace(type="scatter", mode="markers", x=~rt, y=~int,
-#              text=~frags, hoverinfo="text", showlegend=FALSE,
-#              marker=list(color="black"), data = compound_MS2) %>%
-#    layout(annotations=list(x=min(compound_MS2$rt), y=median(compound_MS2$int)*10,
-#                            text="Mouse over to see\nMSMS fragments"))
-
-## ----homarine_fragsearch------------------------------------------------------
-homarine_frag_mz <- 94.06567
-msdata$MS2[fragmz%between%pmppm(homarine_frag_mz, ppm = 5)] %>%
-  head() %>% knitr::kable()
-
-## ----homarine_neutsearch------------------------------------------------------
-homarine_neutral_loss <- homarine_mz - homarine_frag_mz
-
-msdata$MS2 <- mutate(msdata$MS2, neutral_loss=premz-fragmz) %>%
-  select("rt", "premz", "fragmz", "neutral_loss", "int", "voltage", "filename")
-msdata$MS2[neutral_loss%between%pmppm(homarine_neutral_loss, ppm = 5)] %>%
-  arrange(desc(int)) %>% head() %>% knitr::kable()
-
-## ----fig.height=3-------------------------------------------------------------
-chrom_file <- system.file("extdata", "wk_chrom.mzML.gz", package = "RaMS")
-msdata_chroms <- grabMSdata(chrom_file, verbosity = 0, grab_what = "chroms")
-given_chrom <- msdata_chroms$chroms[chrom_type=="SRM iletter1"]
-ptitle <- with(given_chrom, paste0(
-  unique(chrom_type), ": Target m/z = ", unique(target_mz), "; Product m/z = ", 
-  unique(product_mz)
-))
-plot(given_chrom$rt, given_chrom$int, type="l", main=ptitle)
-
-## ----EICdemo------------------------------------------------------------------
-all_data <- grabMSdata(data_files, grab_what = c("MS1", "MS2"))
-
-mzs_of_interest <- c(adenine=136.06232, valine=118.0865)
-small_data <- grabMSdata(data_files, grab_what = c("EIC", "EIC_MS2"),
-                         mz=mzs_of_interest, ppm = 5)
-
-all_data$MS1 %>%
-  mutate(type="All data") %>%
-  rbind(small_data$EIC %>% mutate(type="Extracted data only")) %>%
-  filter(!str_detect(filename, "DDA")) %>%
-  filter(rt%between%c(5, 15)) %>%
-  group_by(rt, filename, type) %>%
-  summarise(TIC=sum(int), .groups="drop") %>%
-  ggplot() +
-  geom_line(aes(x=rt, y=TIC, color=filename)) +
-  facet_wrap(~type, ncol = 1)
-
-# Size reduction factor:
-as.numeric(object.size(all_data)/object.size(small_data))
-
-## ----rtrangedemo--------------------------------------------------------------
-small_data <- grabMSdata(data_files, grab_what = c("MS1", "MS2"), rtrange = c(6, 8))
-
-all_data$MS1 %>%
-  mutate(type="All data") %>%
-  rbind(small_data$MS1 %>% mutate(type="Extracted data only")) %>%
-  filter(!str_detect(filename, "DDA")) %>%
-  group_by(rt, filename, type) %>%
-  summarise(TIC=sum(int), .groups="drop") %>%
-  ggplot() +
-  geom_line(aes(x=rt, y=TIC, color=filename)) +
-  facet_wrap(~type, ncol = 1)
-
-# Size reduction factor:
-as.numeric(object.size(all_data)/object.size(small_data))
-
-## ----verbosedemo--------------------------------------------------------------
-all_data <- grabMSdata(data_files, grab_what = c("MS1", "MS2"), verbosity = 2)
-
-## ----eval=FALSE---------------------------------------------------------------
-#  ## Not run:
-#  library(parallel)
-#  cl <- makeCluster(getOption("cl.cores", detectCores()-1))
-#  output_data <- parLapply(data_files, grabMzmlData, grab_what="everything", cl = cl)
-#  
-#  library(foreach)
-#  library(doParallel)
-#  registerDoParallel(detectCores()-1)
-#  output_data <- foreach (i=data_files) %dopar% {
-#    RaMS::grabMzmlData(i, grab_what="everything")
-#  }
-#  stopImplicitCluster()
-
-## ----eval=FALSE---------------------------------------------------------------
-#  ## Not run:
-#  data_nodes <- xml2::xml_find_all(mzML_nodes, xpath="//d1:precursorMz")
-#  raw_data <- xml2::xml_attr(data_nodes, "value")
-
-## ----eval=FALSE---------------------------------------------------------------
-#  ## Not run:
-#  decoded_binary <- base64enc::base64decode(binary)
-#  raw_binary <- as.raw(decoded_binary)
-#  decomp_binary <- memDecompress(raw_binary, type = file_metadata$compression)
-#  final_binary <- readBin(decomp_binary, what = "double",
-#                          n=length(decomp_binary)/file_metadata$mz_precision,
-#                          size = file_metadata$mz_precision)
-#  
-#  # See https://github.com/ProteoWizard/pwiz/issues/1301
-
diff --git a/doc/Intro-to-RaMS.Rmd b/doc/Intro-to-RaMS.Rmd
deleted file mode 100644
index 0d31915..0000000
--- a/doc/Intro-to-RaMS.Rmd
+++ /dev/null
@@ -1,543 +0,0 @@
----
-title: "Intro to RaMS"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Intro-to-RaMS}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-<!-- This vignette is created from a .Rmd file: please edit that instead. -->
-
-```{r, include = FALSE}
-options(rmarkdown.html_vignette.check_title = FALSE)
-options(tidyverse.quiet = TRUE)
-data.table::setDTthreads(2)
-
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>",
-  out.width = "80%",
-  fig.align = 'center',
-  fig.height = 3,
-  fig.width = 6.5
-)
-```
-
-**Table of contents:**
-
-* **[Basic RaMS usage]**
-  + [TICs, BPCs, and metadata]
-  + [Adding a column: MS1 data]
-  + [Moving along: MS2 data]
-  + [Continuing: chromatograms!]
-* **[Advanced RaMS usage]**
-  + [Saving space: EICs and rtrange]
-  + [Speeding things up]
-  + [Finer control: grabMzmlData, grabMzxmlData]
-  + [The nitty-gritty details]
-
-## Basic RaMS usage
-
-Welcome to `RaMS`! This vignette is designed to provide examples using 
-the package at various levels of complexity. Let's jump right in.
-
-If you have your own data, feel free to load it here. If not, there's a couple
-small example files you're welcome to use in the "extdata" folder. The first of 
-these contains DDA data from a pooled sample, while the others are individual 
-samples. I'll be using these throughout. For more details on the origin of these
-files, see the [minification vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html).
-
-```{r findfiles, message=FALSE}
-library(RaMS)
-
-# Locate the file directory
-msdata_dir <- system.file("extdata", package = "RaMS")
-
-# Identify the files of interest
-data_files <- list.files(msdata_dir, pattern = "mzML", full.names = TRUE)[1:4]
-
-# Check that the files identified are the ones expected
-basename(data_files)
-```
-
-There's only one function to worry about in `RaMS`: the aptly named `grabMSdata`.
-This function has a couple arguments with sensible defaults, but you'll always
-need to tell it two things: one, which files you'd like to process; and two,
-the data you'd like to obtain from those files.
-
-Let's start simple, with a single file and the most basic information about it.
-
-### TICs, BPCs, and metadata
-
-A TIC reports the total intensity measured by the mass analyzer during each 
-scan, so the data is parsed into two columns: retention time (rt) and intensity
-(int). This makes it easy to read and simple to plot:
-
-```{r loadTIC}
-single_file <- data_files[2]
-
-msdata <- grabMSdata(single_file, grab_what = "TIC")
-
-knitr::kable(head(msdata$TIC, 3))
-```
-
-Since we asked for a single thing, the TIC, our `file_data` object is a list
-with a single entry: the TIC. Let's plot that data:
-
-```{r headerTIC}
-plot(msdata$TIC$rt, msdata$TIC$int, type = "l")
-```
-
-Simple enough!
-
-A BPC is just like a TIC except that it records the *maximum* intensity measured,
-rather than the sum of all intensities. This data is also collected by the mass
-analyzer and doesn't need to be calculated.
-
-```{r loadBPC, fig.height=3}
-msdata <- grabMSdata(single_file, grab_what = "BPC")
-```
-
-Since the data is parsed in a ["tidy" format](https://r4ds.had.co.nz/tidy-data.html), 
-it plays nicely with popular packages such as `ggplot2`. Let's use that to plot
-our BPC instead of the base R plotting system:
-
-```{r plotBPC, warning=FALSE}
-library(tidyverse)
-ggplot(msdata$BPC) + geom_line(aes(x=rt, y=int))
-```
-
-The advantages of tidy data and `ggplot` become clear when we load more than
-one file at a time because we can group and color by the third column, the
-name of the file from which the data was read. `RaMS` will also automatically
-enable a loading bar if more than one file is loaded; this can be disabled by
-setting the `verbosity` parameter to 0.
-
-```{r loadmultiBPC}
-msdata <- grabMSdata(data_files[2:4], grab_what = "BPC")
-
-ggplot(msdata$BPC) + geom_line(aes(x=rt, y=int, color=filename))
-```
-
-And of course, this means that all of `ggplot`'s aesthetic power can be brought
-to your chromatograms as well, so customize away!
-
-```{r ggplotdemo, dpi=144}
-ggplot(msdata$BPC) + 
-  geom_polygon(aes(x=rt, y=int, color=filename), lwd=1, fill="#FFFFFF44") +
-  theme(legend.position = c(0.8, 0.7), plot.title = element_text(face="bold"),
-        axis.title = element_text(size = 15)) +
-  scale_y_continuous(labels = c(0, "250M", "500M"), breaks = c(0, 2.5e8, 5e8)) +
-  scale_colour_manual(values = c("#2596be", "#6c25be", "#bea925")) +
-  labs(x="Retention time (minutes)", y="Intensity", 
-       title = "Base peak chromatogram", color="Files:") +
-  coord_cartesian(xlim = c(7.50, 9), ylim = c(0, 5e8))
-```
-
-`RaMS` also provides some basic file metadata extraction capability, although
-the focus for this package is on the actual data and other MS packages handle
-file metadata much more elegantly. This is one area where there are major
-differences between mzML and mzXML file types - the mzXML file type simply
-doesn't encode as much metadata as the mzML filetype, so `RaMS` can't extract
-it.
-
-```{r metadatademo}
-# Since the minification process strips some metadata, I use the 
-# less-minified DDA file here
-grabMSdata(files = data_files[1], grab_what = "metadata")
-```
-
-### Adding a column: MS1 data
-
-MS1 data can be extracted just as easily, by supplying "MS1" to the `grab_what`
-argument of `grabMSdata` function.
-
-```{r MS1demo}
-msdata <- grabMSdata(data_files[2:4], grab_what = "MS1")
-
-knitr::kable(head(msdata$MS1, 3))
-```
-
-So we've now got the *mz* column, corresponding to the mass-to-charge ratio 
-(*m/z*) of an ion. This means that we can now filter our data for specific
-masses and separate out molecules with different masses.
-
-Note that this also makes the data much larger in R's memory - so don't go
-loading hundreds of files simultaneously. If that's necessary, check out the
-section below on saving space.
-
-Because `RaMS` returns [data.tables](https://cran.r-project.org/package=data.table) rather than normal `data.frame`s, indexing
-is super-fast and a bit more intuitive than with base R. Below, I also use the
-`pmppm` function from `RaMS` to produce a mass range from an initial mass and
-spectrometer accuracy (here, 5 parts-per-million).
-
-```{r adenineplot, warning=FALSE, message=FALSE}
-library(data.table)
-
-adenine_mz <- 136.06232
-
-adenine_data <- msdata$MS1[mz%between%pmppm(adenine_mz, ppm=5)]
-
-ggplot(adenine_data) + geom_line(aes(x=rt, y=int, color=filename)) + lims(x=c(4, 9))
-```
-
-This makes it easy to grab the data for multiple compounds of interest with a
-simple loop, provided here by the `purrr` package of the tidyverse:
-
-```{r multicmpdplot}
-mzs_of_interest <- c(adenine=136.06232, valine=118.0865, homarine=138.055503)
-
-mass_data <- imap_dfr(mzs_of_interest, function(mz_i, name){
-  cbind(msdata$MS1[mz%between%pmppm(mz_i, ppm=10)], name)
-})
-
-ggplot(mass_data) + 
-  geom_line(aes(x=rt, y=int, color=filename)) + 
-  facet_wrap(~name, ncol = 1, scales = "free_y") +
-  lims(x=c(4, 9))
-```
-
-### Moving along: MS2 data
-
-`RaMS` also handles MS<sup>2</sup> data elegantly. Request it with the "MS2" 
-option for `grab_what`, although it's often a good idea to grab the MS1 data
-alongside.
-
-```{r MS2data}
-msdata <- grabMSdata(data_files[1], grab_what = "everything")
-knitr::kable(head(msdata$MS2, 3))
-```
-
-DDA data can be plotted nicely with `ggplot2` as well. Typically it makes sense
-to filter for a precursor mass, then render the fragments obtained.
-
-```{r MS2demo}
-homarine_mz <- 137.047678+1.007276
-
-homarine_MS2 <- msdata$MS2[premz%between%pmppm(homarine_mz, 5)]
-homarine_MS2$int <- homarine_MS2$int/max(homarine_MS2$int)
-
-ggplot(homarine_MS2) +
-  geom_point(aes(x=fragmz, y=int)) +
-  geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0)) +
-  scale_y_continuous(breaks = c(0, .5, 1), labels = c("0%", "50%", "100%")) +
-  labs(x="Fragment m/z", y="")
-```
-
-This is also the perfect place to enable some interactivity with packages such
-as `plotly`, making data exploration not only simple but also highly intuitive. 
-
-```{r plotly, eval=FALSE}
-## Not run to save space in the vignette:
-library(plotly)
-msdata <- grabMSdata(data_files, grab_what = c("MS1", "MS2", "BPC"))
-
-compound_MS1 <- msdata$MS1 %>% 
-  filter(mz%between%pmppm(homarine_mz, 10)) %>%
-  filter(!str_detect(filename, "DDA"))
-
-compound_MS2 <- msdata$MS2[premz%between%pmppm(homarine_mz, 10)] %>% 
-  group_by(rt) %>%
-  arrange(desc(int)) %>%
-  summarise(frags=paste(
-    paste(round(fragmz, digits = 3), round(int), sep = ": "), collapse = "\n"),
-    .groups="drop"
-  ) %>%
-  mutate(int=approx(x = compound_MS1$rt, y=compound_MS1$int, xout = rt)$y)
-plot_ly(compound_MS1) %>%
-  add_trace(type="scatter", mode="lines", x=~rt, y=~int, color=~filename,
-            hoverinfo="none") %>%
-  add_trace(type="scatter", mode="markers", x=~rt, y=~int,
-            text=~frags, hoverinfo="text", showlegend=FALSE,
-            marker=list(color="black"), data = compound_MS2) %>%
-  layout(annotations=list(x=min(compound_MS2$rt), y=median(compound_MS2$int)*10, 
-                          text="Mouse over to see\nMSMS fragments"))
-```
-
-Easy access to MS<sup>2</sup> data also allows us to rapidly perform 
-simple operations such as searching for a
-specific fragment mass. For example, if we know that homarine typically
-produces a fragment with a mass of 94.06567, we simply subset the MS<sup>2</sup> data
-for fragments in a range around that mass:
-
-```{r homarine_fragsearch}
-homarine_frag_mz <- 94.06567
-msdata$MS2[fragmz%between%pmppm(homarine_frag_mz, ppm = 5)] %>%
-  head() %>% knitr::kable()
-```
-
-We find that there's not only homarine that produces that fragment,
-but at least one other compound. Precursor masses like this can then
-be searched manually in online databases or, since the data is already in R,
-passed to a script that automatically queries them.
-
-Similarly, we can easily search instead for neutral losses with this method. If
-we suspect other molecules are producing a similar neutral loss as homarine:
-
-```{r homarine_neutsearch}
-homarine_neutral_loss <- homarine_mz - homarine_frag_mz
-
-msdata$MS2 <- mutate(msdata$MS2, neutral_loss=premz-fragmz) %>%
-  select("rt", "premz", "fragmz", "neutral_loss", "int", "voltage", "filename")
-msdata$MS2[neutral_loss%between%pmppm(homarine_neutral_loss, ppm = 5)] %>%
-  arrange(desc(int)) %>% head() %>% knitr::kable()
-```
-
-We can again confirm our suspicions that there's another signal with
-a similar neutral loss: one with a mass of 104.0710.
-
-### Continuing: chromatograms!
-
-mzMLs can also contain precompiled chromatograms. Version 1.3 of RaMS enabled
-the extraction of these chromatograms just like MS^1^ or MS^2^ data and added
-the new `chroms` slot to hold them. However, these need to be requested using
-`grab_what = "chroms"`and will create this new slot in the returned object.
-Chromatograms are returned as a `data.table` with seven columns: chromatogram 
-type (usually TIC, BPC or SRM info), chromatogram index, target mz, product mz, 
-retention time (rt), and intensity (int). This allows simple plotting of
-various chromatograms such as those returned by MRM:
-
-```{r, fig.height=3}
-chrom_file <- system.file("extdata", "wk_chrom.mzML.gz", package = "RaMS")
-msdata_chroms <- grabMSdata(chrom_file, verbosity = 0, grab_what = "chroms")
-given_chrom <- msdata_chroms$chroms[chrom_type=="SRM iletter1"]
-ptitle <- with(given_chrom, paste0(
-  unique(chrom_type), ": Target m/z = ", unique(target_mz), "; Product m/z = ", 
-  unique(product_mz)
-))
-plot(given_chrom$rt, given_chrom$int, type="l", main=ptitle)
-```
-
-Version 1.3 also enabled support for DAD (diode array detection) data. This data
-type can be requested using `grab_what = DAD` and will return an additional
-item in the list with columns for retention time, wavelength, and intensity.
-
-## Advanced RaMS usage
-
-The sections below will likely be of interest to users who are already familiar
-with `RaMS` and are looking to optimize speed, reduce memory requirements, or 
-are otherwise interested in the details of what `RaMS` does under the hood. If
-you're just getting started, I strongly recommend applying `RaMS` to your own
-data before you read on. For a more detailed analysis of the first two sections
-on saving space and speeding things up, consider also browsing the [speed & size comparison vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html)
-
-### Saving space: EICs and rtrange
-
-Mass-spec files are typically tens or hundreds of megabytes in size, which means
-that the simultaneous analysis of many files can easily exceed a computer's
-memory. Since `RaMS` stores all data in R's working memory, this can become a
-problem for large analyses.
-
-However, much of the usage envisioned for `RaMS` on this scale doesn't require
-access to the entire file, the entire time. Instead, users are typically 
-interested in a few masses of interest or a specific time window. This means
-that while each file still needs to be read into R in full to find the data
-of interest, extraneous data can be discarded before the next file is loaded.
-
-This functionality can be enabled by passing "EIC" and/or "EIC_MS2" to the
-`grab_what` argument of `grabMSdata`, along with a vector of masses to extract
-(mz) and the instrument's ppm accuracy. When this is enabled, files are read
-into R's memory sequentially, the mass window is extracted, and the rest of
-the data is discarded.
-
-```{r EICdemo}
-all_data <- grabMSdata(data_files, grab_what = c("MS1", "MS2"))
-
-mzs_of_interest <- c(adenine=136.06232, valine=118.0865)
-small_data <- grabMSdata(data_files, grab_what = c("EIC", "EIC_MS2"),
-                         mz=mzs_of_interest, ppm = 5)
-
-all_data$MS1 %>%
-  mutate(type="All data") %>%
-  rbind(small_data$EIC %>% mutate(type="Extracted data only")) %>%
-  filter(!str_detect(filename, "DDA")) %>%
-  filter(rt%between%c(5, 15)) %>%
-  group_by(rt, filename, type) %>%
-  summarise(TIC=sum(int), .groups="drop") %>%
-  ggplot() +
-  geom_line(aes(x=rt, y=TIC, color=filename)) +
-  facet_wrap(~type, ncol = 1)
-
-# Size reduction factor:
-as.numeric(object.size(all_data)/object.size(small_data))
-```
-
-As expected, the size of the `small_data` object is much smaller than the
-`all_data` object, here by a factor of about 15x. For files that haven't 
-already been "minified", that size reduction will be even 
-more significant. Of course, this comes with the cost of needing to re-load
-the data a second time if a new mass feature becomes of interest but this 
-shrinkage is especially valuable for targeted analyses where the analytes of
-interest are known in advance.
-
-A second way of reducing file size is to constrain the retention time dimension
-rather than the m/z dimension. This can be done with the `rtrange` argument,
-which expects a length-two vector corresponding to the upper and lower bounds
-on retention time. This is useful when a small time window is of interest, and
-only the data between those bounds is relevant. Below, peaks are deliberately
-sliced in half to show that this is filtering on retention time instead of mass.
-
-```{r rtrangedemo}
-small_data <- grabMSdata(data_files, grab_what = c("MS1", "MS2"), rtrange = c(6, 8))
-
-all_data$MS1 %>%
-  mutate(type="All data") %>%
-  rbind(small_data$MS1 %>% mutate(type="Extracted data only")) %>%
-  filter(!str_detect(filename, "DDA")) %>%
-  group_by(rt, filename, type) %>%
-  summarise(TIC=sum(int), .groups="drop") %>%
-  ggplot() +
-  geom_line(aes(x=rt, y=TIC, color=filename)) +
-  facet_wrap(~type, ncol = 1)
-
-# Size reduction factor:
-as.numeric(object.size(all_data)/object.size(small_data))
-```
-
-The savings are also worth noting here, but constraining
-the retention time also speeds up data retrieval slightly. Since *m/z* and
-intensity data is encoded in mzML and mzXML files while retention time 
-information is not, eliminating scans with a retention time window removes
-the need to decode the intensity and *m/z* information in those scans. 
-
-However, decoding is rarely the rate-limiting step and for more information 
-about speeding things up, continue to the next section.
-
-### Speeding things up
-
-So, `RaMS` isn't fast enough for you? Let's see what we can do to improve that.
-The first step in speeding things up is discovering what's slow. Typically,
-this is the process of reading in the mzML/mzXML file rather than any processing 
-that occurs afterward, but this is not always the case. To examine bottlenecks,
-`RaMS` includes timing information that's produced if the `verbosity` argument
-is set to 2 or higher.
-
-```{r verbosedemo}
-all_data <- grabMSdata(data_files, grab_what = c("MS1", "MS2"), verbosity = 2)
-```
-
-In general, slow file read times can be improved by compressing the data. mzML
-files are highly compressible, with options to compress both the data itself
-using the `zlib` method and the files as a whole using `gzip`. 
-
-`gzip` is the simplest one to use, as a plethora of methods exist to compress
-files this way, including online sites. `RaMS` can read the data directly
-from a gzipped file, no decompression necessary, so this is an easy way to
-reduce file size and read times. Sharp-eyed users will have noticed that the
-demo files are already gzipped, which is part of the reason they are so small.
-
-`zlib` compression is slightly trickier, and is most often performed with tools
-such as [Proteowizard](https://proteowizard.sourceforge.io/doc_users.html)'s 
-`msconvert` tool with the option "-z". 
-
-Read times may also be slow if files are being accessed via the Internet, either
-through a VPN or network drive. If your files are stored elsewhere,
-consider first moving those files somewhere more local before reading data from
-them.
-
-If the bottleneck appears when reading MS<sup>1</sup> data, consider restricting
-the retention time range with the `rtrange` argument or using more detailed
-profiling tools such as RStudio's "Profile" options or the `profvis` package.
-Pull requests that improve data processing speed are always welcome!
-
-While the package is not currently set up for parallel processing, this is
-a potential future feature if a strong need is demonstrated.
-
-### Finer control: grabMzmlData, grabMzxmlData
-
-While there's only one main function (`grabMSdata`) in `RaMS`, you may have noticed that two
-other functions have been exposed that perform similar tasks: `grabMzmlData`
-and `grabMzxmlData`. The main function `grabMSdata` serves as a wrapper around
-these two functions, which detects the file type, adds the "filename"
-column to the data, and loops over multiple files if provided. However, there's
-often reason to use these internal functions separately.
-
-For one, the objects themselves are smaller because they don't have the filename
-column attached yet. You as the user will need to keep track of which data
-belongs to which files in this case.
-
-Another use case might be applying functions to each file individually, perhaps
-aligning to a reference chromatogram or identifying peaks. Rather than spending
-the time to bind the files together and immediately separate them again, these
-functions have been exposed to skip that step.
-
-Finally, these functions are useful for parallelization. Because iterating
-over each mass-spec file is often the largest reasonable chunk, these functions
-can be passed directly to parallel processes like `mclapply` or `doParallel`.
-However, parallelization is a beast best handled by individual users because
-its actual implementation often differs wildly and its utility depends strongly
-on individual setups (remember that parallelization won't help with slow I/O
-times, so it may not always improve data processing speed.)
-
-```{r, eval=FALSE}
-## Not run:
-library(parallel)
-cl <- makeCluster(getOption("cl.cores", detectCores()-1))
-output_data <- parLapply(data_files, grabMzmlData, grab_what="everything", cl = cl)
-
-library(foreach)
-library(doParallel)
-registerDoParallel(detectCores()-1)
-output_data <- foreach (i=data_files) %dopar% {
-  RaMS::grabMzmlData(i, grab_what="everything")
-}
-stopImplicitCluster()
-```
-
-### The nitty-gritty details
-
-`RaMS` is possible because mzML and mzXML documents are fundamentally
-[XML](https://en.wikipedia.org/wiki/XML)-based. This means that we can leverage
-speedy and robust XML parsing packages such as `xml2` to extract the data.
-Fundamentally, `RaMS` relies on [XPath](https://www.w3schools.com/xml/xpath_intro.asp)
-navigation to collect various bits of mass-spec data, and the format of mzML
-and mzXML files provides the tags necessary. That means a lot of `RaMS` code
-consists of lines like the following:
-
-```{r, eval=FALSE}
-## Not run:
-data_nodes <- xml2::xml_find_all(mzML_nodes, xpath="//d1:precursorMz")
-raw_data <- xml2::xml_attr(data_nodes, "value")
-```
-
-...plus a lot of data handling to get the output into the tidy format.
-
-The other tricky bit of data extraction is converting the (possibly compressed)
-binary data into R-friendly objects. This is usually handled with code like that
-shown below. Many of the settings can be deduced from the file, but sometimes
-compression types need to be guessed at and will throw a warning if so.
-
-```{r, eval=FALSE}
-## Not run:
-decoded_binary <- base64enc::base64decode(binary)
-raw_binary <- as.raw(decoded_binary)
-decomp_binary <- memDecompress(raw_binary, type = file_metadata$compression)
-final_binary <- readBin(decomp_binary, what = "double",
-                        n=length(decomp_binary)/file_metadata$mz_precision,
-                        size = file_metadata$mz_precision)
-
-# See https://github.com/ProteoWizard/pwiz/issues/1301
-```
-
-Fundamentally, mass-spectrometry data is formatted as a 
-[ragged array](https://en.wikipedia.org/wiki/Jagged_array), with an unknown
-number of *m/z* and intensity values for a given scan. This makes it difficult
-to encode neatly without interpolating, but tidy data provides a solution by
-stacking those arrays rather than aligning them into some sort of matrix.
-
-This ragged shape is also the reason that subsetting mass-spec data by retention
-time is trivial - grab the scans that correspond to the desired retention times
-and you're done. Subsetting by mass, on the other hand, requires decoding each
-and every scan's *m/z* and intensity data. If you're reading a book and only
-want a couple chapters, it's easy to flip to those sections. If you're looking
-instead for every time a specific word shows up, you've gotta read the whole
-thing.
-
-For more information about the mzML data format and its history, check out the
-specification at https://www.psidev.info/mzML.
-
-
-***
-
-Vignette last built on `r Sys.Date()`
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-<body>
-
-
-
-
-<h1 class="title toc-ignore">Intro to RaMS</h1>
-
-
-
-<!-- This vignette is created from a .Rmd file: please edit that instead. -->
-<p><strong>Table of contents:</strong></p>
-<ul>
-<li><strong><a href="#basic-rams-usage">Basic RaMS usage</a></strong>
-<ul>
-<li><a href="#tics-bpcs-and-metadata">TICs, BPCs, and metadata</a></li>
-<li><a href="#adding-a-column-ms1-data">Adding a column: MS1
-data</a></li>
-<li><a href="#moving-along-ms2-data">Moving along: MS2 data</a></li>
-<li><a href="#continuing-chromatograms">Continuing:
-chromatograms!</a></li>
-</ul></li>
-<li><strong><a href="#advanced-rams-usage">Advanced RaMS
-usage</a></strong>
-<ul>
-<li><a href="#saving-space-eics-and-rtrange">Saving space: EICs and
-rtrange</a></li>
-<li><a href="#speeding-things-up">Speeding things up</a></li>
-<li><a href="#finer-control-grabmzmldata-grabmzxmldata">Finer control:
-grabMzmlData, grabMzxmlData</a></li>
-<li><a href="#the-nitty-gritty-details">The nitty-gritty
-details</a></li>
-</ul></li>
-</ul>
-<div id="basic-rams-usage" class="section level2">
-<h2>Basic RaMS usage</h2>
-<p>Welcome to <code>RaMS</code>! This vignette is designed to provide
-examples using the package at various levels of complexity. Let’s jump
-right in.</p>
-<p>If you have your own data, feel free to load it here. If not, there’s
-a couple small example files you’re welcome to use in the “extdata”
-folder. The first of these contains DDA data from a pooled sample, while
-the others are individual samples. I’ll be using these throughout. For
-more details on the origin of these files, see the <a href="https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html">minification
-vignette</a>.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(RaMS)</span>
-<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a></span>
-<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a><span class="co"># Locate the file directory</span></span>
-<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a>msdata_dir <span class="ot">&lt;-</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="at">package =</span> <span class="st">&quot;RaMS&quot;</span>)</span>
-<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a></span>
-<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a><span class="co"># Identify the files of interest</span></span>
-<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a>data_files <span class="ot">&lt;-</span> <span class="fu">list.files</span>(msdata_dir, <span class="at">pattern =</span> <span class="st">&quot;mzML&quot;</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>)[<span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>]</span>
-<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a></span>
-<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a><span class="co"># Check that the files identified are the ones expected</span></span>
-<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a><span class="fu">basename</span>(data_files)</span>
-<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a><span class="co">#&gt; [1] &quot;DDApos_2.mzML.gz&quot;  &quot;LB12HL_AB.mzML.gz&quot; &quot;LB12HL_CD.mzML.gz&quot;</span></span>
-<span id="cb1-12"><a href="#cb1-12" tabindex="-1"></a><span class="co">#&gt; [4] &quot;LB12HL_EF.mzML.gz&quot;</span></span></code></pre></div>
-<p>There’s only one function to worry about in <code>RaMS</code>: the
-aptly named <code>grabMSdata</code>. This function has a couple
-arguments with sensible defaults, but you’ll always need to tell it two
-things: one, which files you’d like to process; and two, the data you’d
-like to obtain from those files.</p>
-<p>Let’s start simple, with a single file and the most basic information
-about it.</p>
-<div id="tics-bpcs-and-metadata" class="section level3">
-<h3>TICs, BPCs, and metadata</h3>
-<p>A TIC reports the total intensity measured by the mass analyzer
-during each scan, so the data is parsed into two columns: retention time
-(rt) and intensity (int). This makes it easy to read and simple to
-plot:</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a>single_file <span class="ot">&lt;-</span> data_files[<span class="dv">2</span>]</span>
-<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a></span>
-<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(single_file, <span class="at">grab_what =</span> <span class="st">&quot;TIC&quot;</span>)</span>
-<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a><span class="co">#&gt; Reading file LB12HL_AB.mzML.gz... 0.05 secs </span></span>
-<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a><span class="co">#&gt; Reading TIC...0.11 secs </span></span>
-<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a><span class="co">#&gt; Binding files together into single data.table</span></span>
-<span id="cb2-8"><a href="#cb2-8" tabindex="-1"></a><span class="co">#&gt; Total time: 0.16 secs</span></span>
-<span id="cb2-9"><a href="#cb2-9" tabindex="-1"></a></span>
-<span id="cb2-10"><a href="#cb2-10" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(<span class="fu">head</span>(msdata<span class="sc">$</span>TIC, <span class="dv">3</span>))</span></code></pre></div>
-<table>
-<thead>
-<tr class="header">
-<th align="right">rt</th>
-<th align="right">int</th>
-<th align="left">filename</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">4.009000</td>
-<td align="right">24680889</td>
-<td align="left">LB12HL_AB.mzML.gz</td>
-</tr>
-<tr class="even">
-<td align="right">4.024533</td>
-<td align="right">22832946</td>
-<td align="left">LB12HL_AB.mzML.gz</td>
-</tr>
-<tr class="odd">
-<td align="right">4.040133</td>
-<td align="right">23881353</td>
-<td align="left">LB12HL_AB.mzML.gz</td>
-</tr>
-</tbody>
-</table>
-<p>Since we asked for a single thing, the TIC, our
-<code>file_data</code> object is a list with a single entry: the TIC.
-Let’s plot that data:</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="fu">plot</span>(msdata<span class="sc">$</span>TIC<span class="sc">$</span>rt, msdata<span class="sc">$</span>TIC<span class="sc">$</span>int, <span class="at">type =</span> <span class="st">&quot;l&quot;</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-<p>Simple enough!</p>
-<p>A BPC is just like a TIC except that it records the <em>maximum</em>
-intensity measured, rather than the sum of all intensities. This data is
-also collected by the mass analyzer and doesn’t need to be
-calculated.</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(single_file, <span class="at">grab_what =</span> <span class="st">&quot;BPC&quot;</span>)</span>
-<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a><span class="co">#&gt; Reading file LB12HL_AB.mzML.gz... 0.05 secs </span></span>
-<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a><span class="co">#&gt; Reading BPC...0.07 secs </span></span>
-<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a><span class="co">#&gt; Binding files together into single data.table</span></span>
-<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a><span class="co">#&gt; Total time: 0.12 secs</span></span></code></pre></div>
-<p>Since the data is parsed in a <a href="https://r4ds.had.co.nz/tidy-data.html">“tidy” format</a>, it plays
-nicely with popular packages such as <code>ggplot2</code>. Let’s use
-that to plot our BPC instead of the base R plotting system:</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
-<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="fu">ggplot</span>(msdata<span class="sc">$</span>BPC) <span class="sc">+</span> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>rt, <span class="at">y=</span>int))</span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-<p>The advantages of tidy data and <code>ggplot</code> become clear when
-we load more than one file at a time because we can group and color by
-the third column, the name of the file from which the data was read.
-<code>RaMS</code> will also automatically enable a loading bar if more
-than one file is loaded; this can be disabled by setting the
-<code>verbosity</code> parameter to 0.</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(data_files[<span class="dv">2</span><span class="sc">:</span><span class="dv">4</span>], <span class="at">grab_what =</span> <span class="st">&quot;BPC&quot;</span>)</span>
-<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>  <span class="er">|</span>                                                                      <span class="er">|</span>   <span class="dv">0</span>%</span>
-<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a>  <span class="er">|=======================</span>                                               <span class="er">|</span>  <span class="dv">33</span>%</span>
-<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb6-8"><a href="#cb6-8" tabindex="-1"></a>  <span class="er">|===============================================</span>                       <span class="er">|</span>  <span class="dv">67</span>%</span>
-<span id="cb6-9"><a href="#cb6-9" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb6-10"><a href="#cb6-10" tabindex="-1"></a>  <span class="er">|======================================================================|</span> <span class="dv">100</span>%</span>
-<span id="cb6-11"><a href="#cb6-11" tabindex="-1"></a><span class="co">#&gt; Total time: 0.32 secs</span></span>
-<span id="cb6-12"><a href="#cb6-12" tabindex="-1"></a></span>
-<span id="cb6-13"><a href="#cb6-13" tabindex="-1"></a><span class="fu">ggplot</span>(msdata<span class="sc">$</span>BPC) <span class="sc">+</span> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>rt, <span class="at">y=</span>int, <span class="at">color=</span>filename))</span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-<p>And of course, this means that all of <code>ggplot</code>’s aesthetic
-power can be brought to your chromatograms as well, so customize
-away!</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="fu">ggplot</span>(msdata<span class="sc">$</span>BPC) <span class="sc">+</span> </span>
-<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>  <span class="fu">geom_polygon</span>(<span class="fu">aes</span>(<span class="at">x=</span>rt, <span class="at">y=</span>int, <span class="at">color=</span>filename), <span class="at">lwd=</span><span class="dv">1</span>, <span class="at">fill=</span><span class="st">&quot;#FFFFFF44&quot;</span>) <span class="sc">+</span></span>
-<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="fu">c</span>(<span class="fl">0.8</span>, <span class="fl">0.7</span>), <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
-<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a>        <span class="at">axis.title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">15</span>)) <span class="sc">+</span></span>
-<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="st">&quot;250M&quot;</span>, <span class="st">&quot;500M&quot;</span>), <span class="at">breaks =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="fl">2.5e8</span>, <span class="fl">5e8</span>)) <span class="sc">+</span></span>
-<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a>  <span class="fu">scale_colour_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;#2596be&quot;</span>, <span class="st">&quot;#6c25be&quot;</span>, <span class="st">&quot;#bea925&quot;</span>)) <span class="sc">+</span></span>
-<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;Retention time (minutes)&quot;</span>, <span class="at">y=</span><span class="st">&quot;Intensity&quot;</span>, </span>
-<span id="cb7-8"><a href="#cb7-8" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">&quot;Base peak chromatogram&quot;</span>, <span class="at">color=</span><span class="st">&quot;Files:&quot;</span>) <span class="sc">+</span></span>
-<span id="cb7-9"><a href="#cb7-9" tabindex="-1"></a>  <span class="fu">coord_cartesian</span>(<span class="at">xlim =</span> <span class="fu">c</span>(<span class="fl">7.50</span>, <span class="dv">9</span>), <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="fl">5e8</span>))</span></code></pre></div>
-<p><img 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" width="80%" style="display: block; margin: auto;" /></p>
-<p><code>RaMS</code> also provides some basic file metadata extraction
-capability, although the focus for this package is on the actual data
-and other MS packages handle file metadata much more elegantly. This is
-one area where there are major differences between mzML and mzXML file
-types - the mzXML file type simply doesn’t encode as much metadata as
-the mzML filetype, so <code>RaMS</code> can’t extract it.</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="co"># Since the minification process strips some metadata, I use the </span></span>
-<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="co"># less-minified DDA file here</span></span>
-<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a><span class="fu">grabMSdata</span>(<span class="at">files =</span> data_files[<span class="dv">1</span>], <span class="at">grab_what =</span> <span class="st">&quot;metadata&quot;</span>)</span>
-<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a><span class="co">#&gt; Reading file DDApos_2.mzML.gz... 0.12 secs </span></span>
-<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a><span class="co">#&gt; Reading file metadata...0.38 secs </span></span>
-<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a><span class="co">#&gt; Binding files together into single data.table</span></span>
-<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a><span class="co">#&gt; Total time: 0.51 secs</span></span>
-<span id="cb8-9"><a href="#cb8-9" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
-<span id="cb8-10"><a href="#cb8-10" tabindex="-1"></a><span class="co">#&gt;                                   source_file  inst_data       config_data</span></span>
-<span id="cb8-11"><a href="#cb8-11" tabindex="-1"></a><span class="co">#&gt; 1: 170223_Poo_AllCyanoAqExtracts_DDApos_2.raw None found &lt;data.frame[1x3]&gt;</span></span>
-<span id="cb8-12"><a href="#cb8-12" tabindex="-1"></a><span class="co">#&gt;    timestamp n_spectra n_chromatograms ms_levels mz_lowest mz_highest</span></span>
-<span id="cb8-13"><a href="#cb8-13" tabindex="-1"></a><span class="co">#&gt; 1:      &lt;NA&gt;      1994               0  MS1, MS2   50.0298    613.158</span></span>
-<span id="cb8-14"><a href="#cb8-14" tabindex="-1"></a><span class="co">#&gt;    lambda_lowest lambda_highest rt_start   rt_end centroided polarity</span></span>
-<span id="cb8-15"><a href="#cb8-15" tabindex="-1"></a><span class="co">#&gt; 1:            NA             NA  4.00085 14.99712       TRUE positive</span></span>
-<span id="cb8-16"><a href="#cb8-16" tabindex="-1"></a><span class="co">#&gt;            filename</span></span>
-<span id="cb8-17"><a href="#cb8-17" tabindex="-1"></a><span class="co">#&gt; 1: DDApos_2.mzML.gz</span></span></code></pre></div>
-</div>
-<div id="adding-a-column-ms1-data" class="section level3">
-<h3>Adding a column: MS1 data</h3>
-<p>MS1 data can be extracted just as easily, by supplying “MS1” to the
-<code>grab_what</code> argument of <code>grabMSdata</code> function.</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(data_files[<span class="dv">2</span><span class="sc">:</span><span class="dv">4</span>], <span class="at">grab_what =</span> <span class="st">&quot;MS1&quot;</span>)</span>
-<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a>  <span class="er">|</span>                                                                      <span class="er">|</span>   <span class="dv">0</span>%</span>
-<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a>  <span class="er">|=======================</span>                                               <span class="er">|</span>  <span class="dv">33</span>%</span>
-<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a>  <span class="er">|===============================================</span>                       <span class="er">|</span>  <span class="dv">67</span>%</span>
-<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb9-10"><a href="#cb9-10" tabindex="-1"></a>  <span class="er">|======================================================================|</span> <span class="dv">100</span>%</span>
-<span id="cb9-11"><a href="#cb9-11" tabindex="-1"></a><span class="co">#&gt; Total time: 0.8 secs</span></span>
-<span id="cb9-12"><a href="#cb9-12" tabindex="-1"></a></span>
-<span id="cb9-13"><a href="#cb9-13" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(<span class="fu">head</span>(msdata<span class="sc">$</span>MS1, <span class="dv">3</span>))</span></code></pre></div>
-<table>
-<thead>
-<tr class="header">
-<th align="right">rt</th>
-<th align="right">mz</th>
-<th align="right">int</th>
-<th align="left">filename</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">4.009</td>
-<td align="right">139.0503</td>
-<td align="right">1800550.12</td>
-<td align="left">LB12HL_AB.mzML.gz</td>
-</tr>
-<tr class="even">
-<td align="right">4.009</td>
-<td align="right">148.0967</td>
-<td align="right">206310.81</td>
-<td align="left">LB12HL_AB.mzML.gz</td>
-</tr>
-<tr class="odd">
-<td align="right">4.009</td>
-<td align="right">136.0618</td>
-<td align="right">71907.15</td>
-<td align="left">LB12HL_AB.mzML.gz</td>
-</tr>
-</tbody>
-</table>
-<p>So we’ve now got the <em>mz</em> column, corresponding to the
-mass-to-charge ratio (<em>m/z</em>) of an ion. This means that we can
-now filter our data for specific masses and separate out molecules with
-different masses.</p>
-<p>Note that this also makes the data much larger in R’s memory - so
-don’t go loading hundreds of files simultaneously. If that’s necessary,
-check out the section below on saving space.</p>
-<p>Because <code>RaMS</code> returns <a href="https://cran.r-project.org/package=data.table">data.tables</a>
-rather than normal <code>data.frame</code>s, indexing is super-fast and
-a bit more intuitive than with base R. Below, I also use the
-<code>pmppm</code> function from <code>RaMS</code> to produce a mass
-range from an initial mass and spectrometer accuracy (here, 5
-parts-per-million).</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a><span class="fu">library</span>(data.table)</span>
-<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a></span>
-<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a>adenine_mz <span class="ot">&lt;-</span> <span class="fl">136.06232</span></span>
-<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a></span>
-<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a>adenine_data <span class="ot">&lt;-</span> msdata<span class="sc">$</span>MS1[mz<span class="sc">%between%</span><span class="fu">pmppm</span>(adenine_mz, <span class="at">ppm=</span><span class="dv">5</span>)]</span>
-<span id="cb10-6"><a href="#cb10-6" tabindex="-1"></a></span>
-<span id="cb10-7"><a href="#cb10-7" tabindex="-1"></a><span class="fu">ggplot</span>(adenine_data) <span class="sc">+</span> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>rt, <span class="at">y=</span>int, <span class="at">color=</span>filename)) <span class="sc">+</span> <span class="fu">lims</span>(<span class="at">x=</span><span class="fu">c</span>(<span class="dv">4</span>, <span class="dv">9</span>))</span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-<p>This makes it easy to grab the data for multiple compounds of
-interest with a simple loop, provided here by the <code>purrr</code>
-package of the tidyverse:</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>mzs_of_interest <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="at">adenine=</span><span class="fl">136.06232</span>, <span class="at">valine=</span><span class="fl">118.0865</span>, <span class="at">homarine=</span><span class="fl">138.055503</span>)</span>
-<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a></span>
-<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a>mass_data <span class="ot">&lt;-</span> <span class="fu">imap_dfr</span>(mzs_of_interest, <span class="cf">function</span>(mz_i, name){</span>
-<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a>  <span class="fu">cbind</span>(msdata<span class="sc">$</span>MS1[mz<span class="sc">%between%</span><span class="fu">pmppm</span>(mz_i, <span class="at">ppm=</span><span class="dv">10</span>)], name)</span>
-<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a>})</span>
-<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a></span>
-<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a><span class="fu">ggplot</span>(mass_data) <span class="sc">+</span> </span>
-<span id="cb11-8"><a href="#cb11-8" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>rt, <span class="at">y=</span>int, <span class="at">color=</span>filename)) <span class="sc">+</span> </span>
-<span id="cb11-9"><a href="#cb11-9" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>name, <span class="at">ncol =</span> <span class="dv">1</span>, <span class="at">scales =</span> <span class="st">&quot;free_y&quot;</span>) <span class="sc">+</span></span>
-<span id="cb11-10"><a href="#cb11-10" tabindex="-1"></a>  <span class="fu">lims</span>(<span class="at">x=</span><span class="fu">c</span>(<span class="dv">4</span>, <span class="dv">9</span>))</span>
-<span id="cb11-11"><a href="#cb11-11" tabindex="-1"></a><span class="co">#&gt; Warning: Removed 1154 rows containing missing values (`geom_line()`).</span></span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-</div>
-<div id="moving-along-ms2-data" class="section level3">
-<h3>Moving along: MS2 data</h3>
-<p><code>RaMS</code> also handles MS<sup>2</sup> data elegantly. Request
-it with the “MS2” option for <code>grab_what</code>, although it’s often
-a good idea to grab the MS1 data alongside.</p>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(data_files[<span class="dv">1</span>], <span class="at">grab_what =</span> <span class="st">&quot;everything&quot;</span>)</span>
-<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a><span class="co">#&gt; Reading file DDApos_2.mzML.gz... 0.41 secs </span></span>
-<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="co">#&gt; Reading MS1 data...0.29 secs </span></span>
-<span id="cb12-5"><a href="#cb12-5" tabindex="-1"></a><span class="co">#&gt; Reading MS2 data...0.11 secs </span></span>
-<span id="cb12-6"><a href="#cb12-6" tabindex="-1"></a><span class="co">#&gt; Reading BPC...0.17 secs </span></span>
-<span id="cb12-7"><a href="#cb12-7" tabindex="-1"></a><span class="co">#&gt; Reading TIC...0.17 secs </span></span>
-<span id="cb12-8"><a href="#cb12-8" tabindex="-1"></a><span class="co">#&gt; Reading file metadata...0.37 secs </span></span>
-<span id="cb12-9"><a href="#cb12-9" tabindex="-1"></a><span class="co">#&gt; Binding files together into single data.table</span></span>
-<span id="cb12-10"><a href="#cb12-10" tabindex="-1"></a><span class="co">#&gt; Total time: 1.52 secs</span></span>
-<span id="cb12-11"><a href="#cb12-11" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(<span class="fu">head</span>(msdata<span class="sc">$</span>MS2, <span class="dv">3</span>))</span></code></pre></div>
-<table>
-<thead>
-<tr class="header">
-<th align="right">rt</th>
-<th align="right">premz</th>
-<th align="right">fragmz</th>
-<th align="right">int</th>
-<th align="right">voltage</th>
-<th align="left">filename</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">4.008767</td>
-<td align="right">104.071</td>
-<td align="right">54.06526</td>
-<td align="right">1067.209</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="even">
-<td align="right">4.008767</td>
-<td align="right">104.071</td>
-<td align="right">54.45449</td>
-<td align="right">1073.694</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="odd">
-<td align="right">4.008767</td>
-<td align="right">104.071</td>
-<td align="right">57.03421</td>
-<td align="right">2519.131</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-</tbody>
-</table>
-<p>DDA data can be plotted nicely with <code>ggplot2</code> as well.
-Typically it makes sense to filter for a precursor mass, then render the
-fragments obtained.</p>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>homarine_mz <span class="ot">&lt;-</span> <span class="fl">137.047678+1.007276</span></span>
-<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a></span>
-<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a>homarine_MS2 <span class="ot">&lt;-</span> msdata<span class="sc">$</span>MS2[premz<span class="sc">%between%</span><span class="fu">pmppm</span>(homarine_mz, <span class="dv">5</span>)]</span>
-<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a>homarine_MS2<span class="sc">$</span>int <span class="ot">&lt;-</span> homarine_MS2<span class="sc">$</span>int<span class="sc">/</span><span class="fu">max</span>(homarine_MS2<span class="sc">$</span>int)</span>
-<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a></span>
-<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a><span class="fu">ggplot</span>(homarine_MS2) <span class="sc">+</span></span>
-<span id="cb13-7"><a href="#cb13-7" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x=</span>fragmz, <span class="at">y=</span>int)) <span class="sc">+</span></span>
-<span id="cb13-8"><a href="#cb13-8" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x=</span>fragmz, <span class="at">xend=</span>fragmz, <span class="at">y=</span>int, <span class="at">yend=</span><span class="dv">0</span>)) <span class="sc">+</span></span>
-<span id="cb13-9"><a href="#cb13-9" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">breaks =</span> <span class="fu">c</span>(<span class="dv">0</span>, .<span class="dv">5</span>, <span class="dv">1</span>), <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;0%&quot;</span>, <span class="st">&quot;50%&quot;</span>, <span class="st">&quot;100%&quot;</span>)) <span class="sc">+</span></span>
-<span id="cb13-10"><a href="#cb13-10" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;Fragment m/z&quot;</span>, <span class="at">y=</span><span class="st">&quot;&quot;</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-<p>This is also the perfect place to enable some interactivity with
-packages such as <code>plotly</code>, making data exploration not only
-simple but also highly intuitive.</p>
-<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="do">## Not run to save space in the vignette:</span></span>
-<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a><span class="fu">library</span>(plotly)</span>
-<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(data_files, <span class="at">grab_what =</span> <span class="fu">c</span>(<span class="st">&quot;MS1&quot;</span>, <span class="st">&quot;MS2&quot;</span>, <span class="st">&quot;BPC&quot;</span>))</span>
-<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a></span>
-<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a>compound_MS1 <span class="ot">&lt;-</span> msdata<span class="sc">$</span>MS1 <span class="sc">%&gt;%</span> </span>
-<span id="cb14-6"><a href="#cb14-6" tabindex="-1"></a>  <span class="fu">filter</span>(mz<span class="sc">%between%</span><span class="fu">pmppm</span>(homarine_mz, <span class="dv">10</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb14-7"><a href="#cb14-7" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">str_detect</span>(filename, <span class="st">&quot;DDA&quot;</span>))</span>
-<span id="cb14-8"><a href="#cb14-8" tabindex="-1"></a></span>
-<span id="cb14-9"><a href="#cb14-9" tabindex="-1"></a>compound_MS2 <span class="ot">&lt;-</span> msdata<span class="sc">$</span>MS2[premz<span class="sc">%between%</span><span class="fu">pmppm</span>(homarine_mz, <span class="dv">10</span>)] <span class="sc">%&gt;%</span> </span>
-<span id="cb14-10"><a href="#cb14-10" tabindex="-1"></a>  <span class="fu">group_by</span>(rt) <span class="sc">%&gt;%</span></span>
-<span id="cb14-11"><a href="#cb14-11" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(int)) <span class="sc">%&gt;%</span></span>
-<span id="cb14-12"><a href="#cb14-12" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">frags=</span><span class="fu">paste</span>(</span>
-<span id="cb14-13"><a href="#cb14-13" tabindex="-1"></a>    <span class="fu">paste</span>(<span class="fu">round</span>(fragmz, <span class="at">digits =</span> <span class="dv">3</span>), <span class="fu">round</span>(int), <span class="at">sep =</span> <span class="st">&quot;: &quot;</span>), <span class="at">collapse =</span> <span class="st">&quot;</span><span class="sc">\n</span><span class="st">&quot;</span>),</span>
-<span id="cb14-14"><a href="#cb14-14" tabindex="-1"></a>    <span class="at">.groups=</span><span class="st">&quot;drop&quot;</span></span>
-<span id="cb14-15"><a href="#cb14-15" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
-<span id="cb14-16"><a href="#cb14-16" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">int=</span><span class="fu">approx</span>(<span class="at">x =</span> compound_MS1<span class="sc">$</span>rt, <span class="at">y=</span>compound_MS1<span class="sc">$</span>int, <span class="at">xout =</span> rt)<span class="sc">$</span>y)</span>
-<span id="cb14-17"><a href="#cb14-17" tabindex="-1"></a><span class="fu">plot_ly</span>(compound_MS1) <span class="sc">%&gt;%</span></span>
-<span id="cb14-18"><a href="#cb14-18" tabindex="-1"></a>  <span class="fu">add_trace</span>(<span class="at">type=</span><span class="st">&quot;scatter&quot;</span>, <span class="at">mode=</span><span class="st">&quot;lines&quot;</span>, <span class="at">x=</span><span class="sc">~</span>rt, <span class="at">y=</span><span class="sc">~</span>int, <span class="at">color=</span><span class="sc">~</span>filename,</span>
-<span id="cb14-19"><a href="#cb14-19" tabindex="-1"></a>            <span class="at">hoverinfo=</span><span class="st">&quot;none&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb14-20"><a href="#cb14-20" tabindex="-1"></a>  <span class="fu">add_trace</span>(<span class="at">type=</span><span class="st">&quot;scatter&quot;</span>, <span class="at">mode=</span><span class="st">&quot;markers&quot;</span>, <span class="at">x=</span><span class="sc">~</span>rt, <span class="at">y=</span><span class="sc">~</span>int,</span>
-<span id="cb14-21"><a href="#cb14-21" tabindex="-1"></a>            <span class="at">text=</span><span class="sc">~</span>frags, <span class="at">hoverinfo=</span><span class="st">&quot;text&quot;</span>, <span class="at">showlegend=</span><span class="cn">FALSE</span>,</span>
-<span id="cb14-22"><a href="#cb14-22" tabindex="-1"></a>            <span class="at">marker=</span><span class="fu">list</span>(<span class="at">color=</span><span class="st">&quot;black&quot;</span>), <span class="at">data =</span> compound_MS2) <span class="sc">%&gt;%</span></span>
-<span id="cb14-23"><a href="#cb14-23" tabindex="-1"></a>  <span class="fu">layout</span>(<span class="at">annotations=</span><span class="fu">list</span>(<span class="at">x=</span><span class="fu">min</span>(compound_MS2<span class="sc">$</span>rt), <span class="at">y=</span><span class="fu">median</span>(compound_MS2<span class="sc">$</span>int)<span class="sc">*</span><span class="dv">10</span>, </span>
-<span id="cb14-24"><a href="#cb14-24" tabindex="-1"></a>                          <span class="at">text=</span><span class="st">&quot;Mouse over to see</span><span class="sc">\n</span><span class="st">MSMS fragments&quot;</span>))</span></code></pre></div>
-<p>Easy access to MS<sup>2</sup> data also allows us to rapidly perform
-simple operations such as searching for a specific fragment mass. For
-example, if we know that homarine typically produces a fragment with a
-mass of 94.06567, we simply subset the MS<sup>2</sup> data for fragments
-in a range around that mass:</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>homarine_frag_mz <span class="ot">&lt;-</span> <span class="fl">94.06567</span></span>
-<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a>msdata<span class="sc">$</span>MS2[fragmz<span class="sc">%between%</span><span class="fu">pmppm</span>(homarine_frag_mz, <span class="at">ppm =</span> <span class="dv">5</span>)] <span class="sc">%&gt;%</span></span>
-<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a>  <span class="fu">head</span>() <span class="sc">%&gt;%</span> knitr<span class="sc">::</span><span class="fu">kable</span>()</span></code></pre></div>
-<table>
-<thead>
-<tr class="header">
-<th align="right">rt</th>
-<th align="right">premz</th>
-<th align="right">fragmz</th>
-<th align="right">int</th>
-<th align="right">voltage</th>
-<th align="left">filename</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">4.202267</td>
-<td align="right">138.0550</td>
-<td align="right">94.06548</td>
-<td align="right">2639.737</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="even">
-<td align="right">4.561233</td>
-<td align="right">138.0550</td>
-<td align="right">94.06560</td>
-<td align="right">4301.696</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="odd">
-<td align="right">4.900883</td>
-<td align="right">138.0550</td>
-<td align="right">94.06550</td>
-<td align="right">1678.091</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="even">
-<td align="right">5.719433</td>
-<td align="right">138.0550</td>
-<td align="right">94.06572</td>
-<td align="right">32145.883</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="odd">
-<td align="right">5.832950</td>
-<td align="right">139.0520</td>
-<td align="right">94.06569</td>
-<td align="right">698355.375</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="even">
-<td align="right">6.063700</td>
-<td align="right">138.0549</td>
-<td align="right">94.06567</td>
-<td align="right">15944193.000</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-</tbody>
-</table>
-<p>We find that there’s not only homarine that produces that fragment,
-but at least one other compound. Precursor masses like this can then be
-searched manually in online databases or, since the data is already in
-R, passed to a script that automatically queries them.</p>
-<p>Similarly, we can easily search instead for neutral losses with this
-method. If we suspect other molecules are producing a similar neutral
-loss as homarine:</p>
-<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>homarine_neutral_loss <span class="ot">&lt;-</span> homarine_mz <span class="sc">-</span> homarine_frag_mz</span>
-<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a></span>
-<span id="cb16-3"><a href="#cb16-3" tabindex="-1"></a>msdata<span class="sc">$</span>MS2 <span class="ot">&lt;-</span> <span class="fu">mutate</span>(msdata<span class="sc">$</span>MS2, <span class="at">neutral_loss=</span>premz<span class="sc">-</span>fragmz) <span class="sc">%&gt;%</span></span>
-<span id="cb16-4"><a href="#cb16-4" tabindex="-1"></a>  <span class="fu">select</span>(<span class="st">&quot;rt&quot;</span>, <span class="st">&quot;premz&quot;</span>, <span class="st">&quot;fragmz&quot;</span>, <span class="st">&quot;neutral_loss&quot;</span>, <span class="st">&quot;int&quot;</span>, <span class="st">&quot;voltage&quot;</span>, <span class="st">&quot;filename&quot;</span>)</span>
-<span id="cb16-5"><a href="#cb16-5" tabindex="-1"></a>msdata<span class="sc">$</span>MS2[neutral_loss<span class="sc">%between%</span><span class="fu">pmppm</span>(homarine_neutral_loss, <span class="at">ppm =</span> <span class="dv">5</span>)] <span class="sc">%&gt;%</span></span>
-<span id="cb16-6"><a href="#cb16-6" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(int)) <span class="sc">%&gt;%</span> <span class="fu">head</span>() <span class="sc">%&gt;%</span> knitr<span class="sc">::</span><span class="fu">kable</span>()</span></code></pre></div>
-<table>
-<colgroup>
-<col width="12%" />
-<col width="11%" />
-<col width="11%" />
-<col width="16%" />
-<col width="14%" />
-<col width="10%" />
-<col width="22%" />
-</colgroup>
-<thead>
-<tr class="header">
-<th align="right">rt</th>
-<th align="right">premz</th>
-<th align="right">fragmz</th>
-<th align="right">neutral_loss</th>
-<th align="right">int</th>
-<th align="right">voltage</th>
-<th align="left">filename</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">6.063700</td>
-<td align="right">138.0549</td>
-<td align="right">94.06567</td>
-<td align="right">43.98918</td>
-<td align="right">15944193.0</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="even">
-<td align="right">6.399933</td>
-<td align="right">138.0549</td>
-<td align="right">94.06569</td>
-<td align="right">43.98921</td>
-<td align="right">11094394.0</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="odd">
-<td align="right">8.438167</td>
-<td align="right">138.0549</td>
-<td align="right">94.06567</td>
-<td align="right">43.98921</td>
-<td align="right">1218680.0</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="even">
-<td align="right">11.810417</td>
-<td align="right">104.0710</td>
-<td align="right">60.08154</td>
-<td align="right">43.98949</td>
-<td align="right">556172.8</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="odd">
-<td align="right">11.469117</td>
-<td align="right">104.0710</td>
-<td align="right">60.08154</td>
-<td align="right">43.98947</td>
-<td align="right">509088.7</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-<tr class="even">
-<td align="right">6.739650</td>
-<td align="right">138.0550</td>
-<td align="right">94.06568</td>
-<td align="right">43.98931</td>
-<td align="right">334129.3</td>
-<td align="right">35</td>
-<td align="left">DDApos_2.mzML.gz</td>
-</tr>
-</tbody>
-</table>
-<p>We can again confirm our suspicions that there’s another signal with
-a similar neutral loss: one with a mass of 104.0710.</p>
-</div>
-<div id="continuing-chromatograms" class="section level3">
-<h3>Continuing: chromatograms!</h3>
-<p>mzMLs can also contain precompiled chromatograms. Version 1.3 of RaMS
-enabled the extraction of these chromatograms just like MS<sup>1</sup>
-or MS<sup>2</sup> data and added the new <code>chroms</code> slot to
-hold them. However, these need to be requested using
-<code>grab_what = &quot;chroms&quot;</code>and will create this new slot in the
-returned object. Chromatograms are returned as a <code>data.table</code>
-with seven columns: chromatogram type (usually TIC, BPC or SRM info),
-chromatogram index, target mz, product mz, retention time (rt), and
-intensity (int). This allows simple plotting of various chromatograms
-such as those returned by MRM:</p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a>chrom_file <span class="ot">&lt;-</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;wk_chrom.mzML.gz&quot;</span>, <span class="at">package =</span> <span class="st">&quot;RaMS&quot;</span>)</span>
-<span id="cb17-2"><a href="#cb17-2" tabindex="-1"></a>msdata_chroms <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(chrom_file, <span class="at">verbosity =</span> <span class="dv">0</span>, <span class="at">grab_what =</span> <span class="st">&quot;chroms&quot;</span>)</span>
-<span id="cb17-3"><a href="#cb17-3" tabindex="-1"></a>given_chrom <span class="ot">&lt;-</span> msdata_chroms<span class="sc">$</span>chroms[chrom_type<span class="sc">==</span><span class="st">&quot;SRM iletter1&quot;</span>]</span>
-<span id="cb17-4"><a href="#cb17-4" tabindex="-1"></a>ptitle <span class="ot">&lt;-</span> <span class="fu">with</span>(given_chrom, <span class="fu">paste0</span>(</span>
-<span id="cb17-5"><a href="#cb17-5" tabindex="-1"></a>  <span class="fu">unique</span>(chrom_type), <span class="st">&quot;: Target m/z = &quot;</span>, <span class="fu">unique</span>(target_mz), <span class="st">&quot;; Product m/z = &quot;</span>, </span>
-<span id="cb17-6"><a href="#cb17-6" tabindex="-1"></a>  <span class="fu">unique</span>(product_mz)</span>
-<span id="cb17-7"><a href="#cb17-7" tabindex="-1"></a>))</span>
-<span id="cb17-8"><a href="#cb17-8" tabindex="-1"></a><span class="fu">plot</span>(given_chrom<span class="sc">$</span>rt, given_chrom<span class="sc">$</span>int, <span class="at">type=</span><span class="st">&quot;l&quot;</span>, <span class="at">main=</span>ptitle)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-<p>Version 1.3 also enabled support for DAD (diode array detection)
-data. This data type can be requested using <code>grab_what = DAD</code>
-and will return an additional item in the list with columns for
-retention time, wavelength, and intensity.</p>
-</div>
-</div>
-<div id="advanced-rams-usage" class="section level2">
-<h2>Advanced RaMS usage</h2>
-<p>The sections below will likely be of interest to users who are
-already familiar with <code>RaMS</code> and are looking to optimize
-speed, reduce memory requirements, or are otherwise interested in the
-details of what <code>RaMS</code> does under the hood. If you’re just
-getting started, I strongly recommend applying <code>RaMS</code> to your
-own data before you read on. For a more detailed analysis of the first
-two sections on saving space and speeding things up, consider also
-browsing the <a href="https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html">speed
-&amp; size comparison vignette</a></p>
-<div id="saving-space-eics-and-rtrange" class="section level3">
-<h3>Saving space: EICs and rtrange</h3>
-<p>Mass-spec files are typically tens or hundreds of megabytes in size,
-which means that the simultaneous analysis of many files can easily
-exceed a computer’s memory. Since <code>RaMS</code> stores all data in
-R’s working memory, this can become a problem for large analyses.</p>
-<p>However, much of the usage envisioned for <code>RaMS</code> on this
-scale doesn’t require access to the entire file, the entire time.
-Instead, users are typically interested in a few masses of interest or a
-specific time window. This means that while each file still needs to be
-read into R in full to find the data of interest, extraneous data can be
-discarded before the next file is loaded.</p>
-<p>This functionality can be enabled by passing “EIC” and/or “EIC_MS2”
-to the <code>grab_what</code> argument of <code>grabMSdata</code>, along
-with a vector of masses to extract (mz) and the instrument’s ppm
-accuracy. When this is enabled, files are read into R’s memory
-sequentially, the mass window is extracted, and the rest of the data is
-discarded.</p>
-<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>all_data <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(data_files, <span class="at">grab_what =</span> <span class="fu">c</span>(<span class="st">&quot;MS1&quot;</span>, <span class="st">&quot;MS2&quot;</span>))</span>
-<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb18-4"><a href="#cb18-4" tabindex="-1"></a>  <span class="er">|</span>                                                                      <span class="er">|</span>   <span class="dv">0</span>%</span>
-<span id="cb18-5"><a href="#cb18-5" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb18-6"><a href="#cb18-6" tabindex="-1"></a>  <span class="er">|==================</span>                                                    <span class="er">|</span>  <span class="dv">25</span>%</span>
-<span id="cb18-7"><a href="#cb18-7" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb18-8"><a href="#cb18-8" tabindex="-1"></a>  <span class="er">|===================================</span>                                   <span class="er">|</span>  <span class="dv">50</span>%</span>
-<span id="cb18-9"><a href="#cb18-9" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb18-10"><a href="#cb18-10" tabindex="-1"></a>  <span class="er">|====================================================</span>                  <span class="er">|</span>  <span class="dv">75</span>%</span>
-<span id="cb18-11"><a href="#cb18-11" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb18-12"><a href="#cb18-12" tabindex="-1"></a>  <span class="er">|======================================================================|</span> <span class="dv">100</span>%</span>
-<span id="cb18-13"><a href="#cb18-13" tabindex="-1"></a><span class="co">#&gt; Total time: 1.72 secs</span></span>
-<span id="cb18-14"><a href="#cb18-14" tabindex="-1"></a></span>
-<span id="cb18-15"><a href="#cb18-15" tabindex="-1"></a>mzs_of_interest <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="at">adenine=</span><span class="fl">136.06232</span>, <span class="at">valine=</span><span class="fl">118.0865</span>)</span>
-<span id="cb18-16"><a href="#cb18-16" tabindex="-1"></a>small_data <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(data_files, <span class="at">grab_what =</span> <span class="fu">c</span>(<span class="st">&quot;EIC&quot;</span>, <span class="st">&quot;EIC_MS2&quot;</span>),</span>
-<span id="cb18-17"><a href="#cb18-17" tabindex="-1"></a>                         <span class="at">mz=</span>mzs_of_interest, <span class="at">ppm =</span> <span class="dv">5</span>)</span>
-<span id="cb18-18"><a href="#cb18-18" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb18-19"><a href="#cb18-19" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb18-20"><a href="#cb18-20" tabindex="-1"></a>  <span class="er">|</span>                                                                      <span class="er">|</span>   <span class="dv">0</span>%</span>
-<span id="cb18-21"><a href="#cb18-21" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb18-22"><a href="#cb18-22" tabindex="-1"></a>  <span class="er">|==================</span>                                                    <span class="er">|</span>  <span class="dv">25</span>%</span>
-<span id="cb18-23"><a href="#cb18-23" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb18-24"><a href="#cb18-24" tabindex="-1"></a>  <span class="er">|===================================</span>                                   <span class="er">|</span>  <span class="dv">50</span>%</span>
-<span id="cb18-25"><a href="#cb18-25" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb18-26"><a href="#cb18-26" tabindex="-1"></a>  <span class="er">|====================================================</span>                  <span class="er">|</span>  <span class="dv">75</span>%</span>
-<span id="cb18-27"><a href="#cb18-27" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb18-28"><a href="#cb18-28" tabindex="-1"></a>  <span class="er">|======================================================================|</span> <span class="dv">100</span>%</span>
-<span id="cb18-29"><a href="#cb18-29" tabindex="-1"></a><span class="co">#&gt; Total time: 1.63 secs</span></span>
-<span id="cb18-30"><a href="#cb18-30" tabindex="-1"></a></span>
-<span id="cb18-31"><a href="#cb18-31" tabindex="-1"></a>all_data<span class="sc">$</span>MS1 <span class="sc">%&gt;%</span></span>
-<span id="cb18-32"><a href="#cb18-32" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">type=</span><span class="st">&quot;All data&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb18-33"><a href="#cb18-33" tabindex="-1"></a>  <span class="fu">rbind</span>(small_data<span class="sc">$</span>EIC <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">type=</span><span class="st">&quot;Extracted data only&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb18-34"><a href="#cb18-34" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">str_detect</span>(filename, <span class="st">&quot;DDA&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb18-35"><a href="#cb18-35" tabindex="-1"></a>  <span class="fu">filter</span>(rt<span class="sc">%between%</span><span class="fu">c</span>(<span class="dv">5</span>, <span class="dv">15</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb18-36"><a href="#cb18-36" tabindex="-1"></a>  <span class="fu">group_by</span>(rt, filename, type) <span class="sc">%&gt;%</span></span>
-<span id="cb18-37"><a href="#cb18-37" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">TIC=</span><span class="fu">sum</span>(int), <span class="at">.groups=</span><span class="st">&quot;drop&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb18-38"><a href="#cb18-38" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span></span>
-<span id="cb18-39"><a href="#cb18-39" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>rt, <span class="at">y=</span>TIC, <span class="at">color=</span>filename)) <span class="sc">+</span></span>
