From 7beab42172dc92462804a616463f250d5f36d730 Mon Sep 17 00:00:00 2001 From: Johannes Zauner <112665672+JZauner@users.noreply.github.com> Date: Tue, 18 Jun 2024 11:22:09 +0200 Subject: [PATCH 1/4] Added articles --- DESCRIPTION | 2 +- NEWS.md | 4 + vignettes/articles/.DS_Store | Bin 6148 -> 6148 bytes vignettes/articles/Import.Rmd | 142 +++++++++ vignettes/articles/Metrics.Rmd | 300 ++++++++++++++++++++ vignettes/articles/cleaned_data/ll_data.rds | Bin 0 -> 24835833 bytes vignettes/articles/images/Day.png | Bin 543009 -> 533411 bytes 7 files changed, 447 insertions(+), 1 deletion(-) create mode 100644 vignettes/articles/Import.Rmd create mode 100644 vignettes/articles/Metrics.Rmd create mode 100644 vignettes/articles/cleaned_data/ll_data.rds diff --git a/DESCRIPTION b/DESCRIPTION index a858825..652c971 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: LightLogR Title: Work With Data from Wearable Light Loggers and Optical Radiation Dosimeters -Version: 0.3.5 +Version: 0.3.6 Authors@R: c( person("Johannes", "Zauner", email = "johannes.zauner@tum.de", role = c("aut", "cre"), diff --git a/NEWS.md b/NEWS.md index 3a39927..30bd9e4 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,7 @@ +# LightLogR 0.3.6 + +* Added articles on `Import & Cleaning`, `Metrics`, and `Visualizations` to the website. + # LightLogR 0.3.5 * Added the function `aggregate_Date()` to aggregate long datasets to one day per group. diff --git a/vignettes/articles/.DS_Store b/vignettes/articles/.DS_Store index 752ff5d57856e7c7ce0660938340a42d2d0334dd..d5651262b444772e77f29fc7866261c2c28b48a5 100644 GIT binary patch delta 386 zcmZoMXfc=|#>B)qu~2NHo}wrZ0|Nsi1A_nqLmopaLkdGY5GQV4$he%b9wf!Xkj#(+ zlt@IDW+-7u%t<#4PR`FQ0IGojoN9CPU0jlK@{@oHISjJDypa?*<_KitQJ(@-Ur)S- zf(#7Duz>u5!=Z3FWQVmJ?R~%vaTrA280gE~e2_~Z4#7|Wlt;ICGY{iwmd)%O{2ag# Z28QBy=E?jbmK?wU1|kNA%>g1?m;qkkQnmm9 delta 83 zcmZoMXfc=|#>B`mu~2NHo}wrV0|Nsi1A_nqLn=dYPP$=ma(-^X#KPr_ESqhZPqS=( kz-+*@nVo~51E^*5Mdt6!lles~Ie?l#x|lWxh-_g706?=7egFUf diff --git a/vignettes/articles/Import.Rmd b/vignettes/articles/Import.Rmd new file mode 100644 index 0000000..aa809ec --- /dev/null +++ b/vignettes/articles/Import.Rmd @@ -0,0 +1,142 @@ +--- +title: "Import & Cleaning" +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +This article focuses on the import from multiple files and participants, as well as the cleaning of the data. We need these packages + +```{r setup, message = FALSE} +library(LightLogR) +library(tidyverse) +library(gghighlight) +``` + +# Importing Data + +The first step in every analysis is data import. We will work with data collected as part the Master Thesis *Insights into real-world human light exposure: relating self-report with eye-level light logging* by Carolina Guidolin (2023). The data is stored in 17 text files in the *data/* folder. You can access the data yourself through the [LightLogR GitHub repository](https://github.com/tscnlab/LightLogR/tree/main/vignettes/articles/data). + +```{r, files} +path <- "data" +files <- list.files(path, full.names = TRUE) +#show how many files are listes +length(files) +``` + +Next we require a time zone of data collection. If uncertain which time zones are valid, use the `OlsonNames()` function. Our data was collected in the "Europe/Berlin" time zone. + +```{r, tz} +#first six time zones from OlsonNames() +head(OlsonNames()) + +#our time zone +tz <- "Europe/Berlin" +``` + +Lastly, the participant Ids are stored in the file names. We will extract them and store them in a column called `Id`. The following code defines the pattern as a *regular expression*, which will extract the first three digits from the file name. + +```{r, Id pattern} +pattern <- "^(\\d{3})" +``` + +Now we can import the data. Data were collected with the ActLumus device by Condor Instruments. The right way to specify this is through the `import` function. + +```{r, import} +data <- import$ActLumus(files, tz = tz, auto.id = pattern, print_n=33) +``` + +# Data cleaning #1 + +Before we can dive into the analysis part, we need to