I have a data frame containing scores of several sub-scales of the same test (columns: participant, session, group, total score, one column per sub-scale). I am trying to run assumption checks for a two-way mixed ANOVA for each sub-scale. For convenience, I would like to write one loop per assumption check, that gives me the output for all sub-scales. This worked well for checking outliers, running Box's M test and for generating the actual ANOVA output. However, I get an error when trying the same thing with Levene's test. See code and errors below:
subscales <- c("awareness", "clarity", "impulse", "goals", "nonacceptance",
"strategies") # these correspond to the column names in the df
for (scale in subscales) {
ders %>%
group_by(session) %>%
levene_test(scale ~ group) %>%
kable(caption = scale) %>% print()
}
Error in mutate(., data = map(.data$data, .f, ...)) : Caused by error in
model.frame.default(): ! variable lengths differ (found for 'group')
How can I run Levene's test for all columns in my df without just repeating the same code over and over? I'm new to R, so maybe I'm trying in a too pythonist kind of way and should use something like lapply() instead?