-<span id="cb18-40"><a href="#cb18-40" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>type, <span class="at">ncol =</span> <span class="dv">1</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a></span>
-<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a><span class="co"># Size reduction factor:</span></span>
-<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a><span class="fu">as.numeric</span>(<span class="fu">object.size</span>(all_data)<span class="sc">/</span><span class="fu">object.size</span>(small_data))</span>
-<span id="cb19-4"><a href="#cb19-4" tabindex="-1"></a><span class="co">#&gt; [1] 16.10698</span></span></code></pre></div>
-<p>As expected, the size of the <code>small_data</code> object is much
-smaller than the <code>all_data</code> object, here by a factor of about
-15x. For files that haven’t already been “minified”, that size reduction
-will be even more significant. Of course, this comes with the cost of
-needing to re-load the data a second time if a new mass feature becomes
-of interest but this shrinkage is especially valuable for targeted
-analyses where the analytes of interest are known in advance.</p>
-<p>A second way of reducing file size is to constrain the retention time
-dimension rather than the m/z dimension. This can be done with the
-<code>rtrange</code> argument, which expects a length-two vector
-corresponding to the upper and lower bounds on retention time. This is
-useful when a small time window is of interest, and only the data
-between those bounds is relevant. Below, peaks are deliberately sliced
-in half to show that this is filtering on retention time instead of
-mass.</p>
-<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a>small_data <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(data_files, <span class="at">grab_what =</span> <span class="fu">c</span>(<span class="st">&quot;MS1&quot;</span>, <span class="st">&quot;MS2&quot;</span>), <span class="at">rtrange =</span> <span class="fu">c</span>(<span class="dv">6</span>, <span class="dv">8</span>))</span>
-<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb20-3"><a href="#cb20-3" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb20-4"><a href="#cb20-4" tabindex="-1"></a>  <span class="er">|</span>                                                                      <span class="er">|</span>   <span class="dv">0</span>%</span>
-<span id="cb20-5"><a href="#cb20-5" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb20-6"><a href="#cb20-6" tabindex="-1"></a>  <span class="er">|==================</span>                                                    <span class="er">|</span>  <span class="dv">25</span>%</span>
-<span id="cb20-7"><a href="#cb20-7" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb20-8"><a href="#cb20-8" tabindex="-1"></a>  <span class="er">|===================================</span>                                   <span class="er">|</span>  <span class="dv">50</span>%</span>
-<span id="cb20-9"><a href="#cb20-9" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb20-10"><a href="#cb20-10" tabindex="-1"></a>  <span class="er">|====================================================</span>                  <span class="er">|</span>  <span class="dv">75</span>%</span>
-<span id="cb20-11"><a href="#cb20-11" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb20-12"><a href="#cb20-12" tabindex="-1"></a>  <span class="er">|======================================================================|</span> <span class="dv">100</span>%</span>
-<span id="cb20-13"><a href="#cb20-13" tabindex="-1"></a><span class="co">#&gt; Total time: 1.28 secs</span></span>
-<span id="cb20-14"><a href="#cb20-14" tabindex="-1"></a></span>
-<span id="cb20-15"><a href="#cb20-15" tabindex="-1"></a>all_data<span class="sc">$</span>MS1 <span class="sc">%&gt;%</span></span>
-<span id="cb20-16"><a href="#cb20-16" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">type=</span><span class="st">&quot;All data&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb20-17"><a href="#cb20-17" tabindex="-1"></a>  <span class="fu">rbind</span>(small_data<span class="sc">$</span>MS1 <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">type=</span><span class="st">&quot;Extracted data only&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb20-18"><a href="#cb20-18" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">str_detect</span>(filename, <span class="st">&quot;DDA&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb20-19"><a href="#cb20-19" tabindex="-1"></a>  <span class="fu">group_by</span>(rt, filename, type) <span class="sc">%&gt;%</span></span>
-<span id="cb20-20"><a href="#cb20-20" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">TIC=</span><span class="fu">sum</span>(int), <span class="at">.groups=</span><span class="st">&quot;drop&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb20-21"><a href="#cb20-21" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span></span>
-<span id="cb20-22"><a href="#cb20-22" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>rt, <span class="at">y=</span>TIC, <span class="at">color=</span>filename)) <span class="sc">+</span></span>
-<span id="cb20-23"><a href="#cb20-23" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>type, <span class="at">ncol =</span> <span class="dv">1</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a></span>
-<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a><span class="co"># Size reduction factor:</span></span>
-<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a><span class="fu">as.numeric</span>(<span class="fu">object.size</span>(all_data)<span class="sc">/</span><span class="fu">object.size</span>(small_data))</span>
-<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a><span class="co">#&gt; [1] 6.731661</span></span></code></pre></div>
-<p>The savings are also worth noting here, but constraining the
-retention time also speeds up data retrieval slightly. Since
-<em>m/z</em> and intensity data is encoded in mzML and mzXML files while
-retention time information is not, eliminating scans with a retention
-time window removes the need to decode the intensity and <em>m/z</em>
-information in those scans.</p>
-<p>However, decoding is rarely the rate-limiting step and for more
-information about speeding things up, continue to the next section.</p>
-</div>
-<div id="speeding-things-up" class="section level3">
-<h3>Speeding things up</h3>
-<p>So, <code>RaMS</code> isn’t fast enough for you? Let’s see what we
-can do to improve that. The first step in speeding things up is
-discovering what’s slow. Typically, this is the process of reading in
-the mzML/mzXML file rather than any processing that occurs afterward,
-but this is not always the case. To examine bottlenecks,
-<code>RaMS</code> includes timing information that’s produced if the
-<code>verbosity</code> argument is set to 2 or higher.</p>
-<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a>all_data <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(data_files, <span class="at">grab_what =</span> <span class="fu">c</span>(<span class="st">&quot;MS1&quot;</span>, <span class="st">&quot;MS2&quot;</span>), <span class="at">verbosity =</span> <span class="dv">2</span>)</span>
-<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a>  <span class="er">|</span>                                                                      <span class="er">|</span>   <span class="dv">0</span>%</span>
-<span id="cb22-5"><a href="#cb22-5" tabindex="-1"></a><span class="co">#&gt; Reading file DDApos_2.mzML.gz... 0.43 secs </span></span>
-<span id="cb22-6"><a href="#cb22-6" tabindex="-1"></a><span class="co">#&gt; Reading MS1 data...0.3 secs </span></span>
-<span id="cb22-7"><a href="#cb22-7" tabindex="-1"></a><span class="co">#&gt; Reading MS2 data...0.11 secs </span></span>
-<span id="cb22-8"><a href="#cb22-8" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb22-9"><a href="#cb22-9" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb22-10"><a href="#cb22-10" tabindex="-1"></a>  <span class="er">|==================</span>                                                    <span class="er">|</span>  <span class="dv">25</span>%</span>
-<span id="cb22-11"><a href="#cb22-11" tabindex="-1"></a><span class="co">#&gt; Reading file LB12HL_AB.mzML.gz... 0.15 secs </span></span>
-<span id="cb22-12"><a href="#cb22-12" tabindex="-1"></a><span class="co">#&gt; Reading MS1 data...0.12 secs </span></span>
-<span id="cb22-13"><a href="#cb22-13" tabindex="-1"></a><span class="co">#&gt; Reading MS2 data...0.01 secs </span></span>
-<span id="cb22-14"><a href="#cb22-14" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb22-15"><a href="#cb22-15" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb22-16"><a href="#cb22-16" tabindex="-1"></a>  <span class="er">|===================================</span>                                   <span class="er">|</span>  <span class="dv">50</span>%</span>
-<span id="cb22-17"><a href="#cb22-17" tabindex="-1"></a><span class="co">#&gt; Reading file LB12HL_CD.mzML.gz... 0.15 secs </span></span>
-<span id="cb22-18"><a href="#cb22-18" tabindex="-1"></a><span class="co">#&gt; Reading MS1 data...0.12 secs </span></span>
-<span id="cb22-19"><a href="#cb22-19" tabindex="-1"></a><span class="co">#&gt; Reading MS2 data...0.01 secs </span></span>
-<span id="cb22-20"><a href="#cb22-20" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb22-21"><a href="#cb22-21" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb22-22"><a href="#cb22-22" tabindex="-1"></a>  <span class="er">|====================================================</span>                  <span class="er">|</span>  <span class="dv">75</span>%</span>
-<span id="cb22-23"><a href="#cb22-23" tabindex="-1"></a><span class="co">#&gt; Reading file LB12HL_EF.mzML.gz... 0.13 secs </span></span>
-<span id="cb22-24"><a href="#cb22-24" tabindex="-1"></a><span class="co">#&gt; Reading MS1 data...0.11 secs </span></span>
-<span id="cb22-25"><a href="#cb22-25" tabindex="-1"></a><span class="co">#&gt; Reading MS2 data...0.01 secs </span></span>
-<span id="cb22-26"><a href="#cb22-26" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb22-27"><a href="#cb22-27" tabindex="-1"></a>  <span class="sc">|</span>                                                                            </span>
-<span id="cb22-28"><a href="#cb22-28" tabindex="-1"></a>  <span class="er">|======================================================================|</span> <span class="dv">100</span>%</span>
-<span id="cb22-29"><a href="#cb22-29" tabindex="-1"></a><span class="co">#&gt; Binding files together into single data.table</span></span>
-<span id="cb22-30"><a href="#cb22-30" tabindex="-1"></a><span class="co">#&gt; Total time: 1.65 secs</span></span></code></pre></div>
-<p>In general, slow file read times can be improved by compressing the
-data. mzML files are highly compressible, with options to compress both
-the data itself using the <code>zlib</code> method and the files as a
-whole using <code>gzip</code>.</p>
-<p><code>gzip</code> is the simplest one to use, as a plethora of
-methods exist to compress files this way, including online sites.
-<code>RaMS</code> can read the data directly from a gzipped file, no
-decompression necessary, so this is an easy way to reduce file size and
-read times. Sharp-eyed users will have noticed that the demo files are
-already gzipped, which is part of the reason they are so small.</p>
-<p><code>zlib</code> compression is slightly trickier, and is most often
-performed with tools such as <a href="https://proteowizard.sourceforge.io/doc_users.html">Proteowizard</a>’s
-<code>msconvert</code> tool with the option “-z”.</p>
-<p>Read times may also be slow if files are being accessed via the
-Internet, either through a VPN or network drive. If your files are
-stored elsewhere, consider first moving those files somewhere more local
-before reading data from them.</p>
-<p>If the bottleneck appears when reading MS<sup>1</sup> data, consider
-restricting the retention time range with the <code>rtrange</code>
-argument or using more detailed profiling tools such as RStudio’s
-“Profile” options or the <code>profvis</code> package. Pull requests
-that improve data processing speed are always welcome!</p>
-<p>While the package is not currently set up for parallel processing,
-this is a potential future feature if a strong need is demonstrated.</p>
-</div>
-<div id="finer-control-grabmzmldata-grabmzxmldata" class="section level3">
-<h3>Finer control: grabMzmlData, grabMzxmlData</h3>
-<p>While there’s only one main function (<code>grabMSdata</code>) in
-<code>RaMS</code>, you may have noticed that two other functions have
-been exposed that perform similar tasks: <code>grabMzmlData</code> and
-<code>grabMzxmlData</code>. The main function <code>grabMSdata</code>
-serves as a wrapper around these two functions, which detects the file
-type, adds the “filename” column to the data, and loops over multiple
-files if provided. However, there’s often reason to use these internal
-functions separately.</p>
-<p>For one, the objects themselves are smaller because they don’t have
-the filename column attached yet. You as the user will need to keep
-track of which data belongs to which files in this case.</p>
-<p>Another use case might be applying functions to each file
-individually, perhaps aligning to a reference chromatogram or
-identifying peaks. Rather than spending the time to bind the files
-together and immediately separate them again, these functions have been
-exposed to skip that step.</p>
-<p>Finally, these functions are useful for parallelization. Because
-iterating over each mass-spec file is often the largest reasonable
-chunk, these functions can be passed directly to parallel processes like
-<code>mclapply</code> or <code>doParallel</code>. However,
-parallelization is a beast best handled by individual users because its
-actual implementation often differs wildly and its utility depends
-strongly on individual setups (remember that parallelization won’t help
-with slow I/O times, so it may not always improve data processing
-speed.)</p>
-<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="do">## Not run:</span></span>
-<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a><span class="fu">library</span>(parallel)</span>
-<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a>cl <span class="ot">&lt;-</span> <span class="fu">makeCluster</span>(<span class="fu">getOption</span>(<span class="st">&quot;cl.cores&quot;</span>, <span class="fu">detectCores</span>()<span class="sc">-</span><span class="dv">1</span>))</span>
-<span id="cb23-4"><a href="#cb23-4" tabindex="-1"></a>output_data <span class="ot">&lt;-</span> <span class="fu">parLapply</span>(data_files, grabMzmlData, <span class="at">grab_what=</span><span class="st">&quot;everything&quot;</span>, <span class="at">cl =</span> cl)</span>
-<span id="cb23-5"><a href="#cb23-5" tabindex="-1"></a></span>
-<span id="cb23-6"><a href="#cb23-6" tabindex="-1"></a><span class="fu">library</span>(foreach)</span>
-<span id="cb23-7"><a href="#cb23-7" tabindex="-1"></a><span class="fu">library</span>(doParallel)</span>
-<span id="cb23-8"><a href="#cb23-8" tabindex="-1"></a><span class="fu">registerDoParallel</span>(<span class="fu">detectCores</span>()<span class="sc">-</span><span class="dv">1</span>)</span>
-<span id="cb23-9"><a href="#cb23-9" tabindex="-1"></a>output_data <span class="ot">&lt;-</span> <span class="fu">foreach</span> (<span class="at">i=</span>data_files) <span class="sc">%dopar%</span> {</span>
-<span id="cb23-10"><a href="#cb23-10" tabindex="-1"></a>  RaMS<span class="sc">::</span><span class="fu">grabMzmlData</span>(i, <span class="at">grab_what=</span><span class="st">&quot;everything&quot;</span>)</span>
-<span id="cb23-11"><a href="#cb23-11" tabindex="-1"></a>}</span>
-<span id="cb23-12"><a href="#cb23-12" tabindex="-1"></a><span class="fu">stopImplicitCluster</span>()</span></code></pre></div>
-</div>
-<div id="the-nitty-gritty-details" class="section level3">
-<h3>The nitty-gritty details</h3>
-<p><code>RaMS</code> is possible because mzML and mzXML documents are
-fundamentally <a href="https://en.wikipedia.org/wiki/XML">XML</a>-based.
-This means that we can leverage speedy and robust XML parsing packages
-such as <code>xml2</code> to extract the data. Fundamentally,
-<code>RaMS</code> relies on <a href="https://www.w3schools.com/xml/xpath_intro.asp">XPath</a>
-navigation to collect various bits of mass-spec data, and the format of
-mzML and mzXML files provides the tags necessary. That means a lot of
-<code>RaMS</code> code consists of lines like the following:</p>
-<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a><span class="do">## Not run:</span></span>
-<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a>data_nodes <span class="ot">&lt;-</span> xml2<span class="sc">::</span><span class="fu">xml_find_all</span>(mzML_nodes, <span class="at">xpath=</span><span class="st">&quot;//d1:precursorMz&quot;</span>)</span>
-<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a>raw_data <span class="ot">&lt;-</span> xml2<span class="sc">::</span><span class="fu">xml_attr</span>(data_nodes, <span class="st">&quot;value&quot;</span>)</span></code></pre></div>
-<p>…plus a lot of data handling to get the output into the tidy
-format.</p>
-<p>The other tricky bit of data extraction is converting the (possibly
-compressed) binary data into R-friendly objects. This is usually handled
-with code like that shown below. Many of the settings can be deduced
-from the file, but sometimes compression types need to be guessed at and
-will throw a warning if so.</p>
-<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="do">## Not run:</span></span>
-<span id="cb25-2"><a href="#cb25-2" tabindex="-1"></a>decoded_binary <span class="ot">&lt;-</span> base64enc<span class="sc">::</span><span class="fu">base64decode</span>(binary)</span>
-<span id="cb25-3"><a href="#cb25-3" tabindex="-1"></a>raw_binary <span class="ot">&lt;-</span> <span class="fu">as.raw</span>(decoded_binary)</span>
-<span id="cb25-4"><a href="#cb25-4" tabindex="-1"></a>decomp_binary <span class="ot">&lt;-</span> <span class="fu">memDecompress</span>(raw_binary, <span class="at">type =</span> file_metadata<span class="sc">$</span>compression)</span>
-<span id="cb25-5"><a href="#cb25-5" tabindex="-1"></a>final_binary <span class="ot">&lt;-</span> <span class="fu">readBin</span>(decomp_binary, <span class="at">what =</span> <span class="st">&quot;double&quot;</span>,</span>
-<span id="cb25-6"><a href="#cb25-6" tabindex="-1"></a>                        <span class="at">n=</span><span class="fu">length</span>(decomp_binary)<span class="sc">/</span>file_metadata<span class="sc">$</span>mz_precision,</span>
-<span id="cb25-7"><a href="#cb25-7" tabindex="-1"></a>                        <span class="at">size =</span> file_metadata<span class="sc">$</span>mz_precision)</span>
-<span id="cb25-8"><a href="#cb25-8" tabindex="-1"></a></span>
-<span id="cb25-9"><a href="#cb25-9" tabindex="-1"></a><span class="co"># See https://github.com/ProteoWizard/pwiz/issues/1301</span></span></code></pre></div>
-<p>Fundamentally, mass-spectrometry data is formatted as a <a href="https://en.wikipedia.org/wiki/Jagged_array">ragged array</a>, with
-an unknown number of <em>m/z</em> and intensity values for a given scan.
-This makes it difficult to encode neatly without interpolating, but tidy
-data provides a solution by stacking those arrays rather than aligning
-them into some sort of matrix.</p>
-<p>This ragged shape is also the reason that subsetting mass-spec data
-by retention time is trivial - grab the scans that correspond to the
-desired retention times and you’re done. Subsetting by mass, on the
-other hand, requires decoding each and every scan’s <em>m/z</em> and
-intensity data. If you’re reading a book and only want a couple
-chapters, it’s easy to flip to those sections. If you’re looking instead
-for every time a specific word shows up, you’ve gotta read the whole
-thing.</p>
-<p>For more information about the mzML data format and its history,
-check out the specification at <a href="https://www.psidev.info/mzML" class="uri">https://www.psidev.info/mzML</a>.</p>
-<hr />
-<p>Vignette last built on 2023-12-13</p>
-</div>
-</div>
-
-
-
-<!-- code folding -->
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
-  (function () {
-    var script = document.createElement("script");
-    script.type = "text/javascript";
-    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
-    document.getElementsByTagName("head")[0].appendChild(script);
-  })();
-</script>
-
-</body>
-</html>
diff --git a/doc/Intro-to-tmzML.R b/doc/Intro-to-tmzML.R
deleted file mode 100644
index 9f1deed..0000000
--- a/doc/Intro-to-tmzML.R
+++ /dev/null
@@ -1,69 +0,0 @@
-## ----setup, include = FALSE---------------------------------------------------
-options(rmarkdown.html_vignette.check_title = FALSE)
-options(tidyverse.quiet = TRUE)
-data.table::setDTthreads(2)
-
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>",
-  out.width = "80%",
-  fig.align = 'center',
-  fig.height = 3,
-  fig.width = 6.5
-)
-
-## ----listfiles----------------------------------------------------------------
-files_to_convert <- list.files(
-  system.file("extdata", package = "RaMS"), full.names = TRUE, pattern = "mzML"
-)[2:4]
-
-## ----maketmzmls---------------------------------------------------------------
-library(RaMS)
-# Create a folder to hold the new files
-dir.create("tmzMLs")
-
-# Convert a single file
-file_to_create <- "tmzMLs/LB12HL_AB.tmzML"
-tmzmlMaker(input_filename = files_to_convert[1], output_filename = file_to_create)
-
-# Convert multiple files
-files_to_create <- paste0("tmzMLs/", basename(files_to_convert))
-# Make sure they end in .tmzML!
-files_to_create <- gsub(x = files_to_create, "\\.mzML.*", ".tmzML")
-# Loop over each file input/output pair
-created_files <- mapply(tmzmlMaker, files_to_convert, files_to_create)
-
-## ----grabmsdata---------------------------------------------------------------
-msdata <- grabMSdata(created_files, verbosity=0)
-
-## ----subset$------------------------------------------------------------------
-ms_data_table <- msdata$MS1[mz%between%pmppm(152.05723, 5)]
-
-## ----ggplot, fig.width=8, warning=FALSE---------------------------------------
-library(ggplot2)
-ggplot(ms_data_table) + geom_line(aes(x=rt, y=int, color=filename)) + xlim(8, 9.5)
-
-## ----dplyr--------------------------------------------------------------------
-library(dplyr)
-ms_data_table %>%
-  filter(rt%between%c(8.4, 8.9)) %>%
-  group_by(filename) %>%
-  summarise(area=sum(int))
-
-## ----print msdata_connection--------------------------------------------------
-print(msdata)
-
-## ----str msdata_connection----------------------------------------------------
-str(msdata)
-
-## ----expected errors, error=TRUE----------------------------------------------
-# Cannot order data by mass
-msdata$MS1[-mz]
-# Cannot request the top few rows
-msdata[1:10,]
-# Cannot subset by retention time
-msdata$MS1[rt %between% c(8, 9.5)]
-
-## ----cleanup------------------------------------------------------------------
-unlink("tmzMLs", recursive = TRUE)
-
diff --git a/doc/Intro-to-tmzML.Rmd b/doc/Intro-to-tmzML.Rmd
deleted file mode 100644
index 143ad60..0000000
--- a/doc/Intro-to-tmzML.Rmd
+++ /dev/null
@@ -1,246 +0,0 @@
----
-title: "Introduction to the tmzML file type"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Intro-to-tmzML}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-<!-- This vignette.md is generated from an .Rmd file. Please edit that file instead -->
-
-```{r setup, include = FALSE}
-options(rmarkdown.html_vignette.check_title = FALSE)
-options(tidyverse.quiet = TRUE)
-data.table::setDTthreads(2)
-
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>",
-  out.width = "80%",
-  fig.align = 'center',
-  fig.height = 3,
-  fig.width = 6.5
-)
-```
-
-**Warning:**
-
-This vignette deals with a feature still undergoing active development. While I
-can't promise to update it immediately with changes, I will do my best to keep
-you in the loop as this functionality evolves. Please also see the [speed & size comparison vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html) for more stable alternatives.
-
-**Table of contents:**
-
-* [What is tmzML?]
-* [Why tmzML?]
-* [Getting started with tmzML documents]
-* [Why not tmzML?]
-* [tmzML internals]
-
-## What is tmzML?
-
-A tmzML document is a new type of mass-spectrometry file that's optimized for
-EIC extraction and visualization. These were introduced in version 1.2.0 of
-RaMS and aren't used anywhere else. Internally, they're just XML documents
-with the same MS data in the mzML file they're created from, but the 
-organization means that accessing data by *m/z* value is much faster and 
-requires far less memory.
-
-The name stands for "transposed mzML" and is inspired by the idea that mzML
-documents are organized by scan number. This makes sense given that the
-original MS data was produced one scan at a time, but this format also makes
-EIC extraction from the original files slower because there's no indexing by 
-*m/z*. What RaMS does here is "transposes" the original document so that
-data are grouped by *m/z* value instead of scan. This means that to extract
-a chromatogram, only a small portion of the file needs to be read instead of
-the whole thing.
-
-## Why tmzML?
-
-RaMS is great because it supplies rapid, intuitive access to mzML documents.
-However, this access comes at a cost - the entire file needs to be loaded into
-R's memory simultaneously. Given that MS files can easily be tens or hundreds
-of megabytes in size, this limits the number of files that can be open at once
-to the computer memory available.
-
-With tmzMLs, much of the "hard" work is done in advance by reorganizing the
-structure of the file on disk. This allows us to read only the essential data
-into memory, vastly improving initial read time and enormously reducing the
-amount of memory required. This makes it possible to open hundreds or even
-thousands of tmzML documents simultaneously and extract a chromatogram
-from each one of them, all on a basic laptop. Within a minute. Below are some
-benchmarking tests comparing the new tmzML file type to the traditional RaMS
-code and MSnbase's MSnExp and OnDiskMSnExp.
-
-![Alt text: Speed comparison between various load methods, showing the new tmzML type to be faster than other methods for most functions and requiring several orders of magnitude less memory](../vignettes/figures/speedsizecomp.png){width=100%}
-
-The tmzML documents take essentially no time to load and require barely
-kilobytes of memory. This is because we aren't doing any data handling yet -
-we're just creating an R object that points to our files and manages the
-provided parameters elegantly. When we actually request the data, the tmzML
-file is opened, scanned, and read into memory. This is a little slower than
-requesting the data the traditional way because data from mzML files read into
-RaMS is already stored in memory, but it still takes fractions of a second and
-is an order of magnitude faster than the MSnbase functions.
-
-To the user, the end result is almost identical: a list of `data.table`s named
-by what the user requested (MS1, MS2, BPC, EIC, etc.) in tidy format. This means
-all your favorite tidyverse tricks and the code you've written to handle the
-old RaMS output will work identically. However,
-the full data file is never read into memory so there are some quirks that I 
-discuss [below](#why-not-tmzml).
-
-## Getting started with tmzML documents
-
-First, mzML/mzXML documents must be converted to tmzML. Again, these files
-might feel similar but they're very different internally. We'll use a couple
-of the files packaged with RaMS and convert them in a temporary directory.
-
-```{r listfiles}
-files_to_convert <- list.files(
-  system.file("extdata", package = "RaMS"), full.names = TRUE, pattern = "mzML"
-)[2:4]
-```
-
-The function `tmzmlMaker` is the workhorse here. It's not vectorized, so you'll
-need to loop over multiple files if you've got them. And while it has some
-basic renaming logic internally, it's safest to pass a list of output filenames
-(just remember to end them with file type .tmzML!). My usual project setup
-has a folder for the mzML files of the project, so creating a similar tmzML
-folder has worked well for me.
-
-```{r maketmzmls}
-library(RaMS)
-# Create a folder to hold the new files
-dir.create("tmzMLs")
-
-# Convert a single file
-file_to_create <- "tmzMLs/LB12HL_AB.tmzML"
-tmzmlMaker(input_filename = files_to_convert[1], output_filename = file_to_create)
-
-# Convert multiple files
-files_to_create <- paste0("tmzMLs/", basename(files_to_convert))
-# Make sure they end in .tmzML!
-files_to_create <- gsub(x = files_to_create, "\\.mzML.*", ".tmzML")
-# Loop over each file input/output pair
-created_files <- mapply(tmzmlMaker, files_to_convert, files_to_create)
-```
-
-These files should now exist in the folder and can be opened with Notepad or
-a similar word processer if you're curious, but they're mostly encoded so it
-may not be incredibly informative.
-
-Extracting a chromatogram from a tmzML document should feel very familiar to
-anyone already used to using RaMS. `grabMSdata` is smart enough to realize
-that it's dealing with .tmzML files instead of .mzML files from the filename,
-so you can reuse the exact same function as before. However, unlike when
-loading mzMLs this step should be instantaneous.
-
-```{r grabmsdata}
-msdata <- grabMSdata(created_files, verbosity=0)
-```
-
-If you're planning to extract a chromatogram, the syntax is identical to 
-traditional RaMS:
-
-```{r subset$}
-ms_data_table <- msdata$MS1[mz%between%pmppm(152.05723, 5)]
-```
-
-Which is a perfectly normal `data.table` object ready to be passed to more 
-familiar functions:
-
-```{r ggplot, fig.width=8, warning=FALSE}
-library(ggplot2)
-ggplot(ms_data_table) + geom_line(aes(x=rt, y=int, color=filename)) + xlim(8, 9.5)
-```
-
-```{r dplyr}
-library(dplyr)
-ms_data_table %>%
-  filter(rt%between%c(8.4, 8.9)) %>%
-  group_by(filename) %>%
-  summarise(area=sum(int))
-```
-
-## Why not tmzML?
-
-However, there are some quirks to the tmzML object itself. If you're used to
-inspecting the initial RaMS output by printing it to the console, you'll get
-a new message instead of the expected list of data tables:
-
-```{r print msdata_connection}
-print(msdata)
-```
-
-This is because the `msdata` object in this case isn't a list anymore - it's a
-custom object that really only contains the names of the files and the settings
-you initially provided to `grabMSdata`. You can see this with `str()`:
-
-```{r str msdata_connection}
-str(msdata)
-```
-
-Eagle-eyed browsers may have already noticed that the `msdata` object is a 
-little different than before because there's this new `connection` option that
-shows up when tab-completing the extraction code above. The MS1 and MS2 objects
-here will actually always be NULL - I just added them so that tab-completion
-will remind you what things you can ask `msdata` for via `$` notation. If you
-see the message above, it means that you've asked a tmzML for something that
-you should ask with traditional RaMS instead because it's outside the scope of
-the tmzML optimization.
-
-The `$` and `[` syntax provided above is really the only thing that will work
-with tmzMLs. Any request that's meant to operate on the data as a whole
-will fail and subsetting with head() or tail() won't work either. Also,
-subsetting based on retention time needs to be done separately because these
-poor files have been indexed within an inch of their lives for a single purpose.
-
-```{r expected errors, error=TRUE}
-# Cannot order data by mass
-msdata$MS1[-mz]
-# Cannot request the top few rows
-msdata[1:10,]
-# Cannot subset by retention time
-msdata$MS1[rt %between% c(8, 9.5)]
-```
-
-In general, if you get an error mentioning the `msdata_connection` object you
-should probably be using traditional RaMS on mzMLs instead of the new tmzMLs.
-
-## tmzML internals
-
-If you haven't yet played with tmzMLs already, please consider doing so now. The
-remainder of this is really geared towards useRs who have already seen what
-tmzMLs do and are interested in some of the magic behind it.
-
-RaMS works on tmzMLs by defining a new S3 object class, the `msdata_connection`.
-This allowed me to define new behavior for `$` and `[` so that the syntax is
-identical to the traditional RaMS arrangement but requires some complicated
-internal mapping. The `$` remapping that occurs can be viewed with `r cat("RaMS:::\x60$.msdata_connection\x60")` but is really just a basic way to
-pass a single MS level at a time to the `[` function. The `[` function, on the other
-hand, is really where the magic happens. You can view the source code again with
-`r cat("RaMS:::\x60[.msdata_connection\x60")` but what basically happens is that
-we manually parse the subset function using `substitute` and `eval.parent`,
-finally open up the file, and access only those XML nodes that have *m/z*
-values of interest. This returns a data table containing all the *m/z* values
-in a large bin (defaulting to 3 Da) that's then subset further down to just the
-requested values.
-
-I'm still deciding whether XML documents are the way to go. Having it be XML
-means that I can expand it effortlessly and essentially future-proofs it for
-additional functionality that I'd like to add later, but does require that the
-whole file is read in at a high level. Something simpler like a pure-text
-document could have a header that's read in separately from the rest of the
-document that then contains pointers to the data. Preliminary tests suggest
-this plain-text method is 5-10 times faster than the XML style but tmzMLs are
-already plenty fast so we may be approaching diminishing returns.
-
-```{r cleanup}
-unlink("tmzMLs", recursive = TRUE)
-```
-
-***
-
-Vignette last built on `r Sys.Date()`
diff --git a/doc/Intro-to-tmzML.html b/doc/Intro-to-tmzML.html
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-
-<h1 class="title toc-ignore">Introduction to the tmzML file type</h1>
-
-
-
-<!-- This vignette.md is generated from an .Rmd file. Please edit that file instead -->
-<p><strong>Warning:</strong></p>
-<p>This vignette deals with a feature still undergoing active
-development. While I can’t promise to update it immediately with
-changes, I will do my best to keep you in the loop as this functionality
-evolves. Please also see the <a href="https://cran.r-project.org/web/packages/RaMS/vignettes/speed_size_comparison.html">speed
-&amp; size comparison vignette</a> for more stable alternatives.</p>
-<p><strong>Table of contents:</strong></p>
-<ul>
-<li><a href="#what-is-tmzml">What is tmzML?</a></li>
-<li><a href="#why-tmzml">Why tmzML?</a></li>
-<li><a href="#getting-started-with-tmzml-documents">Getting started with
-tmzML documents</a></li>
-<li><a href="#why-not-tmzml">Why not tmzML?</a></li>
-<li><a href="#tmzml-internals">tmzML internals</a></li>
-</ul>
-<div id="what-is-tmzml" class="section level2">
-<h2>What is tmzML?</h2>
-<p>A tmzML document is a new type of mass-spectrometry file that’s
-optimized for EIC extraction and visualization. These were introduced in
-version 1.2.0 of RaMS and aren’t used anywhere else. Internally, they’re
-just XML documents with the same MS data in the mzML file they’re
-created from, but the organization means that accessing data by
-<em>m/z</em> value is much faster and requires far less memory.</p>
-<p>The name stands for “transposed mzML” and is inspired by the idea
-that mzML documents are organized by scan number. This makes sense given
-that the original MS data was produced one scan at a time, but this
-format also makes EIC extraction from the original files slower because
-there’s no indexing by <em>m/z</em>. What RaMS does here is “transposes”
-the original document so that data are grouped by <em>m/z</em> value
-instead of scan. This means that to extract a chromatogram, only a small
-portion of the file needs to be read instead of the whole thing.</p>
-</div>
-<div id="why-tmzml" class="section level2">
-<h2>Why tmzML?</h2>
-<p>RaMS is great because it supplies rapid, intuitive access to mzML
-documents. However, this access comes at a cost - the entire file needs
-to be loaded into R’s memory simultaneously. Given that MS files can
-easily be tens or hundreds of megabytes in size, this limits the number
-of files that can be open at once to the computer memory available.</p>
-<p>With tmzMLs, much of the “hard” work is done in advance by
-reorganizing the structure of the file on disk. This allows us to read
-only the essential data into memory, vastly improving initial read time
-and enormously reducing the amount of memory required. This makes it
-possible to open hundreds or even thousands of tmzML documents
-simultaneously and extract a chromatogram from each one of them, all on
-a basic laptop. Within a minute. Below are some benchmarking tests
-comparing the new tmzML file type to the traditional RaMS code and
-MSnbase’s MSnExp and OnDiskMSnExp.</p>
-<div class="float">
-<img 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" style="width:100.0%" alt="Alt text: Speed comparison between various load methods, showing the new tmzML type to be faster than other methods for most functions and requiring several orders of magnitude less memory" />
-<div class="figcaption">Alt text: Speed comparison between various load
-methods, showing the new tmzML type to be faster than other methods for
-most functions and requiring several orders of magnitude less
-memory</div>
-</div>
-<p>The tmzML documents take essentially no time to load and require
-barely kilobytes of memory. This is because we aren’t doing any data
-handling yet - we’re just creating an R object that points to our files
-and manages the provided parameters elegantly. When we actually request
-the data, the tmzML file is opened, scanned, and read into memory. This
-is a little slower than requesting the data the traditional way because
-data from mzML files read into RaMS is already stored in memory, but it
-still takes fractions of a second and is an order of magnitude faster
-than the MSnbase functions.</p>
-<p>To the user, the end result is almost identical: a list of
-<code>data.table</code>s named by what the user requested (MS1, MS2,
-BPC, EIC, etc.) in tidy format. This means all your favorite tidyverse
-tricks and the code you’ve written to handle the old RaMS output will
-work identically. However, the full data file is never read into memory
-so there are some quirks that I discuss <a href="#why-not-tmzml">below</a>.</p>
-</div>
-<div id="getting-started-with-tmzml-documents" class="section level2">
-<h2>Getting started with tmzML documents</h2>
-<p>First, mzML/mzXML documents must be converted to tmzML. Again, these
-files might feel similar but they’re very different internally. We’ll
-use a couple of the files packaged with RaMS and convert them in a
-temporary directory.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a>files_to_convert <span class="ot">&lt;-</span> <span class="fu">list.files</span>(</span>
-<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>  <span class="fu">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="at">package =</span> <span class="st">&quot;RaMS&quot;</span>), <span class="at">full.names =</span> <span class="cn">TRUE</span>, <span class="at">pattern =</span> <span class="st">&quot;mzML&quot;</span></span>
-<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>)[<span class="dv">2</span><span class="sc">:</span><span class="dv">4</span>]</span></code></pre></div>
-<p>The function <code>tmzmlMaker</code> is the workhorse here. It’s not
-vectorized, so you’ll need to loop over multiple files if you’ve got
-them. And while it has some basic renaming logic internally, it’s safest
-to pass a list of output filenames (just remember to end them with file
-type .tmzML!). My usual project setup has a folder for the mzML files of
-the project, so creating a similar tmzML folder has worked well for
-me.</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="fu">library</span>(RaMS)</span>
-<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="co"># Create a folder to hold the new files</span></span>
-<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a><span class="fu">dir.create</span>(<span class="st">&quot;tmzMLs&quot;</span>)</span>
-<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a></span>
-<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a><span class="co"># Convert a single file</span></span>
-<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a>file_to_create <span class="ot">&lt;-</span> <span class="st">&quot;tmzMLs/LB12HL_AB.tmzML&quot;</span></span>
-<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a><span class="fu">tmzmlMaker</span>(<span class="at">input_filename =</span> files_to_convert[<span class="dv">1</span>], <span class="at">output_filename =</span> file_to_create)</span>
-<span id="cb2-8"><a href="#cb2-8" tabindex="-1"></a><span class="co">#&gt; [1] &quot;tmzMLs/LB12HL_AB.tmzML&quot;</span></span>
-<span id="cb2-9"><a href="#cb2-9" tabindex="-1"></a></span>
-<span id="cb2-10"><a href="#cb2-10" tabindex="-1"></a><span class="co"># Convert multiple files</span></span>
-<span id="cb2-11"><a href="#cb2-11" tabindex="-1"></a>files_to_create <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">&quot;tmzMLs/&quot;</span>, <span class="fu">basename</span>(files_to_convert))</span>
-<span id="cb2-12"><a href="#cb2-12" tabindex="-1"></a><span class="co"># Make sure they end in .tmzML!</span></span>
-<span id="cb2-13"><a href="#cb2-13" tabindex="-1"></a>files_to_create <span class="ot">&lt;-</span> <span class="fu">gsub</span>(<span class="at">x =</span> files_to_create, <span class="st">&quot;</span><span class="sc">\\</span><span class="st">.mzML.*&quot;</span>, <span class="st">&quot;.tmzML&quot;</span>)</span>
-<span id="cb2-14"><a href="#cb2-14" tabindex="-1"></a><span class="co"># Loop over each file input/output pair</span></span>
-<span id="cb2-15"><a href="#cb2-15" tabindex="-1"></a>created_files <span class="ot">&lt;-</span> <span class="fu">mapply</span>(tmzmlMaker, files_to_convert, files_to_create)</span></code></pre></div>
-<p>These files should now exist in the folder and can be opened with
-Notepad or a similar word processer if you’re curious, but they’re
-mostly encoded so it may not be incredibly informative.</p>
-<p>Extracting a chromatogram from a tmzML document should feel very
-familiar to anyone already used to using RaMS. <code>grabMSdata</code>
-is smart enough to realize that it’s dealing with .tmzML files instead
-of .mzML files from the filename, so you can reuse the exact same
-function as before. However, unlike when loading mzMLs this step should
-be instantaneous.</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(created_files, <span class="at">verbosity=</span><span class="dv">0</span>)</span></code></pre></div>
-<p>If you’re planning to extract a chromatogram, the syntax is identical
-to traditional RaMS:</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>ms_data_table <span class="ot">&lt;-</span> msdata<span class="sc">$</span>MS1[mz<span class="sc">%between%</span><span class="fu">pmppm</span>(<span class="fl">152.05723</span>, <span class="dv">5</span>)]</span></code></pre></div>
-<p>Which is a perfectly normal <code>data.table</code> object ready to
-be passed to more familiar functions:</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
-<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="fu">ggplot</span>(ms_data_table) <span class="sc">+</span> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>rt, <span class="at">y=</span>int, <span class="at">color=</span>filename)) <span class="sc">+</span> <span class="fu">xlim</span>(<span class="dv">8</span>, <span class="fl">9.5</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
-<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a>ms_data_table <span class="sc">%&gt;%</span></span>
-<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a>  <span class="fu">filter</span>(rt<span class="sc">%between%</span><span class="fu">c</span>(<span class="fl">8.4</span>, <span class="fl">8.9</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>  <span class="fu">group_by</span>(filename) <span class="sc">%&gt;%</span></span>
-<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">area=</span><span class="fu">sum</span>(int))</span>
-<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="co">#&gt; # A tibble: 3 × 2</span></span>
-<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a><span class="co">#&gt;   filename             area</span></span>
-<span id="cb6-8"><a href="#cb6-8" tabindex="-1"></a><span class="co">#&gt;   &lt;chr&gt;               &lt;dbl&gt;</span></span>
-<span id="cb6-9"><a href="#cb6-9" tabindex="-1"></a><span class="co">#&gt; 1 LB12HL_AB.tmzML 13057831.</span></span>
-<span id="cb6-10"><a href="#cb6-10" tabindex="-1"></a><span class="co">#&gt; 2 LB12HL_CD.tmzML 24925878.</span></span>
-<span id="cb6-11"><a href="#cb6-11" tabindex="-1"></a><span class="co">#&gt; 3 LB12HL_EF.tmzML 21913457.</span></span></code></pre></div>
-</div>
-<div id="why-not-tmzml" class="section level2">
-<h2>Why not tmzML?</h2>
-<p>However, there are some quirks to the tmzML object itself. If you’re
-used to inspecting the initial RaMS output by printing it to the
-console, you’ll get a new message instead of the expected list of data
-tables:</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="fu">print</span>(msdata)</span>
-<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="co">#&gt; Hey, I&#39;m not actually an object, sorry!</span></span>
-<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a><span class="co">#&gt; But you can pretend I&#39;m a list containing data.tables:</span></span>
-<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="co">#&gt; MS1; MS2</span></span>
-<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a><span class="co">#&gt; from the following files:</span></span>
-<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="co">#&gt; tmzMLs/LB12HL_AB.tmzML</span></span>
-<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a><span class="co">#&gt; tmzMLs/LB12HL_CD.tmzML</span></span>
-<span id="cb7-8"><a href="#cb7-8" tabindex="-1"></a><span class="co">#&gt; tmzMLs/LB12HL_EF.tmzML</span></span>
-<span id="cb7-9"><a href="#cb7-9" tabindex="-1"></a><span class="co">#&gt; and access the data inside with $ and [ subsetting</span></span></code></pre></div>
-<p>This is because the <code>msdata</code> object in this case isn’t a
-list anymore - it’s a custom object that really only contains the names
-of the files and the settings you initially provided to
-<code>grabMSdata</code>. You can see this with <code>str()</code>:</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">str</span>(msdata)</span>
-<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="co">#&gt; List of 3</span></span>
-<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a><span class="co">#&gt;  $ MS1       : NULL</span></span>
-<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a><span class="co">#&gt;  $ MS2       : NULL</span></span>
-<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a><span class="co">#&gt;  $ connection:List of 3</span></span>
-<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a><span class="co">#&gt;   ..$ files    : Named chr [1:3] &quot;tmzMLs/LB12HL_AB.tmzML&quot; &quot;tmzMLs/LB12HL_CD.tmzML&quot; &quot;tmzMLs/LB12HL_EF.tmzML&quot;</span></span>
-<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a><span class="co">#&gt;   .. ..- attr(*, &quot;names&quot;)= chr [1:3] &quot;C:/Users/willi/AppData/Local/Temp/RtmpQ9wT8y/temp_libpath49a055ab1e9b/RaMS/extdata/LB12HL_AB.mzML.gz&quot; &quot;C:/Users/willi/AppData/Local/Temp/RtmpQ9wT8y/temp_libpath49a055ab1e9b/RaMS/extdata/LB12HL_CD.mzML.gz&quot; &quot;C:/Users/willi/AppData/Local/Temp/RtmpQ9wT8y/temp_libpath49a055ab1e9b/RaMS/extdata/LB12HL_EF.mzML.gz&quot;</span></span>
-<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a><span class="co">#&gt;   ..$ grab_what: chr [1:2] &quot;MS1&quot; &quot;MS2&quot;</span></span>
-<span id="cb8-9"><a href="#cb8-9" tabindex="-1"></a><span class="co">#&gt;   ..$ verbosity: num 0</span></span>
-<span id="cb8-10"><a href="#cb8-10" tabindex="-1"></a><span class="co">#&gt;  - attr(*, &quot;class&quot;)= chr &quot;msdata_connection&quot;</span></span></code></pre></div>
-<p>Eagle-eyed browsers may have already noticed that the
-<code>msdata</code> object is a little different than before because
-there’s this new <code>connection</code> option that shows up when
-tab-completing the extraction code above. The MS1 and MS2 objects here
-will actually always be NULL - I just added them so that tab-completion
-will remind you what things you can ask <code>msdata</code> for via
-<code>$</code> notation. If you see the message above, it means that
-you’ve asked a tmzML for something that you should ask with traditional
-RaMS instead because it’s outside the scope of the tmzML
-optimization.</p>
-<p>The <code>$</code> and <code>[</code> syntax provided above is really
-the only thing that will work with tmzMLs. Any request that’s meant to
-operate on the data as a whole will fail and subsetting with head() or
-tail() won’t work either. Also, subsetting based on retention time needs
-to be done separately because these poor files have been indexed within
-an inch of their lives for a single purpose.</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="co"># Cannot order data by mass</span></span>
-<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>msdata<span class="sc">$</span>MS1[<span class="sc">-</span>mz]</span>
-<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a><span class="co">#&gt; Error in isub[[3]]: subscript out of bounds</span></span>
-<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a><span class="co"># Cannot request the top few rows</span></span>
-<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a>msdata[<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>,]</span>
-<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a><span class="co">#&gt; Error in `[.msdata_connection`(msdata, 1:10, ): unused argument (alist())</span></span>
-<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a><span class="co"># Cannot subset by retention time</span></span>
-<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a>msdata<span class="sc">$</span>MS1[rt <span class="sc">%between%</span> <span class="fu">c</span>(<span class="dv">8</span>, <span class="fl">9.5</span>)]</span>
-<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a><span class="co">#&gt; Error in `[.msdata_connection`(msdata$MS1, rt %between% c(8, 9.5)): tmzML documents currently only support subsetting by mz!</span></span></code></pre></div>
-<p>In general, if you get an error mentioning the
-<code>msdata_connection</code> object you should probably be using
-traditional RaMS on mzMLs instead of the new tmzMLs.</p>
-</div>
-<div id="tmzml-internals" class="section level2">
-<h2>tmzML internals</h2>
-<p>If you haven’t yet played with tmzMLs already, please consider doing
-so now. The remainder of this is really geared towards useRs who have
-already seen what tmzMLs do and are interested in some of the magic
-behind it.</p>
-<p>RaMS works on tmzMLs by defining a new S3 object class, the
-<code>msdata_connection</code>. This allowed me to define new behavior
-for <code>$</code> and <code>[</code> so that the syntax is identical to
-the traditional RaMS arrangement but requires some complicated internal
-mapping. The <code>$</code> remapping that occurs can be viewed with but
-is really just a basic way to pass a single MS level at a time to the
-<code>[</code> function. The <code>[</code> function, on the other hand,
-is really where the magic happens. You can view the source code again
-with but what basically happens is that we manually parse the subset
-function using <code>substitute</code> and <code>eval.parent</code>,
-finally open up the file, and access only those XML nodes that have
-<em>m/z</em> values of interest. This returns a data table containing
-all the <em>m/z</em> values in a large bin (defaulting to 3 Da) that’s
-then subset further down to just the requested values.</p>
-<p>I’m still deciding whether XML documents are the way to go. Having it
-be XML means that I can expand it effortlessly and essentially
-future-proofs it for additional functionality that I’d like to add
-later, but does require that the whole file is read in at a high level.
-Something simpler like a pure-text document could have a header that’s
-read in separately from the rest of the document that then contains
-pointers to the data. Preliminary tests suggest this plain-text method
-is 5-10 times faster than the XML style but tmzMLs are already plenty
-fast so we may be approaching diminishing returns.</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;tmzMLs&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
-<hr />
-<p>Vignette last built on 2023-12-13</p>
-</div>
-
-
-
-<!-- code folding -->
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
-  (function () {
-    var script = document.createElement("script");
-    script.type = "text/javascript";
-    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
-    document.getElementsByTagName("head")[0].appendChild(script);
-  })();
-</script>
-
-</body>
-</html>
diff --git a/doc/Minifying-files-with-RaMS.R b/doc/Minifying-files-with-RaMS.R
deleted file mode 100644
index 9fe91e8..0000000
--- a/doc/Minifying-files-with-RaMS.R
+++ /dev/null
@@ -1,118 +0,0 @@
-## ----setup, include = FALSE---------------------------------------------------
-options(rmarkdown.html_vignette.check_title = FALSE)
-options(tidyverse.quiet = TRUE)
-data.table::setDTthreads(2)
-
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>",
-  out.width = "80%",
-  fig.align = 'center',
-  fig.height = 3,
-  fig.width = 6.5
-)
-
-## ----minifyfile, results='hide'-----------------------------------------------
-library(RaMS)
-msdata_files <- list.files(
-  system.file("extdata", package = "RaMS"), full.names = TRUE, pattern = "mzML"
-)[1:4]
-
-initial_filename <- msdata_files[1]
-output_filename <- gsub(x=paste0("minified_", basename(initial_filename)), "\\.gz", "")
-
-masses_of_interest <- c(118.0865, 138.0555)
-minifyMSdata(files = initial_filename, output_files = output_filename, 
-             mz_include = masses_of_interest, ppm = 10, warn = FALSE)
-
-## ----opendata, results='hide'-------------------------------------------------
-init_msdata <- grabMSdata(initial_filename)
-msdata <- grabMSdata(output_filename)
-
-## ----showmini-----------------------------------------------------------------
-knitr::kable(head(msdata$MS1, 3))
-knitr::kable(head(msdata$MS2, 3))
-
-## ----TICBPCdiffs--------------------------------------------------------------
-par(mfrow=c(2, 1), mar=c(2.1, 2.1, 1.1, 0.1))
-plot(init_msdata$BPC$rt, init_msdata$BPC$int, type = "l", main = "Initial BPC")
-plot(msdata$BPC$rt, msdata$BPC$int, type = "l", main = "New BPC")
-
-## ----cleanmini, include=FALSE-------------------------------------------------
-layout(1)
-unlink(output_filename)
-
-## ----vectorizedmini-----------------------------------------------------------
-dir.create("mini_mzMLs/")
-output_files <- paste0("mini_mzMLs/", basename(msdata_files))
-output_files <- gsub(x=output_files, "\\.gz", "")
-
-minifyMSdata(files = msdata_files, output_files = output_files, verbosity = 0,
-             mz_include = masses_of_interest, ppm = 10, warn = FALSE)
-
-## ----ggplotmini, fig.height=2-------------------------------------------------
-mini_msdata <- grabMSdata(output_files, verbosity = 0)
-
-library(ggplot2)
-ggplot(mini_msdata$BPC) + geom_line(aes(x=rt, y=int, color=filename)) + theme_bw()
-
-## ----cleanminimulti, include=FALSE--------------------------------------------
-unlink("mini_mzMLs", recursive = TRUE)
-
-## ----mzs to include, eval=FALSE-----------------------------------------------
-#  raw_stans <- read.csv(paste0("https://raw.githubusercontent.com/",
-#                               "IngallsLabUW/Ingalls_Standards/",
-#                               "b098927ea0089b6e7a31e1758e7c7eaad5408535/",
-#                               "Ingalls_Lab_Standards_NEW.csv"))
-#  
-#  mzs_to_include <- as.numeric(unique(raw_stans[raw_stans$Fraction1=="HILICPos",]$m.z))
-#  # Include glycine betaine isotopes for README demo
-#  mzs_to_include <- c(mzs_to_include, 119.0899, 119.0835)
-
-## ----download the mzXMLs, eval=FALSE------------------------------------------
-#  if(!dir.exists("vignettes/data"))dir.create("vignettes/data")
-#  base_url <- "ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS703/"
-#  chosen_files <- paste0(base_url, "170223_Smp_LB12HL_", c("AB", "CD", "EF"), "_pos.mzXML")
-#  new_names <- gsub(x=basename(chosen_files), "170223_Smp_", "")
-#  
-#  mapply(download.file, chosen_files, paste0("vignettes/data/", new_names),
-#         mode = "wb", method = "libcurl")
-
-## ----get DDA, eval=FALSE------------------------------------------------------
-#  file.copy(from = paste0("Z:/1_QEdata/2016/2016_Katherine_1335_LightB12_",
-#                          "Experiment/170223_KRH_Rerun_1335_LightB12_Exp_HILIC/",
-#                          "positive/",
-#                          "170223_Poo_AllCyanoAqExtracts_DDApos_2.mzXML"),
-#            to = "vignettes/data/DDApos_2.mzXML", overwrite = TRUE)
-
-## ----minify, warning=FALSE, eval=FALSE----------------------------------------
-#  library(RaMS)
-#  
-#  if(!dir.exists("inst/extdata"))dir.create("inst/extdata", recursive = TRUE)
-#  init_files <- list.files("vignettes/data/", full.names = TRUE)
-#  out_files <- paste0("inst/extdata/", basename(init_files))
-#  minifyMSdata(files = init_files, output_files = out_files, warn = FALSE,
-#               mz_include = mzs_to_include, ppm = 20)
-
-## ----msconvertchunk, eval=FALSE-----------------------------------------------
-#  system("msconvert inst/extdata/*.mzXML -o inst/extdata/temp --noindex")
-#  system("msconvert --mzXML inst/extdata/*.mzXML -o inst/extdata/temp --noindex")
-#  system('msconvert inst/extdata/temp/*.mzML --filter \"scanTime [240,900]\" -o inst/extdata -g')
-#  system('msconvert inst/extdata/temp/*.mzXML --mzXML --filter \"scanTime [240,900]\" -o inst/extdata -g')
-
-## ----renameremove, eval=FALSE-------------------------------------------------
-#  init_files <- list.files("inst/extdata", full.names = TRUE)
-#  new_names <- paste0("inst/extdata/", gsub(x=init_files, ".*(Smp_|Extracts_)", ""))
-#  file.rename(init_files, new_names)
-#  
-#  unlink("inst/extdata/temp", recursive = TRUE)
-#  file.remove(list.files("inst/extdata", pattern = "mzXML$", full.names = TRUE))
-#  file.remove(paste0("inst/extdata/", c("LB12HL_CD.mzXML.gz", "LB12HL_EF.mzXML.gz")))
-
-## ----checkquality, eval=FALSE-------------------------------------------------
-#  MSnbase::readMSData(list.files("inst/extdata", full.names = TRUE)[1], msLevel. = 1)
-#  RaMS::grabMSdata(new_names[1])
-
-## ----vigdata cleanup, eval=FALSE----------------------------------------------
-#  unlink("vignettes/data", recursive = TRUE)
-
diff --git a/doc/Minifying-files-with-RaMS.Rmd b/doc/Minifying-files-with-RaMS.Rmd
deleted file mode 100644
index f2e9ea4..0000000
--- a/doc/Minifying-files-with-RaMS.Rmd
+++ /dev/null
@@ -1,230 +0,0 @@
----
-title: "Minifying files with RaMS"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Minifying-files-with-RaMS}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-<!-- This vignette is created from a .Rmd file: please edit that instead. -->
-
-```{r setup, include = FALSE}
-options(rmarkdown.html_vignette.check_title = FALSE)
-options(tidyverse.quiet = TRUE)
-data.table::setDTthreads(2)
-
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>",
-  out.width = "80%",
-  fig.align = 'center',
-  fig.height = 3,
-  fig.width = 6.5
-)
-```
-
-As of version 1.1.0, `RaMS` also has functions that allow irrelevant data to
-be removed from the file to reduce file sizes. Like `grabMSdata`, there's one
-wrapper function `minifyMSdata` that accepts mzML or mzXML files, plus a vector
-of *m/z* values that should either be kept (`mz_include`) or removed 
-(`mz_exclude`). The function then opens up the provided MS files and removes
-data points in the MS^1^ and MS^2^ spectra that fall outside the accepted bounds.
-`mz_include` is useful when only a few masses are of interest, as in targeted
-metabolomics. `mz_exclude` is useful when many masses are known to be 
-contaminants or interfere with peakpicking/plotting abilities. This minification
-can shrink a file over three orders of magnitude, decreasing both
-processing time and memory allocation later in the pipeline.
-
-This is also very useful for creating demo MS files - `RaMS` uses these
-functions to produce the sample data in `extdata`, with 6 MS files taking up
-less than 5 megabytes of disk space. Many other programs provide the ability
-to shrink files, but none (known to me) shrink files by excluding *m/z*
-values and instead can only remove certain retention times.
-
-Below, we begin with a large MS file containing both MS^1^ and MS^2^ data 
-and extract only the data corresponding to
-valine/glycine betaine and homarine.
-
-```{r minifyfile, results='hide'}
-library(RaMS)
-msdata_files <- list.files(
-  system.file("extdata", package = "RaMS"), full.names = TRUE, pattern = "mzML"
-)[1:4]
-
-initial_filename <- msdata_files[1]
-output_filename <- gsub(x=paste0("minified_", basename(initial_filename)), "\\.gz", "")
-
-masses_of_interest <- c(118.0865, 138.0555)
-minifyMSdata(files = initial_filename, output_files = output_filename, 
-             mz_include = masses_of_interest, ppm = 10, warn = FALSE)
-```
-
-Then, when we open the file up (with `RaMS` or other software) we are left with
-the data corresponding only to those compounds:
-
-```{r opendata, results='hide'}
-init_msdata <- grabMSdata(initial_filename)
-msdata <- grabMSdata(output_filename)
-```
-
-```{r showmini}
-knitr::kable(head(msdata$MS1, 3))
-knitr::kable(head(msdata$MS2, 3))
-```
-
-Both the TIC and BPC are updated to reflect the smaller file size as well:
-
-```{r TICBPCdiffs}
-par(mfrow=c(2, 1), mar=c(2.1, 2.1, 1.1, 0.1))
-plot(init_msdata$BPC$rt, init_msdata$BPC$int, type = "l", main = "Initial BPC")
-plot(msdata$BPC$rt, msdata$BPC$int, type = "l", main = "New BPC")
-```
-
-```{r cleanmini, include=FALSE}
-layout(1)
-unlink(output_filename)
-```
-
-***
-
-The `minifyMSdata` function is vectorized so the exact same syntax can be used for multiple files:
-
-```{r vectorizedmini}
-dir.create("mini_mzMLs/")
-output_files <- paste0("mini_mzMLs/", basename(msdata_files))
-output_files <- gsub(x=output_files, "\\.gz", "")
-
-minifyMSdata(files = msdata_files, output_files = output_files, verbosity = 0,
-             mz_include = masses_of_interest, ppm = 10, warn = FALSE)
-```
-
-```{r ggplotmini, fig.height=2}
-mini_msdata <- grabMSdata(output_files, verbosity = 0)
-
-library(ggplot2)
-ggplot(mini_msdata$BPC) + geom_line(aes(x=rt, y=int, color=filename)) + theme_bw()
-```
-
-These new files are valid according to the validator provided in MSnbase, which
-means that most programs should be able to open them, but this feature is still
-experimental and may break on quirky data. If that happens, please feel free to
-submit a bug report at 
-
-```{r cleanminimulti, include=FALSE}
-unlink("mini_mzMLs", recursive = TRUE)
-```
-
-***
-
-As an example of how I use this minification function, here's the code used to
-create the minified files in the `\extdata` folder that ships with the package.
-This was especially useful because the package can't be more than 5MB but it's
-incredibly useful to include some standalone MS data for demos and vignettes
-like this one. I don't actually run this code in the vignette itself to save
-compilation time but it will run if you test it yourself.
-
-These files originate from the [Ingalls Lab](https://sites.google.com/view/anitra-ingalls) at the University of Washington, USA and are published in the manuscript [**"Metabolic consequences of cobalamin scarcity in diatoms as revealed through metabolomics"**](https://www.sciencedirect.com/science/article/pii/S1434461018301184). Files are downloaded from the corresponding [Metabolights repository](https://www.ebi.ac.uk/metabolights/MTBLS703/files).
-
-First, we identify the *m/z* values we'd like to keep in the minified files. For
-the demo data, I'll use the Ingalls Lab list of targeted compounds - those we
-have authentic standards for.
-
-```{r mzs to include, eval=FALSE}
-raw_stans <- read.csv(paste0("https://raw.githubusercontent.com/",
-                             "IngallsLabUW/Ingalls_Standards/",
-                             "b098927ea0089b6e7a31e1758e7c7eaad5408535/",
-                             "Ingalls_Lab_Standards_NEW.csv"))
-
-mzs_to_include <- as.numeric(unique(raw_stans[raw_stans$Fraction1=="HILICPos",]$m.z))
-# Include glycine betaine isotopes for README demo
-mzs_to_include <- c(mzs_to_include, 119.0899, 119.0835)
-```
-
-Then, we download the raw MS data from the online repository into which it's
-been deposited.
-
-```{r download the mzXMLs, eval=FALSE}
-if(!dir.exists("vignettes/data"))dir.create("vignettes/data")
-base_url <- "ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS703/"
-chosen_files <- paste0(base_url, "170223_Smp_LB12HL_", c("AB", "CD", "EF"), "_pos.mzXML")
-new_names <- gsub(x=basename(chosen_files), "170223_Smp_", "")
-
-mapply(download.file, chosen_files, paste0("vignettes/data/", new_names), 
-       mode = "wb", method = "libcurl")
-```
-
-The MSMS data wasn't uploaded, so we handle that separately by pulling it off
-the lab computer manually and copying it over to our temporary directory. If
-you're following along, you can skip this chunk or use your own DDA data.
-
-```{r get DDA, eval=FALSE}
-file.copy(from = paste0("Z:/1_QEdata/2016/2016_Katherine_1335_LightB12_",
-                        "Experiment/170223_KRH_Rerun_1335_LightB12_Exp_HILIC/",
-                        "positive/",
-                        "170223_Poo_AllCyanoAqExtracts_DDApos_2.mzXML"),
-          to = "vignettes/data/DDApos_2.mzXML", overwrite = TRUE)
-```
-
-Then we can actually perform the minification:
-
-```{r minify, warning=FALSE, eval=FALSE}
-library(RaMS)
-
-if(!dir.exists("inst/extdata"))dir.create("inst/extdata", recursive = TRUE)
-init_files <- list.files("vignettes/data/", full.names = TRUE)
-out_files <- paste0("inst/extdata/", basename(init_files))
-minifyMSdata(files = init_files, output_files = out_files, warn = FALSE,
-             mz_include = mzs_to_include, ppm = 20)
-```
-
-Now we have four minified mzXML files in our inst/extdata folder. However, we'd
-like to be able to demo the mzML functionality as well as that of mzXMLs, so
-we can use [Proteowizard's](https://proteowizard.sourceforge.io/tools/msconvert.html) 
-`msconvert` tool because `RaMS` can't convert between mzML and mzXML or vice 
-versa. You'll need to install `msconvert` and add it to your path for this
-step.
-
-We also use `msconvert` to trim the files by retention time, keeping data 
-between 4 and 15 minutes.
-
-Finally, we gzip the files to get them as small as possible, also using `msconvert`.
-
-```{r msconvertchunk, eval=FALSE}
-system("msconvert inst/extdata/*.mzXML -o inst/extdata/temp --noindex")
-system("msconvert --mzXML inst/extdata/*.mzXML -o inst/extdata/temp --noindex")
-system('msconvert inst/extdata/temp/*.mzML --filter \"scanTime [240,900]\" -o inst/extdata -g')
-system('msconvert inst/extdata/temp/*.mzXML --mzXML --filter \"scanTime [240,900]\" -o inst/extdata -g')
-```
-
-And then for the last few steps, we again rename the files (since `msconvert`
-expands them to their full .raw names) and remove the ones we don't need for
-the demos.
-
-```{r renameremove, eval=FALSE}
-init_files <- list.files("inst/extdata", full.names = TRUE)
-new_names <- paste0("inst/extdata/", gsub(x=init_files, ".*(Smp_|Extracts_)", ""))
-file.rename(init_files, new_names)
-
-unlink("inst/extdata/temp", recursive = TRUE)
-file.remove(list.files("inst/extdata", pattern = "mzXML$", full.names = TRUE))
-file.remove(paste0("inst/extdata/", c("LB12HL_CD.mzXML.gz", "LB12HL_EF.mzXML.gz")))
-```
-
-To check that the new files look ok, we can see if we can read them with `RaMS`
-and `MSnbase`.
-
-```{r checkquality, eval=FALSE}
-MSnbase::readMSData(list.files("inst/extdata", full.names = TRUE)[1], msLevel. = 1)
-RaMS::grabMSdata(new_names[1])
-```
-
-Finally, remember to clean up the original downloads folder
-
-```{r vigdata cleanup, eval=FALSE}
-unlink("vignettes/data", recursive = TRUE)
-```
-
-***
-
-README last built on `r Sys.Date()`
diff --git a/doc/Minifying-files-with-RaMS.html b/doc/Minifying-files-with-RaMS.html
deleted file mode 100644
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-<!DOCTYPE html>
-
-<html>
-
-<head>
-
-<meta charset="utf-8" />
-<meta name="generator" content="pandoc" />
-<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
-
-<meta name="viewport" content="width=device-width, initial-scale=1" />
-
-
-
-<title>Minifying files with RaMS</title>
-
-<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
-// be compatible with the behavior of Pandoc < 2.8).
-document.addEventListener('DOMContentLoaded', function(e) {
-  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
-  var i, h, a;
-  for (i = 0; i < hs.length; i++) {
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-    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
-    a = h.attributes;
-    while (a.length > 0) h.removeAttribute(a[0].name);
-  }
-});
-</script>
-
-<style type="text/css">
-code{white-space: pre-wrap;}
-span.smallcaps{font-variant: small-caps;}
-span.underline{text-decoration: underline;}
-div.column{display: inline-block; vertical-align: top; width: 50%;}
-div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
-ul.task-list{list-style: none;}
-</style>
-
-
-
-<style type="text/css">
-code {
-white-space: pre;
-}
-.sourceCode {
-overflow: visible;
-}
-</style>
-<style type="text/css" data-origin="pandoc">
-pre > code.sourceCode { white-space: pre; position: relative; }
-pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
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-.sourceCode { overflow: visible; }
-code.sourceCode > span { color: inherit; text-decoration: inherit; }
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-pre.sourceCode { margin: 0; }
-@media screen {
-div.sourceCode { overflow: auto; }
-}
-@media print {
-pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
-}
-pre.numberSource code
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-
-
-
-<h1 class="title toc-ignore">Minifying files with RaMS</h1>
-
-
-
-<!-- This vignette is created from a .Rmd file: please edit that instead. -->
-<p>As of version 1.1.0, <code>RaMS</code> also has functions that allow
-irrelevant data to be removed from the file to reduce file sizes. Like
-<code>grabMSdata</code>, there’s one wrapper function
-<code>minifyMSdata</code> that accepts mzML or mzXML files, plus a
-vector of <em>m/z</em> values that should either be kept
-(<code>mz_include</code>) or removed (<code>mz_exclude</code>). The
-function then opens up the provided MS files and removes data points in
-the MS<sup>1</sup> and MS<sup>2</sup> spectra that fall outside the
-accepted bounds. <code>mz_include</code> is useful when only a few
-masses are of interest, as in targeted metabolomics.
-<code>mz_exclude</code> is useful when many masses are known to be
-contaminants or interfere with peakpicking/plotting abilities. This
-minification can shrink a file over three orders of magnitude,
-decreasing both processing time and memory allocation later in the
-pipeline.</p>
-<p>This is also very useful for creating demo MS files -
-<code>RaMS</code> uses these functions to produce the sample data in
-<code>extdata</code>, with 6 MS files taking up less than 5 megabytes of
-disk space. Many other programs provide the ability to shrink files, but
-none (known to me) shrink files by excluding <em>m/z</em> values and
-instead can only remove certain retention times.</p>
-<p>Below, we begin with a large MS file containing both MS<sup>1</sup>
-and MS<sup>2</sup> data and extract only the data corresponding to
-valine/glycine betaine and homarine.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(RaMS)</span>
-<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>msdata_files <span class="ot">&lt;-</span> <span class="fu">list.files</span>(</span>
-<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>  <span class="fu">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="at">package =</span> <span class="st">&quot;RaMS&quot;</span>), <span class="at">full.names =</span> <span class="cn">TRUE</span>, <span class="at">pattern =</span> <span class="st">&quot;mzML&quot;</span></span>
-<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a>)[<span class="dv">1</span><span class="sc">:</span><span class="dv">4</span>]</span>
-<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a></span>
-<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a>initial_filename <span class="ot">&lt;-</span> msdata_files[<span class="dv">1</span>]</span>
-<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a>output_filename <span class="ot">&lt;-</span> <span class="fu">gsub</span>(<span class="at">x=</span><span class="fu">paste0</span>(<span class="st">&quot;minified_&quot;</span>, <span class="fu">basename</span>(initial_filename)), <span class="st">&quot;</span><span class="sc">\\</span><span class="st">.gz&quot;</span>, <span class="st">&quot;&quot;</span>)</span>
-<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a></span>
-<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a>masses_of_interest <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fl">118.0865</span>, <span class="fl">138.0555</span>)</span>
-<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a><span class="fu">minifyMSdata</span>(<span class="at">files =</span> initial_filename, <span class="at">output_files =</span> output_filename, </span>
-<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a>             <span class="at">mz_include =</span> masses_of_interest, <span class="at">ppm =</span> <span class="dv">10</span>, <span class="at">warn =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
-<p>Then, when we open the file up (with <code>RaMS</code> or other
-software) we are left with the data corresponding only to those
-compounds:</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a>init_msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(initial_filename)</span>
-<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(output_filename)</span></code></pre></div>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(<span class="fu">head</span>(msdata<span class="sc">$</span>MS1, <span class="dv">3</span>))</span></code></pre></div>
-<table>
-<thead>
-<tr class="header">
-<th align="right">rt</th>
-<th align="right">mz</th>
-<th align="right">int</th>
-<th align="left">filename</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">4.00085</td>
-<td align="right">118.0865</td>