make sure we have a clean dataset. The import summary shows us two problems with the data: + +- two files have data that crosses daylight saving time (DST) changes. Because the ActLumus device does not adjust for DST, we need to correct for this. +- Multiple Ids have single datapoints at the beginning of the dataset with gaps before actual data collection starts. These are test measurements to check equipment, but must be removed from the dataset. + +Let us first deal with the DST change. LightLogR has an in-built function to correct for this during import. We thus will re-import the data, but make the import silent as to not clutter the output. + +```{r, dst change} +data <- + import$ActLumus(files, tz = tz, auto.id = pattern, dst_adjustment = TRUE, + auto.plot = FALSE, silent = TRUE) +``` + +The second problem requires the filtering of certain Ids. The `filter_Datetime_multiple()` function is ideal for this. We can provide a length (1 week), starting from the end of data collection and backwards. The variable `arguments provide variable arguments to the filter function, they have to be provided in list form and expressions have to be quoted through`quote()`. Fixed arguments, like the length and`length_from_start\` are provided as named arguments and only have to be specified once, as they are the same for all Ids. + +```{r, start shift} +data <- + data %>% + filter_Datetime_multiple( + arguments = list( + list(only_Id = quote(Id == 216)), + list(only_Id = quote(Id == 219)), + list(only_Id = quote(Id == 214)), + list(only_Id = quote(Id == 206)) + ), length = "1 week", length_from_start = FALSE) + +``` + +Let's have a look at the data again with the `gg_overview()` function. + +```{r, overview} +data %>% gg_overview() +``` + +Looks much better now. Also, because there is no longer a hint about gaps in the lower right corner, we can be sure that all gaps have been removed. The function gap_finder() shows us, however, that there are still irregularities in the data and the function count_difftime() reveals where they are. + +```{r, irregularities} +data %>% gap_finder() +data %>% count_difftime() %>% print(n=22) +``` + +This means we have to look at and take care of the irregularities for the Ids 215, 218, and 221. + +# Data cleaning #2 + +Let us first visualize where the irregularities are. We can use `gg_days()` for that. + +```{r} +#create two columns to show the irregularities and gaps for relevant ids +difftimes <- + data %>% + filter(Id %in% c(215, 218, 221)) %>% + mutate(difftime = difftime(lead(Datetime), Datetime, units = "secs"), + end = Datetime + seconds(difftime)) + +#visualize where those points are +difftimes %>% + gg_days(geom = "point", + x.axis.breaks = ~Datetime_breaks(.x, by = "2 days" ) + ) + + geom_rect(data = difftimes %>% filter(difftime !=10), + aes(xmin = Datetime, xmax = end, ymin = -Inf, ymax = Inf), + fill = "red", col = "red", linewidth = 0.2, alpha = 0.2) + + gghighlight(difftime != 10 | lag(difftime !=10)) + +``` + +All irregular data appear at the very beginning of the data collection. As we are interestet in one whole week of data, we can similarly apply a one week filter on these Ids and see if that removed the irregular data points. + +```{r} +data <- + data %>% + filter_Datetime_multiple( + arguments = list( + list(only_Id = quote(Id == 215)), + list(only_Id = quote(Id == 218)), + list(only_Id = quote(Id == 221)) + ), length = "1 week", length_from_start = FALSE) + +data %>% gap_finder() +data %>% count_difftime() %>% print(n=17) +``` + +The data is now clean and we can proceed with the analysis. This dataset will be needed in other articles, so we will save it as an RDS file. + +```{r} +# saveRDS(data, "cleaned_data/ll_data.rds") +``` diff --git a/vignettes/articles/Metrics.Rmd b/vignettes/articles/Metrics.Rmd new file mode 100644 index 0000000..b4f5709 --- /dev/null +++ b/vignettes/articles/Metrics.Rmd @@ -0,0 +1,300 @@ +--- +title: "Metrics" +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +This article focuses on two