Apply feedback 📝

- reference vignettes - reference integration with labelled, openxlsx2, skimr - reference openssf - production deployment nod - table update - new citations
CHOP-CGTInformatics · Nov 13, 2023 · afed5ac · afed5ac
1 parent 52156ad
commit afed5ac
Show file tree

Hide file tree

Showing 2 changed files with 54 additions and 18 deletions.
diff --git a/paper/paper.bib b/paper/paper.bib
@@ -50,21 +50,42 @@ @Manual{r_citation
 }
 
 @Manual{redcapr_cit,
- title = {REDCapR: Interaction Between R and REDCap},
- author = {{Beasley, Will}},
- organization = {Biomedical and Behavioral Methodology Core (University of Oklahoma Health Sciences Center)},
- address = {Oklahoma City, Oklahoma},
- year = {2022},
- url = {https://cran.r-project.org/web/packages/REDCapR/index.html},
+  title = {REDCapR: Interaction Between R and REDCap},
+  author = {Will Beasley},
+  year = {2023},
+  note = {https://ouhscbbmc.github.io/REDCapR/, https://github.com/OuhscBbmc/REDCapR, https://www.ouhsc.edu/bbmc/, https://project-redcap.org},
 }
 
 @Manual{redcapapi_cit,
- title = {redcapAPI: Interface to 'REDCap'},
- author = {{Garbett, Shawn}},
- organization = {Vanderbilt Biostatistics},
- address = {Nashville, Tennessee},
+ title = {{redcapAPI}: Accessing data from REDCap projects using the API},
+ author = {Benjamin Nutter and Stephen Lane},
  year = {2023},
- url = {https://cran.r-project.org/web/packages/redcapAPI/index.html},
+ doi = {10.5281/zenodo.11826},
+ note = {R package version 2.8.0},
+ url = {https://github.com/nutterb/redcapAPI/wiki},
+}
+
+@Manual{labelled_cit,
+  title = {labelled: Manipulating Labelled Data},
+  author = {Joseph Larmarange},
+  year = {2023},
+  note = {R package version 2.12.0},
+  url = {https://larmarange.github.io/labelled/},
+}
+
+@Manual{openxlsx2_cit,
+  title = {openxlsx2: Read, Write and Edit 'xlsx' Files},
+  year = {2023},
+  note = {R package version 1.1},
+  url = {https://janmarvin.github.io/openxlsx2/},
+}
+
+@Manual{skimr_cit,
+  title = {skimr: Compact and Flexible Summaries of Data},
+  author = {Elin Waring and Michael Quinn and Amelia McNamara and Eduardo {Arino de la Rubia} and Hao Zhu and Shannon Ellis},
+  year = {2023},
+  note = {R package version 2.1.5, https://github.com/ropensci/skimr/},
+  url = {https://docs.ropensci.org/skimr/ (website)},
 }
 
 @Article{tidyverse_cit,
@@ -76,4 +97,13 @@ @Article{tidyverse_cit
  number = {43},
  pages = {1686},
  doi = {10.21105/joss.01686},
+}
+
+@Misc{openssf_cit,
+ title = {OpenOpen Source Security Foundation_2023},
+ url={https://openssf.org/},
+ journal={Open Source Security Foundation},
+ publisher={The Linux Foundation},
+ year={2023},
+ month={Oct}
 }
diff --git a/paper/paper.md b/paper/paper.md
@@ -60,13 +60,17 @@ As of 2023, the REDCap Consortium boasts nearly 3 million users across over 150
 
 While there are a few existing REDCap tools for R documented by [`REDCap-tools`](https://redcap-tools.github.io/projects/), `REDCapTidieR` occupies a unique space by providing analysts with an opinionated framework that quickly prepares them for data analysis. Although some of the aforementioned tools also offer functions for data processing, such as the [`tidyREDCap`](https://raymondbalise.github.io/tidyREDCap/) and [`REDCapDM`](https://ubidi.github.io/REDCapDM/index.html) packages, `REDCapTidieR` is unique in how it restructures the block matrix into a format that is easily interpretable within the user's programmatic environment. Of the tools available, `REDCapTidieR` is the only one that fundamentally restructures the block matrix in its entirety.
 
-| Package     | Data Export Support | Data Import Support | Data Manipulation | Data Tidying |
-|-------------|--------------------|--------------------|------------------|--------------|
-| redcapAPI   | x                  | x                  |                  |              |
-| REDCapR     | x                  | x                  |                  |              |
-| tidyREDCap  | x                  |                    | x                |              |
-| REDCapDM    | x                  |                    | x                |              |
-| REDCapTidieR| x                  |                    | x                | x            |
+REDCapTidieR was developed with deployment in production environments as a key consideration. To ensure the utmost confidence in the handling of user data, we've implemented an extensive test suite that exhibits 98% code coverage, as of the package's version 1.0 release. Ample documentation is accessible through a collection of package vignettes and articles, offering detailed insights into the opinionated framework, design structure, and a comprehensive glossary of terms associated with the REDCapTidieR package. These considerations have earned the package an [OpenSSF Best Practices certification](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], which certifies open source projects that meet stringent criteria for delivering high-quality and secure software.
+
+| Package     | Data Export Support | Data Import Support | Data Manipulation | Data Tidying | Production Capability |
+|-------------|---------------------|---------------------|-------------------|--------------| --------------------- |
+| redcapAPI   | x                   | x                   |                   |              |                       |
+| REDCapR     | x                   | x                   |                   |              | x                     |
+| tidyREDCap  | x                   |                     | x                 |              |                       |
+| REDCapDM    | x                   |                     | x                 |              |                       |
+| REDCapTidieR| x                   |                     | x                 | x            | x                     |
+
+Table 1: Comparative breakdown of the landscape for REDCap tools in R.
 
 # Design
 
@@ -79,6 +83,8 @@ Figure 1: Comparative model showing REDCap API export formats between the defaul
 
 In this example, the supertibble displays three REDCap database instruments, with one repeating and two non-repeating. Below, one of each of these instrument types is expanded to show how `REDCapTidieR` separates these instruments into their own tabular list elements structured with only the identifiers necessary to pinpoint a specific record. This format makes tables easily joinable by analysts for whatever operations they may need later in their work.
 
+Additionally, REDCapTidieR comes equipped with features that address common requirements of analysts. Seamless integration with the `labelled` [@labelled_cit] package facilitates effortless application of variable labels to both data and metadata. An extension utilizing the `skimr` [@skimr_cit] package provides comprehensive metric summaries of metadata for exported REDCap databases. Lastly, through an extension leveraging the `openxlsx2` [@openxlsx2_cit] package, users can easily export REDCapTidieR data tibbles to individual XLSX sheets.
+
 # Installation
 
 `REDCapTidieR` is available on [GitHub](https://github.com/CHOP-CGTInformatics/REDCapTidieR) and [CRAN](https://cran.r-project.org/web/packages/REDCapTidieR/index.html) and has been tested for functionality on all major operating systems.