-<td align="right">15968431.0</td>
-<td align="left">minified_DDApos_2.mzML</td>
-</tr>
-<tr class="even">
-<td align="right">4.00085</td>
-<td align="right">138.0550</td>
-<td align="right">174591.6</td>
-<td align="left">minified_DDApos_2.mzML</td>
-</tr>
-<tr class="odd">
-<td align="right">4.00085</td>
-<td align="right">138.0550</td>
-<td align="right">174591.6</td>
-<td align="left">minified_DDApos_2.mzML</td>
-</tr>
-</tbody>
-</table>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(<span class="fu">head</span>(msdata<span class="sc">$</span>MS2, <span class="dv">3</span>))</span></code></pre></div>
-<table>
-<colgroup>
-<col width="13%" />
-<col width="13%" />
-<col width="13%" />
-<col width="15%" />
-<col width="11%" />
-<col width="33%" />
-</colgroup>
-<thead>
-<tr class="header">
-<th align="right">rt</th>
-<th align="right">premz</th>
-<th align="right">fragmz</th>
-<th align="right">int</th>
-<th align="right">voltage</th>
-<th align="left">filename</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">4.182333</td>
-<td align="right">118.0864</td>
-<td align="right">51.81098</td>
-<td align="right">3809.649</td>
-<td align="right">35</td>
-<td align="left">minified_DDApos_2.mzML</td>
-</tr>
-<tr class="even">
-<td align="right">4.182333</td>
-<td align="right">118.0864</td>
-<td align="right">58.06422</td>
-<td align="right">10133.438</td>
-<td align="right">35</td>
-<td align="left">minified_DDApos_2.mzML</td>
-</tr>
-<tr class="odd">
-<td align="right">4.182333</td>
-<td align="right">118.0864</td>
-<td align="right">58.06590</td>
-<td align="right">390179.500</td>
-<td align="right">35</td>
-<td align="left">minified_DDApos_2.mzML</td>
-</tr>
-</tbody>
-</table>
-<p>Both the TIC and BPC are updated to reflect the smaller file size as
-well:</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mfrow=</span><span class="fu">c</span>(<span class="dv">2</span>, <span class="dv">1</span>), <span class="at">mar=</span><span class="fu">c</span>(<span class="fl">2.1</span>, <span class="fl">2.1</span>, <span class="fl">1.1</span>, <span class="fl">0.1</span>))</span>
-<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="fu">plot</span>(init_msdata<span class="sc">$</span>BPC<span class="sc">$</span>rt, init_msdata<span class="sc">$</span>BPC<span class="sc">$</span>int, <span class="at">type =</span> <span class="st">&quot;l&quot;</span>, <span class="at">main =</span> <span class="st">&quot;Initial BPC&quot;</span>)</span>
-<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="fu">plot</span>(msdata<span class="sc">$</span>BPC<span class="sc">$</span>rt, msdata<span class="sc">$</span>BPC<span class="sc">$</span>int, <span class="at">type =</span> <span class="st">&quot;l&quot;</span>, <span class="at">main =</span> <span class="st">&quot;New BPC&quot;</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-<hr />
-<p>The <code>minifyMSdata</code> function is vectorized so the exact
-same syntax can be used for multiple files:</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="fu">dir.create</span>(<span class="st">&quot;mini_mzMLs/&quot;</span>)</span>
-<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a>output_files <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">&quot;mini_mzMLs/&quot;</span>, <span class="fu">basename</span>(msdata_files))</span>
-<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a>output_files <span class="ot">&lt;-</span> <span class="fu">gsub</span>(<span class="at">x=</span>output_files, <span class="st">&quot;</span><span class="sc">\\</span><span class="st">.gz&quot;</span>, <span class="st">&quot;&quot;</span>)</span>
-<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a></span>
-<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="fu">minifyMSdata</span>(<span class="at">files =</span> msdata_files, <span class="at">output_files =</span> output_files, <span class="at">verbosity =</span> <span class="dv">0</span>,</span>
-<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a>             <span class="at">mz_include =</span> masses_of_interest, <span class="at">ppm =</span> <span class="dv">10</span>, <span class="at">warn =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>mini_msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(output_files, <span class="at">verbosity =</span> <span class="dv">0</span>)</span>
-<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a></span>
-<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
-<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="fu">ggplot</span>(mini_msdata<span class="sc">$</span>BPC) <span class="sc">+</span> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x=</span>rt, <span class="at">y=</span>int, <span class="at">color=</span>filename)) <span class="sc">+</span> <span class="fu">theme_bw</span>()</span></code></pre></div>
-<p><img src="data:image/png;base64,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" width="80%" style="display: block; margin: auto;" /></p>
-<p>These new files are valid according to the validator provided in
-MSnbase, which means that most programs should be able to open them, but
-this feature is still experimental and may break on quirky data. If that
-happens, please feel free to submit a bug report at</p>
-<hr />
-<p>As an example of how I use this minification function, here’s the
-code used to create the minified files in the <code>\extdata</code>
-folder that ships with the package. This was especially useful because
-the package can’t be more than 5MB but it’s incredibly useful to include
-some standalone MS data for demos and vignettes like this one. I don’t
-actually run this code in the vignette itself to save compilation time
-but it will run if you test it yourself.</p>
-<p>These files originate from the <a href="https://sites.google.com/view/anitra-ingalls">Ingalls Lab</a> at
-the University of Washington, USA and are published in the manuscript <a href="https://www.sciencedirect.com/science/article/pii/S1434461018301184"><strong>“Metabolic
-consequences of cobalamin scarcity in diatoms as revealed through
-metabolomics”</strong></a>. Files are downloaded from the corresponding
-<a href="https://www.ebi.ac.uk/metabolights/MTBLS703/files">Metabolights
-repository</a>.</p>
-<p>First, we identify the <em>m/z</em> values we’d like to keep in the
-minified files. For the demo data, I’ll use the Ingalls Lab list of
-targeted compounds - those we have authentic standards for.</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>raw_stans <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="fu">paste0</span>(<span class="st">&quot;https://raw.githubusercontent.com/&quot;</span>,</span>
-<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>                             <span class="st">&quot;IngallsLabUW/Ingalls_Standards/&quot;</span>,</span>
-<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a>                             <span class="st">&quot;b098927ea0089b6e7a31e1758e7c7eaad5408535/&quot;</span>,</span>
-<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a>                             <span class="st">&quot;Ingalls_Lab_Standards_NEW.csv&quot;</span>))</span>
-<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a></span>
-<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a>mzs_to_include <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(<span class="fu">unique</span>(raw_stans[raw_stans<span class="sc">$</span>Fraction1<span class="sc">==</span><span class="st">&quot;HILICPos&quot;</span>,]<span class="sc">$</span>m.z))</span>
-<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a><span class="co"># Include glycine betaine isotopes for README demo</span></span>
-<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a>mzs_to_include <span class="ot">&lt;-</span> <span class="fu">c</span>(mzs_to_include, <span class="fl">119.0899</span>, <span class="fl">119.0835</span>)</span></code></pre></div>
-<p>Then, we download the raw MS data from the online repository into
-which it’s been deposited.</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="cf">if</span>(<span class="sc">!</span><span class="fu">dir.exists</span>(<span class="st">&quot;vignettes/data&quot;</span>))<span class="fu">dir.create</span>(<span class="st">&quot;vignettes/data&quot;</span>)</span>
-<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>base_url <span class="ot">&lt;-</span> <span class="st">&quot;ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS703/&quot;</span></span>
-<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a>chosen_files <span class="ot">&lt;-</span> <span class="fu">paste0</span>(base_url, <span class="st">&quot;170223_Smp_LB12HL_&quot;</span>, <span class="fu">c</span>(<span class="st">&quot;AB&quot;</span>, <span class="st">&quot;CD&quot;</span>, <span class="st">&quot;EF&quot;</span>), <span class="st">&quot;_pos.mzXML&quot;</span>)</span>
-<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a>new_names <span class="ot">&lt;-</span> <span class="fu">gsub</span>(<span class="at">x=</span><span class="fu">basename</span>(chosen_files), <span class="st">&quot;170223_Smp_&quot;</span>, <span class="st">&quot;&quot;</span>)</span>
-<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a></span>
-<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a><span class="fu">mapply</span>(download.file, chosen_files, <span class="fu">paste0</span>(<span class="st">&quot;vignettes/data/&quot;</span>, new_names), </span>
-<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a>       <span class="at">mode =</span> <span class="st">&quot;wb&quot;</span>, <span class="at">method =</span> <span class="st">&quot;libcurl&quot;</span>)</span></code></pre></div>
-<p>The MSMS data wasn’t uploaded, so we handle that separately by
-pulling it off the lab computer manually and copying it over to our
-temporary directory. If you’re following along, you can skip this chunk
-or use your own DDA data.</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a><span class="fu">file.copy</span>(<span class="at">from =</span> <span class="fu">paste0</span>(<span class="st">&quot;Z:/1_QEdata/2016/2016_Katherine_1335_LightB12_&quot;</span>,</span>
-<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a>                        <span class="st">&quot;Experiment/170223_KRH_Rerun_1335_LightB12_Exp_HILIC/&quot;</span>,</span>
-<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a>                        <span class="st">&quot;positive/&quot;</span>,</span>
-<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a>                        <span class="st">&quot;170223_Poo_AllCyanoAqExtracts_DDApos_2.mzXML&quot;</span>),</span>
-<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a>          <span class="at">to =</span> <span class="st">&quot;vignettes/data/DDApos_2.mzXML&quot;</span>, <span class="at">overwrite =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
-<p>Then we can actually perform the minification:</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="fu">library</span>(RaMS)</span>
-<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a></span>
-<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a><span class="cf">if</span>(<span class="sc">!</span><span class="fu">dir.exists</span>(<span class="st">&quot;inst/extdata&quot;</span>))<span class="fu">dir.create</span>(<span class="st">&quot;inst/extdata&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a>init_files <span class="ot">&lt;-</span> <span class="fu">list.files</span>(<span class="st">&quot;vignettes/data/&quot;</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a>out_files <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">&quot;inst/extdata/&quot;</span>, <span class="fu">basename</span>(init_files))</span>
-<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a><span class="fu">minifyMSdata</span>(<span class="at">files =</span> init_files, <span class="at">output_files =</span> out_files, <span class="at">warn =</span> <span class="cn">FALSE</span>,</span>
-<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a>             <span class="at">mz_include =</span> mzs_to_include, <span class="at">ppm =</span> <span class="dv">20</span>)</span></code></pre></div>
-<p>Now we have four minified mzXML files in our inst/extdata folder.
-However, we’d like to be able to demo the mzML functionality as well as
-that of mzXMLs, so we can use <a href="https://proteowizard.sourceforge.io/tools/msconvert.html">Proteowizard’s</a>
-<code>msconvert</code> tool because <code>RaMS</code> can’t convert
-between mzML and mzXML or vice versa. You’ll need to install
-<code>msconvert</code> and add it to your path for this step.</p>
-<p>We also use <code>msconvert</code> to trim the files by retention
-time, keeping data between 4 and 15 minutes.</p>
-<p>Finally, we gzip the files to get them as small as possible, also
-using <code>msconvert</code>.</p>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="fu">system</span>(<span class="st">&quot;msconvert inst/extdata/*.mzXML -o inst/extdata/temp --noindex&quot;</span>)</span>
-<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a><span class="fu">system</span>(<span class="st">&quot;msconvert --mzXML inst/extdata/*.mzXML -o inst/extdata/temp --noindex&quot;</span>)</span>
-<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a><span class="fu">system</span>(<span class="st">&#39;msconvert inst/extdata/temp/*.mzML --filter </span><span class="sc">\&quot;</span><span class="st">scanTime [240,900]</span><span class="sc">\&quot;</span><span class="st"> -o inst/extdata -g&#39;</span>)</span>
-<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="fu">system</span>(<span class="st">&#39;msconvert inst/extdata/temp/*.mzXML --mzXML --filter </span><span class="sc">\&quot;</span><span class="st">scanTime [240,900]</span><span class="sc">\&quot;</span><span class="st"> -o inst/extdata -g&#39;</span>)</span></code></pre></div>
-<p>And then for the last few steps, we again rename the files (since
-<code>msconvert</code> expands them to their full .raw names) and remove
-the ones we don’t need for the demos.</p>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>init_files <span class="ot">&lt;-</span> <span class="fu">list.files</span>(<span class="st">&quot;inst/extdata&quot;</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a>new_names <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">&quot;inst/extdata/&quot;</span>, <span class="fu">gsub</span>(<span class="at">x=</span>init_files, <span class="st">&quot;.*(Smp_|Extracts_)&quot;</span>, <span class="st">&quot;&quot;</span>))</span>
-<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a><span class="fu">file.rename</span>(init_files, new_names)</span>
-<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a></span>
-<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;inst/extdata/temp&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a><span class="fu">file.remove</span>(<span class="fu">list.files</span>(<span class="st">&quot;inst/extdata&quot;</span>, <span class="at">pattern =</span> <span class="st">&quot;mzXML$&quot;</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>))</span>
-<span id="cb13-7"><a href="#cb13-7" tabindex="-1"></a><span class="fu">file.remove</span>(<span class="fu">paste0</span>(<span class="st">&quot;inst/extdata/&quot;</span>, <span class="fu">c</span>(<span class="st">&quot;LB12HL_CD.mzXML.gz&quot;</span>, <span class="st">&quot;LB12HL_EF.mzXML.gz&quot;</span>)))</span></code></pre></div>
-<p>To check that the new files look ok, we can see if we can read them
-with <code>RaMS</code> and <code>MSnbase</code>.</p>
-<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>MSnbase<span class="sc">::</span><span class="fu">readMSData</span>(<span class="fu">list.files</span>(<span class="st">&quot;inst/extdata&quot;</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>)[<span class="dv">1</span>], <span class="at">msLevel. =</span> <span class="dv">1</span>)</span>
-<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a>RaMS<span class="sc">::</span><span class="fu">grabMSdata</span>(new_names[<span class="dv">1</span>])</span></code></pre></div>
-<p>Finally, remember to clean up the original downloads folder</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;vignettes/data&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
-<hr />
-<p>README last built on 2023-12-13</p>
-
-
-
-<!-- code folding -->
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
-  (function () {
-    var script = document.createElement("script");
-    script.type = "text/javascript";
-    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
-    document.getElementsByTagName("head")[0].appendChild(script);
-  })();
-</script>
-
-</body>
-</html>
diff --git a/doc/RaMS-and-friends.R b/doc/RaMS-and-friends.R
deleted file mode 100644
index a1d47a7..0000000
--- a/doc/RaMS-and-friends.R
+++ /dev/null
@@ -1,83 +0,0 @@
-## ----setup, include = FALSE---------------------------------------------------
-options(rmarkdown.html_vignette.check_title = FALSE)
-data.table::setDTthreads(2)
-
-## -----------------------------------------------------------------------------
-library(RaMS)
-
-# Locate an MS file
-single_file <- system.file("extdata", "LB12HL_AB.mzML.gz", package = "RaMS")
-
-# Grab the MS data
-msdata <- grabMSdata(single_file, grab_what = "everything")
-
-# Write out MS1 data to .csv file
-write.csv(x = msdata$MS1, file = "MS1_data.csv")
-
-# Clean up afterward
-file.remove("MS1_data.csv")
-
-## -----------------------------------------------------------------------------
-library(openxlsx)
-
-# Locate an MS2 file
-MS2_file <- system.file("extdata", "DDApos_2.mzML.gz", package = "RaMS")
-
-# Grab the MS1 and MS2 data
-msdata <- grabMSdata(MS2_file, grab_what=c("MS1", "MS2"))
-
-# Write out MS data to Excel file
-# openxlsx writes each object in a list to a unique sheet
-# Produces one sheet for MS1 and one for MS2
-write.xlsx(msdata, file = "MS2_data.xlsx")
-
-# Clean up afterward
-file.remove("MS2_data.xlsx")
-
-## -----------------------------------------------------------------------------
-library(DBI)
-# Get data from multiple files to show off
-mzml_files <- system.file(c("extdata/LB12HL_AB.mzML.gz", 
-                            "extdata/LB12HL_CD.mzML.gz"), 
-                          package = "RaMS")
-msdata <- grabMSdata(mzml_files)
-
-# Create the sqlite database and connect to it
-MSdb <- dbConnect(RSQLite::SQLite(), "MSdata.sqlite")
-
-# Export MS1 and MS2 data to sqlite tables
-dbWriteTable(MSdb, "MS1", msdata$MS1)
-dbWriteTable(MSdb, "MS2", msdata$MS2)
-dbListTables(MSdb)
-
-# Perform a simple query to ensure data was exported correctly
-dbGetQuery(MSdb, 'SELECT * FROM MS1 LIMIT 3')
-
-# Perform EIC extraction in SQL rather than in R
-EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-query_params <- list(lower_bound=118.086, upper_bound=118.087)
-EIC <- dbGetQuery(MSdb, EIC_query, params = query_params)
-
-# Append with additional files
-extra_file <- system.file("extdata", "LB12HL_EF.mzML.gz", package = "RaMS")
-extra_msdata <- grabMSdata(extra_file, grab_what = "everything")
-dbGetQuery(MSdb, 'SELECT COUNT(*) FROM MS1') # Initial number of rows
-dbAppendTable(MSdb, "MS1", extra_msdata$MS1)
-# Confirm three different files exist in DB
-dbGetQuery(MSdb, 'SELECT DISTINCT filename FROM MS1')
-# Confirm new rows have been added
-dbGetQuery(MSdb, 'SELECT COUNT(*) FROM MS1')
-
-# Disconnect after export
-dbDisconnect(MSdb)
-
-# Clean up afterward
-unlink("MSdata.sqlite")
-
-## -----------------------------------------------------------------------------
-# Locate a couple MS files
-data_dir <- system.file("extdata", package = "RaMS")
-file_paths <- list.files(data_dir, pattern = "HL.*mzML", full.names = TRUE)
-
-msdata <- grabMSdata(files = file_paths, grab_what = "BPC")$BPC
-
diff --git a/doc/RaMS-and-friends.Rmd b/doc/RaMS-and-friends.Rmd
deleted file mode 100644
index 0152363..0000000
--- a/doc/RaMS-and-friends.Rmd
+++ /dev/null
@@ -1,156 +0,0 @@
----
-title: "RaMS and friends"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{RaMS-and-friends}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r setup, include = FALSE}
-options(rmarkdown.html_vignette.check_title = FALSE)
-data.table::setDTthreads(2)
-```
-
-**Table of contents:**
-
-* [Standard export to CSV]
-* [Fancier export to Excel]
-* [Exporting to SQL database]
-* [Interfacing with Python via `reticulate`]
-
-
-The strength of `RaMS` is its simple data format. Table-like data structures are
-common in most programming languages, and they can always be converted to the
-nigh-universal matrix format. The goal of this vignette is to illustrate this
-strength by exporting MS data to several formats that can be used outside of R.
-
-## Standard export to CSV
-
-As with all rectangular data, `RaMS` objects can be easily exported to CSV files
-with base R functions. This works best with a few chromatograms at a time, as
-the millions of data points found in most MS files can overwhelm common file
-readers.
-
-```{r}
-library(RaMS)
-
-# Locate an MS file
-single_file <- system.file("extdata", "LB12HL_AB.mzML.gz", package = "RaMS")
-
-# Grab the MS data
-msdata <- grabMSdata(single_file, grab_what = "everything")
-
-# Write out MS1 data to .csv file
-write.csv(x = msdata$MS1, file = "MS1_data.csv")
-
-# Clean up afterward
-file.remove("MS1_data.csv")
-```
-
-## Fancier export to Excel
-
-Excel workbooks are a common format because of their intuitive GUI and
-widespread adoption. They can also encode more information than CSV files
-due to their multiple "sheets" within a single workbook - perfect for encoding
-both MS1 and MS2 information in one place. This vignette uses the 
-[`openxlsx`](https://cran.r-project.org/package=openxlsx)
-package, although there are several alternatives with identical functionality.
-
-```{r}
-library(openxlsx)
-
-# Locate an MS2 file
-MS2_file <- system.file("extdata", "S30657.mzML.gz", package = "RaMS")
-
-# Grab the MS1 and MS2 data
-msdata <- grabMSdata(MS2_file, grab_what=c("MS1", "MS2"))
-
-# Write out MS data to Excel file
-# openxlsx writes each object in a list to a unique sheet
-# Produces one sheet for MS1 and one for MS2
-write.xlsx(msdata, file = "MS2_data.xlsx")
-
-# Clean up afterward
-file.remove("MS2_data.xlsx")
-```
-
-## Exporting to SQL database
-
-For more robust data processing and storage, or to work with larger-than-memory
-data sets, SQL databases are an excellent choice. This vignette will demo the
-[`RSQLite`](https://cran.r-project.org/package=RSQLite) 
-package's engine, although several other database engines have similar
-functionality.
-
-```{r}
-library(DBI)
-# Get data from multiple files to show off
-mzml_files <- system.file(c("extdata/LB12HL_AB.mzML.gz", 
-                            "extdata/LB12HL_CD.mzML.gz"), 
-                          package = "RaMS")
-msdata <- grabMSdata(mzml_files)
-
-# Create the sqlite database and connect to it
-MSdb <- dbConnect(RSQLite::SQLite(), "MSdata.sqlite")
-
-# Export MS1 and MS2 data to sqlite tables
-dbWriteTable(MSdb, "MS1", msdata$MS1)
-dbWriteTable(MSdb, "MS2", msdata$MS2)
-dbListTables(MSdb)
-
-# Perform a simple query to ensure data was exported correctly
-dbGetQuery(MSdb, 'SELECT * FROM MS1 LIMIT 3')
-
-# Perform EIC extraction in SQL rather than in R
-EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-query_params <- list(lower_bound=118.086, upper_bound=118.087)
-EIC <- dbGetQuery(MSdb, EIC_query, params = query_params)
-
-# Append with additional files
-extra_file <- system.file("extdata", "LB12HL_EF.mzML.gz", package = "RaMS")
-extra_msdata <- grabMSdata(extra_file, grab_what = "everything")
-dbGetQuery(MSdb, 'SELECT COUNT(*) FROM MS1') # Initial number of rows
-dbAppendTable(MSdb, "MS1", extra_msdata$MS1)
-# Confirm three different files exist in DB
-dbGetQuery(MSdb, 'SELECT DISTINCT filename FROM MS1')
-# Confirm new rows have been added
-dbGetQuery(MSdb, 'SELECT COUNT(*) FROM MS1')
-
-# Disconnect after export
-dbDisconnect(MSdb)
-
-# Clean up afterward
-unlink("MSdata.sqlite")
-```
-
-## Interfacing with Python via `reticulate`
-
-R and Python are commonly used together, and the 
-[`reticulate`](https://rstudio.github.io/reticulate/) package makes this
-even easier by enabling a Python interpreter within R. RStudio, in which
-this vignette was written, supports both R and Python code chunks as shown
-below.
-
-### R code chunk: {r}
-
-```{r}
-# Locate a couple MS files
-data_dir <- system.file("extdata", package = "RaMS")
-file_paths <- list.files(data_dir, pattern = "HL.*mzML", full.names = TRUE)
-
-msdata <- grabMSdata(files = file_paths, grab_what = "BPC")$BPC
-```
-
-### Python code chunk: {python}
-
-```{python, fig.height=3, eval=FALSE}
-# Not run to pass R CMD check on GitHub
-# Make sure python, matplotlib, and seaborn are installed
-
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-sns.relplot(data=r.msdata, kind="line", x="rt", y="int", hue="filename")
-plt.show()
-```
diff --git a/doc/RaMS-and-friends.html b/doc/RaMS-and-friends.html
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-
-<body>
-
-
-
-
-<h1 class="title toc-ignore">RaMS and friends</h1>
-
-
-
-<p><strong>Table of contents:</strong></p>
-<ul>
-<li><a href="#standard-export-to-csv">Standard export to CSV</a></li>
-<li><a href="#fancier-export-to-excel">Fancier export to Excel</a></li>
-<li><a href="#exporting-to-sql-database">Exporting to SQL
-database</a></li>
-<li><a href="#interfacing-with-python-via-reticulate">Interfacing with
-Python via <code>reticulate</code></a></li>
-</ul>
-<p>The strength of <code>RaMS</code> is its simple data format.
-Table-like data structures are common in most programming languages, and
-they can always be converted to the nigh-universal matrix format. The
-goal of this vignette is to illustrate this strength by exporting MS
-data to several formats that can be used outside of R.</p>
-<div id="standard-export-to-csv" class="section level2">
-<h2>Standard export to CSV</h2>
-<p>As with all rectangular data, <code>RaMS</code> objects can be easily
-exported to CSV files with base R functions. This works best with a few
-chromatograms at a time, as the millions of data points found in most MS
-files can overwhelm common file readers.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(RaMS)</span>
-<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a></span>
-<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a><span class="co"># Locate an MS file</span></span>
-<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a>single_file <span class="ot">&lt;-</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;LB12HL_AB.mzML.gz&quot;</span>, <span class="at">package =</span> <span class="st">&quot;RaMS&quot;</span>)</span>
-<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a></span>
-<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a><span class="co"># Grab the MS data</span></span>
-<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(single_file, <span class="at">grab_what =</span> <span class="st">&quot;everything&quot;</span>)</span></code></pre></div>
-<pre><code>## 
-## Reading file LB12HL_AB.mzML.gz... 0.15 secs 
-## Reading MS1 data...0.11 secs 
-## Reading MS2 data...0.01 secs 
-## Reading BPC...0.08 secs 
-## Reading TIC...0.06 secs 
-## Reading file metadata...0.1 secs 
-## Binding files together into single data.table
-## Total time: 0.51 secs</code></pre>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="co"># Write out MS1 data to .csv file</span></span>
-<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">write.csv</span>(<span class="at">x =</span> msdata<span class="sc">$</span>MS1, <span class="at">file =</span> <span class="st">&quot;MS1_data.csv&quot;</span>)</span>
-<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a></span>
-<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a><span class="co"># Clean up afterward</span></span>
-<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a><span class="fu">file.remove</span>(<span class="st">&quot;MS1_data.csv&quot;</span>)</span></code></pre></div>
-<pre><code>## [1] TRUE</code></pre>
-</div>
-<div id="fancier-export-to-excel" class="section level2">
-<h2>Fancier export to Excel</h2>
-<p>Excel workbooks are a common format because of their intuitive GUI
-and widespread adoption. They can also encode more information than CSV
-files due to their multiple “sheets” within a single workbook - perfect
-for encoding both MS1 and MS2 information in one place. This vignette
-uses the <a href="https://cran.r-project.org/package=openxlsx"><code>openxlsx</code></a>
-package, although there are several alternatives with identical
-functionality.</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">library</span>(openxlsx)</span>
-<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a></span>
-<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="co"># Locate an MS2 file</span></span>
-<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a>MS2_file <span class="ot">&lt;-</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;DDApos_2.mzML.gz&quot;</span>, <span class="at">package =</span> <span class="st">&quot;RaMS&quot;</span>)</span>
-<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a></span>
-<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a><span class="co"># Grab the MS1 and MS2 data</span></span>
-<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(MS2_file, <span class="at">grab_what=</span><span class="fu">c</span>(<span class="st">&quot;MS1&quot;</span>, <span class="st">&quot;MS2&quot;</span>))</span></code></pre></div>
-<pre><code>## 
-## Reading file DDApos_2.mzML.gz... 0.41 secs 
-## Reading MS1 data...0.55 secs 
-## Reading MS2 data...0.11 secs 
-## Binding files together into single data.table
-## Total time: 1.07 secs</code></pre>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="co"># Write out MS data to Excel file</span></span>
-<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="co"># openxlsx writes each object in a list to a unique sheet</span></span>
-<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a><span class="co"># Produces one sheet for MS1 and one for MS2</span></span>
-<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="fu">write.xlsx</span>(msdata, <span class="at">file =</span> <span class="st">&quot;MS2_data.xlsx&quot;</span>)</span>
-<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a></span>
-<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="co"># Clean up afterward</span></span>
-<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a><span class="fu">file.remove</span>(<span class="st">&quot;MS2_data.xlsx&quot;</span>)</span></code></pre></div>
-<pre><code>## [1] TRUE</code></pre>
-</div>
-<div id="exporting-to-sql-database" class="section level2">
-<h2>Exporting to SQL database</h2>
-<p>For more robust data processing and storage, or to work with
-larger-than-memory data sets, SQL databases are an excellent choice.
-This vignette will demo the <a href="https://cran.r-project.org/package=RSQLite"><code>RSQLite</code></a>
-package’s engine, although several other database engines have similar
-functionality.</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">library</span>(DBI)</span>
-<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="co"># Get data from multiple files to show off</span></span>
-<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a>mzml_files <span class="ot">&lt;-</span> <span class="fu">system.file</span>(<span class="fu">c</span>(<span class="st">&quot;extdata/LB12HL_AB.mzML.gz&quot;</span>, </span>
-<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a>                            <span class="st">&quot;extdata/LB12HL_CD.mzML.gz&quot;</span>), </span>
-<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a>                          <span class="at">package =</span> <span class="st">&quot;RaMS&quot;</span>)</span>
-<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(mzml_files)</span></code></pre></div>
-<pre><code>## 
-  |                                                                            
-  |                                                                      |   0%
-  |                                                                            
-  |===================================                                   |  50%
-  |                                                                            
-  |======================================================================| 100%
-## Total time: 0.98 secs</code></pre>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="co"># Create the sqlite database and connect to it</span></span>
-<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a>MSdb <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;MSdata.sqlite&quot;</span>)</span>
-<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a></span>
-<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a><span class="co"># Export MS1 and MS2 data to sqlite tables</span></span>
-<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a><span class="fu">dbWriteTable</span>(MSdb, <span class="st">&quot;MS1&quot;</span>, msdata<span class="sc">$</span>MS1)</span>
-<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a><span class="fu">dbWriteTable</span>(MSdb, <span class="st">&quot;MS2&quot;</span>, msdata<span class="sc">$</span>MS2)</span>
-<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a><span class="fu">dbListTables</span>(MSdb)</span></code></pre></div>
-<pre><code>## [1] &quot;MS1&quot; &quot;MS2&quot;</code></pre>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a><span class="co"># Perform a simple query to ensure data was exported correctly</span></span>
-<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a><span class="fu">dbGetQuery</span>(MSdb, <span class="st">&#39;SELECT * FROM MS1 LIMIT 3&#39;</span>)</span></code></pre></div>
-<pre><code>##      rt       mz        int          filename
-## 1 4.009 139.0503 1800550.12 LB12HL_AB.mzML.gz
-## 2 4.009 148.0967  206310.81 LB12HL_AB.mzML.gz
-## 3 4.009 136.0618   71907.15 LB12HL_AB.mzML.gz</code></pre>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="co"># Perform EIC extraction in SQL rather than in R</span></span>
-<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a>EIC_query <span class="ot">&lt;-</span> <span class="st">&#39;SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound&#39;</span></span>
-<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a>query_params <span class="ot">&lt;-</span> <span class="fu">list</span>(<span class="at">lower_bound=</span><span class="fl">118.086</span>, <span class="at">upper_bound=</span><span class="fl">118.087</span>)</span>
-<span id="cb15-4"><a href="#cb15-4" tabindex="-1"></a>EIC <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(MSdb, EIC_query, <span class="at">params =</span> query_params)</span>
-<span id="cb15-5"><a href="#cb15-5" tabindex="-1"></a></span>
-<span id="cb15-6"><a href="#cb15-6" tabindex="-1"></a><span class="co"># Append with additional files</span></span>
-<span id="cb15-7"><a href="#cb15-7" tabindex="-1"></a>extra_file <span class="ot">&lt;-</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;LB12HL_EF.mzML.gz&quot;</span>, <span class="at">package =</span> <span class="st">&quot;RaMS&quot;</span>)</span>
-<span id="cb15-8"><a href="#cb15-8" tabindex="-1"></a>extra_msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(extra_file, <span class="at">grab_what =</span> <span class="st">&quot;everything&quot;</span>)</span></code></pre></div>
-<pre><code>## 
-## Reading file LB12HL_EF.mzML.gz... 0.15 secs 
-## Reading MS1 data...0.3 secs 
-## Reading MS2 data...0.01 secs 
-## Reading BPC...0.06 secs 
-## Reading TIC...0.06 secs 
-## Reading file metadata...0.1 secs 
-## Binding files together into single data.table
-## Total time: 0.69 secs</code></pre>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a><span class="fu">dbGetQuery</span>(MSdb, <span class="st">&#39;SELECT COUNT(*) FROM MS1&#39;</span>) <span class="co"># Initial number of rows</span></span></code></pre></div>
-<pre><code>##   COUNT(*)
-## 1    42313</code></pre>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="fu">dbAppendTable</span>(MSdb, <span class="st">&quot;MS1&quot;</span>, extra_msdata<span class="sc">$</span>MS1)</span></code></pre></div>
-<pre><code>## [1] 22124</code></pre>
-<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a><span class="co"># Confirm three different files exist in DB</span></span>
-<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a><span class="fu">dbGetQuery</span>(MSdb, <span class="st">&#39;SELECT DISTINCT filename FROM MS1&#39;</span>)</span></code></pre></div>
-<pre><code>##            filename
-## 1 LB12HL_AB.mzML.gz
-## 2 LB12HL_CD.mzML.gz
-## 3 LB12HL_EF.mzML.gz</code></pre>
-<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="co"># Confirm new rows have been added</span></span>
-<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a><span class="fu">dbGetQuery</span>(MSdb, <span class="st">&#39;SELECT COUNT(*) FROM MS1&#39;</span>)</span></code></pre></div>
-<pre><code>##   COUNT(*)
-## 1    64437</code></pre>
-<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="co"># Disconnect after export</span></span>
-<span id="cb25-2"><a href="#cb25-2" tabindex="-1"></a><span class="fu">dbDisconnect</span>(MSdb)</span>
-<span id="cb25-3"><a href="#cb25-3" tabindex="-1"></a></span>
-<span id="cb25-4"><a href="#cb25-4" tabindex="-1"></a><span class="co"># Clean up afterward</span></span>
-<span id="cb25-5"><a href="#cb25-5" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;MSdata.sqlite&quot;</span>)</span></code></pre></div>
-</div>
-<div id="interfacing-with-python-via-reticulate" class="section level2">
-<h2>Interfacing with Python via <code>reticulate</code></h2>
-<p>R and Python are commonly used together, and the <a href="https://rstudio.github.io/reticulate/"><code>reticulate</code></a>
-package makes this even easier by enabling a Python interpreter within
-R. RStudio, in which this vignette was written, supports both R and
-Python code chunks as shown below.</p>
-<div id="r-code-chunk-r" class="section level3">
-<h3>R code chunk: {r}</h3>
-<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a><span class="co"># Locate a couple MS files</span></span>
-<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a>data_dir <span class="ot">&lt;-</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="at">package =</span> <span class="st">&quot;RaMS&quot;</span>)</span>
-<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a>file_paths <span class="ot">&lt;-</span> <span class="fu">list.files</span>(data_dir, <span class="at">pattern =</span> <span class="st">&quot;HL.*mzML&quot;</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb26-4"><a href="#cb26-4" tabindex="-1"></a></span>
-<span id="cb26-5"><a href="#cb26-5" tabindex="-1"></a>msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(<span class="at">files =</span> file_paths, <span class="at">grab_what =</span> <span class="st">&quot;BPC&quot;</span>)<span class="sc">$</span>BPC</span></code></pre></div>
-<pre><code>## 
-  |                                                                            
-  |                                                                      |   0%
-  |                                                                            
-  |=======================                                               |  33%
-  |                                                                            
-  |===============================================                       |  67%
-  |                                                                            
-  |======================================================================| 100%
-## Total time: 0.34 secs</code></pre>
-</div>
-<div id="python-code-chunk-python" class="section level3">
-<h3>Python code chunk: {python}</h3>
-<div class="sourceCode" id="cb28"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="co"># Not run to pass R CMD check on GitHub</span></span>
-<span id="cb28-2"><a href="#cb28-2" tabindex="-1"></a><span class="co"># Make sure python, matplotlib, and seaborn are installed</span></span>
-<span id="cb28-3"><a href="#cb28-3" tabindex="-1"></a></span>
-<span id="cb28-4"><a href="#cb28-4" tabindex="-1"></a><span class="im">import</span> seaborn <span class="im">as</span> sns</span>
-<span id="cb28-5"><a href="#cb28-5" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
-<span id="cb28-6"><a href="#cb28-6" tabindex="-1"></a></span>
-<span id="cb28-7"><a href="#cb28-7" tabindex="-1"></a>sns.relplot(data<span class="op">=</span>r.msdata, kind<span class="op">=</span><span class="st">&quot;line&quot;</span>, x<span class="op">=</span><span class="st">&quot;rt&quot;</span>, y<span class="op">=</span><span class="st">&quot;int&quot;</span>, hue<span class="op">=</span><span class="st">&quot;filename&quot;</span>)</span>
-<span id="cb28-8"><a href="#cb28-8" tabindex="-1"></a>plt.show()</span></code></pre></div>
-</div>
-</div>
-
-
-
-<!-- code folding -->
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
-  (function () {
-    var script = document.createElement("script");
-    script.type = "text/javascript";
-    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
-    document.getElementsByTagName("head")[0].appendChild(script);
-  })();
-</script>
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diff --git a/doc/speed_size_comparison.R b/doc/speed_size_comparison.R
deleted file mode 100644
index d495564..0000000
--- a/doc/speed_size_comparison.R
+++ /dev/null
@@ -1,563 +0,0 @@
-## ----setup, include=FALSE-----------------------------------------------------
-knitr::opts_chunk$set(echo = TRUE, eval=FALSE)
-options(rmarkdown.html_vignette.check_title = FALSE)
-data.table::setDTthreads(2)
-
-## -----------------------------------------------------------------------------
-#  library(RaMS)
-#  library(tidyverse)
-#  library(microbenchmark)
-#  library(MSnbase)
-#  library(Spectra)
-#  library(DBI)
-#  library(arrow)
-#  library(rvest)
-#  library(xml2)
-#  
-#  BiocParallel::register(BiocParallel::SerialParam(stop.on.error = FALSE, progressbar = TRUE))
-
-## ----file download------------------------------------------------------------
-#  set.seed(123)
-#  
-#  n_ms_files <- 10
-#  base_url <- "ftp://massive.ucsd.edu/v01/MSV000080030/peak/" %>%
-#    paste0("Forensic_study_80_volunteers/Forensic_Hands_mzXML/") %>%
-#    paste0("Forensic_Hands_plate_1_mzXML/Sample/")
-#  file_list <- base_url %>%
-#    read_html %>%
-#    xml_text() %>%
-#    strsplit("\\\n") %>%
-#    pluck(1) %>%
-#    str_remove(".*2016 ") %>%
-#    str_remove("\\\r")
-#  chosen_file_list <- sample(file_list, n_ms_files)
-#  
-#  dir.create("vignettes/figures/ssc_vignette_renders/Sample/")
-#  for(i in chosen_file_list){
-#    new_file_path <- paste0("vignettes/figures/ssc_vignette_renders/Sample/", i)
-#    download.file(paste0(base_url, i), destfile = new_file_path, mode = "wb")
-#  }
-#  
-#  ms_files <- list.files("vignettes/figures/ssc_vignette_renders/Sample/", full.names = TRUE)
-
-## ----MSnExp-------------------------------------------------------------------
-#  msnexp_obj <- readMSData(ms_files, mode="inMemory", msLevel. = 1)
-#  plot(chromatogram(msnexp_obj, mz=pmppm(432.2810, ppm = 20)))
-
-## ----onDiskMSnExp-------------------------------------------------------------
-#  ondisk_obj <- readMSData(ms_files, mode="onDisk", msLevel. = 1)
-#  plot(chromatogram(ondisk_obj, mz=pmppm(432.2810, ppm = 20)))
-
-## ----Spectra------------------------------------------------------------------
-#  getIntensities <- function(x, ...) {
-#    if (nrow(x)) {
-#      cbind(mz = NA_real_, intensity = x[, "intensity"])
-#    } else cbind(mz = NA_real_, intensity = NA_real_)
-#  }
-#  
-#  sfs_filtered <- Spectra(ms_files, source=MsBackendMzR()) %>%
-#    filterMsLevel(1) %>%
-#    filterMzRange(pmppm(432.2810, ppm = 20))
-#  sfs_agg <- addProcessing(sfs_filtered, getIntensities)
-#  eic <- cbind(rt=rtime(sfs_agg), int=unlist(intensity(sfs_agg), use.names = FALSE))
-#  plot(eic[,"rt"], eic[,"int"], type="l")
-
-## ----RaMS---------------------------------------------------------------------
-#  rams_obj <- grabMSdata(ms_files, grab_what="MS1")
-#  rams_chrom_data <- rams_obj$MS1[mz%between%pmppm(432.2810, ppm = 20)]
-#  plot(rams_chrom_data$rt, rams_chrom_data$int, type="l")
-
-## ----tmzML--------------------------------------------------------------------
-#  tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/", gsub("mzXML", "tmzML", basename(ms_files)))
-#  dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-#  bpmapply(tmzmlMaker, ms_files, tmzml_names, BPPARAM = SnowParam(workers = 3, progressbar = TRUE, tasks=length(tmzml_names)))
-#  tmzml_obj <- grabMSdata(tmzml_names)
-#  tmzml_chrom_data <- tmzml_obj$MS1[mz%between%pmppm(432.2810, ppm = 20)]
-#  plot(tmzml_chrom_data$rt, tmzml_chrom_data$int, type="l")
-
-## ----arrow--------------------------------------------------------------------
-#  write_dataset(rams_obj$MS1, path = "vignettes/figures/ssc_vignette_renders/pqds")
-#  arrow_data <- open_dataset("vignettes/figures/ssc_vignette_renders/pqds") %>%
-#    filter(mz%between%pmppm(432.2810, ppm = 20)) %>%
-#    dplyr::collect()
-#  plot(arrow_data$rt, arrow_data$int, type="l")
-
-## ----sqlite database----------------------------------------------------------
-#  MSdb <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-#  dbWriteTable(MSdb, "MS1", rams_obj$MS1, overwrite=TRUE)
-#  EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-#  query_params <- as.list(pmppm(432.2810, ppm = 20))
-#  names(query_params) <- c("lower_bound", "upper_bound")
-#  sql_data <- dbGetQuery(MSdb, EIC_query, params = query_params)
-#  plot(sql_data$rt, sql_data$int, type="l")
-#  
-#  rs <- dbSendQuery(MSdb, "CREATE INDEX mz ON MS1 (mz)")
-#  dbClearResult(rs)
-#  sql_data <- dbGetQuery(MSdb, EIC_query, params = query_params)
-#  sql_data <- sql_data[order(sql_data$filename, sql_data$rt),]
-#  plot(sql_data$rt, sql_data$int, type="l")
-#  dbDisconnect(MSdb)
-
-## ----time2make----------------------------------------------------------------
-#  msnexp_make_fun <- function(){
-#    readMSData(ms_files, mode="inMemory", msLevel. = 1)
-#  }
-#  ondisk_make_fun <- function(){
-#    readMSData(ms_files, mode="onDisk", msLevel. = 1)
-#  }
-#  spectra_make_fun <- function(){
-#    Spectra(ms_files, source=MsBackendMzR()) %>% filterMsLevel(1)
-#  }
-#  rams_make_fun <- function(){
-#    grabMSdata(ms_files, grab_what="MS1")
-#  }
-#  tmzml_make_fun <- function(){
-#    tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/",
-#                          gsub("mzXML", "tmzML", basename(ms_files)))
-#    dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-#    mapply(tmzmlMaker, ms_files, tmzml_names)
-#    unlink("vignettes/figures/ssc_vignette_renders/tmzMLs", recursive = TRUE)
-#  }
-#  arrow_make_fun <- function(){
-#    msdata <- grabMSdata(ms_files, grab_what="MS1")
-#    write_dataset(msdata$MS1, path = "vignettes/figures/ssc_vignette_renders/pqds")
-#    unlink("vignettes/figures/ssc_vignette_renders/pqds", recursive = TRUE)
-#  }
-#  sql_make_fun <- function(){
-#    msdata <- grabMSdata(ms_files, grab_what="MS1")
-#    MSdb <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-#    dbWriteTable(MSdb, "MS1", msdata$MS1, overwrite=TRUE)
-#    dbDisconnect(MSdb)
-#    unlink("vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-#  }
-#  sqlidx_make_fun <- function(){
-#    msdata <- grabMSdata(ms_files, grab_what="MS1")
-#    MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-#    dbWriteTable(MSdb_idx, "MS1", msdata$MS1, overwrite=TRUE)
-#    rs <- dbSendQuery(MSdb_idx, "CREATE INDEX mz ON MS1 (mz)")
-#    dbClearResult(rs)
-#    dbDisconnect(MSdb_idx)
-#    unlink("vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-#  }
-#  
-#  make_timings <- microbenchmark(
-#    msnexp_make_fun(), ondisk_make_fun(), spectra_make_fun(), rams_make_fun(),
-#    tmzml_make_fun(), arrow_make_fun(), sql_make_fun(), sqlidx_make_fun(),
-#    times = 10)
-#  saveRDS(make_timings, "vignettes/figures/ssc_vignette_renders/make_timings.rds")
-
-## ----plot time2make-----------------------------------------------------------
-#  make_timings <- readRDS("vignettes/figures/ssc_vignette_renders/make_timings.rds")
-#  make_timings %>%
-#    as.data.frame() %>%
-#    arrange(expr) %>%
-#    mutate(expr=str_remove(expr, "_make_fun\\(\\)")) %>%
-#    mutate(rep_type=case_when(
-#      expr%in%c("msnexp", "ondisk", "spectra", "rams")~"Every R session",
-#      TRUE~"Single-time only"
-#    )) %>%
-#    mutate(expr=factor(expr, levels=c("msnexp", "ondisk", "spectra", "rams",
-#                                      "tmzml", "arrow", "sql", "sqlidx"),
-#                       labels=c("MSnExp", "OnDiskMSnExp", "Spectra", "RaMS",
-#                                "tmzMLs", "Arrow", "SQL", "SQL (indexed)"))) %>%
-#    ggplot() +
-#    geom_boxplot(aes(x=expr, y=time/1e9)) +
-#    geom_hline(yintercept = 0) +
-#    facet_wrap(~rep_type, nrow = 1, scales="free_x") +
-#    labs(y="Seconds", x=NULL) +
-#    theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))
-#  ggsave("vignettes/figures/ssc_vignette_renders/make_time_gp.png", width = 6.5, height = 4, units = "in", device = "png", dpi = 144)
-
-## ----parallel_proc------------------------------------------------------------
-#  unpar_rams <- function(){
-#    print("Unpar RaMS")
-#    lapply(ms_files, grabMSdata)
-#  }
-#  unpar_tmzml <- function(){
-#    print("Unpar tmzML")
-#    tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/",
-#                          gsub("mzXML", "tmzML", basename(ms_files)))
-#    dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-#    pbapply::pbmapply(tmzmlMaker, ms_files, tmzml_names)
-#    unlink("vignettes/figures/ssc_vignette_renders/tmzMLs", recursive = TRUE)
-#  }
-#  
-#  library(BiocParallel)
-#  par_param <- SnowParam(workers = 5, progressbar = TRUE)
-#  par_rams <- function(){
-#    print("Par RaMS")
-#    bplapply(ms_files, grabMSdata, BPPARAM = par_param)
-#  }
-#  par_tmzml <- function(){
-#    print("Par tmzML")
-#    tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/",
-#                          gsub("mzXML", "tmzML", basename(ms_files)))
-#    dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-#    bpmapply(tmzmlMaker, ms_files, tmzml_names, BPPARAM = par_param)
-#    unlink("vignettes/figures/ssc_vignette_renders/tmzMLs", recursive = TRUE)
-#  }
-#  
-#  par_timings <- microbenchmark(par_rams(), unpar_rams(), par_tmzml(), unpar_tmzml(), times = 5)
-#  saveRDS(par_timings, "vignettes/figures/ssc_vignette_renders/par_timings.rds")
-
-## ----parallel proc plot-------------------------------------------------------
-#  par_timings <- readRDS("vignettes/figures/ssc_vignette_renders/par_timings.rds")
-#  par_timings %>%
-#    as.data.frame() %>%
-#    separate(expr, into = c("sub_type", "par_type"), sep = "_") %>%
-#    mutate(par_type=str_remove(par_type, "\\(\\)")) %>%
-#    mutate(sub_type=factor(sub_type, levels=c("unpar", "par"),
-#                           labels=c("Sequential", "Parallel"))) %>%
-#    mutate(par_type=factor(par_type, levels=c("rams", "tmzml"),
-#                           labels=c("RaMS", "tmzMLs"))) %>%
-#    ggplot() +
-#    geom_boxplot(aes(x=par_type, y=time/1e9)) +
-#    geom_hline(yintercept = 0) +
-#    facet_wrap(~sub_type, nrow = 1) +
-#    labs(y="Seconds", x=NULL) +
-#    theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))
-#  ggsave("vignettes/figures/ssc_vignette_renders/par_time_gp.png", width = 6.5, height = 3, units = "in", device = "png", dpi = 144)
-
-## ----time2query---------------------------------------------------------------
-#  msnexp_obj <- readMSData(ms_files, mode="inMemory", msLevel. = 1)
-#  ondisk_obj <- readMSData(ms_files, mode="onDisk", msLevel. = 1)
-#  spectra_obj <- Spectra(ms_files, source=MsBackendMzR()) %>% filterMsLevel(1)
-#  rams_obj <- grabMSdata(ms_files, grab_what="MS1")
-#  
-#  tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/",
-#                        gsub("mzXML", "tmzML", basename(ms_files)))
-#  dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-#  mapply(tmzmlMaker, ms_files, tmzml_names)
-#  
-#  write_dataset(rams_obj$MS1, path = "vignettes/figures/ssc_vignette_renders/pqds")
-#  
-#  MSdb <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-#  dbWriteTable(MSdb, "MS1", rams_obj$MS1, overwrite=TRUE)
-#  dbDisconnect(MSdb)
-#  
-#  MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-#  dbWriteTable(MSdb_idx, "MS1", rams_obj$MS1, overwrite=TRUE)
-#  rs <- dbSendQuery(MSdb_idx, "CREATE INDEX mz ON MS1 (mz)")
-#  dbClearResult(rs)
-#  dbDisconnect(MSdb_idx)
-#  
-#  msnexp_query_fun <- function(){
-#    plot(chromatogram(msnexp_obj, mz=pmppm(432.2810, ppm = 20)))
-#  }
-#  ondisk_query_fun <- function(){
-#    plot(chromatogram(ondisk_obj, mz=pmppm(432.2810, ppm = 20)))
-#  }
-#  spectra_query_fun <- function(){
-#    sfs_filtered <- spectra_obj %>% filterMzRange(pmppm(432.2810, ppm = 20))
-#    getIntensities <- function(x, ...) {
-#      if (nrow(x)) {
-#        cbind(mz = NA_real_, intensity = x[, "intensity"])
-#      } else cbind(mz = NA_real_, intensity = NA_real_)
-#    }
-#    sfs_agg <- addProcessing(sfs_filtered, getIntensities)
-#    eic <- cbind(rt=rtime(sfs_agg), int=unlist(intensity(sfs_agg), use.names = FALSE))
-#    plot(eic[,"rt"], eic[,"int"], type="l")
-#  }
-#  rams_query_fun <- function(){
-#    rams_chrom_data <- rams_obj$MS1[mz%between%pmppm(432.2810, ppm = 20)]
-#    plot(rams_chrom_data$rt, rams_chrom_data$int, type="l")
-#  }
-#  tmzml_query_fun <- function(){
-#    tmzml_names <- list.files("vignettes/figures/ssc_vignette_renders/tmzMLs", full.names = TRUE)
-#    tmzml_obj <- grabMSdata(tmzml_names)
-#    tmzml_chrom_data <- tmzml_obj$MS1[mz%between%pmppm(432.2810, ppm = 20)]
-#    plot(tmzml_chrom_data$rt, tmzml_chrom_data$int, type="l")
-#  }
-#  arrow_query_fun <- function(){
-#    arrow_data <- open_dataset("vignettes/figures/ssc_vignette_renders/pqds") %>%
-#      filter(mz%between%pmppm(432.2810, ppm = 20)) %>%
-#      dplyr::collect()
-#    plot(arrow_data$rt, arrow_data$int, type="l")
-#  }
-#  sql_query_fun <- function(){
-#    MSdb <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-#    EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-#    query_params <- as.list(pmppm(432.2810, ppm = 20))
-#    names(query_params) <- c("lower_bound", "upper_bound")
-#    sql_data <- dbGetQuery(MSdb, EIC_query, params = query_params)
-#    plot(sql_data$rt, sql_data$int, type="l")
-#  }
-#  sqlidx_query_fun <- function(){
-#    MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-#    EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-#    query_params <- as.list(pmppm(432.2810, ppm = 20))
-#    names(query_params) <- c("lower_bound", "upper_bound")
-#    sql_data <- dbGetQuery(MSdb_idx, EIC_query, params = query_params)
-#    sql_data <- sql_data[order(sql_data$filename, sql_data$rt),]
-#    plot(sql_data$rt, sql_data$int, type="l")
-#  }
-#  
-#  query_timings <- microbenchmark(
-#    msnexp_query_fun(), ondisk_query_fun(), spectra_query_fun(), rams_query_fun(),
-#    tmzml_query_fun(), arrow_query_fun(), sql_query_fun(), sqlidx_query_fun(),
-#    times = 10
-#  )
-#  query_timings
-#  saveRDS(query_timings, "vignettes/figures/ssc_vignette_renders/query_timings.rds")
-
-## ----time2query plot----------------------------------------------------------
-#  query_timings <- readRDS("vignettes/figures/ssc_vignette_renders/query_timings.rds")
-#  query_timings %>%
-#    as.data.frame() %>%
-#    arrange(expr) %>%
-#    mutate(expr=str_remove(expr, "_query_fun\\(\\)")) %>%
-#    mutate(rep_type=case_when(
-#      expr%in%c("msnexp", "ondisk", "spectra", "rams")~"Every R session",
-#      TRUE~"Single-time only"
-#    )) %>%
-#    mutate(expr=factor(expr, levels=c("msnexp", "ondisk", "spectra", "rams",
-#                                      "tmzml", "arrow", "sql", "sqlidx"),
-#                       labels=c("MSnExp", "OnDiskMSnExp", "Spectra", "RaMS",
-#                                "tmzMLs", "Arrow", "SQL", "SQL (indexed)"))) %>%
-#    ggplot() +
-#    geom_boxplot(aes(x=expr, y=time/1e9)) +
-#    scale_y_log10() +
-#    labs(y="Seconds", x=NULL) +
-#    theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))
-#  ggsave("vignettes/figures/ssc_vignette_renders/query_time_gp.png", width = 6.5, height = 5, units = "in", device = "png", dpi = 144)
-
-## ----double-check sql_idx vs RaMS---------------------------------------------
-#  MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-#  EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-#  query_params <- as.list(pmppm(432.2810, ppm = 20))
-#  names(query_params) <- c("lower_bound", "upper_bound")
-#  dbGetQuery(MSdb_idx, EIC_query, params = query_params) %>% qplotMS1data()
-#  
-#  rams_obj$MS1[mz%between%pmppm(432.2810, ppm = 20)] %>% qplotMS1data()
-
-## ----make query strings-------------------------------------------------------
-#  rams_obj <- grabMSdata(ms_files, grab_what="MS1")
-#  grouped_ms1 <- rams_obj$MS1 %>%
-#    arrange(desc(int)) %>%
-#    mutate(mz_group=mz_group(mz, ppm = 10, max_groups = 10, min_group_size=20)) %>%
-#    drop_na()
-#  # grouped_ms1 %>%
-#  #   qplotMS1data(facet_col="mz_group", facet_args = list(ncol=2))
-#  mzs_to_grab <- grouped_ms1 %>%
-#    group_by(mz_group) %>%
-#    summarise(mean_mz=mean(mz), sd_mz=sd(mz), mean_rt=mean(rt)) %>%
-#    pull(mean_mz)
-#  
-#  rams_arrow_call <- lapply(mzs_to_grab, function(mz_i){
-#    mzrange <- pmppm(mz_i, 10)
-#    call("between", as.name("mz"), mzrange[[1]], mzrange[[2]])
-#  }) %>% paste(collapse = "|")
-#  
-#  sql_comb_call <- sapply(mzs_to_grab, function(mz_i){
-#    paste("mz BETWEEN", pmppm(mz_i, 10)[1], "AND", pmppm(mz_i, 10)[2])
-#  }) %>% paste(collapse = " OR ") %>% paste("SELECT * FROM MS1 WHERE", .)
-#  
-#  print(rams_arrow_call)
-#  print(sql_comb_call)
-
-## ----multichrom query timing--------------------------------------------------
-#  rams_uni_fun <- function(){
-#    print("RaMS unified")
-#    rams_obj$MS1[eval(parse(text=rams_arrow_call))]
-#  }
-#  rams_loop_fun <- function(){
-#    print("RaMS loop")
-#    lapply(mzs_to_grab, function(mz_i){
-#      rams_obj$MS1[mz%between%pmppm(mz_i, 10)]
-#    }) %>% bind_rows() %>% distinct()
-#  }
-#  
-#  arrow_ds <- open_dataset("vignettes/figures/ssc_vignette_renders/pqds")
-#  arrow_uni_fun <- function(){
-#    print("Arrow unified")
-#    arrow_ds %>%
-#      filter(eval(parse(text = rams_arrow_call))) %>%
-#      collect()
-#  }
-#  arrow_loop_fun <- function(){
-#    print("Arrow loop")
-#    lapply(mzs_to_grab, function(mz_i){
-#      arrow_ds %>%
-#        filter(mz%between%pmppm(mz_i, 10)) %>%
-#        collect()
-#    }) %>% bind_rows() %>% distinct()
-#  }
-#  
-#  MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-#  sql_uni_fun <- function(){
-#    print("SQL unified")
-#    dbGetQuery(MSdb_idx, sql_comb_call)
-#  }
-#  sql_query_base <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-#  sql_loop_fun <- function(){
-#    print("SQL loop")
-#    lapply(mzs_to_grab, function(mz_i){
-#      query_params <- as.list(pmppm(mz_i, ppm = 20))
-#      names(query_params) <- c("lower_bound", "upper_bound")
-#      sql_data <- dbGetQuery(MSdb_idx, sql_query_base, params = query_params)
-#    }) %>% bind_rows() %>% distinct()
-#  }
-#  
-#  multichrom_timings <- microbenchmark(
-#    rams_uni_fun(), rams_loop_fun(), arrow_uni_fun(), arrow_loop_fun(),
-#    sql_uni_fun(), sql_loop_fun(), times = 10
-#  )
-#  saveRDS(multichrom_timings, "vignettes/figures/ssc_vignette_renders/multichrom_timings.rds")
-#  
-#  multichrom_timings <- readRDS("vignettes/figures/ssc_vignette_renders/multichrom_timings.rds")
-#  multichrom_timings %>%
-#    as.data.frame() %>%
-#    arrange(expr) %>%
-#    mutate(expr=str_remove(expr, "_fun\\(\\)")) %>%
-#    separate(expr, into = c("expr", "query_type"), sep = "_") %>%
-#    mutate(expr=factor(expr, levels=c("rams", "arrow", "sql"),
-#                       labels=c("RaMS", "Arrow", "SQL"))) %>%
-#    mutate(query_type=factor(query_type, levels=c("uni", "loop"),
-#                             labels=c("Unified query", "Query loop"))) %>%
-#    ggplot() +
-#    geom_boxplot(aes(x=query_type, y=time/1e9), lwd=1) +
-#    facet_wrap(~expr, nrow=1) +
-#    scale_y_log10() +
-#    labs(y="Seconds", x=NULL, color=NULL) +
-#    theme_bw()
-#  ggsave("vignettes/figures/ssc_vignette_renders/multichrom_gp.png", width = 6.5, height = 4, units = "in", device = "png", dpi = 144)
-
-## ----sizing info--------------------------------------------------------------
-#  size_list <- list()
-#  
-#  size_list$mzXML <- sum(file.size(ms_files))
-#  
-#  msnexp_obj <- readMSData(ms_files, mode="inMemory", msLevel. = 1)
-#  size_list$msnexp_obj <- pryr::object_size(msnexp_obj)
-#  rm(msnexp_obj)
-#  
-#  ondisk_obj <- readMSData(ms_files, mode="onDisk", msLevel. = 1)
-#  size_list$ondisk_obj <- pryr::object_size(ondisk_obj)
-#  rm(ondisk_obj)
-#  
-#  sfs_filtered <- Spectra(ms_files, source=MsBackendMzR()) %>%
-#    filterMsLevel(1)
-#  size_list$spectra <- pryr::object_size(sfs_filtered)
-#  rm(sfs_filtered)
-#  
-#  rams_obj <- grabMSdata(ms_files, grab_what="MS1")
-#  size_list$rams <- pryr::object_size(rams_obj)
-#  
-#  tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/", gsub("mzXML", "tmzML", basename(ms_files)))
-#  dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-#  bpmapply(tmzmlMaker, ms_files, tmzml_names, BPPARAM = SnowParam(workers = 5, progressbar = TRUE, tasks=length(tmzml_names)))
-#  size_list$tmzml <- sum(file.size(list.files("vignettes/figures/ssc_vignette_renders/tmzMLs", full.names = TRUE)))
-#  unlink("vignettes/figures/ssc_vignette_renders/tmzMLs", recursive = TRUE)
-#  
-#  write_dataset(rams_obj$MS1, path = "vignettes/figures/ssc_vignette_renders/pqds")
-#  size_list$arrow <- sum(file.size(list.files("vignettes/figures/ssc_vignette_renders/pqds", full.names = TRUE)))
-#  unlink("vignettes/figures/ssc_vignette_renders/pqds", recursive = TRUE)
-#  
-#  MSdb <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-#  dbWriteTable(MSdb, "MS1", rams_obj$MS1, overwrite=TRUE)
-#  dbDisconnect(MSdb)
-#  size_list$MSdb <- file.size("vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-#  
-#  
-#  MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-#  rs <- dbSendQuery(MSdb_idx, "CREATE INDEX mz ON MS1 (mz)")
-#  dbClearResult(rs)
-#  dbDisconnect(MSdb_idx)
-#  size_list$MSdb_idx <- file.size("vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-#  unlink("vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-#  
-#  saveRDS(size_list, "vignettes/figures/ssc_vignette_renders/size_list.rds")
-
-## ----plot size info-----------------------------------------------------------
-#  size_list <- readRDS("vignettes/figures/ssc_vignette_renders/size_list.rds")
-#  size_list %>%
-#    within(rm(mzXML)) %>%
-#    sapply(as.numeric) %>%
-#    data.frame(bytes=.) %>%
-#    rownames_to_column("expr") %>%
-#    mutate(expr=str_remove(expr, "_obj")) %>%
-#    mutate(expr=str_replace(expr, "MSdb_?", "sql")) %>%
-#    mutate(mem_type=case_when(
-#      expr%in%c("msnexp", "ondisk", "spectra", "rams")~"Memory",
-#      TRUE~"Disk"
-#    )) %>%
-#    mutate(mem_type=factor(mem_type, levels=c("Memory", "Disk"))) %>%
-#    mutate(expr=factor(expr, levels=c("msnexp", "ondisk", "spectra", "rams",
-#                                      "tmzml", "arrow", "sql", "sqlidx"),
-#                       labels=c("MSnExp", "OnDiskMSnExp", "Spectra", "RaMS",
-#                                "tmzMLs", "Arrow", "SQL", "SQL (indexed)"))) %>%
-#    ggplot() +
-#    geom_hline(yintercept = size_list$mzXML/(1024^3)) +
-#    geom_point(aes(x=expr, y=bytes/(1024^3))) +
-#    scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10), labels=c("1MB", "10MB", "100MB", "1GB", "10GB"),
-#                  limits=c(0.001, 10)) +
-#    facet_wrap(~mem_type, scales = "free_x") +
-#    labs(x=NULL, y=NULL)
-#  ggsave("vignettes/figures/ssc_vignette_renders/size_cons.png", width = 6.5, height = 4, units = "in", device = "png", dpi = 144)
-
-## ----summary plot-------------------------------------------------------------
-#  make_timings <- readRDS("vignettes/figures/ssc_vignette_renders/make_timings.rds")
-#  query_timings <- readRDS("vignettes/figures/ssc_vignette_renders/query_timings.rds")
-#  size_df <- readRDS("vignettes/figures/ssc_vignette_renders/size_list.rds") %>%
-#    sapply(as.numeric) %>%
-#    data.frame(size=.) %>%
-#    mutate(size=size/1024^3) %>%
-#    rownames_to_column("expr") %>%
-#    mutate(expr=str_remove(expr, "_obj")) %>%
-#    mutate(expr=str_replace(expr, "MSdb_?", "sql"))
-#  
-#  bind_rows(make_timings, query_timings) %>%
-#    as.data.frame() %>%
-#    group_by(expr) %>%
-#    mutate(time=time/1e9) %>%
-#    summarise(med_time=median(time), IQR_time=IQR(time)) %>%
-#    mutate(expr=str_remove(expr, "_fun\\(\\)")) %>%
-#    separate(expr, into = c("expr", "time_type")) %>%
-#    left_join(size_df) %>%
-#    mutate(rep_type=case_when(
-#      expr%in%c("msnexp", "ondisk", "spectra", "rams")~"Memory",
-#      TRUE~"Disk"
-#    )) %>%
-#    mutate(expr=factor(expr, levels=c("msnexp", "ondisk", "spectra", "rams",
-#                                      "tmzml", "arrow", "sql", "sqlidx"),
-#                       labels=c("MSnExp", "OnDiskMSnExp", "Spectra", "RaMS",
-#                                "tmzMLs", "Arrow", "SQL", "SQL (indexed)"))) %>%
-#    pivot_wider(names_from = time_type, values_from=c("med_time", "IQR_time")) %>%
-#    ggplot() +
-#    geom_vline(xintercept = 0) +
-#    geom_linerange(aes(x=med_time_make, ymin=med_time_query-IQR_time_query*2,
-#                       ymax=med_time_query+IQR_time_query*2, color=expr)) +
-#    geom_linerange(aes(y=med_time_query, xmin=med_time_make-IQR_time_make*2,
-#                       xmax=med_time_make+IQR_time_make*2, color=expr)) +
-#    geom_point(aes(x=med_time_make, y=med_time_query, color=expr,
-#                   size=size, shape=rep_type)) +
-#    scale_shape_manual(values=c(16, 15)) +
-#    scale_y_log10() +
-#    coord_flip() +
-#    guides(color = guide_legend(order = 1), shape = guide_legend(order = 2),
-#           size=guide_legend(order=3)) +
-#    labs(x="Time to transform (s)", y="Time to query (s)", color=NULL, size="Size (GB)",
-#         shape="Storage") +
-#    theme_bw()
-#  ggsave("vignettes/figures/ssc_vignette_renders/sum_plot.png", width = 6.5, height = 5, units = "in", device = "png", dpi = 144)
-#  
-#  
-#  # bind_rows(make_timings, query_timings) %>%
-#  #   as.data.frame() %>%
-#  #   group_by(expr) %>%
-#  #   mutate(time=time/1e9) %>%
-#  #   summarise(med_time=median(time)) %>%
-#  #   mutate(expr=str_remove(expr, "_fun\\(\\)")) %>%
-#  #   separate(expr, into = c("expr", "time_type")) %>%
-#  #   pivot_wider(names_from = time_type, values_from=med_time) %>%
-#  #   left_join(size_df) %>%
-#  #   plotly::plot_ly(x=~make, y=~query, z=~size, hovertext=~expr,
-#  #                   type="scatter3d", mode="markers")
-
-## ----cleanup------------------------------------------------------------------
-#  unlink("vignettes/figures/ssc_vignette_renders/tmzMLs", recursive = TRUE)
-#  unlink("vignettes/figures/ssc_vignette_renders/pqds", recursive = TRUE)
-#  unlink("vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-#  unlink("vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-#  unlink("vignettes/figures/ssc_vignette_renders/Sample/", recursive = TRUE)
-
diff --git a/doc/speed_size_comparison.Rmd b/doc/speed_size_comparison.Rmd
deleted file mode 100644
index 2350f2d..0000000
--- a/doc/speed_size_comparison.Rmd
+++ /dev/null
@@ -1,685 +0,0 @@
----
-title: "Benchmarking - RaMS, MSnBase, Spectra, Arrow, and SQL"
-author: "William Kumler"
-date: "`r Sys.Date()`"
-output: 
-  rmarkdown::html_vignette:
-    code_folding: hide
-vignette: >
-  %\VignetteIndexEntry{Benchmarking}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE, eval=FALSE)
-options(rmarkdown.html_vignette.check_title = FALSE)
-data.table::setDTthreads(2)
-```
-
-```{r}
-library(RaMS)
-library(tidyverse)
-library(microbenchmark)
-library(MSnbase)
-library(Spectra)
-library(DBI)
-library(arrow)
-library(rvest)
-library(xml2)
-
-BiocParallel::register(BiocParallel::SerialParam(stop.on.error = FALSE, progressbar = TRUE))
-```
-
-```{r file download}
-set.seed(123)
-
-n_ms_files <- 10
-base_url <- "ftp://massive.ucsd.edu/v01/MSV000080030/peak/" %>%
-  paste0("Forensic_study_80_volunteers/Forensic_Hands_mzXML/") %>% 
-  paste0("Forensic_Hands_plate_1_mzXML/Sample/")
-file_list <- base_url %>%
-  read_html %>%
-  xml_text() %>%
-  strsplit("\\\n") %>%
-  pluck(1) %>%
-  str_remove(".*2016 ") %>%
-  str_remove("\\\r")
-chosen_file_list <- sample(file_list, n_ms_files)
-
-dir.create("vignettes/figures/ssc_vignette_renders/Sample/")
-for(i in chosen_file_list){
-  new_file_path <- paste0("vignettes/figures/ssc_vignette_renders/Sample/", i)
-  download.file(paste0(base_url, i), destfile = new_file_path, mode = "wb")
-}
-
-ms_files <- list.files("vignettes/figures/ssc_vignette_renders/Sample/", full.names = TRUE)
-```
-
-## Intro
-
-As noted in the [RaMS and friends vignette](https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html), one of the major strengths of the 'tidy' data format is how nicely it plays with other packages, allowing for the use of powerful packages that can't normally be applied to MS data. After playing with a few different ones I wanted to do some structured benchmarking to see which is the best use case for various situations.
-
-The general constraints I'm interested in are speed and space, with a minor consideration for simplicity afterward. My question of interest is how costly in these areas access to the MS data is, a process that can be broken down into two relevant steps - the transformation of the MS data from mz(X)ML format into a queryable format, and then the actual query itself. The query itself is relatively straightforward and can be measured in just the amount of time it takes, but the transformation has a caveat that it can be stored either in memory (and therefore must be recreated every time R is restarted) or it can be stored on disk (which is robust across restarts). So I'm interested in
-
-1. Time to create
-2. Size in memory/size on disk
-3. Time to access
-
-I'll be measuring these constraints across eight different ways of accessing the MS data.
-
-#### Original MSnExp
-
-Not used much these days since there's a big push to XCMS3 and the new "on-disk" infrastructure but still provides a useful baseline. The object is stored in memory and is 10/10 in simplicity.
-
-```{r MSnExp}
-msnexp_obj <- readMSData(ms_files, mode="inMemory", msLevel. = 1)
-plot(chromatogram(msnexp_obj, mz=pmppm(432.2810, ppm = 20)))
-```
-
-#### New onDiskMSnExp infrastructure
-
-The newest version of an XCMS object, using a system of pointers to reduce memory load. The object itself is still stored in memory but is equally easy to construct.
-
-```{r onDiskMSnExp}
-ondisk_obj <- readMSData(ms_files, mode="onDisk", msLevel. = 1)
-plot(chromatogram(ondisk_obj, mz=pmppm(432.2810, ppm = 20)))
-```
-
-#### Spectra package
-
-The Spectra package "defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data." It doesn't handle chromatograms very well but is a useful benchmark for next-gen read and write times. For benchmarking comparisons to other data sets, see the [vignette in the Spectra package](https://bioconductor.org/packages/release/bioc/vignettes/MsBackendSql/inst/doc/MsBackendSql.html#4_Performance_comparison_with_other_backends).
-
-```{r Spectra}
-getIntensities <- function(x, ...) {
-  if (nrow(x)) {
-    cbind(mz = NA_real_, intensity = x[, "intensity"])
-  } else cbind(mz = NA_real_, intensity = NA_real_)
-}
-
-sfs_filtered <- Spectra(ms_files, source=MsBackendMzR()) %>%
-  filterMsLevel(1) %>%
-  filterMzRange(pmppm(432.2810, ppm = 20))
-sfs_agg <- addProcessing(sfs_filtered, getIntensities)
-eic <- cbind(rt=rtime(sfs_agg), int=unlist(intensity(sfs_agg), use.names = FALSE))
-plot(eic[,"rt"], eic[,"int"], type="l")
-```
-
-#### RaMS package
-
-The eponymous package, notoriously heavy on memory but very speedy and still pretty darn simple.
-
-```{r RaMS}
-rams_obj <- grabMSdata(ms_files, grab_what="MS1")
-rams_chrom_data <- rams_obj$MS1[mz%between%pmppm(432.2810, ppm = 20)]
-plot(rams_chrom_data$rt, rams_chrom_data$int, type="l")
-```
-
-#### tmzML files
-
-I designed the tmzMLs to be a "transposed" mzML where data are stored as binned chromatograms rather than individual scans, thus making it much more efficient to extract the data with a minimal memory requirement. This was before I was more familiar with formats like Parquet and SQL though.
-
-```{r tmzML}
-tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/", gsub("mzXML", "tmzML", basename(ms_files)))
-dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-bpmapply(tmzmlMaker, ms_files, tmzml_names, BPPARAM = SnowParam(workers = 3, progressbar = TRUE, tasks=length(tmzml_names)))
-tmzml_obj <- grabMSdata(tmzml_names)
-tmzml_chrom_data <- tmzml_obj$MS1[mz%between%pmppm(432.2810, ppm = 20)]
-plot(tmzml_chrom_data$rt, tmzml_chrom_data$int, type="l")
-```
-
-#### Arrow package
-
-Apache's arrow package is designed to interface with parquet files, a column-based instead of row-based file type, with fast access to large datasets with a minimal memory footprint. There's an excellent package that allows seamless interfacing with R and makes reading and writing files almost trivial. This package was the one that actually made me originally want to write about this because it outperformed tmzMLs and other databases so overwhelmingly.
-
-```{r arrow}
-write_dataset(rams_obj$MS1, path = "vignettes/figures/ssc_vignette_renders/pqds")
-arrow_data <- open_dataset("vignettes/figures/ssc_vignette_renders/pqds") %>%
-  filter(mz%between%pmppm(432.2810, ppm = 20)) %>%
-  dplyr::collect()
-plot(arrow_data$rt, arrow_data$int, type="l")
-```
-
-#### SQL(ite)
-
-Finally, another important baseline is SQL databases. In theory these are the "proper" way to store large amounts of MS data but I haven't found them to be incredibly efficient previously, both slow to query and very large on disk. Here I'm testing both the default SQL database as well as one indexed by m/z to optimize for chromatogram extraction.
-
-```{r sqlite database}
-MSdb <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-dbWriteTable(MSdb, "MS1", rams_obj$MS1, overwrite=TRUE)
-EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-query_params <- as.list(pmppm(432.2810, ppm = 20))
-names(query_params) <- c("lower_bound", "upper_bound")
-sql_data <- dbGetQuery(MSdb, EIC_query, params = query_params)
-plot(sql_data$rt, sql_data$int, type="l")
-
-rs <- dbSendQuery(MSdb, "CREATE INDEX mz ON MS1 (mz)")
-dbClearResult(rs)
-sql_data <- dbGetQuery(MSdb, EIC_query, params = query_params)
-sql_data <- sql_data[order(sql_data$filename, sql_data$rt),]
-plot(sql_data$rt, sql_data$int, type="l")
-dbDisconnect(MSdb)
-```
-
-## MS data transformation timings
-
-Below, I've pulled out the "transformation" step from each of the eight methods above that gets the MS data out of the mzML documents and into an R-readable structure. For the first four (MSnExp, onDiskMSnExp, Spectra, and RaMS) this creates an object in the R environment that is wiped each time R is restarted, so they must be recreated every time. For the last four, files are created on the hard drive that are robust across R sessions and only need to be created once.