important aspects of light logger analysis: structuring data into relevant groups and calculating personal light exposure metrics for them. LightLogR contains a large set of over 60 metrics and sub-metrics across multiple functions, where each constitutes a family of light exposure metrics. The following packages are needed for the analysis: + +```{r setup, message = FALSE} +library(LightLogR) +library(tidyverse) +library(gt) +library(gtsummary) +``` + +# Importing Data + +We will use data imported and cleaned already in the article [Import & Cleaning](https://tscnlab.github.io/LightLogR/articles/Import.html). + +```{r, import} +data <- readRDS("cleaned_data/ll_data.rds") +``` + +As can be seen by using `gg_overview()`, the dataset contains 17 ids with one weeks worth of data each, and one to three participants per week. + +```{r, overview} +data %>% gg_overview() +``` + +# Metric principles + +There are a lot of metrics associated with personal light exposure. You can find the function reference to all of them in the appropriate [reference section](https://tscnlab.github.io/LightLogR/reference/index.html#metrics). There are a few important distinctions between metrics that are important to understand: + +* Some metrics require or work best with a specific time frame, usually one day, while others are calculated over an arbitrary length of time. For example, the function `interdaily_stability()` calculates a metric over multiple days, while a function like `midpointCE()` calculates the midpoint of the cumulative light exposure within the given time series - this is less useful for multiple days, where the midpoint is just a time point during these days. E.g., for two similar light exposure patterns across two days, the cumulative light exposure across those two days will be around midnight, which is not particularly informative. Much more sensible is the midpoint of the light exposure for each day. To enable this, data has to be grouped within days (or other relevant time frames, like sleep/wake-phase). + +* Some metrics are submetrics within a family and have to be actively chosen through the arguments of the function. An example is `duration_above_threshold()` that, despite its name also provides the metrics `duration below threshold` and `duration within threshold`. Depending on its `comparison` argument, and whether one or two `threshold`s are provided, the function will calculate different metrics. + +* Some metric functions calculate multiple submetrics at once, like `bright_dark_period()`. As stated above, this type of function contains metrics accessible through a function argument, `period` in this case, which allows to specify whether the brightest or darkest periods of the day are required. Independent of this, the function will calculate multiple submetrics at once, which are the `onset`, `midpoint`, and `offset` of the respective period, and also the `mean` light level during that period. + +We will cover the practical considerations following from these aspects in the following sections. Further, every function documentation explicitly states whether different metrics are accessible through parameters, and which metrics are calculated by default. + +# Metric calculation: basics + +All metric functions are by default agnostic to the type of data. They require vectors of light data and commonly also of datetimes. This means that the functions can be used outside of the LightLogR framework, if applied correctly. Let us try this with a simple example for a days worth of light data for one participant across two functions. + +```{r, Id 201} +data_Id201 <- data %>% filter(Id == 201 & date(Datetime) == "2023-08-15") +data_Id201 %>% gg_day() +``` + +## Time above threshold (TAT) + +The first example metric we will calculate is the *time above threshold* (or TAT) for a threshold of 250 lx mel EDI. TAT is calculated by the function `duration_above_threshold()`. + +```{r, TAT} +duration_above_threshold( + Light.vector = data_Id201$MEDI, + Time.vector = data_Id201$Datetime, + threshold = 250 +) +``` + +Specifying the argument `comparison = "below"` will calculate the time below the threshold. + +```{r, TBT} +duration_above_threshold( + Light.vector = data_Id201$MEDI, + Time.vector = data_Id201$Datetime, + threshold = 250, + comparison = "below" +) +``` + +And specifying two thresholds will calculate the time within the thresholds. + +```{r, TWT} +duration_above_threshold( + Light.vector = data_Id201$MEDI, + Time.vector = data_Id201$Datetime, + threshold = c(10,250) +) +``` + +## Brightest 10 hours of the day (L10) + +The second example metric yields multiple submetrics at once. The function `bright_dark_period()` calculates the brightest and darkest periods of the day. By default, it calculates the brightest 10 hour period of the day. By setting `as_df = TRUE`, the function will return a data frame we can pipe to `gt()` for a nice output + + +```{r, L10} +bright_dark_period( + Light.vector = data_Id201$MEDI, + Time.vector = data_Id201$Datetime, + as.df = TRUE +) %>% gt() +``` + +### Looping + +Calculating the darkest period of the day is tricky, as it likely traverses midnight. In the following code we can see that the darkest 10-hour period of day begins at midnight and ends at 10 am, which would be very coincidental. (Note that commonly, the darkest 5-hour period is calculated. We deviate from this to make this point.) +```{r, M10_wrong} +M10_wrong <- +bright_dark_period( + Light.vector = data_Id201$MEDI, + Time.vector = data_Id201$Datetime, + as.df = TRUE, + period = "darkest", + timespan = "10 hours" +) +M10_wrong %>% gt() + +``` + +We also see that this makes little sense, if we visualize this portion. The yellow color indicates the darkest 10-hour period of the day. + +```{r, M10_wrong_plot} +Onset <- M10_wrong$darkest_10h_onset +Offset <- M10_wrong$darkest_10h_offset + +data_Id201 %>% + gg_day(aes_col = Datetime >= Onset & Datetime <= Offset) + + guides(color = "none") + +``` + +To solve this, `bright_dark_period()` and some other functions have the option to `loop` the day. + +```{r, M10} +M10 <- +bright_dark_period( + Light.vector = data_Id201$MEDI, + Time.vector = data_Id201$Datetime, + as.df = TRUE, + period = "darkest", + timespan = "10 hours", + loop = TRUE +) +M10 %>% gt() +``` + +This is more plausible, and can also be visualized easily. + +```{r, M10_plot} +Onset <- M10$darkest_10h_onset +Offset <- M10$darkest_10h_offset + +data_Id201 %>% + gg_day(aes_col = Datetime >= Onset | Datetime <= (Offset -days())) + + guides(color = "none") +``` + +# Metric calculation: advanced + +More often than not, metrics are calculated for many participants over prolonged periods of time. In this case, the singular calculation as shown above is inefficient. The `dplyr` family of `dplyr::summarize()` and `dplyr::reframe()` make this much easier. + +## Preparation + +As we only want to calculate metrics for days with full data, we will exclude Mondays from the data set. + +```{r} +data <- data %>% dplyr::filter(weekdays(Datetime) != "Monday") +``` + +## Summarize + +The `dplyr::summarize()` function is used to calculate metrics for each group of data. In the following example, we will calculate Interdaily Stability (IS) for all participants in the data set, giving us the variability of the 24h light exposure patterns across the full 6 days of data compared to their average, ranging between 0 (Gaussian noise) and 1 (Perfect stability). For brevity, only the first 6 Ids will be shown. + +```{r} +data %>% + summarize( + IS = interdaily_stability( + Light.vector = MEDI, + Datetime.vector = Datetime + ) + ) %>% + head() %>% gt() +``` + +## Grouping + +By default, data imported with `LightLogR` is grouped by `Id`, which represents individual participants. When using the `dplyr` family of functions, grouping is essential, as it specifies the subgroups of data for which the metrics are calculated. In the following example, we will calculate the TAT 250 lx MEDI for all participants in the data set. We only show the first 6 participants, as