Interactive Dropout Analysis with dropR

library(dropR)

Though the low-hurdle of the online version with its graphical user interface (GUI) is appealing for many use cases, there are good reasons to directly use dropR’s backend in the R console without the GUI: Some data.frames may need extra formatting or additional cleaning before they suit the dropR input format or you may want adapt and extend your analysis in a way the GUI does not allow to. If you are writing a report directly in RMarkdown, you can also make use of automatically reporting your results in your document or embedding a dropout plot from dropR.

Dropout Analysis Walkthrough

This section describes how to extract information on dropout from the demo data set without using the dropR shiny UI. First, let’s make sure the demo data set is loaded and available. The data set should look like this:

library(dropR)
# Use demo dataset in a new data frame to be edited
df <- dropRdemo
obs_id experimental_condition vi_1 vi_2 vi_3 … vi_51 vi_52
7a9f33 11 1 1 1 1 1
e11f94 22 1 NA 1 NA NA
e72a50 22 1 NA 1 1 1
f90f5f 11 1 1 1 1 1
20bc72 12 1 NA 1 1 1

Basic Dropout Statistics

Now, let’s extract dropout, i.e., information on when a participant dropped out of the questionnaire and never returned. For this, we need to identify the last question that someone filled out before only missing data is present a.k.a NAs. We will use the add_dropout_idx function on the demo data set and add the position of all question variables in the data. In the demo data, questions are easily identified by their prefix vi_:

qs <- which(grepl("vi_", names(df)))
# add numeric drop out position to original dataset
df <- add_dropout_idx(df, q_pos = qs)
kable(head(df[,c(1:3,(ncol(df)-1):ncol(df))]))
obs_id experimental_condition vi_1 vi_52 do_idx
7a9f33 11 1 1 0
e11f94 22 1 NA 6
e72a50 22 1 1 0
f90f5f 11 1 1 0
20bc72 12 1 1 0
76b97a 22 1 NA 27

The experimental_condition column indicates belonging to a sub sample, each of which was treated differently. For example, groups receive a different sequence of questions or different wording.

Next we’ll compute a table containing basic dropout statistics for each item using the compute_stats function.

stats <- compute_stats(df,
                       by_cond = "experimental_condition",
                       no_of_vars = length(qs))
kable(head(stats))
q_idx condition cs N remain pct_remain
1 total 11 246 235 0.9552846
2 total 14 246 232 0.9430894
3 total 23 246 223 0.9065041
4 total 26 246 220 0.8943089
5 total 27 246 219 0.8902439
6 total 31 246 215 0.8739837

Out of 246 participants in total in the demo sample, 235 participants remain in the survey at the first question, accounting for 95.53 percent of the sample. At the last question of the experiment, 61.79% of all participants “survived”. The cs column shows the absolute cumulative dropout count.

The stats table shows the dropout statistics for the total sample first and if defined in the function by_cond, it also shows the same statistics for each experimental condition separately. This table is the basis for many further analyses and can easily be reported.

Plotting Dropout Curves

Based on the above statistics table, dropR plots dropout curves very conveniently.

plot_do_curve(stats)

By default, the function to plot dropout curves chooses a color palette which is designed to de distinguishable for color blind individuals. Adhering to some journal guidelines, you may also choose a gray color palette, distinguishing the lines by line type and gray value.

Full Workflow Example using tidyverse

To wrap up this Walkthrough, we want to show you a full analysis example in just six lines of code using tidyverse workflow with functions from magrittr and ggplot2. Specifically, it is very easy to pipe several dropR functions to create the full analysis as well as plotting all at once. Moreover, it is easy to customize the plot further using common ggplot2 functions as shown. Assuming we want to create a similar analysis as before with a customized plot output, we can achieve this like so:

library(ggplot2)

dropRdemo %>% 
  add_dropout_idx(3:54) %>% 
  compute_stats(by_cond = "experimental_condition", no_of_vars = 52) %>% 
  plot_do_curve(linetypes = F, full_scale = F, show_confbands = T) +
  labs(title = "Dropout Plot with tidyverse Workflow") +
  scale_color_brewer(palette = "Dark2") + scale_fill_brewer(palette = "Dark2")
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.

Next, you may want to run more statistical dropout analyses using dropR. You can find an in-depth tutorial in our test article.