-
-```{r time2make}
-msnexp_make_fun <- function(){
-  readMSData(ms_files, mode="inMemory", msLevel. = 1)
-}
-ondisk_make_fun <- function(){
-  readMSData(ms_files, mode="onDisk", msLevel. = 1)
-}
-spectra_make_fun <- function(){
-  Spectra(ms_files, source=MsBackendMzR()) %>% filterMsLevel(1)
-}
-rams_make_fun <- function(){
-  grabMSdata(ms_files, grab_what="MS1")
-}
-tmzml_make_fun <- function(){
-  tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/", 
-                        gsub("mzXML", "tmzML", basename(ms_files)))
-  dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-  mapply(tmzmlMaker, ms_files, tmzml_names)
-  unlink("vignettes/figures/ssc_vignette_renders/tmzMLs", recursive = TRUE)
-}
-arrow_make_fun <- function(){
-  msdata <- grabMSdata(ms_files, grab_what="MS1")
-  write_dataset(msdata$MS1, path = "vignettes/figures/ssc_vignette_renders/pqds")
-  unlink("vignettes/figures/ssc_vignette_renders/pqds", recursive = TRUE)
-}
-sql_make_fun <- function(){
-  msdata <- grabMSdata(ms_files, grab_what="MS1")
-  MSdb <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-  dbWriteTable(MSdb, "MS1", msdata$MS1, overwrite=TRUE)
-  dbDisconnect(MSdb)
-  unlink("vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-}
-sqlidx_make_fun <- function(){
-  msdata <- grabMSdata(ms_files, grab_what="MS1")
-  MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-  dbWriteTable(MSdb_idx, "MS1", msdata$MS1, overwrite=TRUE)
-  rs <- dbSendQuery(MSdb_idx, "CREATE INDEX mz ON MS1 (mz)")
-  dbClearResult(rs)
-  dbDisconnect(MSdb_idx)
-  unlink("vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-}
-
-make_timings <- microbenchmark(
-  msnexp_make_fun(), ondisk_make_fun(), spectra_make_fun(), rams_make_fun(),
-  tmzml_make_fun(), arrow_make_fun(), sql_make_fun(), sqlidx_make_fun(), 
-  times = 10)
-saveRDS(make_timings, "vignettes/figures/ssc_vignette_renders/make_timings.rds")
-```
-
-```{r plot time2make}
-make_timings <- readRDS("vignettes/figures/ssc_vignette_renders/make_timings.rds")
-make_timings %>%
-  as.data.frame() %>%
-  arrange(expr) %>%
-  mutate(expr=str_remove(expr, "_make_fun\\(\\)")) %>%
-  mutate(rep_type=case_when(
-    expr%in%c("msnexp", "ondisk", "spectra", "rams")~"Every R session",
-    TRUE~"Single-time only"
-  )) %>%
-  mutate(expr=factor(expr, levels=c("msnexp", "ondisk", "spectra", "rams",
-                                    "tmzml", "arrow", "sql", "sqlidx"),
-                     labels=c("MSnExp", "OnDiskMSnExp", "Spectra", "RaMS",
-                              "tmzMLs", "Arrow", "SQL", "SQL (indexed)"))) %>%
-  ggplot() + 
-  geom_boxplot(aes(x=expr, y=time/1e9)) +
-  geom_hline(yintercept = 0) +
-  facet_wrap(~rep_type, nrow = 1, scales="free_x") +
-  labs(y="Seconds", x=NULL) +
-  theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))
-ggsave("vignettes/figures/ssc_vignette_renders/make_time_gp.png", width = 6.5, height = 4, units = "in", device = "png", dpi = 144)
-```
-
-![](../vignettes/figures/ssc_vignette_renders/make_time_gp.png){width=100%}
-
-Nothing super surprising here. It should be noted that I ran all of these without using parallel processing to ensure a fair comparison, but RaMS and the tmzMLs are almost perfectly parallelizable and can be read/written in series for a huge time saving. Testing this with 5 cores across the 10 files resulted in a 3-fold speed improvement (from ~250 seconds to ~85, placing it about on par with the SQL database) for tmzMLs but not for default RaMS. This makes sense if I/O is the slow step but reading can be done separately from writing, thus preventing multiple simultaneous reads but allowing one tmzML to be written while another is read. Also worth noting is that because `tmzmlMaker` calls `grabMSdata` internally, the RaMS values are a lower bound on the maximum tmzML speed. The MSnExp, OnDiskMSnExp, and Spectra packages focus instead on parallelizing their query step and I can't seem to run those in parallel. The SQL and Arrow options similarly write to a single file that's presumably going to be "locked" during writing and don't have parallelization options available either. Also worth noting is that performing this test on 10 files (~1GB MS data) doesn't really challenge memory requirements, so this could be very different if a larger number of files was used.
-
-```{r parallel_proc}
-unpar_rams <- function(){
-  print("Unpar RaMS")
-  lapply(ms_files, grabMSdata)
-}
-unpar_tmzml <- function(){
-  print("Unpar tmzML")
-  tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/", 
-                        gsub("mzXML", "tmzML", basename(ms_files)))
-  dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-  pbapply::pbmapply(tmzmlMaker, ms_files, tmzml_names)
-  unlink("vignettes/figures/ssc_vignette_renders/tmzMLs", recursive = TRUE)
-}
-
-library(BiocParallel)
-par_param <- SnowParam(workers = 5, progressbar = TRUE)
-par_rams <- function(){
-  print("Par RaMS")
-  bplapply(ms_files, grabMSdata, BPPARAM = par_param)
-}
-par_tmzml <- function(){
-  print("Par tmzML")
-  tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/", 
-                        gsub("mzXML", "tmzML", basename(ms_files)))
-  dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-  bpmapply(tmzmlMaker, ms_files, tmzml_names, BPPARAM = par_param)
-  unlink("vignettes/figures/ssc_vignette_renders/tmzMLs", recursive = TRUE)
-}
-
-par_timings <- microbenchmark(par_rams(), unpar_rams(), par_tmzml(), unpar_tmzml(), times = 5)
-saveRDS(par_timings, "vignettes/figures/ssc_vignette_renders/par_timings.rds")
-```
-
-```{r parallel proc plot}
-par_timings <- readRDS("vignettes/figures/ssc_vignette_renders/par_timings.rds")
-par_timings %>%
-  as.data.frame() %>%
-  separate(expr, into = c("sub_type", "par_type"), sep = "_") %>%
-  mutate(par_type=str_remove(par_type, "\\(\\)")) %>%
-  mutate(sub_type=factor(sub_type, levels=c("unpar", "par"),
-                         labels=c("Sequential", "Parallel"))) %>%
-  mutate(par_type=factor(par_type, levels=c("rams", "tmzml"),
-                         labels=c("RaMS", "tmzMLs"))) %>%
-  ggplot() + 
-  geom_boxplot(aes(x=par_type, y=time/1e9)) +
-  geom_hline(yintercept = 0) +
-  facet_wrap(~sub_type, nrow = 1) +
-  labs(y="Seconds", x=NULL) +
-  theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))
-ggsave("vignettes/figures/ssc_vignette_renders/par_time_gp.png", width = 6.5, height = 3, units = "in", device = "png", dpi = 144)
-```
-
-![](../vignettes/figures/ssc_vignette_renders/par_time_gp.png){width=100%}
-
-## MS data query timings
-
-After the MS data has been transformed into an accessible format, it still needs to be queried for a single chromatogram of interest. This typically means performing a search across the ~75 millon entries of mz/rt/int data for the m/z values within a certain window, and can be performed either on an object in memory (e.g. MSnExp & RaMS) or on a disk object.
-
-```{r time2query}
-msnexp_obj <- readMSData(ms_files, mode="inMemory", msLevel. = 1)
-ondisk_obj <- readMSData(ms_files, mode="onDisk", msLevel. = 1)
-spectra_obj <- Spectra(ms_files, source=MsBackendMzR()) %>% filterMsLevel(1)
-rams_obj <- grabMSdata(ms_files, grab_what="MS1")
-
-tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/", 
-                      gsub("mzXML", "tmzML", basename(ms_files)))
-dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-mapply(tmzmlMaker, ms_files, tmzml_names)
-
-write_dataset(rams_obj$MS1, path = "vignettes/figures/ssc_vignette_renders/pqds")
-
-MSdb <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-dbWriteTable(MSdb, "MS1", rams_obj$MS1, overwrite=TRUE)
-dbDisconnect(MSdb)
-
-MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-dbWriteTable(MSdb_idx, "MS1", rams_obj$MS1, overwrite=TRUE)
-rs <- dbSendQuery(MSdb_idx, "CREATE INDEX mz ON MS1 (mz)")
-dbClearResult(rs)
-dbDisconnect(MSdb_idx)
-
-msnexp_query_fun <- function(){
-  plot(chromatogram(msnexp_obj, mz=pmppm(432.2810, ppm = 20)))
-}
-ondisk_query_fun <- function(){
-  plot(chromatogram(ondisk_obj, mz=pmppm(432.2810, ppm = 20)))
-}
-spectra_query_fun <- function(){
-  sfs_filtered <- spectra_obj %>% filterMzRange(pmppm(432.2810, ppm = 20))
-  getIntensities <- function(x, ...) {
-    if (nrow(x)) {
-      cbind(mz = NA_real_, intensity = x[, "intensity"])
-    } else cbind(mz = NA_real_, intensity = NA_real_)
-  }
-  sfs_agg <- addProcessing(sfs_filtered, getIntensities)
-  eic <- cbind(rt=rtime(sfs_agg), int=unlist(intensity(sfs_agg), use.names = FALSE))
-  plot(eic[,"rt"], eic[,"int"], type="l")
-}
-rams_query_fun <- function(){
-  rams_chrom_data <- rams_obj$MS1[mz%between%pmppm(432.2810, ppm = 20)]
-  plot(rams_chrom_data$rt, rams_chrom_data$int, type="l")
-}
-tmzml_query_fun <- function(){
-  tmzml_names <- list.files("vignettes/figures/ssc_vignette_renders/tmzMLs", full.names = TRUE)
-  tmzml_obj <- grabMSdata(tmzml_names)
-  tmzml_chrom_data <- tmzml_obj$MS1[mz%between%pmppm(432.2810, ppm = 20)]
-  plot(tmzml_chrom_data$rt, tmzml_chrom_data$int, type="l")
-}
-arrow_query_fun <- function(){
-  arrow_data <- open_dataset("vignettes/figures/ssc_vignette_renders/pqds") %>%
-    filter(mz%between%pmppm(432.2810, ppm = 20)) %>%
-    dplyr::collect()
-  plot(arrow_data$rt, arrow_data$int, type="l")
-}
-sql_query_fun <- function(){
-  MSdb <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-  EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-  query_params <- as.list(pmppm(432.2810, ppm = 20))
-  names(query_params) <- c("lower_bound", "upper_bound")
-  sql_data <- dbGetQuery(MSdb, EIC_query, params = query_params)
-  plot(sql_data$rt, sql_data$int, type="l")
-}
-sqlidx_query_fun <- function(){
-  MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-  EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-  query_params <- as.list(pmppm(432.2810, ppm = 20))
-  names(query_params) <- c("lower_bound", "upper_bound")
-  sql_data <- dbGetQuery(MSdb_idx, EIC_query, params = query_params)
-  sql_data <- sql_data[order(sql_data$filename, sql_data$rt),]
-  plot(sql_data$rt, sql_data$int, type="l")
-}
-
-query_timings <- microbenchmark(
-  msnexp_query_fun(), ondisk_query_fun(), spectra_query_fun(), rams_query_fun(),
-  tmzml_query_fun(), arrow_query_fun(), sql_query_fun(), sqlidx_query_fun(),
-  times = 10
-)
-query_timings
-saveRDS(query_timings, "vignettes/figures/ssc_vignette_renders/query_timings.rds")
-```
-
-```{r time2query plot}
-query_timings <- readRDS("vignettes/figures/ssc_vignette_renders/query_timings.rds")
-query_timings %>%
-  as.data.frame() %>%
-  arrange(expr) %>%
-  mutate(expr=str_remove(expr, "_query_fun\\(\\)")) %>%
-  mutate(rep_type=case_when(
-    expr%in%c("msnexp", "ondisk", "spectra", "rams")~"Every R session",
-    TRUE~"Single-time only"
-  )) %>%
-  mutate(expr=factor(expr, levels=c("msnexp", "ondisk", "spectra", "rams",
-                                    "tmzml", "arrow", "sql", "sqlidx"),
-                     labels=c("MSnExp", "OnDiskMSnExp", "Spectra", "RaMS",
-                              "tmzMLs", "Arrow", "SQL", "SQL (indexed)"))) %>%
-  ggplot() + 
-  geom_boxplot(aes(x=expr, y=time/1e9)) +
-  scale_y_log10() +
-  labs(y="Seconds", x=NULL) +
-  theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))
-ggsave("vignettes/figures/ssc_vignette_renders/query_time_gp.png", width = 6.5, height = 5, units = "in", device = "png", dpi = 144)
-```
-
-![](../vignettes/figures/ssc_vignette_renders/query_time_gp.png){width=100%}
-
-There's a few surprises in the above plot. Most notably, the incredible speed of the mz-indexed SQL database. It's apparently faster to query the database and read that data into R than it is to index through the big RaMS object entirely within memory. That's a pretty darn compelling reason to switch to SQL databases (although note the importance of indexing them - the non-indexed one took 100x longer!). Side note: I originally expected this to be quite slow because I previously *thought* I was creating an index on the mz data using `dbExecute` instead of `dbSendQuery` but apparently that's not how the function works. This actually disagrees with a [similar comparison](https://bioconductor.org/packages/release/bioc/vignettes/MsBackendSql/inst/doc/MsBackendSql.html#4_Performance_comparison_with_other_backends) done by the Spectra package that found the SQL backend to be consistently slower than the in-memory option. This result was so unexpected that I wrote a quick followup to double-check and can confirm that in real time the SQL query felt just as fast as the RaMS object. Wild.
-
-```{r double-check sql_idx vs RaMS}
-MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-query_params <- as.list(pmppm(432.2810, ppm = 20))
-names(query_params) <- c("lower_bound", "upper_bound")
-dbGetQuery(MSdb_idx, EIC_query, params = query_params) %>% qplotMS1data()
-
-rams_obj$MS1[mz%between%pmppm(432.2810, ppm = 20)] %>% qplotMS1data()
-```
-
-Also surprising is the performance of the tmzMLs - I thought they'd do much worse and instead they're about on par with the arrow method. I'm pleasantly surprised that my hacky file type can compete at all. Of course, up at the top are the traditional MS formats. They're not designed for this or optimized for chromatogram extraction at all so it's not especially surprising that they struggle, but the graph does also nicely show the improved performance of more recent versions over older ones.
-
-#### Multiple chromatograms
-
-Of course, the above tests assume that only a single chromatogram is being requested. This is a little unrealistic. While there are times when I just want to see a single mass, I also often want to extract multiple masses of interest simultaneously and some of these methods are more friendly to this approach than others. This usually requires constructing a string of `between` statements that we can then pass to the evaluator. Fortunately, the same string can be passed to both Arrow and RaMS so that makes the task a little easier. The nice thing about sending a single query is also that the data does not then need to be deduplicated, which can happen during loops if the m/z windows overlap.
-
-```{r make query strings}
-rams_obj <- grabMSdata(ms_files, grab_what="MS1")
-grouped_ms1 <- rams_obj$MS1 %>%
-  arrange(desc(int)) %>% 
-  mutate(mz_group=mz_group(mz, ppm = 10, max_groups = 10, min_group_size=20)) %>%
-  drop_na()
-# grouped_ms1 %>%
-#   qplotMS1data(facet_col="mz_group", facet_args = list(ncol=2))
-mzs_to_grab <- grouped_ms1 %>%
-  group_by(mz_group) %>%
-  summarise(mean_mz=mean(mz), sd_mz=sd(mz), mean_rt=mean(rt)) %>%
-  pull(mean_mz)
-
-rams_arrow_call <- lapply(mzs_to_grab, function(mz_i){
-  mzrange <- pmppm(mz_i, 10)
-  call("between", as.name("mz"), mzrange[[1]], mzrange[[2]])
-}) %>% paste(collapse = "|")
-
-sql_comb_call <- sapply(mzs_to_grab, function(mz_i){
-  paste("mz BETWEEN", pmppm(mz_i, 10)[1], "AND", pmppm(mz_i, 10)[2])
-}) %>% paste(collapse = " OR ") %>% paste("SELECT * FROM MS1 WHERE", .)
-
-print(rams_arrow_call)
-print(sql_comb_call)
-```
-
-Finally we can test the timing of the multiple chromatograms both in loop form and in single-query form.
-
-```{r multichrom query timing}
-rams_uni_fun <- function(){
-  print("RaMS unified")
-  rams_obj$MS1[eval(parse(text=rams_arrow_call))]
-}
-rams_loop_fun <- function(){
-  print("RaMS loop")
-  lapply(mzs_to_grab, function(mz_i){
-    rams_obj$MS1[mz%between%pmppm(mz_i, 10)]
-  }) %>% bind_rows() %>% distinct()
-}
-
-arrow_ds <- open_dataset("vignettes/figures/ssc_vignette_renders/pqds")
-arrow_uni_fun <- function(){
-  print("Arrow unified")
-  arrow_ds %>%
-    filter(eval(parse(text = rams_arrow_call))) %>%
-    collect()
-}
-arrow_loop_fun <- function(){
-  print("Arrow loop")
-  lapply(mzs_to_grab, function(mz_i){
-    arrow_ds %>%
-      filter(mz%between%pmppm(mz_i, 10)) %>%
-      collect()
-  }) %>% bind_rows() %>% distinct()
-}
-
-MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-sql_uni_fun <- function(){
-  print("SQL unified")
-  dbGetQuery(MSdb_idx, sql_comb_call)
-}
-sql_query_base <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
-sql_loop_fun <- function(){
-  print("SQL loop")
-  lapply(mzs_to_grab, function(mz_i){
-    query_params <- as.list(pmppm(mz_i, ppm = 20))
-    names(query_params) <- c("lower_bound", "upper_bound")
-    sql_data <- dbGetQuery(MSdb_idx, sql_query_base, params = query_params)
-  }) %>% bind_rows() %>% distinct()
-}
-
-multichrom_timings <- microbenchmark(
-  rams_uni_fun(), rams_loop_fun(), arrow_uni_fun(), arrow_loop_fun(), 
-  sql_uni_fun(), sql_loop_fun(), times = 10
-)
-saveRDS(multichrom_timings, "vignettes/figures/ssc_vignette_renders/multichrom_timings.rds")
-
-multichrom_timings <- readRDS("vignettes/figures/ssc_vignette_renders/multichrom_timings.rds")
-multichrom_timings %>%
-  as.data.frame() %>%
-  arrange(expr) %>%
-  mutate(expr=str_remove(expr, "_fun\\(\\)")) %>%
-  separate(expr, into = c("expr", "query_type"), sep = "_") %>%
-  mutate(expr=factor(expr, levels=c("rams", "arrow", "sql"),
-                     labels=c("RaMS", "Arrow", "SQL"))) %>%
-  mutate(query_type=factor(query_type, levels=c("uni", "loop"),
-                           labels=c("Unified query", "Query loop"))) %>%
-  ggplot() + 
-  geom_boxplot(aes(x=query_type, y=time/1e9), lwd=1) +
-  facet_wrap(~expr, nrow=1) +
-  scale_y_log10() +
-  labs(y="Seconds", x=NULL, color=NULL) +
-  theme_bw()
-ggsave("vignettes/figures/ssc_vignette_renders/multichrom_gp.png", width = 6.5, height = 4, units = "in", device = "png", dpi = 144)
-```
-
-![](../vignettes/figures/ssc_vignette_renders/multichrom_gp.png){width=100%}
-
-Once again the indexed SQL database blows the others out of the water, with the unified query performing ~10x better in SQL than Arrow and ~100x better than RaMS. Curiously, the unified query actually takes *longer* when processed via RaMS/data.table than looping. Good to know.
-
-## MS object sizing info
-
-Of course, there's no free lunch. The speedier options tend to be much larger in space, as shown in the below graph of the object/file sizes. For the MSnBase, Spectra, and RaMS objects this is space occupied in the computer's memory. For the tmzML, Arrow, and SQL databases this is on the computer's disk.
-
-```{r sizing info}
-size_list <- list()
-
-size_list$mzXML <- sum(file.size(ms_files))
-
-msnexp_obj <- readMSData(ms_files, mode="inMemory", msLevel. = 1)
-size_list$msnexp_obj <- pryr::object_size(msnexp_obj)
-rm(msnexp_obj)
-
-ondisk_obj <- readMSData(ms_files, mode="onDisk", msLevel. = 1)
-size_list$ondisk_obj <- pryr::object_size(ondisk_obj)
-rm(ondisk_obj)
-
-sfs_filtered <- Spectra(ms_files, source=MsBackendMzR()) %>%
-  filterMsLevel(1)
-size_list$spectra <- pryr::object_size(sfs_filtered)
-rm(sfs_filtered)
-
-rams_obj <- grabMSdata(ms_files, grab_what="MS1")
-size_list$rams <- pryr::object_size(rams_obj)
-
-tmzml_names <- paste0(dirname(dirname(ms_files)), "/tmzMLs/", gsub("mzXML", "tmzML", basename(ms_files)))
-dir.create("vignettes/figures/ssc_vignette_renders/tmzMLs")
-bpmapply(tmzmlMaker, ms_files, tmzml_names, BPPARAM = SnowParam(workers = 5, progressbar = TRUE, tasks=length(tmzml_names)))
-size_list$tmzml <- sum(file.size(list.files("vignettes/figures/ssc_vignette_renders/tmzMLs", full.names = TRUE)))
-unlink("vignettes/figures/ssc_vignette_renders/tmzMLs", recursive = TRUE)
-
-write_dataset(rams_obj$MS1, path = "vignettes/figures/ssc_vignette_renders/pqds")
-size_list$arrow <- sum(file.size(list.files("vignettes/figures/ssc_vignette_renders/pqds", full.names = TRUE)))
-unlink("vignettes/figures/ssc_vignette_renders/pqds", recursive = TRUE)
-
-MSdb <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-dbWriteTable(MSdb, "MS1", rams_obj$MS1, overwrite=TRUE)
-dbDisconnect(MSdb)
-size_list$MSdb <- file.size("vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-
-
-MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-rs <- dbSendQuery(MSdb_idx, "CREATE INDEX mz ON MS1 (mz)")
-dbClearResult(rs)
-dbDisconnect(MSdb_idx)
-size_list$MSdb_idx <- file.size("vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-unlink("vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-
-saveRDS(size_list, "vignettes/figures/ssc_vignette_renders/size_list.rds")
-```
-
-```{r plot size info}
-size_list <- readRDS("vignettes/figures/ssc_vignette_renders/size_list.rds")
-size_list %>%
-  within(rm(mzXML)) %>%
-  sapply(as.numeric) %>%
-  data.frame(bytes=.) %>%
-  rownames_to_column("expr") %>%
-  mutate(expr=str_remove(expr, "_obj")) %>%
-  mutate(expr=str_replace(expr, "MSdb_?", "sql")) %>%
-  mutate(mem_type=case_when(
-    expr%in%c("msnexp", "ondisk", "spectra", "rams")~"Memory",
-    TRUE~"Disk"
-  )) %>%
-  mutate(mem_type=factor(mem_type, levels=c("Memory", "Disk"))) %>%
-  mutate(expr=factor(expr, levels=c("msnexp", "ondisk", "spectra", "rams",
-                                    "tmzml", "arrow", "sql", "sqlidx"),
-                     labels=c("MSnExp", "OnDiskMSnExp", "Spectra", "RaMS",
-                              "tmzMLs", "Arrow", "SQL", "SQL (indexed)"))) %>%
-  ggplot() +
-  geom_hline(yintercept = size_list$mzXML/(1024^3)) +
-  geom_point(aes(x=expr, y=bytes/(1024^3))) +
-  scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10), labels=c("1MB", "10MB", "100MB", "1GB", "10GB"),
-                limits=c(0.001, 10)) +
-  facet_wrap(~mem_type, scales = "free_x") +
-  labs(x=NULL, y=NULL)
-ggsave("vignettes/figures/ssc_vignette_renders/size_cons.png", width = 6.5, height = 4, units = "in", device = "png", dpi = 144)
-```
-
-![](../vignettes/figures/ssc_vignette_renders/size_cons.png){width=100%}
-
-Unsurprisingly, the SQL databases are massive, weighing in at 5-10x the size of the original mzXML files (black horizontal line in above plot). RaMS is similarly enormous (about 2x the mzXML size) with an additional penalty of existing in memory instead of on disk. The original MSnExp object is about the same size (slightly larger than mzXMLs, maybe because the m/z & int data has been decompressed?) and also exists in memory. tmzMLs and Arrow do slightly better, actually reducing the size of the dataset while remaining on disk, and of course the pointer objects in Spectra and the OnDiskMSnExp object are microscopic in comparison.
-
-## Summary
-
-I started out this project because I'd recently learned about `arrow` and was blown away by how fast it was while still remaining essentially memory-free. At the end of it, I think there's more nuance than I was really expecting, though there are a few clear losers. The MSnExp objects should be almost never used, as they're dominated pretty clearly by the OnDisk equivalent. The Spectra package provides even more improvement on this but extracting a chromatogram is more obnoxious and you're still dealing with a complicated S4 object instead of an intuitive data frame. Similarly, the tmzMLs seem to be unilaterally worse than `arrow`, with access at-best equivalent and a make time much larger. Arrow also plays nicely with `dplyr` commands and has good performance when requesting multiple EICs, though longtime RaMS users may find the intuitive syntax of the tmzMLs (and progress bar!) friendlier as long as they don't need to extract more than a single chromatogram.
-
-In general, it seems to come down more to the size of the dataset than anything else. If your dataset is small (<1GB) or you're only loading a small-ish subset of it, RaMS is the go-to. If it's larger than memory but not enormous (10-100GB), Arrow and SQL seem like good contenders. The SQL database size ballooned scarily (but had insanely fast access times) while Arrow provided a file size reduction and more familiar syntax. For truly large datasets (100GB+) I can't imagine using anything but the OnDisk or Spectra infrastructure because it's just not worth trying to duplicate the dataset at all, so reading the data out of the files is the only real option. Of course, if you're looking at data that large then you may actually come all the way back around to Arrow or SQL anyway so you can host the data in the cloud or on a cluster instead of on a personal computer.
-
-```{r summary plot}
-make_timings <- readRDS("vignettes/figures/ssc_vignette_renders/make_timings.rds")
-query_timings <- readRDS("vignettes/figures/ssc_vignette_renders/query_timings.rds")
-size_df <- readRDS("vignettes/figures/ssc_vignette_renders/size_list.rds") %>%
-  sapply(as.numeric) %>%
-  data.frame(size=.) %>%
-  mutate(size=size/1024^3) %>%
-  rownames_to_column("expr") %>%
-  mutate(expr=str_remove(expr, "_obj")) %>%
-  mutate(expr=str_replace(expr, "MSdb_?", "sql"))
-
-bind_rows(make_timings, query_timings) %>%
-  as.data.frame() %>%
-  group_by(expr) %>%
-  mutate(time=time/1e9) %>%
-  summarise(med_time=median(time), IQR_time=IQR(time)) %>%
-  mutate(expr=str_remove(expr, "_fun\\(\\)")) %>%
-  separate(expr, into = c("expr", "time_type")) %>%
-  left_join(size_df) %>%
-  mutate(rep_type=case_when(
-    expr%in%c("msnexp", "ondisk", "spectra", "rams")~"Memory",
-    TRUE~"Disk"
-  )) %>%
-  mutate(expr=factor(expr, levels=c("msnexp", "ondisk", "spectra", "rams",
-                                    "tmzml", "arrow", "sql", "sqlidx"),
-                     labels=c("MSnExp", "OnDiskMSnExp", "Spectra", "RaMS",
-                              "tmzMLs", "Arrow", "SQL", "SQL (indexed)"))) %>%
-  pivot_wider(names_from = time_type, values_from=c("med_time", "IQR_time")) %>%
-  ggplot() +
-  geom_vline(xintercept = 0) +
-  geom_linerange(aes(x=med_time_make, ymin=med_time_query-IQR_time_query*2, 
-                     ymax=med_time_query+IQR_time_query*2, color=expr)) +
-  geom_linerange(aes(y=med_time_query, xmin=med_time_make-IQR_time_make*2, 
-                     xmax=med_time_make+IQR_time_make*2, color=expr)) +
-  geom_point(aes(x=med_time_make, y=med_time_query, color=expr, 
-                 size=size, shape=rep_type)) +
-  scale_shape_manual(values=c(16, 15)) +
-  scale_y_log10() +
-  coord_flip() +
-  guides(color = guide_legend(order = 1), shape = guide_legend(order = 2), 
-         size=guide_legend(order=3)) +
-  labs(x="Time to transform (s)", y="Time to query (s)", color=NULL, size="Size (GB)",
-       shape="Storage") +
-  theme_bw()
-ggsave("vignettes/figures/ssc_vignette_renders/sum_plot.png", width = 6.5, height = 5, units = "in", device = "png", dpi = 144)
-
-
-# bind_rows(make_timings, query_timings) %>%
-#   as.data.frame() %>%
-#   group_by(expr) %>%
-#   mutate(time=time/1e9) %>%
-#   summarise(med_time=median(time)) %>%
-#   mutate(expr=str_remove(expr, "_fun\\(\\)")) %>%
-#   separate(expr, into = c("expr", "time_type")) %>%
-#   pivot_wider(names_from = time_type, values_from=med_time) %>%
-#   left_join(size_df) %>%
-#   plotly::plot_ly(x=~make, y=~query, z=~size, hovertext=~expr, 
-#                   type="scatter3d", mode="markers")
-```
-
-![](../vignettes/figures/ssc_vignette_renders/sum_plot.png){width=100%}
-
-```{r cleanup}
-unlink("vignettes/figures/ssc_vignette_renders/tmzMLs", recursive = TRUE)
-unlink("vignettes/figures/ssc_vignette_renders/pqds", recursive = TRUE)
-unlink("vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
-unlink("vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
-unlink("vignettes/figures/ssc_vignette_renders/Sample/", recursive = TRUE)
-```
-
diff --git a/doc/speed_size_comparison.html b/doc/speed_size_comparison.html
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-<h1 class="title toc-ignore">Benchmarking - RaMS, MSnBase, Spectra,
-Arrow, and SQL</h1>
-<h4 class="author">William Kumler</h4>
-<h4 class="date">2023-12-13</h4>
-
-
-
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(RaMS)</span>
-<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
-<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a><span class="fu">library</span>(microbenchmark)</span>
-<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="fu">library</span>(MSnbase)</span>
-<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a><span class="fu">library</span>(Spectra)</span>
-<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a><span class="fu">library</span>(DBI)</span>
-<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a><span class="fu">library</span>(arrow)</span>
-<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a><span class="fu">library</span>(rvest)</span>
-<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a><span class="fu">library</span>(xml2)</span>
-<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a></span>
-<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a>BiocParallel<span class="sc">::</span><span class="fu">register</span>(BiocParallel<span class="sc">::</span><span class="fu">SerialParam</span>(<span class="at">stop.on.error =</span> <span class="cn">FALSE</span>, <span class="at">progressbar =</span> <span class="cn">TRUE</span>))</span></code></pre></div>
-</details>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
-<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a></span>
-<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a>n_ms_files <span class="ot">&lt;-</span> <span class="dv">10</span></span>
-<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a>base_url <span class="ot">&lt;-</span> <span class="st">&quot;ftp://massive.ucsd.edu/v01/MSV000080030/peak/&quot;</span> <span class="sc">%&gt;%</span></span>
-<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a>  <span class="fu">paste0</span>(<span class="st">&quot;Forensic_study_80_volunteers/Forensic_Hands_mzXML/&quot;</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a>  <span class="fu">paste0</span>(<span class="st">&quot;Forensic_Hands_plate_1_mzXML/Sample/&quot;</span>)</span>
-<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a>file_list <span class="ot">&lt;-</span> base_url <span class="sc">%&gt;%</span></span>
-<span id="cb2-8"><a href="#cb2-8" tabindex="-1"></a>  read_html <span class="sc">%&gt;%</span></span>
-<span id="cb2-9"><a href="#cb2-9" tabindex="-1"></a>  <span class="fu">xml_text</span>() <span class="sc">%&gt;%</span></span>
-<span id="cb2-10"><a href="#cb2-10" tabindex="-1"></a>  <span class="fu">strsplit</span>(<span class="st">&quot;</span><span class="sc">\\\n</span><span class="st">&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb2-11"><a href="#cb2-11" tabindex="-1"></a>  <span class="fu">pluck</span>(<span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb2-12"><a href="#cb2-12" tabindex="-1"></a>  <span class="fu">str_remove</span>(<span class="st">&quot;.*2016 &quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb2-13"><a href="#cb2-13" tabindex="-1"></a>  <span class="fu">str_remove</span>(<span class="st">&quot;</span><span class="sc">\\\r</span><span class="st">&quot;</span>)</span>
-<span id="cb2-14"><a href="#cb2-14" tabindex="-1"></a>chosen_file_list <span class="ot">&lt;-</span> <span class="fu">sample</span>(file_list, n_ms_files)</span>
-<span id="cb2-15"><a href="#cb2-15" tabindex="-1"></a></span>
-<span id="cb2-16"><a href="#cb2-16" tabindex="-1"></a><span class="fu">dir.create</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/Sample/&quot;</span>)</span>
-<span id="cb2-17"><a href="#cb2-17" tabindex="-1"></a><span class="cf">for</span>(i <span class="cf">in</span> chosen_file_list){</span>
-<span id="cb2-18"><a href="#cb2-18" tabindex="-1"></a>  new_file_path <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/Sample/&quot;</span>, i)</span>
-<span id="cb2-19"><a href="#cb2-19" tabindex="-1"></a>  <span class="fu">download.file</span>(<span class="fu">paste0</span>(base_url, i), <span class="at">destfile =</span> new_file_path, <span class="at">mode =</span> <span class="st">&quot;wb&quot;</span>)</span>
-<span id="cb2-20"><a href="#cb2-20" tabindex="-1"></a>}</span>
-<span id="cb2-21"><a href="#cb2-21" tabindex="-1"></a></span>
-<span id="cb2-22"><a href="#cb2-22" tabindex="-1"></a>ms_files <span class="ot">&lt;-</span> <span class="fu">list.files</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/Sample/&quot;</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
-</details>
-<div id="intro" class="section level2">
-<h2>Intro</h2>
-<p>As noted in the <a href="https://cran.r-project.org/web/packages/RaMS/vignettes/Minifying-files-with-RaMS.html">RaMS
-and friends vignette</a>, one of the major strengths of the ‘tidy’ data
-format is how nicely it plays with other packages, allowing for the use
-of powerful packages that can’t normally be applied to MS data. After
-playing with a few different ones I wanted to do some structured
-benchmarking to see which is the best use case for various
-situations.</p>
-<p>The general constraints I’m interested in are speed and space, with a
-minor consideration for simplicity afterward. My question of interest is
-how costly in these areas access to the MS data is, a process that can
-be broken down into two relevant steps - the transformation of the MS
-data from mz(X)ML format into a queryable format, and then the actual
-query itself. The query itself is relatively straightforward and can be
-measured in just the amount of time it takes, but the transformation has
-a caveat that it can be stored either in memory (and therefore must be
-recreated every time R is restarted) or it can be stored on disk (which
-is robust across restarts). So I’m interested in</p>
-<ol style="list-style-type: decimal">
-<li>Time to create</li>
-<li>Size in memory/size on disk</li>
-<li>Time to access</li>
-</ol>
-<p>I’ll be measuring these constraints across eight different ways of
-accessing the MS data.</p>
-<div id="original-msnexp" class="section level4">
-<h4>Original MSnExp</h4>
-<p>Not used much these days since there’s a big push to XCMS3 and the
-new “on-disk” infrastructure but still provides a useful baseline. The
-object is stored in memory and is 10/10 in simplicity.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a>msnexp_obj <span class="ot">&lt;-</span> <span class="fu">readMSData</span>(ms_files, <span class="at">mode=</span><span class="st">&quot;inMemory&quot;</span>, <span class="at">msLevel. =</span> <span class="dv">1</span>)</span>
-<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">plot</span>(<span class="fu">chromatogram</span>(msnexp_obj, <span class="at">mz=</span><span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>)))</span></code></pre></div>
-</details>
-</div>
-<div id="new-ondiskmsnexp-infrastructure" class="section level4">
-<h4>New onDiskMSnExp infrastructure</h4>
-<p>The newest version of an XCMS object, using a system of pointers to
-reduce memory load. The object itself is still stored in memory but is
-equally easy to construct.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>ondisk_obj <span class="ot">&lt;-</span> <span class="fu">readMSData</span>(ms_files, <span class="at">mode=</span><span class="st">&quot;onDisk&quot;</span>, <span class="at">msLevel. =</span> <span class="dv">1</span>)</span>
-<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="fu">plot</span>(<span class="fu">chromatogram</span>(ondisk_obj, <span class="at">mz=</span><span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>)))</span></code></pre></div>
-</details>
-</div>
-<div id="spectra-package" class="section level4">
-<h4>Spectra package</h4>
-<p>The Spectra package “defines an efficient infrastructure for storing
-and handling mass spectrometry spectra and functionality to subset,
-process, visualize and compare spectra data.” It doesn’t handle
-chromatograms very well but is a useful benchmark for next-gen read and
-write times. For benchmarking comparisons to other data sets, see the <a href="https://bioconductor.org/packages/release/bioc/vignettes/MsBackendSql/inst/doc/MsBackendSql.html#4_Performance_comparison_with_other_backends">vignette
-in the Spectra package</a>.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a>getIntensities <span class="ot">&lt;-</span> <span class="cf">function</span>(x, ...) {</span>
-<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">nrow</span>(x)) {</span>
-<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a>    <span class="fu">cbind</span>(<span class="at">mz =</span> <span class="cn">NA_real_</span>, <span class="at">intensity =</span> x[, <span class="st">&quot;intensity&quot;</span>])</span>
-<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a>  } <span class="cf">else</span> <span class="fu">cbind</span>(<span class="at">mz =</span> <span class="cn">NA_real_</span>, <span class="at">intensity =</span> <span class="cn">NA_real_</span>)</span>
-<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>}</span>
-<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a></span>
-<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a>sfs_filtered <span class="ot">&lt;-</span> <span class="fu">Spectra</span>(ms_files, <span class="at">source=</span><span class="fu">MsBackendMzR</span>()) <span class="sc">%&gt;%</span></span>
-<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a>  <span class="fu">filterMsLevel</span>(<span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a>  <span class="fu">filterMzRange</span>(<span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>))</span>
-<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a>sfs_agg <span class="ot">&lt;-</span> <span class="fu">addProcessing</span>(sfs_filtered, getIntensities)</span>
-<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a>eic <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="at">rt=</span><span class="fu">rtime</span>(sfs_agg), <span class="at">int=</span><span class="fu">unlist</span>(<span class="fu">intensity</span>(sfs_agg), <span class="at">use.names =</span> <span class="cn">FALSE</span>))</span>
-<span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a><span class="fu">plot</span>(eic[,<span class="st">&quot;rt&quot;</span>], eic[,<span class="st">&quot;int&quot;</span>], <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span></code></pre></div>
-</details>
-</div>
-<div id="rams-package" class="section level4">
-<h4>RaMS package</h4>
-<p>The eponymous package, notoriously heavy on memory but very speedy
-and still pretty darn simple.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>rams_obj <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(ms_files, <span class="at">grab_what=</span><span class="st">&quot;MS1&quot;</span>)</span>
-<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a>rams_chrom_data <span class="ot">&lt;-</span> rams_obj<span class="sc">$</span>MS1[mz<span class="sc">%between%</span><span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>)]</span>
-<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="fu">plot</span>(rams_chrom_data<span class="sc">$</span>rt, rams_chrom_data<span class="sc">$</span>int, <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span></code></pre></div>
-</details>
-</div>
-<div id="tmzml-files" class="section level4">
-<h4>tmzML files</h4>
-<p>I designed the tmzMLs to be a “transposed” mzML where data are stored
-as binned chromatograms rather than individual scans, thus making it
-much more efficient to extract the data with a minimal memory
-requirement. This was before I was more familiar with formats like
-Parquet and SQL though.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>tmzml_names <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="fu">dirname</span>(<span class="fu">dirname</span>(ms_files)), <span class="st">&quot;/tmzMLs/&quot;</span>, <span class="fu">gsub</span>(<span class="st">&quot;mzXML&quot;</span>, <span class="st">&quot;tmzML&quot;</span>, <span class="fu">basename</span>(ms_files)))</span>
-<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="fu">dir.create</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>)</span>
-<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a><span class="fu">bpmapply</span>(tmzmlMaker, ms_files, tmzml_names, <span class="at">BPPARAM =</span> <span class="fu">SnowParam</span>(<span class="at">workers =</span> <span class="dv">3</span>, <span class="at">progressbar =</span> <span class="cn">TRUE</span>, <span class="at">tasks=</span><span class="fu">length</span>(tmzml_names)))</span>
-<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a>tmzml_obj <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(tmzml_names)</span>
-<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a>tmzml_chrom_data <span class="ot">&lt;-</span> tmzml_obj<span class="sc">$</span>MS1[mz<span class="sc">%between%</span><span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>)]</span>
-<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="fu">plot</span>(tmzml_chrom_data<span class="sc">$</span>rt, tmzml_chrom_data<span class="sc">$</span>int, <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span></code></pre></div>
-</details>
-</div>
-<div id="arrow-package" class="section level4">
-<h4>Arrow package</h4>
-<p>Apache’s arrow package is designed to interface with parquet files, a
-column-based instead of row-based file type, with fast access to large
-datasets with a minimal memory footprint. There’s an excellent package
-that allows seamless interfacing with R and makes reading and writing
-files almost trivial. This package was the one that actually made me
-originally want to write about this because it outperformed tmzMLs and
-other databases so overwhelmingly.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">write_dataset</span>(rams_obj<span class="sc">$</span>MS1, <span class="at">path =</span> <span class="st">&quot;vignettes/figures/ssc_vignette_renders/pqds&quot;</span>)</span>
-<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>arrow_data <span class="ot">&lt;-</span> <span class="fu">open_dataset</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/pqds&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a>  <span class="fu">filter</span>(mz<span class="sc">%between%</span><span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">collect</span>()</span>
-<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a><span class="fu">plot</span>(arrow_data<span class="sc">$</span>rt, arrow_data<span class="sc">$</span>int, <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span></code></pre></div>
-</details>
-</div>
-<div id="sqlite" class="section level4">
-<h4>SQL(ite)</h4>
-<p>Finally, another important baseline is SQL databases. In theory these
-are the “proper” way to store large amounts of MS data but I haven’t
-found them to be incredibly efficient previously, both slow to query and
-very large on disk. Here I’m testing both the default SQL database as
-well as one indexed by m/z to optimize for chromatogram extraction.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>MSdb <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata.sqlite&quot;</span>)</span>
-<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="fu">dbWriteTable</span>(MSdb, <span class="st">&quot;MS1&quot;</span>, rams_obj<span class="sc">$</span>MS1, <span class="at">overwrite=</span><span class="cn">TRUE</span>)</span>
-<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a>EIC_query <span class="ot">&lt;-</span> <span class="st">&#39;SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound&#39;</span></span>
-<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a>query_params <span class="ot">&lt;-</span> <span class="fu">as.list</span>(<span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>))</span>
-<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a><span class="fu">names</span>(query_params) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;lower_bound&quot;</span>, <span class="st">&quot;upper_bound&quot;</span>)</span>
-<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a>sql_data <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(MSdb, EIC_query, <span class="at">params =</span> query_params)</span>
-<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a><span class="fu">plot</span>(sql_data<span class="sc">$</span>rt, sql_data<span class="sc">$</span>int, <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span>
-<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a></span>
-<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a>rs <span class="ot">&lt;-</span> <span class="fu">dbSendQuery</span>(MSdb, <span class="st">&quot;CREATE INDEX mz ON MS1 (mz)&quot;</span>)</span>
-<span id="cb9-10"><a href="#cb9-10" tabindex="-1"></a><span class="fu">dbClearResult</span>(rs)</span>
-<span id="cb9-11"><a href="#cb9-11" tabindex="-1"></a>sql_data <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(MSdb, EIC_query, <span class="at">params =</span> query_params)</span>
-<span id="cb9-12"><a href="#cb9-12" tabindex="-1"></a>sql_data <span class="ot">&lt;-</span> sql_data[<span class="fu">order</span>(sql_data<span class="sc">$</span>filename, sql_data<span class="sc">$</span>rt),]</span>
-<span id="cb9-13"><a href="#cb9-13" tabindex="-1"></a><span class="fu">plot</span>(sql_data<span class="sc">$</span>rt, sql_data<span class="sc">$</span>int, <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span>
-<span id="cb9-14"><a href="#cb9-14" tabindex="-1"></a><span class="fu">dbDisconnect</span>(MSdb)</span></code></pre></div>
-</details>
-</div>
-</div>
-<div id="ms-data-transformation-timings" class="section level2">
-<h2>MS data transformation timings</h2>
-<p>Below, I’ve pulled out the “transformation” step from each of the
-eight methods above that gets the MS data out of the mzML documents and
-into an R-readable structure. For the first four (MSnExp, onDiskMSnExp,
-Spectra, and RaMS) this creates an object in the R environment that is
-wiped each time R is restarted, so they must be recreated every time.
-For the last four, files are created on the hard drive that are robust
-across R sessions and only need to be created once.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>msnexp_make_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a>  <span class="fu">readMSData</span>(ms_files, <span class="at">mode=</span><span class="st">&quot;inMemory&quot;</span>, <span class="at">msLevel. =</span> <span class="dv">1</span>)</span>
-<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a>}</span>
-<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a>ondisk_make_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a>  <span class="fu">readMSData</span>(ms_files, <span class="at">mode=</span><span class="st">&quot;onDisk&quot;</span>, <span class="at">msLevel. =</span> <span class="dv">1</span>)</span>
-<span id="cb10-6"><a href="#cb10-6" tabindex="-1"></a>}</span>
-<span id="cb10-7"><a href="#cb10-7" tabindex="-1"></a>spectra_make_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb10-8"><a href="#cb10-8" tabindex="-1"></a>  <span class="fu">Spectra</span>(ms_files, <span class="at">source=</span><span class="fu">MsBackendMzR</span>()) <span class="sc">%&gt;%</span> <span class="fu">filterMsLevel</span>(<span class="dv">1</span>)</span>
-<span id="cb10-9"><a href="#cb10-9" tabindex="-1"></a>}</span>
-<span id="cb10-10"><a href="#cb10-10" tabindex="-1"></a>rams_make_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb10-11"><a href="#cb10-11" tabindex="-1"></a>  <span class="fu">grabMSdata</span>(ms_files, <span class="at">grab_what=</span><span class="st">&quot;MS1&quot;</span>)</span>
-<span id="cb10-12"><a href="#cb10-12" tabindex="-1"></a>}</span>
-<span id="cb10-13"><a href="#cb10-13" tabindex="-1"></a>tmzml_make_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb10-14"><a href="#cb10-14" tabindex="-1"></a>  tmzml_names <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="fu">dirname</span>(<span class="fu">dirname</span>(ms_files)), <span class="st">&quot;/tmzMLs/&quot;</span>, </span>
-<span id="cb10-15"><a href="#cb10-15" tabindex="-1"></a>                        <span class="fu">gsub</span>(<span class="st">&quot;mzXML&quot;</span>, <span class="st">&quot;tmzML&quot;</span>, <span class="fu">basename</span>(ms_files)))</span>
-<span id="cb10-16"><a href="#cb10-16" tabindex="-1"></a>  <span class="fu">dir.create</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>)</span>
-<span id="cb10-17"><a href="#cb10-17" tabindex="-1"></a>  <span class="fu">mapply</span>(tmzmlMaker, ms_files, tmzml_names)</span>
-<span id="cb10-18"><a href="#cb10-18" tabindex="-1"></a>  <span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb10-19"><a href="#cb10-19" tabindex="-1"></a>}</span>
-<span id="cb10-20"><a href="#cb10-20" tabindex="-1"></a>arrow_make_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb10-21"><a href="#cb10-21" tabindex="-1"></a>  msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(ms_files, <span class="at">grab_what=</span><span class="st">&quot;MS1&quot;</span>)</span>
-<span id="cb10-22"><a href="#cb10-22" tabindex="-1"></a>  <span class="fu">write_dataset</span>(msdata<span class="sc">$</span>MS1, <span class="at">path =</span> <span class="st">&quot;vignettes/figures/ssc_vignette_renders/pqds&quot;</span>)</span>
-<span id="cb10-23"><a href="#cb10-23" tabindex="-1"></a>  <span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/pqds&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb10-24"><a href="#cb10-24" tabindex="-1"></a>}</span>
-<span id="cb10-25"><a href="#cb10-25" tabindex="-1"></a>sql_make_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb10-26"><a href="#cb10-26" tabindex="-1"></a>  msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(ms_files, <span class="at">grab_what=</span><span class="st">&quot;MS1&quot;</span>)</span>
-<span id="cb10-27"><a href="#cb10-27" tabindex="-1"></a>  MSdb <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata.sqlite&quot;</span>)</span>
-<span id="cb10-28"><a href="#cb10-28" tabindex="-1"></a>  <span class="fu">dbWriteTable</span>(MSdb, <span class="st">&quot;MS1&quot;</span>, msdata<span class="sc">$</span>MS1, <span class="at">overwrite=</span><span class="cn">TRUE</span>)</span>
-<span id="cb10-29"><a href="#cb10-29" tabindex="-1"></a>  <span class="fu">dbDisconnect</span>(MSdb)</span>
-<span id="cb10-30"><a href="#cb10-30" tabindex="-1"></a>  <span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata.sqlite&quot;</span>)</span>
-<span id="cb10-31"><a href="#cb10-31" tabindex="-1"></a>}</span>
-<span id="cb10-32"><a href="#cb10-32" tabindex="-1"></a>sqlidx_make_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb10-33"><a href="#cb10-33" tabindex="-1"></a>  msdata <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(ms_files, <span class="at">grab_what=</span><span class="st">&quot;MS1&quot;</span>)</span>
-<span id="cb10-34"><a href="#cb10-34" tabindex="-1"></a>  MSdb_idx <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite&quot;</span>)</span>
-<span id="cb10-35"><a href="#cb10-35" tabindex="-1"></a>  <span class="fu">dbWriteTable</span>(MSdb_idx, <span class="st">&quot;MS1&quot;</span>, msdata<span class="sc">$</span>MS1, <span class="at">overwrite=</span><span class="cn">TRUE</span>)</span>
-<span id="cb10-36"><a href="#cb10-36" tabindex="-1"></a>  rs <span class="ot">&lt;-</span> <span class="fu">dbSendQuery</span>(MSdb_idx, <span class="st">&quot;CREATE INDEX mz ON MS1 (mz)&quot;</span>)</span>
-<span id="cb10-37"><a href="#cb10-37" tabindex="-1"></a>  <span class="fu">dbClearResult</span>(rs)</span>
-<span id="cb10-38"><a href="#cb10-38" tabindex="-1"></a>  <span class="fu">dbDisconnect</span>(MSdb_idx)</span>
-<span id="cb10-39"><a href="#cb10-39" tabindex="-1"></a>  <span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite&quot;</span>)</span>
-<span id="cb10-40"><a href="#cb10-40" tabindex="-1"></a>}</span>
-<span id="cb10-41"><a href="#cb10-41" tabindex="-1"></a></span>
-<span id="cb10-42"><a href="#cb10-42" tabindex="-1"></a>make_timings <span class="ot">&lt;-</span> <span class="fu">microbenchmark</span>(</span>
-<span id="cb10-43"><a href="#cb10-43" tabindex="-1"></a>  <span class="fu">msnexp_make_fun</span>(), <span class="fu">ondisk_make_fun</span>(), <span class="fu">spectra_make_fun</span>(), <span class="fu">rams_make_fun</span>(),</span>
-<span id="cb10-44"><a href="#cb10-44" tabindex="-1"></a>  <span class="fu">tmzml_make_fun</span>(), <span class="fu">arrow_make_fun</span>(), <span class="fu">sql_make_fun</span>(), <span class="fu">sqlidx_make_fun</span>(), </span>
-<span id="cb10-45"><a href="#cb10-45" tabindex="-1"></a>  <span class="at">times =</span> <span class="dv">10</span>)</span>
-<span id="cb10-46"><a href="#cb10-46" tabindex="-1"></a><span class="fu">saveRDS</span>(make_timings, <span class="st">&quot;vignettes/figures/ssc_vignette_renders/make_timings.rds&quot;</span>)</span></code></pre></div>
-</details>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>make_timings <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/make_timings.rds&quot;</span>)</span>
-<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a>make_timings <span class="sc">%&gt;%</span></span>
-<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a>  <span class="fu">as.data.frame</span>() <span class="sc">%&gt;%</span></span>
-<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a>  <span class="fu">arrange</span>(expr) <span class="sc">%&gt;%</span></span>
-<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">str_remove</span>(expr, <span class="st">&quot;_make_fun</span><span class="sc">\\</span><span class="st">(</span><span class="sc">\\</span><span class="st">)&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">rep_type=</span><span class="fu">case_when</span>(</span>
-<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a>    expr<span class="sc">%in%</span><span class="fu">c</span>(<span class="st">&quot;msnexp&quot;</span>, <span class="st">&quot;ondisk&quot;</span>, <span class="st">&quot;spectra&quot;</span>, <span class="st">&quot;rams&quot;</span>)<span class="sc">~</span><span class="st">&quot;Every R session&quot;</span>,</span>
-<span id="cb11-8"><a href="#cb11-8" tabindex="-1"></a>    <span class="cn">TRUE</span><span class="sc">~</span><span class="st">&quot;Single-time only&quot;</span></span>
-<span id="cb11-9"><a href="#cb11-9" tabindex="-1"></a>  )) <span class="sc">%&gt;%</span></span>
-<span id="cb11-10"><a href="#cb11-10" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">factor</span>(expr, <span class="at">levels=</span><span class="fu">c</span>(<span class="st">&quot;msnexp&quot;</span>, <span class="st">&quot;ondisk&quot;</span>, <span class="st">&quot;spectra&quot;</span>, <span class="st">&quot;rams&quot;</span>,</span>
-<span id="cb11-11"><a href="#cb11-11" tabindex="-1"></a>                                    <span class="st">&quot;tmzml&quot;</span>, <span class="st">&quot;arrow&quot;</span>, <span class="st">&quot;sql&quot;</span>, <span class="st">&quot;sqlidx&quot;</span>),</span>
-<span id="cb11-12"><a href="#cb11-12" tabindex="-1"></a>                     <span class="at">labels=</span><span class="fu">c</span>(<span class="st">&quot;MSnExp&quot;</span>, <span class="st">&quot;OnDiskMSnExp&quot;</span>, <span class="st">&quot;Spectra&quot;</span>, <span class="st">&quot;RaMS&quot;</span>,</span>
-<span id="cb11-13"><a href="#cb11-13" tabindex="-1"></a>                              <span class="st">&quot;tmzMLs&quot;</span>, <span class="st">&quot;Arrow&quot;</span>, <span class="st">&quot;SQL&quot;</span>, <span class="st">&quot;SQL (indexed)&quot;</span>))) <span class="sc">%&gt;%</span></span>
-<span id="cb11-14"><a href="#cb11-14" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
-<span id="cb11-15"><a href="#cb11-15" tabindex="-1"></a>  <span class="fu">geom_boxplot</span>(<span class="fu">aes</span>(<span class="at">x=</span>expr, <span class="at">y=</span>time<span class="sc">/</span><span class="fl">1e9</span>)) <span class="sc">+</span></span>
-<span id="cb11-16"><a href="#cb11-16" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>) <span class="sc">+</span></span>
-<span id="cb11-17"><a href="#cb11-17" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>rep_type, <span class="at">nrow =</span> <span class="dv">1</span>, <span class="at">scales=</span><span class="st">&quot;free_x&quot;</span>) <span class="sc">+</span></span>
-<span id="cb11-18"><a href="#cb11-18" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&quot;Seconds&quot;</span>, <span class="at">x=</span><span class="cn">NULL</span>) <span class="sc">+</span></span>
-<span id="cb11-19"><a href="#cb11-19" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">90</span>, <span class="at">hjust=</span><span class="dv">1</span>, <span class="at">vjust=</span><span class="fl">0.5</span>))</span>
-<span id="cb11-20"><a href="#cb11-20" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/make_time_gp.png&quot;</span>, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>, <span class="at">device =</span> <span class="st">&quot;png&quot;</span>, <span class="at">dpi =</span> <span class="dv">144</span>)</span></code></pre></div>
-</details>
-<p><img 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" style="width:100.0%" /></p>
-<p>Nothing super surprising here. It should be noted that I ran all of
-these without using parallel processing to ensure a fair comparison, but
-RaMS and the tmzMLs are almost perfectly parallelizable and can be
-read/written in series for a huge time saving. Testing this with 5 cores
-across the 10 files resulted in a 3-fold speed improvement (from ~250
-seconds to ~85, placing it about on par with the SQL database) for
-tmzMLs but not for default RaMS. This makes sense if I/O is the slow
-step but reading can be done separately from writing, thus preventing
-multiple simultaneous reads but allowing one tmzML to be written while
-another is read. Also worth noting is that because
-<code>tmzmlMaker</code> calls <code>grabMSdata</code> internally, the
-RaMS values are a lower bound on the maximum tmzML speed. The MSnExp,
-OnDiskMSnExp, and Spectra packages focus instead on parallelizing their
-query step and I can’t seem to run those in parallel. The SQL and Arrow
-options similarly write to a single file that’s presumably going to be
-“locked” during writing and don’t have parallelization options available
-either. Also worth noting is that performing this test on 10 files (~1GB
-MS data) doesn’t really challenge memory requirements, so this could be
-very different if a larger number of files was used.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a>unpar_rams <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a>  <span class="fu">print</span>(<span class="st">&quot;Unpar RaMS&quot;</span>)</span>
-<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a>  <span class="fu">lapply</span>(ms_files, grabMSdata)</span>
-<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a>}</span>
-<span id="cb12-5"><a href="#cb12-5" tabindex="-1"></a>unpar_tmzml <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb12-6"><a href="#cb12-6" tabindex="-1"></a>  <span class="fu">print</span>(<span class="st">&quot;Unpar tmzML&quot;</span>)</span>
-<span id="cb12-7"><a href="#cb12-7" tabindex="-1"></a>  tmzml_names <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="fu">dirname</span>(<span class="fu">dirname</span>(ms_files)), <span class="st">&quot;/tmzMLs/&quot;</span>, </span>
-<span id="cb12-8"><a href="#cb12-8" tabindex="-1"></a>                        <span class="fu">gsub</span>(<span class="st">&quot;mzXML&quot;</span>, <span class="st">&quot;tmzML&quot;</span>, <span class="fu">basename</span>(ms_files)))</span>
-<span id="cb12-9"><a href="#cb12-9" tabindex="-1"></a>  <span class="fu">dir.create</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>)</span>
-<span id="cb12-10"><a href="#cb12-10" tabindex="-1"></a>  pbapply<span class="sc">::</span><span class="fu">pbmapply</span>(tmzmlMaker, ms_files, tmzml_names)</span>
-<span id="cb12-11"><a href="#cb12-11" tabindex="-1"></a>  <span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb12-12"><a href="#cb12-12" tabindex="-1"></a>}</span>
-<span id="cb12-13"><a href="#cb12-13" tabindex="-1"></a></span>
-<span id="cb12-14"><a href="#cb12-14" tabindex="-1"></a><span class="fu">library</span>(BiocParallel)</span>
-<span id="cb12-15"><a href="#cb12-15" tabindex="-1"></a>par_param <span class="ot">&lt;-</span> <span class="fu">SnowParam</span>(<span class="at">workers =</span> <span class="dv">5</span>, <span class="at">progressbar =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb12-16"><a href="#cb12-16" tabindex="-1"></a>par_rams <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb12-17"><a href="#cb12-17" tabindex="-1"></a>  <span class="fu">print</span>(<span class="st">&quot;Par RaMS&quot;</span>)</span>
-<span id="cb12-18"><a href="#cb12-18" tabindex="-1"></a>  <span class="fu">bplapply</span>(ms_files, grabMSdata, <span class="at">BPPARAM =</span> par_param)</span>
-<span id="cb12-19"><a href="#cb12-19" tabindex="-1"></a>}</span>
-<span id="cb12-20"><a href="#cb12-20" tabindex="-1"></a>par_tmzml <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb12-21"><a href="#cb12-21" tabindex="-1"></a>  <span class="fu">print</span>(<span class="st">&quot;Par tmzML&quot;</span>)</span>
-<span id="cb12-22"><a href="#cb12-22" tabindex="-1"></a>  tmzml_names <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="fu">dirname</span>(<span class="fu">dirname</span>(ms_files)), <span class="st">&quot;/tmzMLs/&quot;</span>, </span>
-<span id="cb12-23"><a href="#cb12-23" tabindex="-1"></a>                        <span class="fu">gsub</span>(<span class="st">&quot;mzXML&quot;</span>, <span class="st">&quot;tmzML&quot;</span>, <span class="fu">basename</span>(ms_files)))</span>
-<span id="cb12-24"><a href="#cb12-24" tabindex="-1"></a>  <span class="fu">dir.create</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>)</span>
-<span id="cb12-25"><a href="#cb12-25" tabindex="-1"></a>  <span class="fu">bpmapply</span>(tmzmlMaker, ms_files, tmzml_names, <span class="at">BPPARAM =</span> par_param)</span>
-<span id="cb12-26"><a href="#cb12-26" tabindex="-1"></a>  <span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb12-27"><a href="#cb12-27" tabindex="-1"></a>}</span>
-<span id="cb12-28"><a href="#cb12-28" tabindex="-1"></a></span>
-<span id="cb12-29"><a href="#cb12-29" tabindex="-1"></a>par_timings <span class="ot">&lt;-</span> <span class="fu">microbenchmark</span>(<span class="fu">par_rams</span>(), <span class="fu">unpar_rams</span>(), <span class="fu">par_tmzml</span>(), <span class="fu">unpar_tmzml</span>(), <span class="at">times =</span> <span class="dv">5</span>)</span>
-<span id="cb12-30"><a href="#cb12-30" tabindex="-1"></a><span class="fu">saveRDS</span>(par_timings, <span class="st">&quot;vignettes/figures/ssc_vignette_renders/par_timings.rds&quot;</span>)</span></code></pre></div>
-</details>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>par_timings <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/par_timings.rds&quot;</span>)</span>
-<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a>par_timings <span class="sc">%&gt;%</span></span>
-<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a>  <span class="fu">as.data.frame</span>() <span class="sc">%&gt;%</span></span>
-<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a>  <span class="fu">separate</span>(expr, <span class="at">into =</span> <span class="fu">c</span>(<span class="st">&quot;sub_type&quot;</span>, <span class="st">&quot;par_type&quot;</span>), <span class="at">sep =</span> <span class="st">&quot;_&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">par_type=</span><span class="fu">str_remove</span>(par_type, <span class="st">&quot;</span><span class="sc">\\</span><span class="st">(</span><span class="sc">\\</span><span class="st">)&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sub_type=</span><span class="fu">factor</span>(sub_type, <span class="at">levels=</span><span class="fu">c</span>(<span class="st">&quot;unpar&quot;</span>, <span class="st">&quot;par&quot;</span>),</span>
-<span id="cb13-7"><a href="#cb13-7" tabindex="-1"></a>                         <span class="at">labels=</span><span class="fu">c</span>(<span class="st">&quot;Sequential&quot;</span>, <span class="st">&quot;Parallel&quot;</span>))) <span class="sc">%&gt;%</span></span>
-<span id="cb13-8"><a href="#cb13-8" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">par_type=</span><span class="fu">factor</span>(par_type, <span class="at">levels=</span><span class="fu">c</span>(<span class="st">&quot;rams&quot;</span>, <span class="st">&quot;tmzml&quot;</span>),</span>
-<span id="cb13-9"><a href="#cb13-9" tabindex="-1"></a>                         <span class="at">labels=</span><span class="fu">c</span>(<span class="st">&quot;RaMS&quot;</span>, <span class="st">&quot;tmzMLs&quot;</span>))) <span class="sc">%&gt;%</span></span>
-<span id="cb13-10"><a href="#cb13-10" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
-<span id="cb13-11"><a href="#cb13-11" tabindex="-1"></a>  <span class="fu">geom_boxplot</span>(<span class="fu">aes</span>(<span class="at">x=</span>par_type, <span class="at">y=</span>time<span class="sc">/</span><span class="fl">1e9</span>)) <span class="sc">+</span></span>
-<span id="cb13-12"><a href="#cb13-12" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>) <span class="sc">+</span></span>
-<span id="cb13-13"><a href="#cb13-13" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>sub_type, <span class="at">nrow =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
-<span id="cb13-14"><a href="#cb13-14" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&quot;Seconds&quot;</span>, <span class="at">x=</span><span class="cn">NULL</span>) <span class="sc">+</span></span>
-<span id="cb13-15"><a href="#cb13-15" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">90</span>, <span class="at">hjust=</span><span class="dv">1</span>, <span class="at">vjust=</span><span class="fl">0.5</span>))</span>
-<span id="cb13-16"><a href="#cb13-16" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/par_time_gp.png&quot;</span>, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="dv">3</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>, <span class="at">device =</span> <span class="st">&quot;png&quot;</span>, <span class="at">dpi =</span> <span class="dv">144</span>)</span></code></pre></div>
-</details>
-<p><img 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" style="width:100.0%" /></p>
-</div>
-<div id="ms-data-query-timings" class="section level2">
-<h2>MS data query timings</h2>
-<p>After the MS data has been transformed into an accessible format, it
-still needs to be queried for a single chromatogram of interest. This
-typically means performing a search across the ~75 millon entries of
-mz/rt/int data for the m/z values within a certain window, and can be
-performed either on an object in memory (e.g. MSnExp &amp; RaMS) or on a
-disk object.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>msnexp_obj <span class="ot">&lt;-</span> <span class="fu">readMSData</span>(ms_files, <span class="at">mode=</span><span class="st">&quot;inMemory&quot;</span>, <span class="at">msLevel. =</span> <span class="dv">1</span>)</span>
-<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a>ondisk_obj <span class="ot">&lt;-</span> <span class="fu">readMSData</span>(ms_files, <span class="at">mode=</span><span class="st">&quot;onDisk&quot;</span>, <span class="at">msLevel. =</span> <span class="dv">1</span>)</span>
-<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a>spectra_obj <span class="ot">&lt;-</span> <span class="fu">Spectra</span>(ms_files, <span class="at">source=</span><span class="fu">MsBackendMzR</span>()) <span class="sc">%&gt;%</span> <span class="fu">filterMsLevel</span>(<span class="dv">1</span>)</span>
-<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a>rams_obj <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(ms_files, <span class="at">grab_what=</span><span class="st">&quot;MS1&quot;</span>)</span>
-<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a></span>
-<span id="cb14-6"><a href="#cb14-6" tabindex="-1"></a>tmzml_names <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="fu">dirname</span>(<span class="fu">dirname</span>(ms_files)), <span class="st">&quot;/tmzMLs/&quot;</span>, </span>
-<span id="cb14-7"><a href="#cb14-7" tabindex="-1"></a>                      <span class="fu">gsub</span>(<span class="st">&quot;mzXML&quot;</span>, <span class="st">&quot;tmzML&quot;</span>, <span class="fu">basename</span>(ms_files)))</span>
-<span id="cb14-8"><a href="#cb14-8" tabindex="-1"></a><span class="fu">dir.create</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>)</span>
-<span id="cb14-9"><a href="#cb14-9" tabindex="-1"></a><span class="fu">mapply</span>(tmzmlMaker, ms_files, tmzml_names)</span>
-<span id="cb14-10"><a href="#cb14-10" tabindex="-1"></a></span>
-<span id="cb14-11"><a href="#cb14-11" tabindex="-1"></a><span class="fu">write_dataset</span>(rams_obj<span class="sc">$</span>MS1, <span class="at">path =</span> <span class="st">&quot;vignettes/figures/ssc_vignette_renders/pqds&quot;</span>)</span>
-<span id="cb14-12"><a href="#cb14-12" tabindex="-1"></a></span>
-<span id="cb14-13"><a href="#cb14-13" tabindex="-1"></a>MSdb <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata.sqlite&quot;</span>)</span>
-<span id="cb14-14"><a href="#cb14-14" tabindex="-1"></a><span class="fu">dbWriteTable</span>(MSdb, <span class="st">&quot;MS1&quot;</span>, rams_obj<span class="sc">$</span>MS1, <span class="at">overwrite=</span><span class="cn">TRUE</span>)</span>
-<span id="cb14-15"><a href="#cb14-15" tabindex="-1"></a><span class="fu">dbDisconnect</span>(MSdb)</span>
-<span id="cb14-16"><a href="#cb14-16" tabindex="-1"></a></span>
-<span id="cb14-17"><a href="#cb14-17" tabindex="-1"></a>MSdb_idx <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite&quot;</span>)</span>
-<span id="cb14-18"><a href="#cb14-18" tabindex="-1"></a><span class="fu">dbWriteTable</span>(MSdb_idx, <span class="st">&quot;MS1&quot;</span>, rams_obj<span class="sc">$</span>MS1, <span class="at">overwrite=</span><span class="cn">TRUE</span>)</span>
-<span id="cb14-19"><a href="#cb14-19" tabindex="-1"></a>rs <span class="ot">&lt;-</span> <span class="fu">dbSendQuery</span>(MSdb_idx, <span class="st">&quot;CREATE INDEX mz ON MS1 (mz)&quot;</span>)</span>
-<span id="cb14-20"><a href="#cb14-20" tabindex="-1"></a><span class="fu">dbClearResult</span>(rs)</span>
-<span id="cb14-21"><a href="#cb14-21" tabindex="-1"></a><span class="fu">dbDisconnect</span>(MSdb_idx)</span>
-<span id="cb14-22"><a href="#cb14-22" tabindex="-1"></a></span>
-<span id="cb14-23"><a href="#cb14-23" tabindex="-1"></a>msnexp_query_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb14-24"><a href="#cb14-24" tabindex="-1"></a>  <span class="fu">plot</span>(<span class="fu">chromatogram</span>(msnexp_obj, <span class="at">mz=</span><span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>)))</span>
-<span id="cb14-25"><a href="#cb14-25" tabindex="-1"></a>}</span>
-<span id="cb14-26"><a href="#cb14-26" tabindex="-1"></a>ondisk_query_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb14-27"><a href="#cb14-27" tabindex="-1"></a>  <span class="fu">plot</span>(<span class="fu">chromatogram</span>(ondisk_obj, <span class="at">mz=</span><span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>)))</span>
-<span id="cb14-28"><a href="#cb14-28" tabindex="-1"></a>}</span>
-<span id="cb14-29"><a href="#cb14-29" tabindex="-1"></a>spectra_query_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb14-30"><a href="#cb14-30" tabindex="-1"></a>  sfs_filtered <span class="ot">&lt;-</span> spectra_obj <span class="sc">%&gt;%</span> <span class="fu">filterMzRange</span>(<span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>))</span>
-<span id="cb14-31"><a href="#cb14-31" tabindex="-1"></a>  getIntensities <span class="ot">&lt;-</span> <span class="cf">function</span>(x, ...) {</span>
-<span id="cb14-32"><a href="#cb14-32" tabindex="-1"></a>    <span class="cf">if</span> (<span class="fu">nrow</span>(x)) {</span>
-<span id="cb14-33"><a href="#cb14-33" tabindex="-1"></a>      <span class="fu">cbind</span>(<span class="at">mz =</span> <span class="cn">NA_real_</span>, <span class="at">intensity =</span> x[, <span class="st">&quot;intensity&quot;</span>])</span>
-<span id="cb14-34"><a href="#cb14-34" tabindex="-1"></a>    } <span class="cf">else</span> <span class="fu">cbind</span>(<span class="at">mz =</span> <span class="cn">NA_real_</span>, <span class="at">intensity =</span> <span class="cn">NA_real_</span>)</span>
-<span id="cb14-35"><a href="#cb14-35" tabindex="-1"></a>  }</span>
-<span id="cb14-36"><a href="#cb14-36" tabindex="-1"></a>  sfs_agg <span class="ot">&lt;-</span> <span class="fu">addProcessing</span>(sfs_filtered, getIntensities)</span>
-<span id="cb14-37"><a href="#cb14-37" tabindex="-1"></a>  eic <span class="ot">&lt;-</span> <span class="fu">cbind</span>(<span class="at">rt=</span><span class="fu">rtime</span>(sfs_agg), <span class="at">int=</span><span class="fu">unlist</span>(<span class="fu">intensity</span>(sfs_agg), <span class="at">use.names =</span> <span class="cn">FALSE</span>))</span>
-<span id="cb14-38"><a href="#cb14-38" tabindex="-1"></a>  <span class="fu">plot</span>(eic[,<span class="st">&quot;rt&quot;</span>], eic[,<span class="st">&quot;int&quot;</span>], <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span>
-<span id="cb14-39"><a href="#cb14-39" tabindex="-1"></a>}</span>
-<span id="cb14-40"><a href="#cb14-40" tabindex="-1"></a>rams_query_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb14-41"><a href="#cb14-41" tabindex="-1"></a>  rams_chrom_data <span class="ot">&lt;-</span> rams_obj<span class="sc">$</span>MS1[mz<span class="sc">%between%</span><span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>)]</span>
-<span id="cb14-42"><a href="#cb14-42" tabindex="-1"></a>  <span class="fu">plot</span>(rams_chrom_data<span class="sc">$</span>rt, rams_chrom_data<span class="sc">$</span>int, <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span>
-<span id="cb14-43"><a href="#cb14-43" tabindex="-1"></a>}</span>
-<span id="cb14-44"><a href="#cb14-44" tabindex="-1"></a>tmzml_query_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb14-45"><a href="#cb14-45" tabindex="-1"></a>  tmzml_names <span class="ot">&lt;-</span> <span class="fu">list.files</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb14-46"><a href="#cb14-46" tabindex="-1"></a>  tmzml_obj <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(tmzml_names)</span>
-<span id="cb14-47"><a href="#cb14-47" tabindex="-1"></a>  tmzml_chrom_data <span class="ot">&lt;-</span> tmzml_obj<span class="sc">$</span>MS1[mz<span class="sc">%between%</span><span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>)]</span>
-<span id="cb14-48"><a href="#cb14-48" tabindex="-1"></a>  <span class="fu">plot</span>(tmzml_chrom_data<span class="sc">$</span>rt, tmzml_chrom_data<span class="sc">$</span>int, <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span>
-<span id="cb14-49"><a href="#cb14-49" tabindex="-1"></a>}</span>
-<span id="cb14-50"><a href="#cb14-50" tabindex="-1"></a>arrow_query_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb14-51"><a href="#cb14-51" tabindex="-1"></a>  arrow_data <span class="ot">&lt;-</span> <span class="fu">open_dataset</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/pqds&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb14-52"><a href="#cb14-52" tabindex="-1"></a>    <span class="fu">filter</span>(mz<span class="sc">%between%</span><span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb14-53"><a href="#cb14-53" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">collect</span>()</span>