it becomes readily apparent that time above threshold for 6 days might not be the most informative parametrization of the metric. + +```{r} +data %>% + summarize( + TAT_250 = duration_above_threshold( + Light.vector = MEDI, + Time.vector = Datetime, + threshold = 250 + ) + ) %>% head() %>% gt() +``` + +Instead, we can calculate the TAT 250 lx MEDI for each participant **and day** of data. This is more informative, as it allows us to see how the metric changes over time. The final output is for the first two Ids. + +```{r} +#create a new column in the data set with the weekday +data$wDay <- wday(data$Datetime, label = TRUE, week_start = 1) + +#group the data and calculate the metrics +TAT_250 <- +data %>% + group_by(wDay, .add = TRUE) %>% + summarize( + TAT_250 = duration_above_threshold( + Light.vector = MEDI, + Time.vector = Datetime, + threshold = 250 + ), .groups = "drop_last" + ) + +TAT_250 %>% head(12) %>% gt() + +``` + +## Metric statistics + +With the dataframe `TAT_250`, we can easily calculate statistics for each participant. This can be done manually, e.g., with another call to `dplyr::summarize()`, or semi-automatic, e.g., with packages like `gtsummary`. In the following example, we will calculate the mean and standard deviation of the TAT 250 lx MEDI for each participant, formatted as `HH:SS` through a styling function. + +```{r} +#styling formula for time +style_time <- function(x, format = "%H:%M"){ + x %>% as.numeric() %>% hms::as_hms() %>% as.POSIXlt() %>% format(format) +} + +#Table output +TAT_250 %>% + tbl_summary(by = Id, include = -wDay, + statistic = list(TAT_250 ~ "{mean} ({sd})"), + digits = list(TAT_250 ~ style_time), + label = list(TAT_250 = "Time above 250 lx mel EDI") + ) +``` + +# Metric calculation: batch + +In the final section, we will add more metrics to the analysis, including ones with multiple submetrics. Further, the let us imagine we wand to know how these metrics change from the first half of the experiment (August/September) to the second half (October/November). Finally, we will include a column `Time.data` to the data set, which will be used to calculate the metrics. This avoids + +```{r} +data <- data %>% + mutate( + Month = case_when(month(Datetime) %in% 8:9 ~ "Aug/Sep", + month(Datetime) %in% 10:11 ~ "Oct/Nov") + ) %>% + create_Timedata() + +metrics <- + data %>% + group_by(Month, Id, wDay) %>% + summarize( + MLIT250 = + timing_above_threshold(MEDI, Time.data, threshold = 250, as.df = TRUE), + TAT250 = + duration_above_threshold(MEDI, Time.data, threshold = 250, as.df = TRUE), + average_MEDI = + mean(MEDI), + light_exposure = + sum(MEDI)/360, # 10 second epochs means 360 epochs in one hour. dividing by 360 gives the light exposure in lx·h + .groups = "drop_last" + ) %>% + unnest(-Id) + +#first 6 rows +metrics %>% head() %>% gt() + +``` + +The operation above yields a dataframe with 6 metrics across 102 participant days (6 days for 17 participants). The grouping for `Month` did not add additional groups, as each participant day is already solely either in the `"Aug/Sep"` or `"Oct/Nov"` group. Next we will regroup the data by `Month` and look at a summary table similar to above, but for more metrics. + +```{r} +metrics <- metrics %>% group_by(Month) %>% select(-Id, -wDay) + +#Table output +metrics %>% + tbl_summary(by = Month, + statistic = list(all_continuous() ~ "{mean} (±{sd})"), + digits = list( + c( + mean_timing_above_250, first_timing_above_250, + last_timing_above_250, duration_above_250 + ) ~ style_time), + label = list( + mean_timing_above_250 = + "mean timing above 250 lx mel EDI (HH:MM)", + first_timing_above_250 = + "first time above 250 lx mel EDI (HH:MM)", + last_timing_above_250 = + "last time above 250 lx mel EDI (HH:MM)", + duration_above_250 = "duration above 250 lx mel EDI (HH:MM)", + average_MEDI = "average mel EDI (lx)", + light_exposure = "light exposure (lx·h)" + ) + ) +``` + +And that is all you need to work with metrics in `LightLogR`. 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