The most three commonly used tools to perform a first inspection on
activity data are:
We start by loading a dataset from the library digiRhythm, we then
remove the outliers and resample it to 15 min. We choose one activity to
study.
library(digiRhythm)
data <- digiRhythm::df691b_1
data <- remove_activity_outliers(data)
data <- resample_dgm(data, 15)
activity = names(data)[2]
head(data)
#> datetime Motion.Index Steps
#> 1 2020-08-25 00:00:00 20 8
#> 2 2020-08-25 00:15:00 52 46
#> 3 2020-08-25 00:30:00 61 39
#> 4 2020-08-25 00:45:00 29 18
#> 5 2020-08-25 01:00:00 83 26
#> 6 2020-08-25 01:15:00 50 23
Actograms
We then proceed to plot the actogram of the activity. We can choose
the start and end date of the actogram. We can also choose an alias for
the activity. This alias is used for aesthetics purpose in the plots.
For instance, if the name of the activity column is motion.index, we
don’t probably want to show this string in the plot’s legend, but rather
have it coded properly as ‘Motion Index’. The actogram function also
offers the possibility to save then graph directly in a path as shown in
the commented line below. Users only provide the path (relative or
physical) without the file name ignoring the extension. All pictures in
digiRhythm are saved with the tiff format as it’s the most widely used
extension for scientific publications.
User have the possibility to save the plot with another extension or
configuration simply by saving the actogram object in a variable (which
is a ggplot object) and control it as they wish to.
start <- "2020-08-25" #year-month-day
end <- "2020-10-11" #year-month-day -->
activity_alias <- 'Motion Index'
#save <- 'sample_results/actogram' #if NULL, don't save the image
my_actogram <- actogram(data, activity, activity_alias , start, end, save = NULL)
Average Activity
Now, we plot the average activity. We follow the same dataframe and
same logic like for the actograms.
start <- "2020-08-25" #year-month-day
end <- "2020-10-11" #year-month-day -->
activity_alias <- 'Motion Index'
#save <- 'sample_results/actogram' #if NULL, don't save the image
my_daa <- daily_average_activity(data, activity, activity_alias , start, end, save = NULL)
Diurnality Index
The diurnality index could be computed and plotted using the
diurnality function as follows.
day_time = c("06:30:00", "16:30:00")
night_time = c("18:00:00", "T05:00:00")
my_di <- diurnality(data, activity,day_time, night_time)