-<span id="cb14-54"><a href="#cb14-54" tabindex="-1"></a>  <span class="fu">plot</span>(arrow_data<span class="sc">$</span>rt, arrow_data<span class="sc">$</span>int, <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span>
-<span id="cb14-55"><a href="#cb14-55" tabindex="-1"></a>}</span>
-<span id="cb14-56"><a href="#cb14-56" tabindex="-1"></a>sql_query_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb14-57"><a href="#cb14-57" tabindex="-1"></a>  MSdb <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata.sqlite&quot;</span>)</span>
-<span id="cb14-58"><a href="#cb14-58" tabindex="-1"></a>  EIC_query <span class="ot">&lt;-</span> <span class="st">&#39;SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound&#39;</span></span>
-<span id="cb14-59"><a href="#cb14-59" tabindex="-1"></a>  query_params <span class="ot">&lt;-</span> <span class="fu">as.list</span>(<span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>))</span>
-<span id="cb14-60"><a href="#cb14-60" tabindex="-1"></a>  <span class="fu">names</span>(query_params) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;lower_bound&quot;</span>, <span class="st">&quot;upper_bound&quot;</span>)</span>
-<span id="cb14-61"><a href="#cb14-61" tabindex="-1"></a>  sql_data <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(MSdb, EIC_query, <span class="at">params =</span> query_params)</span>
-<span id="cb14-62"><a href="#cb14-62" tabindex="-1"></a>  <span class="fu">plot</span>(sql_data<span class="sc">$</span>rt, sql_data<span class="sc">$</span>int, <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span>
-<span id="cb14-63"><a href="#cb14-63" tabindex="-1"></a>}</span>
-<span id="cb14-64"><a href="#cb14-64" tabindex="-1"></a>sqlidx_query_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb14-65"><a href="#cb14-65" tabindex="-1"></a>  MSdb_idx <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite&quot;</span>)</span>
-<span id="cb14-66"><a href="#cb14-66" tabindex="-1"></a>  EIC_query <span class="ot">&lt;-</span> <span class="st">&#39;SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound&#39;</span></span>
-<span id="cb14-67"><a href="#cb14-67" tabindex="-1"></a>  query_params <span class="ot">&lt;-</span> <span class="fu">as.list</span>(<span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>))</span>
-<span id="cb14-68"><a href="#cb14-68" tabindex="-1"></a>  <span class="fu">names</span>(query_params) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;lower_bound&quot;</span>, <span class="st">&quot;upper_bound&quot;</span>)</span>
-<span id="cb14-69"><a href="#cb14-69" tabindex="-1"></a>  sql_data <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(MSdb_idx, EIC_query, <span class="at">params =</span> query_params)</span>
-<span id="cb14-70"><a href="#cb14-70" tabindex="-1"></a>  sql_data <span class="ot">&lt;-</span> sql_data[<span class="fu">order</span>(sql_data<span class="sc">$</span>filename, sql_data<span class="sc">$</span>rt),]</span>
-<span id="cb14-71"><a href="#cb14-71" tabindex="-1"></a>  <span class="fu">plot</span>(sql_data<span class="sc">$</span>rt, sql_data<span class="sc">$</span>int, <span class="at">type=</span><span class="st">&quot;l&quot;</span>)</span>
-<span id="cb14-72"><a href="#cb14-72" tabindex="-1"></a>}</span>
-<span id="cb14-73"><a href="#cb14-73" tabindex="-1"></a></span>
-<span id="cb14-74"><a href="#cb14-74" tabindex="-1"></a>query_timings <span class="ot">&lt;-</span> <span class="fu">microbenchmark</span>(</span>
-<span id="cb14-75"><a href="#cb14-75" tabindex="-1"></a>  <span class="fu">msnexp_query_fun</span>(), <span class="fu">ondisk_query_fun</span>(), <span class="fu">spectra_query_fun</span>(), <span class="fu">rams_query_fun</span>(),</span>
-<span id="cb14-76"><a href="#cb14-76" tabindex="-1"></a>  <span class="fu">tmzml_query_fun</span>(), <span class="fu">arrow_query_fun</span>(), <span class="fu">sql_query_fun</span>(), <span class="fu">sqlidx_query_fun</span>(),</span>
-<span id="cb14-77"><a href="#cb14-77" tabindex="-1"></a>  <span class="at">times =</span> <span class="dv">10</span></span>
-<span id="cb14-78"><a href="#cb14-78" tabindex="-1"></a>)</span>
-<span id="cb14-79"><a href="#cb14-79" tabindex="-1"></a>query_timings</span>
-<span id="cb14-80"><a href="#cb14-80" tabindex="-1"></a><span class="fu">saveRDS</span>(query_timings, <span class="st">&quot;vignettes/figures/ssc_vignette_renders/query_timings.rds&quot;</span>)</span></code></pre></div>
-</details>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>query_timings <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/query_timings.rds&quot;</span>)</span>
-<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a>query_timings <span class="sc">%&gt;%</span></span>
-<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a>  <span class="fu">as.data.frame</span>() <span class="sc">%&gt;%</span></span>
-<span id="cb15-4"><a href="#cb15-4" tabindex="-1"></a>  <span class="fu">arrange</span>(expr) <span class="sc">%&gt;%</span></span>
-<span id="cb15-5"><a href="#cb15-5" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">str_remove</span>(expr, <span class="st">&quot;_query_fun</span><span class="sc">\\</span><span class="st">(</span><span class="sc">\\</span><span class="st">)&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb15-6"><a href="#cb15-6" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">rep_type=</span><span class="fu">case_when</span>(</span>
-<span id="cb15-7"><a href="#cb15-7" tabindex="-1"></a>    expr<span class="sc">%in%</span><span class="fu">c</span>(<span class="st">&quot;msnexp&quot;</span>, <span class="st">&quot;ondisk&quot;</span>, <span class="st">&quot;spectra&quot;</span>, <span class="st">&quot;rams&quot;</span>)<span class="sc">~</span><span class="st">&quot;Every R session&quot;</span>,</span>
-<span id="cb15-8"><a href="#cb15-8" tabindex="-1"></a>    <span class="cn">TRUE</span><span class="sc">~</span><span class="st">&quot;Single-time only&quot;</span></span>
-<span id="cb15-9"><a href="#cb15-9" tabindex="-1"></a>  )) <span class="sc">%&gt;%</span></span>
-<span id="cb15-10"><a href="#cb15-10" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">factor</span>(expr, <span class="at">levels=</span><span class="fu">c</span>(<span class="st">&quot;msnexp&quot;</span>, <span class="st">&quot;ondisk&quot;</span>, <span class="st">&quot;spectra&quot;</span>, <span class="st">&quot;rams&quot;</span>,</span>
-<span id="cb15-11"><a href="#cb15-11" tabindex="-1"></a>                                    <span class="st">&quot;tmzml&quot;</span>, <span class="st">&quot;arrow&quot;</span>, <span class="st">&quot;sql&quot;</span>, <span class="st">&quot;sqlidx&quot;</span>),</span>
-<span id="cb15-12"><a href="#cb15-12" tabindex="-1"></a>                     <span class="at">labels=</span><span class="fu">c</span>(<span class="st">&quot;MSnExp&quot;</span>, <span class="st">&quot;OnDiskMSnExp&quot;</span>, <span class="st">&quot;Spectra&quot;</span>, <span class="st">&quot;RaMS&quot;</span>,</span>
-<span id="cb15-13"><a href="#cb15-13" tabindex="-1"></a>                              <span class="st">&quot;tmzMLs&quot;</span>, <span class="st">&quot;Arrow&quot;</span>, <span class="st">&quot;SQL&quot;</span>, <span class="st">&quot;SQL (indexed)&quot;</span>))) <span class="sc">%&gt;%</span></span>
-<span id="cb15-14"><a href="#cb15-14" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
-<span id="cb15-15"><a href="#cb15-15" tabindex="-1"></a>  <span class="fu">geom_boxplot</span>(<span class="fu">aes</span>(<span class="at">x=</span>expr, <span class="at">y=</span>time<span class="sc">/</span><span class="fl">1e9</span>)) <span class="sc">+</span></span>
-<span id="cb15-16"><a href="#cb15-16" tabindex="-1"></a>  <span class="fu">scale_y_log10</span>() <span class="sc">+</span></span>
-<span id="cb15-17"><a href="#cb15-17" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&quot;Seconds&quot;</span>, <span class="at">x=</span><span class="cn">NULL</span>) <span class="sc">+</span></span>
-<span id="cb15-18"><a href="#cb15-18" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">90</span>, <span class="at">hjust=</span><span class="dv">1</span>, <span class="at">vjust=</span><span class="fl">0.5</span>))</span>
-<span id="cb15-19"><a href="#cb15-19" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/query_time_gp.png&quot;</span>, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>, <span class="at">device =</span> <span class="st">&quot;png&quot;</span>, <span class="at">dpi =</span> <span class="dv">144</span>)</span></code></pre></div>
-</details>
-<p><img 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" style="width:100.0%" /></p>
-<p>There’s a few surprises in the above plot. Most notably, the
-incredible speed of the mz-indexed SQL database. It’s apparently faster
-to query the database and read that data into R than it is to index
-through the big RaMS object entirely within memory. That’s a pretty darn
-compelling reason to switch to SQL databases (although note the
-importance of indexing them - the non-indexed one took 100x longer!).
-Side note: I originally expected this to be quite slow because I
-previously <em>thought</em> I was creating an index on the mz data using
-<code>dbExecute</code> instead of <code>dbSendQuery</code> but
-apparently that’s not how the function works. This actually disagrees
-with a <a href="https://bioconductor.org/packages/release/bioc/vignettes/MsBackendSql/inst/doc/MsBackendSql.html#4_Performance_comparison_with_other_backends">similar
-comparison</a> done by the Spectra package that found the SQL backend to
-be consistently slower than the in-memory option. This result was so
-unexpected that I wrote a quick followup to double-check and can confirm
-that in real time the SQL query felt just as fast as the RaMS object.
-Wild.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>MSdb_idx <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite&quot;</span>)</span>
-<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a>EIC_query <span class="ot">&lt;-</span> <span class="st">&#39;SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound&#39;</span></span>
-<span id="cb16-3"><a href="#cb16-3" tabindex="-1"></a>query_params <span class="ot">&lt;-</span> <span class="fu">as.list</span>(<span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>))</span>
-<span id="cb16-4"><a href="#cb16-4" tabindex="-1"></a><span class="fu">names</span>(query_params) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;lower_bound&quot;</span>, <span class="st">&quot;upper_bound&quot;</span>)</span>
-<span id="cb16-5"><a href="#cb16-5" tabindex="-1"></a><span class="fu">dbGetQuery</span>(MSdb_idx, EIC_query, <span class="at">params =</span> query_params) <span class="sc">%&gt;%</span> <span class="fu">qplotMS1data</span>()</span>
-<span id="cb16-6"><a href="#cb16-6" tabindex="-1"></a></span>
-<span id="cb16-7"><a href="#cb16-7" tabindex="-1"></a>rams_obj<span class="sc">$</span>MS1[mz<span class="sc">%between%</span><span class="fu">pmppm</span>(<span class="fl">432.2810</span>, <span class="at">ppm =</span> <span class="dv">20</span>)] <span class="sc">%&gt;%</span> <span class="fu">qplotMS1data</span>()</span></code></pre></div>
-</details>
-<p>Also surprising is the performance of the tmzMLs - I thought they’d
-do much worse and instead they’re about on par with the arrow method.
-I’m pleasantly surprised that my hacky file type can compete at all. Of
-course, up at the top are the traditional MS formats. They’re not
-designed for this or optimized for chromatogram extraction at all so
-it’s not especially surprising that they struggle, but the graph does
-also nicely show the improved performance of more recent versions over
-older ones.</p>
-<div id="multiple-chromatograms" class="section level4">
-<h4>Multiple chromatograms</h4>
-<p>Of course, the above tests assume that only a single chromatogram is
-being requested. This is a little unrealistic. While there are times
-when I just want to see a single mass, I also often want to extract
-multiple masses of interest simultaneously and some of these methods are
-more friendly to this approach than others. This usually requires
-constructing a string of <code>between</code> statements that we can
-then pass to the evaluator. Fortunately, the same string can be passed
-to both Arrow and RaMS so that makes the task a little easier. The nice
-thing about sending a single query is also that the data does not then
-need to be deduplicated, which can happen during loops if the m/z
-windows overlap.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a>rams_obj <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(ms_files, <span class="at">grab_what=</span><span class="st">&quot;MS1&quot;</span>)</span>
-<span id="cb17-2"><a href="#cb17-2" tabindex="-1"></a>grouped_ms1 <span class="ot">&lt;-</span> rams_obj<span class="sc">$</span>MS1 <span class="sc">%&gt;%</span></span>
-<span id="cb17-3"><a href="#cb17-3" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(int)) <span class="sc">%&gt;%</span> </span>
-<span id="cb17-4"><a href="#cb17-4" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">mz_group=</span><span class="fu">mz_group</span>(mz, <span class="at">ppm =</span> <span class="dv">10</span>, <span class="at">max_groups =</span> <span class="dv">10</span>, <span class="at">min_group_size=</span><span class="dv">20</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb17-5"><a href="#cb17-5" tabindex="-1"></a>  <span class="fu">drop_na</span>()</span>
-<span id="cb17-6"><a href="#cb17-6" tabindex="-1"></a><span class="co"># grouped_ms1 %&gt;%</span></span>
-<span id="cb17-7"><a href="#cb17-7" tabindex="-1"></a><span class="co">#   qplotMS1data(facet_col=&quot;mz_group&quot;, facet_args = list(ncol=2))</span></span>
-<span id="cb17-8"><a href="#cb17-8" tabindex="-1"></a>mzs_to_grab <span class="ot">&lt;-</span> grouped_ms1 <span class="sc">%&gt;%</span></span>
-<span id="cb17-9"><a href="#cb17-9" tabindex="-1"></a>  <span class="fu">group_by</span>(mz_group) <span class="sc">%&gt;%</span></span>
-<span id="cb17-10"><a href="#cb17-10" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">mean_mz=</span><span class="fu">mean</span>(mz), <span class="at">sd_mz=</span><span class="fu">sd</span>(mz), <span class="at">mean_rt=</span><span class="fu">mean</span>(rt)) <span class="sc">%&gt;%</span></span>
-<span id="cb17-11"><a href="#cb17-11" tabindex="-1"></a>  <span class="fu">pull</span>(mean_mz)</span>
-<span id="cb17-12"><a href="#cb17-12" tabindex="-1"></a></span>
-<span id="cb17-13"><a href="#cb17-13" tabindex="-1"></a>rams_arrow_call <span class="ot">&lt;-</span> <span class="fu">lapply</span>(mzs_to_grab, <span class="cf">function</span>(mz_i){</span>
-<span id="cb17-14"><a href="#cb17-14" tabindex="-1"></a>  mzrange <span class="ot">&lt;-</span> <span class="fu">pmppm</span>(mz_i, <span class="dv">10</span>)</span>
-<span id="cb17-15"><a href="#cb17-15" tabindex="-1"></a>  <span class="fu">call</span>(<span class="st">&quot;between&quot;</span>, <span class="fu">as.name</span>(<span class="st">&quot;mz&quot;</span>), mzrange[[<span class="dv">1</span>]], mzrange[[<span class="dv">2</span>]])</span>
-<span id="cb17-16"><a href="#cb17-16" tabindex="-1"></a>}) <span class="sc">%&gt;%</span> <span class="fu">paste</span>(<span class="at">collapse =</span> <span class="st">&quot;|&quot;</span>)</span>
-<span id="cb17-17"><a href="#cb17-17" tabindex="-1"></a></span>
-<span id="cb17-18"><a href="#cb17-18" tabindex="-1"></a>sql_comb_call <span class="ot">&lt;-</span> <span class="fu">sapply</span>(mzs_to_grab, <span class="cf">function</span>(mz_i){</span>
-<span id="cb17-19"><a href="#cb17-19" tabindex="-1"></a>  <span class="fu">paste</span>(<span class="st">&quot;mz BETWEEN&quot;</span>, <span class="fu">pmppm</span>(mz_i, <span class="dv">10</span>)[<span class="dv">1</span>], <span class="st">&quot;AND&quot;</span>, <span class="fu">pmppm</span>(mz_i, <span class="dv">10</span>)[<span class="dv">2</span>])</span>
-<span id="cb17-20"><a href="#cb17-20" tabindex="-1"></a>}) <span class="sc">%&gt;%</span> <span class="fu">paste</span>(<span class="at">collapse =</span> <span class="st">&quot; OR &quot;</span>) <span class="sc">%&gt;%</span> <span class="fu">paste</span>(<span class="st">&quot;SELECT * FROM MS1 WHERE&quot;</span>, .)</span>
-<span id="cb17-21"><a href="#cb17-21" tabindex="-1"></a></span>
-<span id="cb17-22"><a href="#cb17-22" tabindex="-1"></a><span class="fu">print</span>(rams_arrow_call)</span>
-<span id="cb17-23"><a href="#cb17-23" tabindex="-1"></a><span class="fu">print</span>(sql_comb_call)</span></code></pre></div>
-</details>
-<p>Finally we can test the timing of the multiple chromatograms both in
-loop form and in single-query form.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>rams_uni_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a>  <span class="fu">print</span>(<span class="st">&quot;RaMS unified&quot;</span>)</span>
-<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a>  rams_obj<span class="sc">$</span>MS1[<span class="fu">eval</span>(<span class="fu">parse</span>(<span class="at">text=</span>rams_arrow_call))]</span>
-<span id="cb18-4"><a href="#cb18-4" tabindex="-1"></a>}</span>
-<span id="cb18-5"><a href="#cb18-5" tabindex="-1"></a>rams_loop_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb18-6"><a href="#cb18-6" tabindex="-1"></a>  <span class="fu">print</span>(<span class="st">&quot;RaMS loop&quot;</span>)</span>
-<span id="cb18-7"><a href="#cb18-7" tabindex="-1"></a>  <span class="fu">lapply</span>(mzs_to_grab, <span class="cf">function</span>(mz_i){</span>
-<span id="cb18-8"><a href="#cb18-8" tabindex="-1"></a>    rams_obj<span class="sc">$</span>MS1[mz<span class="sc">%between%</span><span class="fu">pmppm</span>(mz_i, <span class="dv">10</span>)]</span>
-<span id="cb18-9"><a href="#cb18-9" tabindex="-1"></a>  }) <span class="sc">%&gt;%</span> <span class="fu">bind_rows</span>() <span class="sc">%&gt;%</span> <span class="fu">distinct</span>()</span>
-<span id="cb18-10"><a href="#cb18-10" tabindex="-1"></a>}</span>
-<span id="cb18-11"><a href="#cb18-11" tabindex="-1"></a></span>
-<span id="cb18-12"><a href="#cb18-12" tabindex="-1"></a>arrow_ds <span class="ot">&lt;-</span> <span class="fu">open_dataset</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/pqds&quot;</span>)</span>
-<span id="cb18-13"><a href="#cb18-13" tabindex="-1"></a>arrow_uni_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb18-14"><a href="#cb18-14" tabindex="-1"></a>  <span class="fu">print</span>(<span class="st">&quot;Arrow unified&quot;</span>)</span>
-<span id="cb18-15"><a href="#cb18-15" tabindex="-1"></a>  arrow_ds <span class="sc">%&gt;%</span></span>
-<span id="cb18-16"><a href="#cb18-16" tabindex="-1"></a>    <span class="fu">filter</span>(<span class="fu">eval</span>(<span class="fu">parse</span>(<span class="at">text =</span> rams_arrow_call))) <span class="sc">%&gt;%</span></span>
-<span id="cb18-17"><a href="#cb18-17" tabindex="-1"></a>    <span class="fu">collect</span>()</span>
-<span id="cb18-18"><a href="#cb18-18" tabindex="-1"></a>}</span>
-<span id="cb18-19"><a href="#cb18-19" tabindex="-1"></a>arrow_loop_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb18-20"><a href="#cb18-20" tabindex="-1"></a>  <span class="fu">print</span>(<span class="st">&quot;Arrow loop&quot;</span>)</span>
-<span id="cb18-21"><a href="#cb18-21" tabindex="-1"></a>  <span class="fu">lapply</span>(mzs_to_grab, <span class="cf">function</span>(mz_i){</span>
-<span id="cb18-22"><a href="#cb18-22" tabindex="-1"></a>    arrow_ds <span class="sc">%&gt;%</span></span>
-<span id="cb18-23"><a href="#cb18-23" tabindex="-1"></a>      <span class="fu">filter</span>(mz<span class="sc">%between%</span><span class="fu">pmppm</span>(mz_i, <span class="dv">10</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb18-24"><a href="#cb18-24" tabindex="-1"></a>      <span class="fu">collect</span>()</span>
-<span id="cb18-25"><a href="#cb18-25" tabindex="-1"></a>  }) <span class="sc">%&gt;%</span> <span class="fu">bind_rows</span>() <span class="sc">%&gt;%</span> <span class="fu">distinct</span>()</span>
-<span id="cb18-26"><a href="#cb18-26" tabindex="-1"></a>}</span>
-<span id="cb18-27"><a href="#cb18-27" tabindex="-1"></a></span>
-<span id="cb18-28"><a href="#cb18-28" tabindex="-1"></a>MSdb_idx <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite&quot;</span>)</span>
-<span id="cb18-29"><a href="#cb18-29" tabindex="-1"></a>sql_uni_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb18-30"><a href="#cb18-30" tabindex="-1"></a>  <span class="fu">print</span>(<span class="st">&quot;SQL unified&quot;</span>)</span>
-<span id="cb18-31"><a href="#cb18-31" tabindex="-1"></a>  <span class="fu">dbGetQuery</span>(MSdb_idx, sql_comb_call)</span>
-<span id="cb18-32"><a href="#cb18-32" tabindex="-1"></a>}</span>
-<span id="cb18-33"><a href="#cb18-33" tabindex="-1"></a>sql_query_base <span class="ot">&lt;-</span> <span class="st">&#39;SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound&#39;</span></span>
-<span id="cb18-34"><a href="#cb18-34" tabindex="-1"></a>sql_loop_fun <span class="ot">&lt;-</span> <span class="cf">function</span>(){</span>
-<span id="cb18-35"><a href="#cb18-35" tabindex="-1"></a>  <span class="fu">print</span>(<span class="st">&quot;SQL loop&quot;</span>)</span>
-<span id="cb18-36"><a href="#cb18-36" tabindex="-1"></a>  <span class="fu">lapply</span>(mzs_to_grab, <span class="cf">function</span>(mz_i){</span>
-<span id="cb18-37"><a href="#cb18-37" tabindex="-1"></a>    query_params <span class="ot">&lt;-</span> <span class="fu">as.list</span>(<span class="fu">pmppm</span>(mz_i, <span class="at">ppm =</span> <span class="dv">20</span>))</span>
-<span id="cb18-38"><a href="#cb18-38" tabindex="-1"></a>    <span class="fu">names</span>(query_params) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;lower_bound&quot;</span>, <span class="st">&quot;upper_bound&quot;</span>)</span>
-<span id="cb18-39"><a href="#cb18-39" tabindex="-1"></a>    sql_data <span class="ot">&lt;-</span> <span class="fu">dbGetQuery</span>(MSdb_idx, sql_query_base, <span class="at">params =</span> query_params)</span>
-<span id="cb18-40"><a href="#cb18-40" tabindex="-1"></a>  }) <span class="sc">%&gt;%</span> <span class="fu">bind_rows</span>() <span class="sc">%&gt;%</span> <span class="fu">distinct</span>()</span>
-<span id="cb18-41"><a href="#cb18-41" tabindex="-1"></a>}</span>
-<span id="cb18-42"><a href="#cb18-42" tabindex="-1"></a></span>
-<span id="cb18-43"><a href="#cb18-43" tabindex="-1"></a>multichrom_timings <span class="ot">&lt;-</span> <span class="fu">microbenchmark</span>(</span>
-<span id="cb18-44"><a href="#cb18-44" tabindex="-1"></a>  <span class="fu">rams_uni_fun</span>(), <span class="fu">rams_loop_fun</span>(), <span class="fu">arrow_uni_fun</span>(), <span class="fu">arrow_loop_fun</span>(), </span>
-<span id="cb18-45"><a href="#cb18-45" tabindex="-1"></a>  <span class="fu">sql_uni_fun</span>(), <span class="fu">sql_loop_fun</span>(), <span class="at">times =</span> <span class="dv">10</span></span>
-<span id="cb18-46"><a href="#cb18-46" tabindex="-1"></a>)</span>
-<span id="cb18-47"><a href="#cb18-47" tabindex="-1"></a><span class="fu">saveRDS</span>(multichrom_timings, <span class="st">&quot;vignettes/figures/ssc_vignette_renders/multichrom_timings.rds&quot;</span>)</span>
-<span id="cb18-48"><a href="#cb18-48" tabindex="-1"></a></span>
-<span id="cb18-49"><a href="#cb18-49" tabindex="-1"></a>multichrom_timings <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/multichrom_timings.rds&quot;</span>)</span>
-<span id="cb18-50"><a href="#cb18-50" tabindex="-1"></a>multichrom_timings <span class="sc">%&gt;%</span></span>
-<span id="cb18-51"><a href="#cb18-51" tabindex="-1"></a>  <span class="fu">as.data.frame</span>() <span class="sc">%&gt;%</span></span>
-<span id="cb18-52"><a href="#cb18-52" tabindex="-1"></a>  <span class="fu">arrange</span>(expr) <span class="sc">%&gt;%</span></span>
-<span id="cb18-53"><a href="#cb18-53" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">str_remove</span>(expr, <span class="st">&quot;_fun</span><span class="sc">\\</span><span class="st">(</span><span class="sc">\\</span><span class="st">)&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb18-54"><a href="#cb18-54" tabindex="-1"></a>  <span class="fu">separate</span>(expr, <span class="at">into =</span> <span class="fu">c</span>(<span class="st">&quot;expr&quot;</span>, <span class="st">&quot;query_type&quot;</span>), <span class="at">sep =</span> <span class="st">&quot;_&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb18-55"><a href="#cb18-55" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">factor</span>(expr, <span class="at">levels=</span><span class="fu">c</span>(<span class="st">&quot;rams&quot;</span>, <span class="st">&quot;arrow&quot;</span>, <span class="st">&quot;sql&quot;</span>),</span>
-<span id="cb18-56"><a href="#cb18-56" tabindex="-1"></a>                     <span class="at">labels=</span><span class="fu">c</span>(<span class="st">&quot;RaMS&quot;</span>, <span class="st">&quot;Arrow&quot;</span>, <span class="st">&quot;SQL&quot;</span>))) <span class="sc">%&gt;%</span></span>
-<span id="cb18-57"><a href="#cb18-57" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">query_type=</span><span class="fu">factor</span>(query_type, <span class="at">levels=</span><span class="fu">c</span>(<span class="st">&quot;uni&quot;</span>, <span class="st">&quot;loop&quot;</span>),</span>
-<span id="cb18-58"><a href="#cb18-58" tabindex="-1"></a>                           <span class="at">labels=</span><span class="fu">c</span>(<span class="st">&quot;Unified query&quot;</span>, <span class="st">&quot;Query loop&quot;</span>))) <span class="sc">%&gt;%</span></span>
-<span id="cb18-59"><a href="#cb18-59" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span> </span>
-<span id="cb18-60"><a href="#cb18-60" tabindex="-1"></a>  <span class="fu">geom_boxplot</span>(<span class="fu">aes</span>(<span class="at">x=</span>query_type, <span class="at">y=</span>time<span class="sc">/</span><span class="fl">1e9</span>), <span class="at">lwd=</span><span class="dv">1</span>) <span class="sc">+</span></span>
-<span id="cb18-61"><a href="#cb18-61" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>expr, <span class="at">nrow=</span><span class="dv">1</span>) <span class="sc">+</span></span>
-<span id="cb18-62"><a href="#cb18-62" tabindex="-1"></a>  <span class="fu">scale_y_log10</span>() <span class="sc">+</span></span>
-<span id="cb18-63"><a href="#cb18-63" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y=</span><span class="st">&quot;Seconds&quot;</span>, <span class="at">x=</span><span class="cn">NULL</span>, <span class="at">color=</span><span class="cn">NULL</span>) <span class="sc">+</span></span>
-<span id="cb18-64"><a href="#cb18-64" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span>
-<span id="cb18-65"><a href="#cb18-65" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/multichrom_gp.png&quot;</span>, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>, <span class="at">device =</span> <span class="st">&quot;png&quot;</span>, <span class="at">dpi =</span> <span class="dv">144</span>)</span></code></pre></div>
-</details>
-<p><img 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7yCgoJ58+YVatQ0zefzLVq0yJSSAAAAIhDBt8o7fPiw1+u9sl1V1e+//z789QAAAEQmgm+V5/P5yrEIAABANgTfKq9JkyY2WxF/R5vNdtNNN4W/HgAAgMhE8K3yYmJiHnjggSIXDR8+PMzFAAAARCyCrxU8/vjjvXv3FkLY7Xa73a4oitvtnjp1atOmTc0uDQAAIFJwHl8rcLlc06dPHzBgwFdffXXmzJkbbrihX79+ycnJZtcFAAAQQQi+1tGxY8cWLVr4fL64uLioqCizywEAAIgsTHUAAACAFAi+AAAAkALBFwAAAFJgjm+F7Nu378iRI0eOHNm9e7fZtYiTJ08al3BTFKVGjRrx8fFmVyQGDBjgdDq7detWvXp1s2sBAACyI/hWyPr16//+97+bXUURcnJyzC5BCCH27dsnhPj73/9O8AUAAKZjqgMAAACkQPAFAACAFAi+AAAAkAJzfAEAv3r//fe//fZbc2vQdT0nJ8fv9wshHA5HTEyM3W43t6RatWpNmTLF3BoAhATBFwAgRo0a9cMPP3i9XlVVza4l4iiK8sUXXzgcjk2bNpldC4AKIfhWyEMPPTRw4MDPP//83XffNbuWSLR06dLo6OjrrrvO7EIAlMDr9ebl5ZldRYTSdT0vL8/hYI8JVHkM4wqpWbNmzZo1U1JSkpKSzK5FCCE0TRNCKIqiKIrZtQghRMOGDWNiYsyuAgAAQAiCb0j079+/f//+ZlchhBCXLl3y+XxxcXFRUVFm1wIAABBZOKsDAAAApEDwBQAAgBSY6gAAEE8++WRWVtZHH320a9cus2uJONdee+2ECRMi5LcTACqC4AsAEG3atBFCHDhw4PLly2bXIoQQqqrqum632yMhbtapU6dHjx5mVwEgBAi+AIBfjRo1atSoUWZXIYQQ58+f13W9Ro0apl+9AoCVMMcXAAAAUiD4AgAAQAoEXwAAAEiB4AsAAAApEHwBAAAgBYIvAAAApEDwBQAAgBQIvgAAAJACwRcAAABSIPgCAABACgRfAAAASIHgCwAAACkQfAEAACAFgi8AAACkQPAFAACAFAi+AAAAkALBFwAAAFIg+AIAAEAKBF8AAABIgeALAAAAKTjMemJVVcePHz9+/PiUlJTg9szMzGXLlu3cuTMpKem+++7r0qVLMSspU2cAAADIzJwjvqqqzpw58+eff9Z1Pbj94sWLo0aN6tmz54oVK6ZOnbps2bJPP/30aispU2cAAABIzoTge+zYsdGjR+/fv//KRQsXLuzdu3e7du0URUlMTJw4ceKSJUsKCgqKXE+ZOgMAAEBy4Q6++fn5Tz31VI8ePWbPnl1okaZp27dvv+222wIt9evX1zTt4MGDV66nTJ0BAACAcAdft9u9fPnye++9NyoqqtCinJwcIURSUlKgxel0xsTEZGZmXrmeMnUGAAAAwv3jNpvNFhMTU+SivLw8IUShQBwfH3/p0qWKdJ4zZ07w/Ifs7GyHw5Gbm1uu8iOdqqpCCK/Xa9xAGATmqVv1TeX1ejVNK76Pqqqqqlr1FfD7/UIIn89n1Q2MZPn5+YqimF1F6OXn5xf6icuVNE3Tdd2q7zpjWFn4cyMyGR/mXq/XeP0txgh7JY4s087qUEpl+sgrsvPHH39spGRD69at69Spk5+fH4LiIpXP5/P5fGZXIR2rvqkKCgpKDL66rmuaZtVXwOD3+y25q4hwHo/H7BIqhdfrLXH3rOu6ruvWHlaqqlp7AyOTVX8NVcrkE0HBNzo6Wgjh9XqDG7OzsxMSEirS+Z577gl+LS5dumS3242HW49xcM7pdDocEfSXtTZd1419s1XfVC6Xy2YrYU6Uoig2m82qr4DP5/P7/Q6Hw+l0ml2LRDwej67rUVFRljzi63a7S9wuRVEURbHqsPL7/T6fz263u1wus2uRiBESXC6X3W43u5bQK+VGRVA8iouLE0KcO3eubt26Rovf78/NzU1OTq5I56eeeir47tKlS9PT02NjY0NefyTw+/2aprnd7iunUKOSaJpmBF+rvqncbneJwddut9vtdqu+Ajk5OX6/3+l0WnUDI1Pg+6Ql99DR0dElBl+bzaYoilXfdfn5+UbwteoGRiafz2eEBLfbbXYtoWccmyh5ZIWlmFKx2+0dO3b89ttvAy3Hjh0TQjRq1KiCnQEAAIAICr5CiOHDh69du3b37t26rl+8eHHmzJkjR4682sHLMnUGAACA5CIr+NauXXvevHmrV68eMmTI5MmThwwZ8pvf/CawdNeuXYMGDTp//nxpOgMAAADBTJvjGx0dvWbNmivbU1JSXnrppSIf0qZNm9WrV5eyMwAAABAsso74AgAAAJWE4AsAAAApEHwBAAAgBYIvAAAApEDwBQAAgBQIvgAAAJACwRcAAABSIPgCAABACgRfAAAASIHgCwAAACkQfAEAACAFgi8AAACkQPAFAACAFAi+AAAAkALBFwAAAFIg+AIAAEAKBF8AAABIgeALAAAAKRB8AQAAIAWCLwAAAKRA8AUAAIAUCL4AAACQAsEXAAAAUiD4AgAAQAoEXwAAAEiB4AsAAAApEHwBAAAgBYIvAAAApEDwBQAAgBQIvgAAAJACwRcAAABSIPgCAABACgRfAAAASIHgCwAAACkQfAEAACAFgi8AAACkQPAFAACAFAi+AAAAkALBFwAAAFIg+AIAAEAKBF8AAABIgeALAAAAKRB8AQAAIAWCLwAAAKRA8AUAAIAUCL4AAACQAsEXAAAAUiD4AgAAQAoEXwAAAEiB4AsAAAApEHwBAAAgBYIvAAAApEDwBQAAgBQIvgAAAJACwRcAAABSIPgCAABACgRfAAAASIHgCwAAACkQfAEAACAFgi8AAACkQPAFAACAFAi+AAAAkALBFwAAAFIg+AIAAEAKBF8AAABIgeALAAAAKRB8AQAAIAWH2QUAQORavnz5999/r6qq3W6/7bbbBg8ebHZFAIDyI/gCwFWlpaVt2rTJuB0fH0/wBYAqjakOAAAAkAJHfAFElh07dvzrX/8yu4pf7d27N3D722+/ff75500sJtjvfve7m2++2ewqAKCKIfgCiCzp6ekbNmwwu4oiZGRkZGRkmF3Fr7p3707wBYCyYqoDAAAApEDwBQAAgBQIvgAAAJACwRcAAABSIPgCAABACgRfAAAASIHgCwAAAClwHl8AkcXtdl9zzTV+vz87O9vsWiJRfHy8w+FwuVxmFwIAVQ/BF0Bk6devX79+/Q4ePLhw4UKzaxFCCL/fr2ma3W632+1m1yKEEGPHjq1Xr57ZVQBAlUTwBRCJmjZtOmvWLLOrEEKInJwcj8cTHR0dGxtrdi0AgAphji8AAACkQPAFAACAFAi+AAAAkIJ0c3xVVfX5fDk5OWYXUilUVRVCeL1ev99vdi2y0HXduGHVN5XH49E0rfg+qqqqqmrVV8Dn8wkhCgoKAn9rhE1eXp6iKGZXEXp5eXklvp00TdN13arDythJ+f1+q25gZDI+zD0ej/GxZjEFBQUiaKd8NdIFX0VRFEWJkF9nh5yxh7DwBkaan3/+eenSpXv37rXb7a1btx4xYkSdOnXMLirEbLaS/y9k7WFl7KFtNptVNzCS2e12Swbf0ryXjA236rvOSGAW/tyIZFb9NDM2qsRPDOmCr81mczgc0dHRZhdSKQoKClRVdblcUVFRZtdifRs2bJgyZYr4nwPtX3zxxX/+85+33nqrQ4cOZpcWSi6Xq8Tsa7PZbDabVYeVqqp+v9/CnxuRKS8vTwjhdrstuYd2u90l7p6N75MWftcVFBTY7XYLb2AE8nq9mqa5XC632212LaFXys8K5vgC5XHmzJlp06YZ/+I3WoxZNJMnT+Y/dwAARCaCL1AeGzZs8Hq9hRo1Tbtw4cLWrVtNKQkAABSP4AuUx5EjR4qcAGC32w8fPhz+egAAQIkIvkB5XHvttUW2a5p2tUUAAMBcBF+gPG699dbA7N5C2rVrF+ZiAABAaRB8gfJo27Zt/fr1r2y/5ZZbrr/++vDXAwAASkTwBcrjhx9+OHbs2JXtaWlp6enp4a8HAACUiOALlMfWrVuL/HGbqqo7duwIfz0AAKBEBF+gPE6ePFnk+eftdntmZmb46wEAACUi+ALl0bBhwyIvCK6qKnN8AQCITARfoDx69ux55VQHRVHi4uI6duxoSkkAAKB4BF+gPK677rqJEycqihK4OLjdbrfZbFOnTq1Ro4a5tQEAgCI5zC4AqKoGDx58/fXXL1iwYN++fXa7vVWrVmPGjGnSpInZdQEAgKIRfIHya9269YIFC86fPy+ufi03AAAQIQi+QEUVeXoHAAAQaZjjCwAAACkQfAEAACAFgi8AAACkQPAFAACAFAi+AAAAkALBFwAAAFIg+AIAAEAKBF8AAABIgeALAAAAKRB8AQAAIAUuWYyq58SJE7qum13FrzRNu3TpkhAiLy/P7Fr+l8PhSE5ONrsKAAAiC8EXVc/DDz9sZE1cTaNGjVasWGF2FQAARBamOgAAAEAKBF8AAADr03Xd7/ebXYXJmOoAAABgZadPn549e/Y333yTk5NTv379YcOGDRo0SFEUs+syAcEXAADAsg4dOjRixIiCggJVVYUQx44de/XVV7///vtp06ZJmH2Z6gAAAGBNmqZNmTLF6/UaqVcIYZwW6bPPPtu0aZOppZmD4AsAAGBNv/zyy+HDhzVNK9Rus9k+++wzU0oyF8EXAADAmn755Zci2zVNO3z4cHhriQgEXwAAAGuqW7duke02m61evXphLiYSEHwBAACsqVGjRklJSTZb4bynaVr37t3NqMhkBF8AAABrcjgc06ZN03XdbrcHGhVFad269cCBA00szCwEXwAAAMtq27bt0qVLGzdubNx1u90PPvjg/PnzrzwMLIMQnMdX13WPxxMdHV3xVQEAACC0WrRo8eGHH2ZmZp4/f75x48YyZ7YyB98zZ85s3bp10KBBxt21a9cOHTo0Nzf3hhtuWLly5c033xzqCoHCpk+fHiEXXczIyJgxY0bg7iuvvFKtWjUT6wmIiYkxuwQAQGSJiYlxuVxyHugNKFvwPX/+fEpKSp06dYzge/z48QEDBgghWrRo8dNPP7Vr1+7YsWN16tSplEqB/9GhQwezS/jVwYMHg++2b9/+mmuuMasYAABQvLKl/rfeesvv97/zzjvG3cWLFwsh3n777X379p07d65GjRqvv/566GsEAAAAKqxswXfdunVPPvlkz549jbuLFi0SQtx///1CiISEhLFjx65ZsybkJQIAAAAVV7bge/Dgwfbt2xu3MzIyzpw507Vr18BswkaNGl3tAiEAAACAucoWfKtXr3727Fnj9pdffimE+P3vfx9YunfvXib4AgAAIDKVLfj269dv8eLFPp/P7/e/9tprQoi+ffsai86dO/eXv/zlrrvuCn2NAAAAQIWVLfhOmDDhwIEDiYmJtWvXPnDgwO23356SkiKEmDRpUu3atbOzsydMmFA5dQIAAAAVUrbg27Rp09TU1AYNGmRlZT388MOffvqp0f7dd9/VrFlz69atTZo0qYQiAQAAgIoq8wUsOnTokJaWVqhxxYoVCQkJkp8SGRKqW7furFmzLl++LISIj4+Pi4szuyIAAHBVIbhksRCCk/ZDTrGxsf/v//2/CxcuCCESExPNLgcAABSHY7QAAACQQslHfGfMmKFpWunXOGnSpArUAwAAAFSKkoPvlClTfD5f6ddI8AUAAEAEKnmqw/79+w8GmTBhQmxs7Lx5844cOXLp0qWcnJyMjIwVK1bUrl27Xr16mZmZYSgaAAAAKKuSj/g2btw4cPv777+fO3fuzz//HHyFttjY2Pvuu69Pnz4NGjT44IMPOJUvAAAAIlDZftw2fvz4CRMmFHld4vj4+KlTpy5cuDBEhQEAAAChVLbgu2PHjvr1619t6TXXXHPixIkKlwQAAACEXtmCb4cOHd5//31d169cpOv6ggULevToEaLCAAAAgFAqW/AdM2bMf//732effTY/Pz+4PScn57HHHtu+ffvYsWNDWh4AAAAQGmW7ctvAgQPvv//+WbNmzQjuS8kAACAASURBVJo1q23bti1atLDb7fv379+xY4cQYuLEiX369KmcOgEAAIAKKVvwVRTl/fffv/vuu6dMmbJz586dO3ca7a1bt37jjTd69epVCRUCAAAAIVC24CuEUBTlnnvuueeee7xe77lz54QQiYmJbre7EmoDAAAAQqbMwTfA7XbXrVs3hKUAAAAAlafMwbegoGDdunU7d+68fPlykad3+Mtf/hKKwgAAAIBQKlvwzcnJad68eXp6ejF9CL4AAACIQGULvtOnT09PTx8/fvzw4cMTEhJstrKdDQ0AAAAwS9mC74cffjho0KCZM2dWUjUAAABAJSnbIdsLFy7cfffdlVQKAAAAUHnKFny7dOmyZ8+eSioFAAAAqDxlC74TJ06cO3fu2bNnK6kaAAAAoJKUbY5vq1athgwZ0qBBg+eff75Dhw7Vq1dXFKVQn/bt24euPAAAACA0yhZ8a9Wq5fP5hBCTJ0++Wp8iT+4LAAAAmKtswXf+/PmaplVSKQAAAEDlKVvwffTRRyupDgAAAKBSlfmSxQZVVY8fP37y5Em73V63bt06derY7fbQVgYAAICKy87OXrhw4bZt27Kyspo2bTps2LDu3bubXZQ5yhx8NU1buHDhhAkT8vLyAo1Op/PFF1987rnnuJYbAABA5Dhx4sSIESOys7NVVRVC7N69e9euXUOHDh0/frzZpZmgzMH3/vvvX758ebVq1UaOHNmsWTNd1/fv379q1aoXXnhh9+7dK1asuPI8DwAAAAg/XdenTp166dKlwG+0jBvLly/v2LFjp06dTK3OBGULvhs3bly+fPm4ceNmzJjhcPzvY99+++2pU6e++uqrI0aM6NOnT6iLBAAAQJmdOHGiyEuP2Wy2tWvXShh8yzYzYc6cOe3atZs5c2Zw6hVCOByOl19+uXPnzosXLw5peQAAACinI0eOFNmuadpPP/0U5mIiQdmC75YtWx599NEiJzMoijJy5MjPP/88RIUBAACgQpKSkopsVxSldu3aYS4mEpQt+DqdzoKCgqstLSgo4NwOAAAAEaJx48YJCQlXnntA1/XOnTubUpK5yhZ8e/fuPXfuXONXgYVomjZnzpxevXqFqDAAAABUiMvl+tOf/qRpWnD2tdlsTZs2/e1vf2tiYWYpW/AdN27coUOHhg0blp2dHdx++fLlhx9+eN++fc8880xIywMAAED5de3ade7cuYmJicZdRVH69ev39ttvF/q9liTKts3t27cfP378m2++uXLlyltvvfWmm25SFOWHH37Yvn27EOKxxx7r0qVL5dQJAACA8ujYseO6desOHDhw9uzZm266qUaNGmZXZJoyh/2ZM2f26tXr2WefTU1NTU1NNRqbNGny+uuv33333aEqS1XV8ePHjx8/PiUlpZhumZmZy5Yt27lzZ1JS0n333UfsBgAAuJKiKHXq1Ln22mtjYmLMrsVM5TnK3adPnz59+uTl5Z0/f17X9WuuuSYuLi6ENamqOnPmzJ9//lnX9WK6Xbx4cdSoUZMnT544ceL58+eff/75S5cu/eY3vwlhJQAAALCM8lxh+OzZs9u2bYuJiUlJSalXr15cXNzixYu//fbbkBR07Nix0aNH79+/v8SeCxcu7N27d7t27RRFSUxMnDhx4pIlS4o56QQAAABkVubgO3/+/Fq1ao0cOTK48bXXXuvQocO4ceMqWE1+fv5TTz3Vo0eP2bNnF99T07Tt27ffdtttgZb69etrmnbw4MEK1gAAAABLKlvw/eabb5588skWLVoUCqabN29+8MEH33rrrU2bNlWkGrfbvXz58nvvvTcqKqr4njk5OeL/npbZ6XTGxMRkZmZWpAAAAABYVdnm+L722muNGzdOS0srdAqMlJSUv/3tb4cOHXrjjTd69OhR7mpsNlsp51zn5eUJIQrl4/j4+EuXLhXq+fzzz3u93sBdh8MRFRVV6HRslmGcYjk/P58pH+Fn1TdVfn5+kafuDqaqqqqqVn0F/H6/EKKgoKDE1wEhl5OTU+S1Qqu63Nzc4n/EIoTQNE3XdasOK2M0+f1+q25gZAqEhOBcZBlG8ilxZJUt+G7ZsuWVV14p8sRviqI8+uijY8aMKdMKQ6vIz8evv/7aSMmG1q1b16lTx9q50EghZlchHau+qfx+f4mfI7qua5pm1VfAwLAyhc/nM7uESuHz+UozrHRdt/awsvznRmQyvsxbTyk/LsoWfB0OR/HrDdsli6Ojo4UQhb6yZGdnJyQkFOo5evTo4Jp/+eUXv98fGxsbhiLDz+PxqKrqcrmcTqfZtchC13Xjm5VV31Rut/vKa10WYrPZ7Ha7VV8Br9fr9/udTqfL5TK7Fonk5eXpuh4TE2PJI77R0dElbpfNZlMUxarDyufzFRQU2O32Emc2IoTy8/M1TXO73Za8dIXxJarEkVW2LR84cOCcOXOefPLJK18yXdfnz58ftksWGydQO3fuXN26dY0Wv9+fm5ubnJxcqOd9990XfHfp0qXp6elGbrYe47+xLpeLj5Kw0TTNCL5WfVO5XK7SBF+bzWbVV0BVVb/f73A4rLqBkckYVm63O2zHU8LJ7XaXuHtWFEVRFAu/64zga+ENjEBer1fTNJfL5Xa7za4l9Er5WVG2H7f94Q9/OHr06ODBgzMyMoLbz5w589BDD+3cuXPChAllWmG52e32jh07Bp9D7dixY0KIRo0ahacAAAAAVC1lC75t27adPn362rVrr7vuuho1anTq1KlTp06JiYlJSUnLli2bOHFix44dK6nQKw0fPnzt2rW7d+/Wdf3ixYszZ84cOXIkRzoBAABQpDKfx/e5555LTU0dPHiwx+PZvn379u3bz58/36dPn61bt86YMaMySgzYtWvXoEGDzp8/b9ytXbv2vHnzVq9ePWTIkMmTJw8ZMoTLtgEAAOBqyjO7uUOHDh9//LGu6/n5+ZX044Po6Og1a9YUamzTps3q1auDW1JSUl566aXQPjUAAAAsqTyXLDZ4PJ5z586lp6cbqdeqZ8cAAACANZQn+G7fvr1z584xMTH169dv1qyZ0ditW7dt27aFtDYAAAAgZMocfP/85z936tRp27ZtLVq0uPnmmwPtJ0+e7Ny5c3p6ekjLAwAAAEKjbMF3x44df/rTn4YNG3bx4sV9+/YFz6/dsWNHTEzM22+/HeoKAQAAgBAoW/B97bXXWrduvWzZsisvkJaYmDhmzJgrf5EGAAAARIKyBd+NGzc+8cQTV7uM0y233PLjjz+GoioAAAAgxMoWfKOioi5dunS1padPn46Pj69wSQAAAEDolS343nXXXTNmzMjNzb1ykaZps2fPvuOOO0JUGAAAABBKZQu+EyZMOH/+/C233JKamqqqaqA9Nzf38ccfP3r06DPPPBPqCgEAAIAQKNuV21q0aDF37twxY8bcdtttNpstLi5OCHHbbbelpqYKISZOnNi+fftKKRMAAAComDKfx3f06NFpaWkDBgzQNC07O1sIkZqa2rlz582bN8+YMaMSKgQAAABCoGxHfA0333zzJ598YgRfTdPi4+MdjvKsBwAAAAib8gdWm8125dl8AQAAgMhUoSO1Pp9vx44dmZmZrVq1atKkiaIooSoLAAAACK3SBt9du3bNmDHjxx9/3LNnj9Fy9OjRtm3bXrx40bjbrl27jRs3ch5fAAAARKZSBd8PPvjggQceEEJERUUZLV6vt1OnThcvXuzateujjz761VdfLV68uHv37t9++63dbq/EegEAAIByKfmsDunp6Q888EBiYuLmzZtzcnKMxg8//PDUqVOdOnXavHnzAw88sGjRokWLFn3//fdr166t5IIBAACA8ig5+L7zzjtCiK+++qpbt26Bo7nz588XQsyaNSswr3fEiBFRUVErVqyotFIBAACA8is5+K5fv/66665r1qxZoCUnJ2fnzp1CiDZt2gQa7XZ7165dv/zyy8qoEgAAAKigkoPvnj17Cl2P7YcffhBCdO/e3el0BrcnJSUFfusGAAAARJSSg6/L5crPzw9u2bRpkxBi8ODBhXr+8ssvtWrVCmFxAAAAQKiUHHw7deq0detWXdeNu7quL1iwQAhxxx13BHfz+/3btm3r0KFDZVQJAAAAVFDJwXfw4MGXL19ev369cXfnzp3p6emJiYlNmzYN7rZq1SpVVXv37l0pZQIAAAAVU3LwHTZsWJ06dfr27Tty5MhJkyZ17txZCLFw4cLA+RxUVf38888feughl8t1//33V269AAAAQLmUfAGLqKio1NTUbt26LVmyxGiZMmXKkCFDAh2Sk5PPnDkjhNi0aVP16tUrqVAAAACgIkp15baUlJQjR44cOnTozJkzzZs3r1mzZvDSBg0a3HnnnZMmTWrevHnlFAkAAABUVKmCrxBCUZSmTZsWmtdrSE1NDWlJAAAAQOiVPMcXAAAAsACCLwAAAKRA8AUAAIAUCL4AAACQAsEXAAAAUiD4AgAAQAoEXwAAAEiB4AsAAAApEHwBAAAgBYIvAAAApEDwBQAAgBQIvgAAAJACwRcAAABSIPgCAABACgRfAAAASIHgCwAAACkQfAEAACAFgi8AAACkQPAFAACAFAi+AAAAkALBFwAAAFIg+AIAAEAKBF8AAABIgeALAAAAKRB8AQAAIAWCLwAAAKRA8AUAAIAUCL4AAACQAsEXAAAAUiD4AgAAQAoEXwAAAEiB4AsAAAApEHwBAAAgBYIvAAAApEDwBQAAgBQIvgAAAJACwRcAAABSIPgCAABACgRfAAAASIHgCwAAACkQfAEAACAFgi8AAACkQPAFAACAFAi+AAAAkALBFwAAAFIg+AIAAEAKBF8AAABIgeALAAAAKRB8AQAAIAWCLwAAAKRA8AUAAIAUCL4AAACQAsEXAAAAUiD4AgAAQAoEXwAAAEiB4AsAAAApEHwBAAAgBYIvAAAApOAwu4Bw8/v9BQUF2dnZZhdSKVRVFULk5+cXFBSYXYt0rPqmys/PN95XxVBVVVVVq74Cfr9fCFFQUFDi64CQy8nJURTF7CpCLzc3V9f14vtomqbrulWHlTGa/H6/VTcwMgVCgtfrNbuW0DOST4kjS7rga7PZ7Ha72+02u5BKYbynnU6n0+k0uxZZ6LpuDDarvqkcDofNVsK/hmw2m6IoVn0FdF3XNM3CnxuRyefzCSFcLleJb7+qqDQf0Ubit+q7zvgmabPZrLqBkck4SOFwOCwZEowhU+JXZYKvpXg8HuM9bdUNjECaphk3rPqaO53OEj9HFEWx8A7M5/P5fD4Lf25EppycHCGEy+Wy2+1m1xJ6LperNMPKwt8nNU3zer0W/tyITPn5+UIIp9NpyZe9lP8dsuA3aQAAAOBKBF8AAABIgeALAAAAKRB8AQAAIAWCLwAAAKRA8AUAAIAUCL4AAACQAsEXAAAAUiD4AgAAQAoEXwAAAEiB4AsAAAApEHwBAAAgBYIvAAAApEDwBQAAgBQIvgAAAJACwRcAAABSIPgCAABACgRfAAAASIHgCwAAACkQfAEAACAFgi8AAACkQPAFAACAFAi+AAAAkALBFwAAAFIg+AIAAEAKBF8AAABIgeALAAAAKRB8AQAAIAWCLwAAAKRA8AUAAIAUCL4AAACQAsEXAAAAUiD4AgAAQAoEXwAAAEiB4AsAAAApEHwBAAAgBYIvAAAApOAwuwAAAAALmj59+rp168yu4n/pui6EUBTF7EL+j5UrVyYnJ4ft6Qi+AAAAoefz+Twej9lVRDojjocNUx0AAAAgBYIvAAAApMBUBwAAgMrVqlWrBx980OwqIsK5c+emT59u1rMTfAEAACpXUlJSt27dzK4iIqSnp5v47ARfAACAyrV3797JkyebXUVEyMvLM/HZCb4AAACV6+TJkydPnjS7CvDjNgAAAMiB4AsAAAApEHwBAAAgBYIvAAAApEDwBQAAgBQIvgAAAJACwRcAAABSIPgCAABACgRfAAAASIErtwEAAIRe48aNO3XqZHYVv9q5c6fX6zVuN2rUKCkpydx6AtxudzifjuALAAAQesOGDRs2bJjZVfwqKyvr4sWLqqrGxcXVqFEjzHEzchB8AQAALC4hIUEI4ff7q1WrJm3qFczxBQAAgCQIvgAAAJACwRcAAABSIPgCAABACgRfAAAASIHgCwAAACkQfAEAACAFgi8AAACkQPAFAACAFAi+AAAAkALBFwAAAFIg+AIAAEAKBF8AAABIgeALAAAAKRB8AQAAIAWCLwAAAKRA8AUAAIAUCL4AAACQAsEXAAAAUiD4AgAAQAoEXwAAAEiB4AsAAAApEHwBAAAgBYIvAAAApEDwBQAAgBQIvgAAAJACwRcAAABSiLjgm5mZOWPGjPvuu2/MmDFfffVVaR6iqurYsWNPnDhR2bUBAACg6oqs4Hvx4sVRo0b17NlzxYoVU6dOXbZs2aefflr8Q1RVnTlz5s8//6zreniKBAAAQFUUWcF34cKFvXv3bteunaIoiYmJEydOXLJkSUFBwdX6Hzt2bPTo0fv37w9nkQAAAKiKIij4apq2ffv22267LdBSv359TdMOHjxYZP/8/PynnnqqR48es2fPDleNAAAAqKocZhfwv3JycoQQSUlJgRan0xkTE5OZmdmyZcsr+7vd7uXLl8fExOTn54evSgAAAFRNERR88/LyhBBRUVHBjfHx8ZcuXSqyv81mi4mJKXG1jz76aHAyTkxMTEhIyMrKqlixEUpVVSFEXl6ex+MxuxbpWPVNlZuba7yviqGqqt/vt+oroGmaEMLr9fp8PrNrkYjxs43Lly+bXUiluHz5svG+KoaqqrquW3tY+Xw+q25gZDI+zHNzcy15xNCYGVviL74iKPgWSVGUCq7h4MGDRqQ2REdHx8fH+/3+Cq42kmmaVuJHKkLOqm8qTdNK/BzRdV3Xdau+AgaGlSms+qYq8cukwfLDyvIbGJms+mlWymEVQcE3OjpaCOH1eoMbs7OzExISKrLaadOmBb8W33//fV5eXrVq1SqyzoiVl5enqmpUVJTT6TS7Flnoum7M0rHqmyoqKsputxffx2632+12q74CHo/H5/O5XC632212LRLJycnRdT02NtZmi6DfooRKTExMiYd1bDaboihWHVYFBQVer9fhcBi7foSHtUOCccS3xJEVQcE3Li5OCHHu3Lm6desaLX6/Pzc3Nzk5uSKr7d69e/Dd9PT09PR0q+7APB6PqqoOh8OqGxiBAt+brfqaO53OEj9HFEWx2WxWfQV8Pp/P57Pb7VbdwMhkfJ90uVwlfu+qilwuV2mGlaIoVn3XaZrm9Xot/LkRmYwZDk6n05IveynnCETQN2m73d6xY8dvv/020HLs2DEhRKNGjcwrCgAAABYRQUd8hRDDhw9//PHH27Vr16pVq6ysrJkzZ44cObLQz90AAEDV9fPPPx88eNDj8Tidzjp16rRp08bsiiCRyAq+tWvXnjdv3pIlS6ZNm1anTp0hQ4b07NkzsHTXrl1//vOflyxZUrNmTROLBAAA5bZhw4YlS5YYt1u3bh24DYRBZAVfIURKSspLL71U5KI2bdqsXr36yvbo6Og1a9ZUcl0AAACo2iJoji8AAABQeQi+AAAAkALBFwAAAFIg+AIAAEAKEffjNgAAEFqqqno8HrOr+JXP5wvc1jQtNzfXxGKCOZ1Ol8tldhWoXARfAAAsLi0t7bHHHjO7iiLs2bOnW7duZlfxq8ceeywyXyWEEFMdAAAAIAWCLwAAAKRA8AUAAIAUmOMLAIBcfvvb39psHPkSQogvvvji3LlzZleB8CH4AgAglxUrVphdAmAOvvABAABACgRfAAAASIHgCwAAACkQfAEAACAFgi8AAACkQPAFAACAFAi+AAAAkALn8QUAQC6PPfaY3W43u4qIsHr16lOnTpldBcKH4AsAgFwWLVpkdgmAOZjqAAAAACkQfAEAACAFpjoAAGBxUVFRDRs2NLuKX128eDErK8u4HRUVVadOHXPrCUhISDC7BFQ6gi8AABbXokWLVatWmV3FrxYuXLhkyRLjdrNmzQK3gTAg+AIAgPBp0aLFgAED/H6/zWa7/vrrzS4HciH4AgCA8OnatWv79u1zc3NdLld8fLzZ5UAu/LgNAAAAUiD4AgAAQAoEXwAAAEiB4AsAAAApEHwBAAAgBYIvAAAApEDwBQAAgBQIvgAAAJACwRcAAABSIPgCAABACgRfAAAASIHgCwAAACkQfAEAACAFgi8AAACkQPAFAACAFAi+AAAAkALBFwAAAFIg+AIAAEAKBF8AAABIgeALAAAAKTjMLsAE//73v7/++muzq6gUmqYJIRRFURTF7FokYrzsNps1v0Z6PJ7mzZuX2G3v3r19+/YNQz3hp+u6rusMqzCz9rDy+/1Op7PEbllZWQwrhJC1Q4Ku66XpppSyn2VcvHjxwoULZldRWebPn3/kyJH77rvv1ltvNbsWWVy8ePHll18WQrz++ut2u93scipFdHR0cnJyMR1yc3NPnToVtnrCbOXKlampqd27d+/fv7/ZtUhk0qRJBQUF48ePL/69V3XZ7fYGDRoU06GgoODEiRPhKifctmzZsmbNmhYtWjzyyCNm1yKRWbNmZWRkPPjgg61atTK7lsrSsGHD4r8wS3fEt0aNGjVq1DC7ispy+fLl9PT0uLi4Ro0amV2LLE6dOpWeni6EuP766x0O6QaUITY21sJvOV3X09PTbTabhbcxAmVkZHi93uTkZGlfdpfLZeFtT01NTU9Pv/766y28jREoKysrPT29evXqMr/s1vwvEgAAAFAIwRcAAABSIPgCAABACpJOSbSq9u3bJyYm1q1b1+xCJBIVFXXHHXcIISz5I1kIIVq0aJGTk9OkSROzC5FLr169fD5fXFyc2YWgUtSvX/+OO+4ozRljEEIdO3asX79+UlKS2YWYSbqzOgAAAEBOTHUAAACAFAi+AAAAkALBFwAAAFIg+JbT8uXLH3jggSvblyxZMmrUqFKuZNy4cR9//LFxW9O0d955Z9iwYUOHDr18+XLwotI7e/bsoEGDsrKyyvpAy7h06dKqVavGjh177733jho16sMPPzx//rzZRQkhxKhRoz777DOzq6gCKj6yQj6shPQji2FV1TGsIhDDyiyc1aGcNE1TVbX07UWaNWtW4PaePXs++eST999/v3r16oqiBC8qPV3XS//s1rN3794pU6aMHDny1VdfjY6Ozs/P37Fjx8iRI//4xz+afg1nVVWNi6SjeBUfWSEfVkLukcWwsgCGVaRhWJmI4BspsrOz3W53QkKC2YVUVadOnXrhhRdeeeWVm266yWiJiYnp3r173bp1x48fP2fOnIYNG5pbIcKPYVVBDCtciWFVQQwrc9lffPFFs2uokvbu3Xvo0KEhQ4YUat+5c+fJkyf79+9v3B03blxsbOz+/ftnzpy5bNmyY8eO3XjjjVFRUYGlOTk5LVq0eOGFF/71r3+pqrpy5UqPx9O6devAIqPnzz//PHfu3L/+9a8bN27Udb1JkyaBs8Zu3bp19uzZ77777tGjR+Pi4r7++utBgwYFnqKQbdu2vfXWW0bnevXqPfzww3feeWd0dLQQYtSoUTabLXCy0rNnz95///2BpcXU8NRTT/n9/n/84x/z5s3zer0XLlyYOHHi4MGDbbZfJ9Kkp6c/9NBDAwYMcDqdoXjtizZnzpyEhIRhw4YVaq9Zs2ZGRsbGjRvvvPNOo6X4LS3mpa7Iln766ac33HBD06ZNjbv79u2bO3fuwoULN27c6Pf7mzRpElhJMYvGjRvndDq/++67mTNnrl69Oicnp3nz5na7PYQvo+lKM7LCMKxEWUZWZQwr8X/fbwcPHpw3b17VHVbFbGl4hlUxSxlWpdlhWXJYmbXDkmFYiQgeWczxrVyaps2ZM8ftdv/1r39dtmyZ3W4fN25c4NzJmqYZt1955ZVx48a53e7Vq1c//PDDwYuEELt37x47dmzfvn0/+uijN99887vvvps2bZqx9PPPP1+0aNH48eM/+uijQYMGzZ49u5hi1q9fP2/evKeffvqjjz4aMGDAxIkTg//NVOi/G4X+CVVMDaqqLl68+IEHHvj73//er1+/Dh06eDyeH374IfDYjRs33nLLLTExMRV9Na9OVdXU1NSePXsWubRLly6HDx/2eDyBzlfb0mI2U4RuS7/88stp06b9/ve/X7FixYwZM/bs2fPHP/7ReJZiFgkhNE2bO3eu1+tdtGjRggUL0tPTn3vuOWv/T6pIlT2sRFlGViUNK/F/32/Dhw+vusNKhOUDpPixU8xShlVAMSPLksPKlB2WJMNKRPDIIvhWuqZNm95+++12u93lcg0dOvTs2bNFzuW32WxFXvpL1/UZM2YMHTq0ffv2NpstNjZ24sSJu3btOnjwoM/nW7hw4dNPP123bl1FUZo0afLkk09erQyfzzd//vwxY8bUr19fUZRmzZo99thjpdyEYmowOnTs2LFevXoOh6NGjRoxMTEdO3b85JNPjEWapq1bt+7uu+8u5XOVz6VLl4QQV7saTZ06dYQQp0+fLn4lJW6mCMWW+ny+efPmjRs3rlmzZoqiVK9e/bnnnjt06NA333xTzKLAwxs2bHj//fc7nc6YmJjx48cfPnx49+7dpXiFrKbyhpUQovQjq1KHlQh6vyUnJ1fRYSXC8gFS/NgpcWQxrAJKM7IsM6xM2WHJM6xEpI4sgm+lC0ziEUIY/54oKCgo/cPPnj2bm5vbuXPnQEtsbGxycvJ333136tQpTdOCr6R64403Xm09p06d0nW9WbNmgZbWrVtXvAbjbuPGjYP7Dxgw4LvvvsvNzRVCHD582OfztWzZspTPVRmMz2ifz1d8txI3U4RiS9PT0zVNC/xbUAjhcrluvvnm1NTUYhYFWoKPE7hcrubNm0fC50j4Vd6wEkKUfmRV6rAS//f9VkWHlQjLB0jxY6fEkcWwCqjID8yceQAABeNJREFUyKqKw0pE2Miy0rASkTqy+HFbOTmdTr/ff2W7z+dzu93BLcF3jfd0mS4TnZ2dLYR46qmngr9hq6p67tw544tj8PpjY2Ovtp6cnBwhRPBUqtL/K6eYGozbhWZoNW/ePCoqavv27b169fr888/79OlT2dN6qlevLoQ4ffp08Kd2wJkzZ4QQNWrUKH4lJW6mCMWWXr58+cr1XHvttWfPni1mUeBuzZo1g5cmJiYGl2cBpRxZlTesxP8ckinNyKrUYVVozVV0WImwfIAUP3ZKHFkMq4CKjKyqOKxE2EeWPMNKROrIIviWU506dTwej6ZpwbO8hRCZmZkNGjQI4RMZA37JkiWJiYmFFp04cUII4fV6XS6X0VLMV/P4+HghhMfjCUyKv7Jz8OSb4G+cxdRQJJvNdvfdd//zn//s2rXrxo0b58yZU5pHVYTdbm/Tps1//vOfXr16BRp1XTc+EbZs2RIdHX3NNdcEFhW5pWXdTFGuLY2LixNCeDye4J8UnD17NikpqZhFgbvGLiF4qbmH/UIuPCOr+L+1sWcqzchiWJU4rERYtrT4sVPiyGJYhURVHFYi7CNLnmElInVkMdWhnBo1aiSEOHnyZHCj3+/ft29fmzZtQvhESUlJDodj165dwc8ybNiwzZs3Jycn2+32n376KbAoeBpTIbVr13a73fv3779a52rVqhnfyA0ZGRmlqeFqT9erV6/09PQPPvggPj6+fv36JW5mxY0cOXLv3r2BDTx//vywYcM2bdp06NChzZs3Dx8+PPC1+GpbWo7NFGXf0uuuu05RlAMHDgRaCgoK9uzZ06pVq2IWBVqCp08VFBQcOHAgtO8304VnZBX/ty79yGJYlTisRFi2tPixU+LIYliFRBUdViLsI0uSYSUidWQRfMspOTn5zjvvnDZtWkZGhq7ruq5nZWXNnj27cePGXbp0CeET2e32p59+evHixT/++KOu6wUFBW+//XZcXFyXLl3sdvszzzwze/Zs49BvRkbGX/7yl2LW88wzz8yd+//buX+QdMI4juPPbxFLWkIIC4eWMAjcD9LFhgiHhEAaWvvjEHINQtBgc0pJQkS16iItgVA4NjTnEJij2SIFgUbD8xsEhx8/L5XzOnver/W5e577es8HvsPjZarVqhCiXq//c/Hi4uLNzU2j0RBCvLy8nJ2d9fIM3Zabmpryer3X19eRSOS/f4Mw3czMzOHh4f7+/t3dXavVmpycTCQSmUxG1/X5+fnO12FE90oHKFP0X6nD4dja2kqlUpVKRUr58fFxdHQ0OzuraZrBUOf2h4eHUqkkpWw2m+l02u/3GxzsHkXWJMv4XfeeLGLVufJnKzXOzrfJIlamGNFYCcuTpUishF2TxVGHwe3s7Nzf319dXT0+Pn5+fs7NzQWDwXg8bvrxoGAw6Ha7c7lcuVx2uVxLS0snJyftVQKBwPj4+PHx8fPz88LCgq7rBwcH3ebRNG1iYiKbzbYvXl9fPz097YyGw+Gvr694PN5qtTRNSyaT29vbvTxDN2tra6lUKhAImPEb9MTv919eXhaLRV3Xa7Xa2NiYz+fzeDylUimbzUaj0fZ5I4NKByhT9F/p8vLy9PT0xcXF09OT2+0OhUJ7e3vtVQyGOvdWKpXz83On07mysrK6umpNA2Qla5Jl/K57Txax+jZWwpJKjbPz7SixMsWIxkpYniwVYiVsmywJVb2+vobD4UajMaT5C4VCIpEY0uR9aTabt7e3b29vQ5rfskp3d3cLhYIFC2FgxMosxAodw46VtE2yfk2spI2TxVEHmE9K+f7+ns/nNzc3f/pZhBDC6XSGQqH2XyvMZbdK8YvZbbMRK/wOttpvxMoCHHWAyaSUGxsbQohYLGbuBy7sRp1K8ePU2WzqVAo7UGS/KVJmj/7Ifj5+CQAAAIwojjoAAABACTS+AAAAUAKNLwAAAJRA4wsAAAAl0PgCAABACTS+AAAAUAKNLwAAAJRA4wsAAAAl0PgCAABACTS+AAAAUMJfvCnzTJj6csQAAAAASUVORK5CYII=" style="width:100.0%" /></p>
-<p>Once again the indexed SQL database blows the others out of the
-water, with the unified query performing ~10x better in SQL than Arrow
-and ~100x better than RaMS. Curiously, the unified query actually takes
-<em>longer</em> when processed via RaMS/data.table than looping. Good to
-know.</p>
-</div>
-</div>
-<div id="ms-object-sizing-info" class="section level2">
-<h2>MS object sizing info</h2>
-<p>Of course, there’s no free lunch. The speedier options tend to be
-much larger in space, as shown in the below graph of the object/file
-sizes. For the MSnBase, Spectra, and RaMS objects this is space occupied
-in the computer’s memory. For the tmzML, Arrow, and SQL databases this
-is on the computer’s disk.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a>size_list <span class="ot">&lt;-</span> <span class="fu">list</span>()</span>
-<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a></span>
-<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a>size_list<span class="sc">$</span>mzXML <span class="ot">&lt;-</span> <span class="fu">sum</span>(<span class="fu">file.size</span>(ms_files))</span>
-<span id="cb19-4"><a href="#cb19-4" tabindex="-1"></a></span>
-<span id="cb19-5"><a href="#cb19-5" tabindex="-1"></a>msnexp_obj <span class="ot">&lt;-</span> <span class="fu">readMSData</span>(ms_files, <span class="at">mode=</span><span class="st">&quot;inMemory&quot;</span>, <span class="at">msLevel. =</span> <span class="dv">1</span>)</span>
-<span id="cb19-6"><a href="#cb19-6" tabindex="-1"></a>size_list<span class="sc">$</span>msnexp_obj <span class="ot">&lt;-</span> pryr<span class="sc">::</span><span class="fu">object_size</span>(msnexp_obj)</span>
-<span id="cb19-7"><a href="#cb19-7" tabindex="-1"></a><span class="fu">rm</span>(msnexp_obj)</span>
-<span id="cb19-8"><a href="#cb19-8" tabindex="-1"></a></span>
-<span id="cb19-9"><a href="#cb19-9" tabindex="-1"></a>ondisk_obj <span class="ot">&lt;-</span> <span class="fu">readMSData</span>(ms_files, <span class="at">mode=</span><span class="st">&quot;onDisk&quot;</span>, <span class="at">msLevel. =</span> <span class="dv">1</span>)</span>
-<span id="cb19-10"><a href="#cb19-10" tabindex="-1"></a>size_list<span class="sc">$</span>ondisk_obj <span class="ot">&lt;-</span> pryr<span class="sc">::</span><span class="fu">object_size</span>(ondisk_obj)</span>
-<span id="cb19-11"><a href="#cb19-11" tabindex="-1"></a><span class="fu">rm</span>(ondisk_obj)</span>
-<span id="cb19-12"><a href="#cb19-12" tabindex="-1"></a></span>
-<span id="cb19-13"><a href="#cb19-13" tabindex="-1"></a>sfs_filtered <span class="ot">&lt;-</span> <span class="fu">Spectra</span>(ms_files, <span class="at">source=</span><span class="fu">MsBackendMzR</span>()) <span class="sc">%&gt;%</span></span>
-<span id="cb19-14"><a href="#cb19-14" tabindex="-1"></a>  <span class="fu">filterMsLevel</span>(<span class="dv">1</span>)</span>
-<span id="cb19-15"><a href="#cb19-15" tabindex="-1"></a>size_list<span class="sc">$</span>spectra <span class="ot">&lt;-</span> pryr<span class="sc">::</span><span class="fu">object_size</span>(sfs_filtered)</span>
-<span id="cb19-16"><a href="#cb19-16" tabindex="-1"></a><span class="fu">rm</span>(sfs_filtered)</span>
-<span id="cb19-17"><a href="#cb19-17" tabindex="-1"></a></span>
-<span id="cb19-18"><a href="#cb19-18" tabindex="-1"></a>rams_obj <span class="ot">&lt;-</span> <span class="fu">grabMSdata</span>(ms_files, <span class="at">grab_what=</span><span class="st">&quot;MS1&quot;</span>)</span>
-<span id="cb19-19"><a href="#cb19-19" tabindex="-1"></a>size_list<span class="sc">$</span>rams <span class="ot">&lt;-</span> pryr<span class="sc">::</span><span class="fu">object_size</span>(rams_obj)</span>
-<span id="cb19-20"><a href="#cb19-20" tabindex="-1"></a></span>
-<span id="cb19-21"><a href="#cb19-21" tabindex="-1"></a>tmzml_names <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="fu">dirname</span>(<span class="fu">dirname</span>(ms_files)), <span class="st">&quot;/tmzMLs/&quot;</span>, <span class="fu">gsub</span>(<span class="st">&quot;mzXML&quot;</span>, <span class="st">&quot;tmzML&quot;</span>, <span class="fu">basename</span>(ms_files)))</span>
-<span id="cb19-22"><a href="#cb19-22" tabindex="-1"></a><span class="fu">dir.create</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>)</span>
-<span id="cb19-23"><a href="#cb19-23" tabindex="-1"></a><span class="fu">bpmapply</span>(tmzmlMaker, ms_files, tmzml_names, <span class="at">BPPARAM =</span> <span class="fu">SnowParam</span>(<span class="at">workers =</span> <span class="dv">5</span>, <span class="at">progressbar =</span> <span class="cn">TRUE</span>, <span class="at">tasks=</span><span class="fu">length</span>(tmzml_names)))</span>
-<span id="cb19-24"><a href="#cb19-24" tabindex="-1"></a>size_list<span class="sc">$</span>tmzml <span class="ot">&lt;-</span> <span class="fu">sum</span>(<span class="fu">file.size</span>(<span class="fu">list.files</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>)))</span>
-<span id="cb19-25"><a href="#cb19-25" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb19-26"><a href="#cb19-26" tabindex="-1"></a></span>
-<span id="cb19-27"><a href="#cb19-27" tabindex="-1"></a><span class="fu">write_dataset</span>(rams_obj<span class="sc">$</span>MS1, <span class="at">path =</span> <span class="st">&quot;vignettes/figures/ssc_vignette_renders/pqds&quot;</span>)</span>
-<span id="cb19-28"><a href="#cb19-28" tabindex="-1"></a>size_list<span class="sc">$</span>arrow <span class="ot">&lt;-</span> <span class="fu">sum</span>(<span class="fu">file.size</span>(<span class="fu">list.files</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/pqds&quot;</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>)))</span>
-<span id="cb19-29"><a href="#cb19-29" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/pqds&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb19-30"><a href="#cb19-30" tabindex="-1"></a></span>
-<span id="cb19-31"><a href="#cb19-31" tabindex="-1"></a>MSdb <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata.sqlite&quot;</span>)</span>
-<span id="cb19-32"><a href="#cb19-32" tabindex="-1"></a><span class="fu">dbWriteTable</span>(MSdb, <span class="st">&quot;MS1&quot;</span>, rams_obj<span class="sc">$</span>MS1, <span class="at">overwrite=</span><span class="cn">TRUE</span>)</span>
-<span id="cb19-33"><a href="#cb19-33" tabindex="-1"></a><span class="fu">dbDisconnect</span>(MSdb)</span>
-<span id="cb19-34"><a href="#cb19-34" tabindex="-1"></a>size_list<span class="sc">$</span>MSdb <span class="ot">&lt;-</span> <span class="fu">file.size</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata.sqlite&quot;</span>)</span>
-<span id="cb19-35"><a href="#cb19-35" tabindex="-1"></a></span>
-<span id="cb19-36"><a href="#cb19-36" tabindex="-1"></a></span>
-<span id="cb19-37"><a href="#cb19-37" tabindex="-1"></a>MSdb_idx <span class="ot">&lt;-</span> <span class="fu">dbConnect</span>(RSQLite<span class="sc">::</span><span class="fu">SQLite</span>(), <span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata.sqlite&quot;</span>)</span>
-<span id="cb19-38"><a href="#cb19-38" tabindex="-1"></a>rs <span class="ot">&lt;-</span> <span class="fu">dbSendQuery</span>(MSdb_idx, <span class="st">&quot;CREATE INDEX mz ON MS1 (mz)&quot;</span>)</span>
-<span id="cb19-39"><a href="#cb19-39" tabindex="-1"></a><span class="fu">dbClearResult</span>(rs)</span>
-<span id="cb19-40"><a href="#cb19-40" tabindex="-1"></a><span class="fu">dbDisconnect</span>(MSdb_idx)</span>
-<span id="cb19-41"><a href="#cb19-41" tabindex="-1"></a>size_list<span class="sc">$</span>MSdb_idx <span class="ot">&lt;-</span> <span class="fu">file.size</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite&quot;</span>)</span>
-<span id="cb19-42"><a href="#cb19-42" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata.sqlite&quot;</span>)</span>
-<span id="cb19-43"><a href="#cb19-43" tabindex="-1"></a></span>
-<span id="cb19-44"><a href="#cb19-44" tabindex="-1"></a><span class="fu">saveRDS</span>(size_list, <span class="st">&quot;vignettes/figures/ssc_vignette_renders/size_list.rds&quot;</span>)</span></code></pre></div>
-</details>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a>size_list <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/size_list.rds&quot;</span>)</span>
-<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a>size_list <span class="sc">%&gt;%</span></span>
-<span id="cb20-3"><a href="#cb20-3" tabindex="-1"></a>  <span class="fu">within</span>(<span class="fu">rm</span>(mzXML)) <span class="sc">%&gt;%</span></span>
-<span id="cb20-4"><a href="#cb20-4" tabindex="-1"></a>  <span class="fu">sapply</span>(as.numeric) <span class="sc">%&gt;%</span></span>
-<span id="cb20-5"><a href="#cb20-5" tabindex="-1"></a>  <span class="fu">data.frame</span>(<span class="at">bytes=</span>.) <span class="sc">%&gt;%</span></span>
-<span id="cb20-6"><a href="#cb20-6" tabindex="-1"></a>  <span class="fu">rownames_to_column</span>(<span class="st">&quot;expr&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb20-7"><a href="#cb20-7" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">str_remove</span>(expr, <span class="st">&quot;_obj&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb20-8"><a href="#cb20-8" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">str_replace</span>(expr, <span class="st">&quot;MSdb_?&quot;</span>, <span class="st">&quot;sql&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb20-9"><a href="#cb20-9" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">mem_type=</span><span class="fu">case_when</span>(</span>
-<span id="cb20-10"><a href="#cb20-10" tabindex="-1"></a>    expr<span class="sc">%in%</span><span class="fu">c</span>(<span class="st">&quot;msnexp&quot;</span>, <span class="st">&quot;ondisk&quot;</span>, <span class="st">&quot;spectra&quot;</span>, <span class="st">&quot;rams&quot;</span>)<span class="sc">~</span><span class="st">&quot;Memory&quot;</span>,</span>
-<span id="cb20-11"><a href="#cb20-11" tabindex="-1"></a>    <span class="cn">TRUE</span><span class="sc">~</span><span class="st">&quot;Disk&quot;</span></span>
-<span id="cb20-12"><a href="#cb20-12" tabindex="-1"></a>  )) <span class="sc">%&gt;%</span></span>
-<span id="cb20-13"><a href="#cb20-13" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">mem_type=</span><span class="fu">factor</span>(mem_type, <span class="at">levels=</span><span class="fu">c</span>(<span class="st">&quot;Memory&quot;</span>, <span class="st">&quot;Disk&quot;</span>))) <span class="sc">%&gt;%</span></span>
-<span id="cb20-14"><a href="#cb20-14" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">factor</span>(expr, <span class="at">levels=</span><span class="fu">c</span>(<span class="st">&quot;msnexp&quot;</span>, <span class="st">&quot;ondisk&quot;</span>, <span class="st">&quot;spectra&quot;</span>, <span class="st">&quot;rams&quot;</span>,</span>
-<span id="cb20-15"><a href="#cb20-15" tabindex="-1"></a>                                    <span class="st">&quot;tmzml&quot;</span>, <span class="st">&quot;arrow&quot;</span>, <span class="st">&quot;sql&quot;</span>, <span class="st">&quot;sqlidx&quot;</span>),</span>
-<span id="cb20-16"><a href="#cb20-16" tabindex="-1"></a>                     <span class="at">labels=</span><span class="fu">c</span>(<span class="st">&quot;MSnExp&quot;</span>, <span class="st">&quot;OnDiskMSnExp&quot;</span>, <span class="st">&quot;Spectra&quot;</span>, <span class="st">&quot;RaMS&quot;</span>,</span>
-<span id="cb20-17"><a href="#cb20-17" tabindex="-1"></a>                              <span class="st">&quot;tmzMLs&quot;</span>, <span class="st">&quot;Arrow&quot;</span>, <span class="st">&quot;SQL&quot;</span>, <span class="st">&quot;SQL (indexed)&quot;</span>))) <span class="sc">%&gt;%</span></span>
-<span id="cb20-18"><a href="#cb20-18" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span></span>
-<span id="cb20-19"><a href="#cb20-19" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> size_list<span class="sc">$</span>mzXML<span class="sc">/</span>(<span class="dv">1024</span><span class="sc">^</span><span class="dv">3</span>)) <span class="sc">+</span></span>
-<span id="cb20-20"><a href="#cb20-20" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x=</span>expr, <span class="at">y=</span>bytes<span class="sc">/</span>(<span class="dv">1024</span><span class="sc">^</span><span class="dv">3</span>))) <span class="sc">+</span></span>
-<span id="cb20-21"><a href="#cb20-21" tabindex="-1"></a>  <span class="fu">scale_y_log10</span>(<span class="at">breaks=</span><span class="fu">c</span>(<span class="fl">0.001</span>, <span class="fl">0.01</span>, <span class="fl">0.1</span>, <span class="dv">1</span>, <span class="dv">10</span>), <span class="at">labels=</span><span class="fu">c</span>(<span class="st">&quot;1MB&quot;</span>, <span class="st">&quot;10MB&quot;</span>, <span class="st">&quot;100MB&quot;</span>, <span class="st">&quot;1GB&quot;</span>, <span class="st">&quot;10GB&quot;</span>),</span>
-<span id="cb20-22"><a href="#cb20-22" tabindex="-1"></a>                <span class="at">limits=</span><span class="fu">c</span>(<span class="fl">0.001</span>, <span class="dv">10</span>)) <span class="sc">+</span></span>
-<span id="cb20-23"><a href="#cb20-23" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>mem_type, <span class="at">scales =</span> <span class="st">&quot;free_x&quot;</span>) <span class="sc">+</span></span>
-<span id="cb20-24"><a href="#cb20-24" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x=</span><span class="cn">NULL</span>, <span class="at">y=</span><span class="cn">NULL</span>)</span>
-<span id="cb20-25"><a href="#cb20-25" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/size_cons.png&quot;</span>, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="dv">4</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>, <span class="at">device =</span> <span class="st">&quot;png&quot;</span>, <span class="at">dpi =</span> <span class="dv">144</span>)</span></code></pre></div>
-</details>
-<p><img src="data:image/png;base64,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" style="width:100.0%" /></p>
-<p>Unsurprisingly, the SQL databases are massive, weighing in at 5-10x
-the size of the original mzXML files (black horizontal line in above
-plot). RaMS is similarly enormous (about 2x the mzXML size) with an
-additional penalty of existing in memory instead of on disk. The
-original MSnExp object is about the same size (slightly larger than
-mzXMLs, maybe because the m/z &amp; int data has been decompressed?) and
-also exists in memory. tmzMLs and Arrow do slightly better, actually
-reducing the size of the dataset while remaining on disk, and of course
-the pointer objects in Spectra and the OnDiskMSnExp object are
-microscopic in comparison.</p>
-</div>
-<div id="summary" class="section level2">
-<h2>Summary</h2>
-<p>I started out this project because I’d recently learned about
-<code>arrow</code> and was blown away by how fast it was while still
-remaining essentially memory-free. At the end of it, I think there’s
-more nuance than I was really expecting, though there are a few clear
-losers. The MSnExp objects should be almost never used, as they’re
-dominated pretty clearly by the OnDisk equivalent. The Spectra package
-provides even more improvement on this but extracting a chromatogram is
-more obnoxious and you’re still dealing with a complicated S4 object
-instead of an intuitive data frame. Similarly, the tmzMLs seem to be
-unilaterally worse than <code>arrow</code>, with access at-best
-equivalent and a make time much larger. Arrow also plays nicely with
-<code>dplyr</code> commands and has good performance when requesting
-multiple EICs, though longtime RaMS users may find the intuitive syntax
-of the tmzMLs (and progress bar!) friendlier as long as they don’t need
-to extract more than a single chromatogram.</p>
-<p>In general, it seems to come down more to the size of the dataset
-than anything else. If your dataset is small (&lt;1GB) or you’re only
-loading a small-ish subset of it, RaMS is the go-to. If it’s larger than
-memory but not enormous (10-100GB), Arrow and SQL seem like good
-contenders. The SQL database size ballooned scarily (but had insanely
-fast access times) while Arrow provided a file size reduction and more
-familiar syntax. For truly large datasets (100GB+) I can’t imagine using
-anything but the OnDisk or Spectra infrastructure because it’s just not
-worth trying to duplicate the dataset at all, so reading the data out of
-the files is the only real option. Of course, if you’re looking at data
-that large then you may actually come all the way back around to Arrow
-or SQL anyway so you can host the data in the cloud or on a cluster
-instead of on a personal computer.</p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>make_timings <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/make_timings.rds&quot;</span>)</span>
-<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a>query_timings <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/query_timings.rds&quot;</span>)</span>
-<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a>size_df <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/size_list.rds&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a>  <span class="fu">sapply</span>(as.numeric) <span class="sc">%&gt;%</span></span>
-<span id="cb21-5"><a href="#cb21-5" tabindex="-1"></a>  <span class="fu">data.frame</span>(<span class="at">size=</span>.) <span class="sc">%&gt;%</span></span>
-<span id="cb21-6"><a href="#cb21-6" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">size=</span>size<span class="sc">/</span><span class="dv">1024</span><span class="sc">^</span><span class="dv">3</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb21-7"><a href="#cb21-7" tabindex="-1"></a>  <span class="fu">rownames_to_column</span>(<span class="st">&quot;expr&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb21-8"><a href="#cb21-8" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">str_remove</span>(expr, <span class="st">&quot;_obj&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb21-9"><a href="#cb21-9" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">str_replace</span>(expr, <span class="st">&quot;MSdb_?&quot;</span>, <span class="st">&quot;sql&quot;</span>))</span>
-<span id="cb21-10"><a href="#cb21-10" tabindex="-1"></a></span>
-<span id="cb21-11"><a href="#cb21-11" tabindex="-1"></a><span class="fu">bind_rows</span>(make_timings, query_timings) <span class="sc">%&gt;%</span></span>
-<span id="cb21-12"><a href="#cb21-12" tabindex="-1"></a>  <span class="fu">as.data.frame</span>() <span class="sc">%&gt;%</span></span>
-<span id="cb21-13"><a href="#cb21-13" tabindex="-1"></a>  <span class="fu">group_by</span>(expr) <span class="sc">%&gt;%</span></span>
-<span id="cb21-14"><a href="#cb21-14" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">time=</span>time<span class="sc">/</span><span class="fl">1e9</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb21-15"><a href="#cb21-15" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">med_time=</span><span class="fu">median</span>(time), <span class="at">IQR_time=</span><span class="fu">IQR</span>(time)) <span class="sc">%&gt;%</span></span>
-<span id="cb21-16"><a href="#cb21-16" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">str_remove</span>(expr, <span class="st">&quot;_fun</span><span class="sc">\\</span><span class="st">(</span><span class="sc">\\</span><span class="st">)&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb21-17"><a href="#cb21-17" tabindex="-1"></a>  <span class="fu">separate</span>(expr, <span class="at">into =</span> <span class="fu">c</span>(<span class="st">&quot;expr&quot;</span>, <span class="st">&quot;time_type&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb21-18"><a href="#cb21-18" tabindex="-1"></a>  <span class="fu">left_join</span>(size_df) <span class="sc">%&gt;%</span></span>
-<span id="cb21-19"><a href="#cb21-19" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">rep_type=</span><span class="fu">case_when</span>(</span>
-<span id="cb21-20"><a href="#cb21-20" tabindex="-1"></a>    expr<span class="sc">%in%</span><span class="fu">c</span>(<span class="st">&quot;msnexp&quot;</span>, <span class="st">&quot;ondisk&quot;</span>, <span class="st">&quot;spectra&quot;</span>, <span class="st">&quot;rams&quot;</span>)<span class="sc">~</span><span class="st">&quot;Memory&quot;</span>,</span>
-<span id="cb21-21"><a href="#cb21-21" tabindex="-1"></a>    <span class="cn">TRUE</span><span class="sc">~</span><span class="st">&quot;Disk&quot;</span></span>
-<span id="cb21-22"><a href="#cb21-22" tabindex="-1"></a>  )) <span class="sc">%&gt;%</span></span>
-<span id="cb21-23"><a href="#cb21-23" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">expr=</span><span class="fu">factor</span>(expr, <span class="at">levels=</span><span class="fu">c</span>(<span class="st">&quot;msnexp&quot;</span>, <span class="st">&quot;ondisk&quot;</span>, <span class="st">&quot;spectra&quot;</span>, <span class="st">&quot;rams&quot;</span>,</span>
-<span id="cb21-24"><a href="#cb21-24" tabindex="-1"></a>                                    <span class="st">&quot;tmzml&quot;</span>, <span class="st">&quot;arrow&quot;</span>, <span class="st">&quot;sql&quot;</span>, <span class="st">&quot;sqlidx&quot;</span>),</span>
-<span id="cb21-25"><a href="#cb21-25" tabindex="-1"></a>                     <span class="at">labels=</span><span class="fu">c</span>(<span class="st">&quot;MSnExp&quot;</span>, <span class="st">&quot;OnDiskMSnExp&quot;</span>, <span class="st">&quot;Spectra&quot;</span>, <span class="st">&quot;RaMS&quot;</span>,</span>
-<span id="cb21-26"><a href="#cb21-26" tabindex="-1"></a>                              <span class="st">&quot;tmzMLs&quot;</span>, <span class="st">&quot;Arrow&quot;</span>, <span class="st">&quot;SQL&quot;</span>, <span class="st">&quot;SQL (indexed)&quot;</span>))) <span class="sc">%&gt;%</span></span>
-<span id="cb21-27"><a href="#cb21-27" tabindex="-1"></a>  <span class="fu">pivot_wider</span>(<span class="at">names_from =</span> time_type, <span class="at">values_from=</span><span class="fu">c</span>(<span class="st">&quot;med_time&quot;</span>, <span class="st">&quot;IQR_time&quot;</span>)) <span class="sc">%&gt;%</span></span>
-<span id="cb21-28"><a href="#cb21-28" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span></span>
-<span id="cb21-29"><a href="#cb21-29" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>) <span class="sc">+</span></span>
-<span id="cb21-30"><a href="#cb21-30" tabindex="-1"></a>  <span class="fu">geom_linerange</span>(<span class="fu">aes</span>(<span class="at">x=</span>med_time_make, <span class="at">ymin=</span>med_time_query<span class="sc">-</span>IQR_time_query<span class="sc">*</span><span class="dv">2</span>, </span>
-<span id="cb21-31"><a href="#cb21-31" tabindex="-1"></a>                     <span class="at">ymax=</span>med_time_query<span class="sc">+</span>IQR_time_query<span class="sc">*</span><span class="dv">2</span>, <span class="at">color=</span>expr)) <span class="sc">+</span></span>
-<span id="cb21-32"><a href="#cb21-32" tabindex="-1"></a>  <span class="fu">geom_linerange</span>(<span class="fu">aes</span>(<span class="at">y=</span>med_time_query, <span class="at">xmin=</span>med_time_make<span class="sc">-</span>IQR_time_make<span class="sc">*</span><span class="dv">2</span>, </span>
-<span id="cb21-33"><a href="#cb21-33" tabindex="-1"></a>                     <span class="at">xmax=</span>med_time_make<span class="sc">+</span>IQR_time_make<span class="sc">*</span><span class="dv">2</span>, <span class="at">color=</span>expr)) <span class="sc">+</span></span>
-<span id="cb21-34"><a href="#cb21-34" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x=</span>med_time_make, <span class="at">y=</span>med_time_query, <span class="at">color=</span>expr, </span>
-<span id="cb21-35"><a href="#cb21-35" tabindex="-1"></a>                 <span class="at">size=</span>size, <span class="at">shape=</span>rep_type)) <span class="sc">+</span></span>
-<span id="cb21-36"><a href="#cb21-36" tabindex="-1"></a>  <span class="fu">scale_shape_manual</span>(<span class="at">values=</span><span class="fu">c</span>(<span class="dv">16</span>, <span class="dv">15</span>)) <span class="sc">+</span></span>
-<span id="cb21-37"><a href="#cb21-37" tabindex="-1"></a>  <span class="fu">scale_y_log10</span>() <span class="sc">+</span></span>
-<span id="cb21-38"><a href="#cb21-38" tabindex="-1"></a>  <span class="fu">coord_flip</span>() <span class="sc">+</span></span>
-<span id="cb21-39"><a href="#cb21-39" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="fu">guide_legend</span>(<span class="at">order =</span> <span class="dv">1</span>), <span class="at">shape =</span> <span class="fu">guide_legend</span>(<span class="at">order =</span> <span class="dv">2</span>), </span>
-<span id="cb21-40"><a href="#cb21-40" tabindex="-1"></a>         <span class="at">size=</span><span class="fu">guide_legend</span>(<span class="at">order=</span><span class="dv">3</span>)) <span class="sc">+</span></span>
-<span id="cb21-41"><a href="#cb21-41" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;Time to transform (s)&quot;</span>, <span class="at">y=</span><span class="st">&quot;Time to query (s)&quot;</span>, <span class="at">color=</span><span class="cn">NULL</span>, <span class="at">size=</span><span class="st">&quot;Size (GB)&quot;</span>,</span>
-<span id="cb21-42"><a href="#cb21-42" tabindex="-1"></a>       <span class="at">shape=</span><span class="st">&quot;Storage&quot;</span>) <span class="sc">+</span></span>
-<span id="cb21-43"><a href="#cb21-43" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span>
-<span id="cb21-44"><a href="#cb21-44" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/sum_plot.png&quot;</span>, <span class="at">width =</span> <span class="fl">6.5</span>, <span class="at">height =</span> <span class="dv">5</span>, <span class="at">units =</span> <span class="st">&quot;in&quot;</span>, <span class="at">device =</span> <span class="st">&quot;png&quot;</span>, <span class="at">dpi =</span> <span class="dv">144</span>)</span>
-<span id="cb21-45"><a href="#cb21-45" tabindex="-1"></a></span>
-<span id="cb21-46"><a href="#cb21-46" tabindex="-1"></a></span>
-<span id="cb21-47"><a href="#cb21-47" tabindex="-1"></a><span class="co"># bind_rows(make_timings, query_timings) %&gt;%</span></span>
-<span id="cb21-48"><a href="#cb21-48" tabindex="-1"></a><span class="co">#   as.data.frame() %&gt;%</span></span>
-<span id="cb21-49"><a href="#cb21-49" tabindex="-1"></a><span class="co">#   group_by(expr) %&gt;%</span></span>
-<span id="cb21-50"><a href="#cb21-50" tabindex="-1"></a><span class="co">#   mutate(time=time/1e9) %&gt;%</span></span>
-<span id="cb21-51"><a href="#cb21-51" tabindex="-1"></a><span class="co">#   summarise(med_time=median(time)) %&gt;%</span></span>
-<span id="cb21-52"><a href="#cb21-52" tabindex="-1"></a><span class="co">#   mutate(expr=str_remove(expr, &quot;_fun\\(\\)&quot;)) %&gt;%</span></span>
-<span id="cb21-53"><a href="#cb21-53" tabindex="-1"></a><span class="co">#   separate(expr, into = c(&quot;expr&quot;, &quot;time_type&quot;)) %&gt;%</span></span>
-<span id="cb21-54"><a href="#cb21-54" tabindex="-1"></a><span class="co">#   pivot_wider(names_from = time_type, values_from=med_time) %&gt;%</span></span>
-<span id="cb21-55"><a href="#cb21-55" tabindex="-1"></a><span class="co">#   left_join(size_df) %&gt;%</span></span>
-<span id="cb21-56"><a href="#cb21-56" tabindex="-1"></a><span class="co">#   plotly::plot_ly(x=~make, y=~query, z=~size, hovertext=~expr, </span></span>
-<span id="cb21-57"><a href="#cb21-57" tabindex="-1"></a><span class="co">#                   type=&quot;scatter3d&quot;, mode=&quot;markers&quot;)</span></span></code></pre></div>
-</details>
-<p><img src="data:image/png;base64,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style="width:100.0%" /></p>
-<details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary>
-<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/tmzMLs&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/pqds&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata.sqlite&quot;</span>)</span>
-<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite&quot;</span>)</span>
-<span id="cb22-5"><a href="#cb22-5" tabindex="-1"></a><span class="fu">unlink</span>(<span class="st">&quot;vignettes/figures/ssc_vignette_renders/Sample/&quot;</span>, <span class="at">recursive =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
-</details>
-</div>
-
-
-
-<!-- code folding -->
-
-
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From 60c7de015f77e3160b5d04d6531d93c2d444667d Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Tue, 17 Sep 2024 18:02:25 -0700
Subject: [PATCH 10/17] Making sure databases are deleted after use

---
 vignettes/speed_size_comparison.Rmd | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/vignettes/speed_size_comparison.Rmd b/vignettes/speed_size_comparison.Rmd
index 64ac46b..caed7c4 100644
--- a/vignettes/speed_size_comparison.Rmd
+++ b/vignettes/speed_size_comparison.Rmd
@@ -626,12 +626,13 @@ MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders
 rs <- dbSendQuery(MSdb_idx, "CREATE INDEX mz ON MS1 (mz)")
 dbClearResult(rs)
 dbDisconnect(MSdb_idx)
-size_list$MSdb_idx <- file.size("vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
+size_list$MSdb_idx <- file.size("vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
 unlink("vignettes/figures/ssc_vignette_renders/MSdata.sqlite")
 
 msduck <- dbConnect(duckdb::duckdb(), "vignettes/figures/ssc_vignette_renders/duckdb")
 dbWriteTable(msduck, "MS1", rams_obj$MS1[order(mz)], overwrite=TRUE)
 size_list$msduck <- file.size("vignettes/figures/ssc_vignette_renders/duckdb")
+dbDisconnect(msduck)
 unlink("vignettes/figures/ssc_vignette_renders/duckdb")
 
 saveRDS(size_list, "vignettes/figures/ssc_vignette_renders/size_list.rds")

From c83c69410df1640a6167e4d91f0a20bd8a3f280e Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Wed, 18 Sep 2024 11:51:06 -0700
Subject: [PATCH 11/17] Adding MS3 info and fixing bugs

---
 vignettes/Intro-to-RaMS.Rmd | 83 ++++++++++++++++---------------------
 1 file changed, 36 insertions(+), 47 deletions(-)

diff --git a/vignettes/Intro-to-RaMS.Rmd b/vignettes/Intro-to-RaMS.Rmd
index 0d31915..d5e4c10 100644
--- a/vignettes/Intro-to-RaMS.Rmd
+++ b/vignettes/Intro-to-RaMS.Rmd
@@ -126,7 +126,8 @@ to your chromatograms as well, so customize away!
 ```{r ggplotdemo, dpi=144}
 ggplot(msdata$BPC) + 
   geom_polygon(aes(x=rt, y=int, color=filename), lwd=1, fill="#FFFFFF44") +
-  theme(legend.position = c(0.8, 0.7), plot.title = element_text(face="bold"),
+  theme(legend.position = "inside", legend.position.inside=c(0.8, 0.7), 
+        plot.title = element_text(face="bold"),
         axis.title = element_text(size = 15)) +
   scale_y_continuous(labels = c(0, "250M", "500M"), breaks = c(0, 2.5e8, 5e8)) +
   scale_colour_manual(values = c("#2596be", "#6c25be", "#bea925")) +
@@ -170,42 +171,44 @@ section below on saving space.
 Because `RaMS` returns [data.tables](https://cran.r-project.org/package=data.table) rather than normal `data.frame`s, indexing
 is super-fast and a bit more intuitive than with base R. Below, I also use the
 `pmppm` function from `RaMS` to produce a mass range from an initial mass and
-spectrometer accuracy (here, 5 parts-per-million).
+spectrometer accuracy (here, 5 parts-per-million) and select only retention
+times that are between 4 and 9 minutes.
 
 ```{r adenineplot, warning=FALSE, message=FALSE}
 library(data.table)
 
 adenine_mz <- 136.06232
 
-adenine_data <- msdata$MS1[mz%between%pmppm(adenine_mz, ppm=5)]
+adenine_data <- msdata$MS1[mz%between%pmppm(adenine_mz, ppm=5)][rt%between%c(4, 9)]
 
-ggplot(adenine_data) + geom_line(aes(x=rt, y=int, color=filename)) + lims(x=c(4, 9))
+ggplot(adenine_data) + geom_line(aes(x=rt, y=int, color=filename))
 ```
 
 This makes it easy to grab the data for multiple compounds of interest with a
 simple loop, provided here by the `purrr` package of the tidyverse:
 
 ```{r multicmpdplot}
-mzs_of_interest <- c(adenine=136.06232, valine=118.0865, homarine=138.055503)
+mzs_of_interest <- c(Adenine=136.06232, Valine=118.0865, Homarine=138.055503)
 
-mass_data <- imap_dfr(mzs_of_interest, function(mz_i, name){
+mass_data <- imap(mzs_of_interest, function(mz_i, name){
   cbind(msdata$MS1[mz%between%pmppm(mz_i, ppm=10)], name)
-})
+}) %>%
+  bind_rows() %>%
+  filter(rt%between%c(4, 9))
 
 ggplot(mass_data) + 
   geom_line(aes(x=rt, y=int, color=filename)) + 
-  facet_wrap(~name, ncol = 1, scales = "free_y") +
-  lims(x=c(4, 9))
+  facet_wrap(~name, ncol = 1, scales = "free_y")
 ```
 
-### Moving along: MS2 data
+### Moving along: MS<sup>n</sup> data
 
 `RaMS` also handles MS<sup>2</sup> data elegantly. Request it with the "MS2" 
 option for `grab_what`, although it's often a good idea to grab the MS1 data
 alongside.
 
 ```{r MS2data}
-msdata <- grabMSdata(data_files[1], grab_what = "everything")
+msdata <- grabMSdata(data_files[1], grab_what = c("MS1", "MS2", "MS3"))
 knitr::kable(head(msdata$MS2, 3))
 ```
 
@@ -213,12 +216,10 @@ DDA data can be plotted nicely with `ggplot2` as well. Typically it makes sense
 to filter for a precursor mass, then render the fragments obtained.
 
 ```{r MS2demo}
-homarine_mz <- 137.047678+1.007276
+iodine_MS2 <- msdata$MS2[premz%between%pmppm(351.0818, 5)][rt==min(rt)]
+iodine_MS2$int <- iodine_MS2$int/max(iodine_MS2$int)
 
-homarine_MS2 <- msdata$MS2[premz%between%pmppm(homarine_mz, 5)]
-homarine_MS2$int <- homarine_MS2$int/max(homarine_MS2$int)
-
-ggplot(homarine_MS2) +
+ggplot(iodine_MS2) +
   geom_point(aes(x=fragmz, y=int)) +
   geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0)) +
   scale_y_continuous(breaks = c(0, .5, 1), labels = c("0%", "50%", "100%")) +
@@ -231,23 +232,23 @@ as `plotly`, making data exploration not only simple but also highly intuitive.
 ```{r plotly, eval=FALSE}
 ## Not run to save space in the vignette:
 library(plotly)
-msdata <- grabMSdata(data_files, grab_what = c("MS1", "MS2", "BPC"))
 
 compound_MS1 <- msdata$MS1 %>% 
-  filter(mz%between%pmppm(homarine_mz, 10)) %>%
-  filter(!str_detect(filename, "DDA"))
+  filter(mz%between%pmppm(351.0818, 10)) %>%
+  filter(!str_detect(filename, "DDA")) %>%
+  slice_max(int, by = rt)
 
-compound_MS2 <- msdata$MS2[premz%between%pmppm(homarine_mz, 10)] %>% 
+compound_MS2 <- msdata$MS2[premz%between%pmppm(351.0818, 10)] %>% 
   group_by(rt) %>%
   arrange(desc(int)) %>%
+  slice(1:10) %>%
   summarise(frags=paste(
     paste(round(fragmz, digits = 3), round(int), sep = ": "), collapse = "\n"),
     .groups="drop"
   ) %>%
   mutate(int=approx(x = compound_MS1$rt, y=compound_MS1$int, xout = rt)$y)
 plot_ly(compound_MS1) %>%
-  add_trace(type="scatter", mode="lines", x=~rt, y=~int, color=~filename,
-            hoverinfo="none") %>%
+  add_trace(type="scatter", mode="lines", x=~rt, y=~int, hoverinfo="none") %>%
   add_trace(type="scatter", mode="markers", x=~rt, y=~int,
             text=~frags, hoverinfo="text", showlegend=FALSE,
             marker=list(color="black"), data = compound_MS2) %>%
@@ -257,35 +258,23 @@ plot_ly(compound_MS1) %>%
 
 Easy access to MS<sup>2</sup> data also allows us to rapidly perform 
 simple operations such as searching for a
-specific fragment mass. For example, if we know that homarine typically
-produces a fragment with a mass of 94.06567, we simply subset the MS<sup>2</sup> data
+specific fragment mass. For example, if we want to find other precursor masses
+that produced a fragment of 57.07, we simply subset the MS<sup>2</sup> data
 for fragments in a range around that mass:
 
 ```{r homarine_fragsearch}
-homarine_frag_mz <- 94.06567
-msdata$MS2[fragmz%between%pmppm(homarine_frag_mz, ppm = 5)] %>%
+msdata$MS2[fragmz%between%pmppm(57.07, ppm = 10)] %>%
   head() %>% knitr::kable()
 ```
 
-We find that there's not only homarine that produces that fragment,
-but at least one other compound. Precursor masses like this can then
-be searched manually in online databases or, since the data is already in R,
-passed to a script that automatically queries them.
-
-Similarly, we can easily search instead for neutral losses with this method. If
-we suspect other molecules are producing a similar neutral loss as homarine:
-
-```{r homarine_neutsearch}
-homarine_neutral_loss <- homarine_mz - homarine_frag_mz
+Support for MS<sup>3</sup> data was added in RaMS 1.4 and includes an additional
+"prepremz" column that contains the original MS<sup>1</sup> value selected for
+later fragmentation.
 
-msdata$MS2 <- mutate(msdata$MS2, neutral_loss=premz-fragmz) %>%
-  select("rt", "premz", "fragmz", "neutral_loss", "int", "voltage", "filename")
-msdata$MS2[neutral_loss%between%pmppm(homarine_neutral_loss, ppm = 5)] %>%
-  arrange(desc(int)) %>% head() %>% knitr::kable()
+```{r}
+msdata$MS3 %>% head() %>% knitr::kable()
 ```
 
-We can again confirm our suspicions that there's another signal with
-a similar neutral loss: one with a mass of 104.0710.
 
 ### Continuing: chromatograms!
 
@@ -310,7 +299,7 @@ plot(given_chrom$rt, given_chrom$int, type="l", main=ptitle)
 ```
 
 Version 1.3 also enabled support for DAD (diode array detection) data. This data
-type can be requested using `grab_what = DAD` and will return an additional
+type can be requested using `grab_what = "DAD"` and will return an additional
 item in the list with columns for retention time, wavelength, and intensity.
 
 ## Advanced RaMS usage
@@ -342,10 +331,10 @@ into R's memory sequentially, the mass window is extracted, and the rest of
 the data is discarded.
 
 ```{r EICdemo}
-all_data <- grabMSdata(data_files, grab_what = c("MS1", "MS2"))
+all_data <- grabMSdata(data_files[-1], grab_what = c("MS1", "MS2"))
 
 mzs_of_interest <- c(adenine=136.06232, valine=118.0865)
-small_data <- grabMSdata(data_files, grab_what = c("EIC", "EIC_MS2"),
+small_data <- grabMSdata(data_files[-1], grab_what = c("EIC", "EIC_MS2"),
                          mz=mzs_of_interest, ppm = 5)
 
 all_data$MS1 %>%
@@ -364,7 +353,7 @@ as.numeric(object.size(all_data)/object.size(small_data))
 ```
 
 As expected, the size of the `small_data` object is much smaller than the
-`all_data` object, here by a factor of about 15x. For files that haven't 
+`all_data` object, here by a factor of about 20x. For files that haven't 
 already been "minified", that size reduction will be even 
 more significant. Of course, this comes with the cost of needing to re-load
 the data a second time if a new mass feature becomes of interest but this 
@@ -379,7 +368,7 @@ only the data between those bounds is relevant. Below, peaks are deliberately
 sliced in half to show that this is filtering on retention time instead of mass.
 
 ```{r rtrangedemo}
-small_data <- grabMSdata(data_files, grab_what = c("MS1", "MS2"), rtrange = c(6, 8))
+small_data <- grabMSdata(data_files[-1], grab_what = c("MS1", "MS2"), rtrange = c(6, 8))
 
 all_data$MS1 %>%
   mutate(type="All data") %>%

From 6a9dc22cafe9f8be956170bfafe43920be746e46 Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Wed, 18 Sep 2024 11:51:32 -0700
Subject: [PATCH 12/17] Adding more duckdb notes

---
 vignettes/speed_size_comparison.Rmd | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/vignettes/speed_size_comparison.Rmd b/vignettes/speed_size_comparison.Rmd
index caed7c4..aae9771 100644
--- a/vignettes/speed_size_comparison.Rmd
+++ b/vignettes/speed_size_comparison.Rmd
@@ -447,7 +447,7 @@ ggsave("vignettes/figures/ssc_vignette_renders/query_time_gp.png", width = 6.5,
 
 ![](../vignettes/figures/ssc_vignette_renders/query_time_gp.png){width=100%}
 
-There's a few surprises in the above plot. Most notably, the incredible speed of the mz-indexed SQL database. It's apparently faster to query the database and read that data into R than it is to index through the big RaMS object entirely within memory. That's a pretty darn compelling reason to switch to SQL databases (although note the importance of indexing them - the non-indexed one took 100x longer!). Side note: I originally expected this to be quite slow because I previously *thought* I was creating an index on the mz data using `dbExecute` instead of `dbSendQuery` but apparently that's not how the function works. This actually disagrees with a [similar comparison](https://bioconductor.org/packages/release/bioc/vignettes/MsBackendSql/inst/doc/MsBackendSql.html#4_Performance_comparison_with_other_backends) done by the Spectra package that found the SQL backend to be consistently slower than the in-memory option. This result was so unexpected that I wrote a quick followup to double-check and can confirm that in real time the SQL query felt just as fast as the RaMS object. Wild.
+There's a few surprises in the above plot. Most notably, the incredible speed of the mz-indexed SQLite database. It's apparently faster to query the database and read that data into R than it is to index through the big RaMS object entirely within memory. That's a pretty darn compelling reason to switch to SQL databases (although note the importance of indexing them - the non-indexed one took 100x longer!). Side note: I originally expected this to be quite slow because I previously *thought* I was creating an index on the mz data using `dbExecute` instead of `dbSendQuery` but apparently that's not how the function works. This actually disagrees with a [similar comparison](https://bioconductor.org/packages/release/bioc/vignettes/MsBackendSql/inst/doc/MsBackendSql.html#4_Performance_comparison_with_other_backends) done by the Spectra package that found the SQL backend to be consistently slower than the in-memory option. This result was so unexpected that I wrote a quick followup to double-check and can confirm that in real time the SQL query felt faster than the RaMS object. DuckDB is nearly as good as SQLite and has some other advantages discussed later.
 
 ```{r double-check sql_idx vs RaMS}
 MSdb_idx <- dbConnect(RSQLite::SQLite(), "vignettes/figures/ssc_vignette_renders/MSdata_idx.sqlite")
@@ -459,7 +459,7 @@ dbGetQuery(MSdb_idx, EIC_query, params = query_params) %>% qplotMS1data()
 rams_obj$MS1[mz%between%pmppm(432.2810, ppm = 20)] %>% qplotMS1data()
 ```
 
-Also surprising is the performance of the tmzMLs - I thought they'd do much worse and instead they're about on par with the arrow method. I'm pleasantly surprised that my hacky file type can compete at all. Of course, up at the top are the traditional MS formats. They're not designed for this or optimized for chromatogram extraction at all so it's not especially surprising that they struggle, but the graph does also nicely show the improved performance of more recent versions over older ones.
+Of course, up at the top are the traditional MS formats. They're not designed for this or optimized for chromatogram extraction at all so it's not especially surprising that they struggle, but the graph does also nicely show the improved performance of more recent versions over older ones.
 
 #### Multiple chromatograms
 
@@ -557,7 +557,10 @@ multichrom_timings <- microbenchmark(
   times = 10
 )
 saveRDS(multichrom_timings, "vignettes/figures/ssc_vignette_renders/multichrom_timings.rds")
+```
+
 
+```{r multichrom query plot}
 multichrom_timings <- readRDS("vignettes/figures/ssc_vignette_renders/multichrom_timings.rds")
 multichrom_timings %>%
   as.data.frame() %>%
@@ -567,7 +570,7 @@ multichrom_timings %>%
   mutate(expr=factor(expr, levels=c("rams", "arrow", "sql", "duckdb"),
                      labels=c("RaMS", "Arrow", "SQL", "DuckDB"))) %>%
   mutate(query_type=factor(query_type, levels=c("uni", "loop"),
-                           labels=c("Unified query", "Query loop"))) %>%
+                           labels=c("Unified query", "Loop"))) %>%
   ggplot() + 
   geom_boxplot(aes(x=query_type, y=time/1e9), lwd=1) +
   facet_wrap(~expr, nrow=1) +
@@ -579,7 +582,7 @@ ggsave("vignettes/figures/ssc_vignette_renders/multichrom_gp.png", width = 6.5,
 
 ![](../vignettes/figures/ssc_vignette_renders/multichrom_gp.png){width=100%}
 
-Once again the indexed SQL database blows the others out of the water, with the unified query performing ~10x better in SQL than Arrow and ~100x better than RaMS. Curiously, the unified query actually takes *longer* when processed via RaMS/data.table than looping. Good to know.
+The indexed SQL database remains about an order of magnitude faster than Arrow and nearly 100x faster than RaMS. DuckDB competes pretty well with SQLite and much prefers looping, much like RaMS does, which is a little surprising to me. Good to know.
 
 ## MS object sizing info
 
@@ -654,8 +657,8 @@ size_list %>%
   mutate(mem_type=factor(mem_type, levels=c("Memory", "Disk"))) %>%
   mutate(expr=factor(expr, levels=c("msnexp", "ondisk", "spectra", "rams",
                                     "tmzml", "arrow", "sql", "sqlidx", "msduck"),
-                     labels=c("MSnExp", "OnDiskMSnExp", "Spectra", "RaMS",
-                              "tmzMLs", "Arrow", "SQL", "SQL (indexed)", "DuckDB"))) %>%
+                     labels=c("MSnExp", "OnDisk\nMSnExp", "Spectra", "RaMS",
+                              "tmzMLs", "Arrow", "SQL", "SQL\n(indexed)", "DuckDB"))) %>%
   ggplot() +
   geom_hline(yintercept = size_list$mzXML/(1024^3)) +
   geom_point(aes(x=expr, y=bytes/(1024^3))) +
@@ -672,9 +675,9 @@ Unsurprisingly, the SQL databases are massive, weighing in at 5-10x the size of
 
 ## Summary
 
-I started out this project because I'd recently learned about `arrow` and was blown away by how fast it was while still remaining essentially memory-free. At the end of it, I think there's more nuance than I was really expecting, though there are a few clear losers. The MSnExp objects should be almost never used, as they're dominated pretty clearly by the OnDisk equivalent. The Spectra package provides even more improvement on this but extracting a chromatogram is more obnoxious and you're still dealing with a complicated S4 object instead of an intuitive data frame. Similarly, the tmzMLs seem to be unilaterally worse than `arrow`, with access at-best equivalent and a make time much larger. Arrow also plays nicely with `dplyr` commands and has good performance when requesting multiple EICs, though longtime RaMS users may find the intuitive syntax of the tmzMLs (and progress bar!) friendlier as long as they don't need to extract more than a single chromatogram.
+I started out this project because I'd recently learned about `arrow` and was blown away by how fast it was while still remaining essentially memory-free. At the end of it, I think there's more nuance than I was really expecting, though there are a few clear losers. The MSnExp objects should be almost never used, as they're dominated pretty clearly by the OnDisk equivalent. The Spectra package provides even more improvement on this but extracting a chromatogram is more obnoxious and you're still dealing with a complicated S4 object instead of an intuitive data frame. Similarly, the tmzMLs seem to be unilaterally worse than `arrow`, with slower access and a make time much larger. Arrow also plays nicely with `dplyr` commands and has good performance when requesting multiple EICs, though longtime RaMS users may find the intuitive syntax of the tmzMLs (and progress bar!) friendlier as long as they don't need to extract more than a single chromatogram. Since then, I found also stumbled across DuckDB which seems to be a faster (but fatter) version of Arrow.
 
-In general, it seems to come down more to the size of the dataset than anything else. If your dataset is small (<1GB) or you're only loading a small-ish subset of it, RaMS is the go-to. If it's larger than memory but not enormous (10-100GB), Arrow and SQL seem like good contenders. The SQL database size ballooned scarily (but had insanely fast access times) while Arrow provided a file size reduction and more familiar syntax. For truly large datasets (100GB+) I can't imagine using anything but the OnDisk or Spectra infrastructure because it's just not worth trying to duplicate the dataset at all, so reading the data out of the files is the only real option. Of course, if you're looking at data that large then you may actually come all the way back around to Arrow or SQL anyway so you can host the data in the cloud or on a cluster instead of on a personal computer.
+In general, it seems to come down more to the size of the dataset than anything else. If your dataset is small (<1GB) or you're only loading a small-ish subset of it, RaMS is the go-to. If it's larger than memory but not enormous (10-100GB), Arrow, DuckDB, and SQLite seem like good contenders. The SQL database size ballooned scarily (but had insanely fast access times) while Arrow provided a file size reduction and more familiar syntax. For truly large datasets (100GB+) I can't imagine using anything but the OnDisk or Spectra infrastructure because it's just not worth trying to duplicate the dataset at all, so reading the data out of the files is the only real option. Of course, if you're looking at data that large then you may actually come all the way back around to the database solutions anyway so you can host the data in the cloud or on a cluster instead of on a personal computer.
 
 ```{r summary plot}
 make_timings <- readRDS("vignettes/figures/ssc_vignette_renders/make_timings.rds")

From 81d9c5763632918584e84700b73ed19264e3b7a7 Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Wed, 18 Sep 2024 11:51:54 -0700
Subject: [PATCH 13/17] Making text in plots overlap less

---
 .../ssc_vignette_renders/multichrom_gp.png    | Bin 20073 -> 19439 bytes
 .../ssc_vignette_renders/size_cons.png        | Bin 21788 -> 23113 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/vignettes/figures/ssc_vignette_renders/multichrom_gp.png b/vignettes/figures/ssc_vignette_renders/multichrom_gp.png
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From 73c31963a1dd26db28bde0316275b7ef0c990568 Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Wed, 18 Sep 2024 12:23:22 -0700
Subject: [PATCH 14/17] Updating NEWS for 1.4.1

---
 NEWS.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index f297a67..4be7dea 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,9 @@
 # RaMS (development version)
 
+# RaMS 1.4.1
+ - Added timezone support to metadata's timestamps (mzML files are all UTC)
+ - Fixed several bugs in the README and vignettes (it's been a minute since I recompiled these!)
+
 # RaMS 1.4
  - Added MS3 support to file reading and minification
    - MS3 data is not automatically requested with "everything" (the default) and must instead be added (e.g. `grab_what=c("everything", "MS3")`)

From 6b1e3bcb9e4a1db521cfcdf125297addaca1a28c Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Wed, 18 Sep 2024 12:29:26 -0700
Subject: [PATCH 15/17] Removing extra speedsizecomp image

---
 speedsizecomp.png | Bin 64401 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 speedsizecomp.png

diff --git a/speedsizecomp.png b/speedsizecomp.png
deleted file mode 100644
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From 46109a1c57fc915cf9ebcc578dc1f333194d9ae5 Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Thu, 19 Sep 2024 11:10:17 -0700
Subject: [PATCH 16/17] Rerunning SSC vignette with CSV instead of RDS

---
 .../ssc_vignette_renders/make_time_gp.png     | Bin 29883 -> 27555 bytes
 .../ssc_vignette_renders/make_timings.csv     |  91 ++++++++++++++++++
 .../ssc_vignette_renders/multichrom_gp.png    | Bin 19439 -> 19594 bytes
 .../multichrom_timings.csv                    |  81 ++++++++++++++++
 .../ssc_vignette_renders/par_time_gp.png      | Bin 15537 -> 16315 bytes
 .../ssc_vignette_renders/par_timings.csv      |  21 ++++
 .../ssc_vignette_renders/query_time_gp.png    | Bin 26335 -> 26128 bytes
 .../ssc_vignette_renders/query_timings.csv    |  91 ++++++++++++++++++
 .../ssc_vignette_renders/size_list.csv        |  11 +++
 .../figures/ssc_vignette_renders/sum_plot.png | Bin 48434 -> 47736 bytes
 vignettes/speed_size_comparison.Rmd           |  53 +++++-----
 11 files changed, 320 insertions(+), 28 deletions(-)
 create mode 100644 vignettes/figures/ssc_vignette_renders/make_timings.csv
 create mode 100644 vignettes/figures/ssc_vignette_renders/multichrom_timings.csv
 create mode 100644 vignettes/figures/ssc_vignette_renders/par_timings.csv
 create mode 100644 vignettes/figures/ssc_vignette_renders/query_timings.csv
 create mode 100644 vignettes/figures/ssc_vignette_renders/size_list.csv

diff --git a/vignettes/figures/ssc_vignette_renders/make_time_gp.png b/vignettes/figures/ssc_vignette_renders/make_time_gp.png
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diff --git a/vignettes/figures/ssc_vignette_renders/make_timings.csv b/vignettes/figures/ssc_vignette_renders/make_timings.csv
new file mode 100644
index 0000000..7475556
--- /dev/null
+++ b/vignettes/figures/ssc_vignette_renders/make_timings.csv
@@ -0,0 +1,91 @@
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diff --git a/vignettes/figures/ssc_vignette_renders/multichrom_gp.png b/vignettes/figures/ssc_vignette_renders/multichrom_gp.png
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new file mode 100644
index 0000000..dbdc3b3
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diff --git a/vignettes/figures/ssc_vignette_renders/par_timings.csv b/vignettes/figures/ssc_vignette_renders/par_timings.csv
new file mode 100644
index 0000000..16296d5
--- /dev/null
+++ b/vignettes/figures/ssc_vignette_renders/par_timings.csv
@@ -0,0 +1,21 @@
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diff --git a/vignettes/figures/ssc_vignette_renders/query_time_gp.png b/vignettes/figures/ssc_vignette_renders/query_time_gp.png
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diff --git a/vignettes/figures/ssc_vignette_renders/query_timings.csv b/vignettes/figures/ssc_vignette_renders/query_timings.csv
new file mode 100644
index 0000000..9e65c3a
--- /dev/null
+++ b/vignettes/figures/ssc_vignette_renders/query_timings.csv
@@ -0,0 +1,91 @@
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diff --git a/vignettes/figures/ssc_vignette_renders/size_list.csv b/vignettes/figures/ssc_vignette_renders/size_list.csv
new file mode 100644
index 0000000..cf72c26
--- /dev/null
+++ b/vignettes/figures/ssc_vignette_renders/size_list.csv
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diff --git a/vignettes/speed_size_comparison.Rmd b/vignettes/speed_size_comparison.Rmd
index aae9771..c5c698e 100644
--- a/vignettes/speed_size_comparison.Rmd
+++ b/vignettes/speed_size_comparison.Rmd
@@ -235,11 +235,11 @@ make_timings <- microbenchmark(
   tmzml_make_fun(), arrow_make_fun(), sql_make_fun(), sqlidx_make_fun(), 
   duckdb_make_fun(),
   times = 10)
-saveRDS(make_timings, "vignettes/figures/ssc_vignette_renders/make_timings.rds")
+write_csv(make_timings, "vignettes/figures/ssc_vignette_renders/make_timings.csv")
 ```
 
 ```{r plot time2make}
-make_timings <- readRDS("vignettes/figures/ssc_vignette_renders/make_timings.rds")
+make_timings <- read_csv("vignettes/figures/ssc_vignette_renders/make_timings.csv")
 make_timings %>%
   as.data.frame() %>%
   arrange(expr) %>%
@@ -295,11 +295,11 @@ par_tmzml <- function(){
 }
 
 par_timings <- microbenchmark(par_rams(), unpar_rams(), par_tmzml(), unpar_tmzml(), times = 5)
-saveRDS(par_timings, "vignettes/figures/ssc_vignette_renders/par_timings.rds")
+write_csv(par_timings, "vignettes/figures/ssc_vignette_renders/par_timings.csv")
 ```
 
 ```{r parallel proc plot}
-par_timings <- readRDS("vignettes/figures/ssc_vignette_renders/par_timings.rds")
+par_timings <- read_csv("vignettes/figures/ssc_vignette_renders/par_timings.csv")
 par_timings %>%
   as.data.frame() %>%
   separate(expr, into = c("sub_type", "par_type"), sep = "_") %>%
@@ -420,11 +420,11 @@ query_timings <- microbenchmark(
   times = 10
 )
 query_timings
-saveRDS(query_timings, "vignettes/figures/ssc_vignette_renders/query_timings.rds")
+write_csv(query_timings, "vignettes/figures/ssc_vignette_renders/query_timings.csv")
 ```
 
 ```{r time2query plot}
-query_timings <- readRDS("vignettes/figures/ssc_vignette_renders/query_timings.rds")
+query_timings <- read_csv("vignettes/figures/ssc_vignette_renders/query_timings.csv")
 query_timings %>%
   as.data.frame() %>%
   arrange(expr) %>%
@@ -556,12 +556,12 @@ multichrom_timings <- microbenchmark(
   sql_uni_fun(), sql_loop_fun(), duckdb_uni_fun(), duckdb_loop_fun(), 
   times = 10
 )
-saveRDS(multichrom_timings, "vignettes/figures/ssc_vignette_renders/multichrom_timings.rds")
+write_csv(multichrom_timings, "vignettes/figures/ssc_vignette_renders/multichrom_timings.csv")
 ```
 
 
 ```{r multichrom query plot}
-multichrom_timings <- readRDS("vignettes/figures/ssc_vignette_renders/multichrom_timings.rds")
+multichrom_timings <- read_csv("vignettes/figures/ssc_vignette_renders/multichrom_timings.csv")
 multichrom_timings %>%
   as.data.frame() %>%
   arrange(expr) %>%
@@ -638,16 +638,17 @@ size_list$msduck <- file.size("vignettes/figures/ssc_vignette_renders/duckdb")
 dbDisconnect(msduck)
 unlink("vignettes/figures/ssc_vignette_renders/duckdb")
 
-saveRDS(size_list, "vignettes/figures/ssc_vignette_renders/size_list.rds")
-```
-
-```{r plot size info}
-size_list <- readRDS("vignettes/figures/ssc_vignette_renders/size_list.rds")
 size_list %>%
-  within(rm(mzXML)) %>%
-  sapply(as.numeric) %>%
+  unlist() %>%
   data.frame(bytes=.) %>%
   rownames_to_column("expr") %>%
+  write_csv("vignettes/figures/ssc_vignette_renders/size_list.csv")
+```
+
+```{r plot size info}
+size_df <- read_csv("vignettes/figures/ssc_vignette_renders/size_list.csv")
+size_df %>%
+  filter(expr!="mzXML") %>%
   mutate(expr=str_remove(expr, "_obj")) %>%
   mutate(expr=str_replace(expr, "MSdb_?", "sql")) %>%
   mutate(mem_type=case_when(
@@ -656,11 +657,11 @@ size_list %>%
   )) %>%
   mutate(mem_type=factor(mem_type, levels=c("Memory", "Disk"))) %>%
   mutate(expr=factor(expr, levels=c("msnexp", "ondisk", "spectra", "rams",
-                                    "tmzml", "arrow", "sql", "sqlidx", "msduck"),
+                                    "tmzml", "arrow", "sql", "sqlidx", "duckdb"),
                      labels=c("MSnExp", "OnDisk\nMSnExp", "Spectra", "RaMS",
                               "tmzMLs", "Arrow", "SQL", "SQL\n(indexed)", "DuckDB"))) %>%
   ggplot() +
-  geom_hline(yintercept = size_list$mzXML/(1024^3)) +
+  geom_hline(yintercept = size_df$bytes[size_df$expr=="mzXML"]/(1024^3)) +
   geom_point(aes(x=expr, y=bytes/(1024^3))) +
   scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10), labels=c("1MB", "10MB", "100MB", "1GB", "10GB"),
                 limits=c(0.001, 10)) +
@@ -675,18 +676,14 @@ Unsurprisingly, the SQL databases are massive, weighing in at 5-10x the size of
 
 ## Summary
 
-I started out this project because I'd recently learned about `arrow` and was blown away by how fast it was while still remaining essentially memory-free. At the end of it, I think there's more nuance than I was really expecting, though there are a few clear losers. The MSnExp objects should be almost never used, as they're dominated pretty clearly by the OnDisk equivalent. The Spectra package provides even more improvement on this but extracting a chromatogram is more obnoxious and you're still dealing with a complicated S4 object instead of an intuitive data frame. Similarly, the tmzMLs seem to be unilaterally worse than `arrow`, with slower access and a make time much larger. Arrow also plays nicely with `dplyr` commands and has good performance when requesting multiple EICs, though longtime RaMS users may find the intuitive syntax of the tmzMLs (and progress bar!) friendlier as long as they don't need to extract more than a single chromatogram. Since then, I found also stumbled across DuckDB which seems to be a faster (but fatter) version of Arrow.
+I started out this project because I'd recently learned about `arrow` and was blown away by how fast it was while still remaining essentially memory-free. At the end of it, I think there's more nuance than I was really expecting, though there are a few clear losers. The MSnExp objects should be almost never used, as they're dominated pretty clearly by the OnDisk equivalent. The Spectra package provides even more improvement on this but extracting a chromatogram is more obnoxious and you're still dealing with a complicated S4 object instead of an intuitive data frame. Similarly, the tmzMLs seem to be unilaterally worse than `arrow`, with slower access and a make time much larger. Arrow also plays nicely with `dplyr` commands and has good performance when requesting multiple EICs, though longtime RaMS users may find the intuitive syntax of the tmzMLs (and progress bar!) friendlier as long as they don't need to extract more than a single chromatogram. Since then, I also stumbled across DuckDB which seems to be a faster (but fatter) version of Arrow.
 
 In general, it seems to come down more to the size of the dataset than anything else. If your dataset is small (<1GB) or you're only loading a small-ish subset of it, RaMS is the go-to. If it's larger than memory but not enormous (10-100GB), Arrow, DuckDB, and SQLite seem like good contenders. The SQL database size ballooned scarily (but had insanely fast access times) while Arrow provided a file size reduction and more familiar syntax. For truly large datasets (100GB+) I can't imagine using anything but the OnDisk or Spectra infrastructure because it's just not worth trying to duplicate the dataset at all, so reading the data out of the files is the only real option. Of course, if you're looking at data that large then you may actually come all the way back around to the database solutions anyway so you can host the data in the cloud or on a cluster instead of on a personal computer.
 
 ```{r summary plot}
-make_timings <- readRDS("vignettes/figures/ssc_vignette_renders/make_timings.rds")
-query_timings <- readRDS("vignettes/figures/ssc_vignette_renders/query_timings.rds")
-size_df <- readRDS("vignettes/figures/ssc_vignette_renders/size_list.rds") %>%
-  sapply(as.numeric) %>%
-  data.frame(size=.) %>%
-  mutate(size=size/1024^3) %>%
-  rownames_to_column("expr") %>%
+make_timings <- read_csv("vignettes/figures/ssc_vignette_renders/make_timings.csv")
+query_timings <- read_csv("vignettes/figures/ssc_vignette_renders/query_timings.csv")
+size_df <- read_csv("vignettes/figures/ssc_vignette_renders/size_list.csv") %>%
   mutate(expr=str_remove(expr, "_obj")) %>%
   mutate(expr=str_replace(expr, "MSdb_?", "sql")) %>%
   mutate(expr=ifelse(expr=="msduck", "duckdb", expr))
@@ -715,7 +712,7 @@ bind_rows(make_timings, query_timings) %>%
   geom_linerange(aes(y=med_time_query, xmin=med_time_make-IQR_time_make*2, 
                      xmax=med_time_make+IQR_time_make*2, color=expr)) +
   geom_point(aes(x=med_time_make, y=med_time_query, color=expr, 
-                 size=size, shape=rep_type)) +
+                 size=bytes, shape=rep_type)) +
   scale_y_log10() +
   coord_flip() +
   guides(color = guide_legend(order = 1), shape = guide_legend(order = 2), 
@@ -757,9 +754,9 @@ bind_rows(make_timings, query_timings) %>%
                               "tmzMLs", "Arrow", "SQL", "SQL (indexed)", "DuckDB"))) %>%
   pivot_wider(names_from = time_type, values_from=c("med_time", "IQR_time")) %>%
   ggplot() +
-  geom_linerange(aes(x=size, ymin=med_time_query-IQR_time_query, 
+  geom_linerange(aes(x=bytes, ymin=med_time_query-IQR_time_query, 
                      ymax=med_time_query+IQR_time_query, color=expr), linewidth=1) +
-  geom_point(aes(x=size, y=med_time_query, color=expr), size=3) +
+  geom_point(aes(x=bytes, y=med_time_query, color=expr), size=3) +
   scale_y_log10() +
   guides(color = guide_legend(order = 1)) +
   labs(x="Size (GB)", y="Time to query (s)") +

From 451a7cc52e03393321c7e4e749959d529f9877e0 Mon Sep 17 00:00:00 2001
From: wkumler <wkumler@uw.edu>
Date: Thu, 19 Sep 2024 11:10:27 -0700
Subject: [PATCH 17/17] Adding duckdb to .gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 8857dc3..2c672db 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,6 +7,7 @@
 *.mzXML
 *.sqlite
 *.parquet
+*duckdb
 
 .Rproj.user
 README.Rmd