Efficient way to create stratified subsamples of a data frame, depending on frequency of a category

Question

I want to create a sub-sample of data frame df, depending on the frequency of a given category in one of its columns, e.g. a.

Let's assume we have a data frame like this:

df <- data.frame(a = rep(1:4, c(3, 9, 4, 8)),
                 b = runif(24)) 

then I want to get a sub-sample of rows, proportional to the categories in column a, first in a random way:

smpl <- unlist(lapply(1:4, \(x) sample(c(TRUE, FALSE), 
                                       size = sum(x==df$a), 
                                       replace = TRUE)))
df[smpl,]

Here sample leads to the intended effect, that half of the records are returned on average for each category. However, it may be more or less (and even zero) for a category in a specific case.

I am also looking for second "more deterministic" approach, where only the cases are selected at random, but returns for each category either 50% of cases in the even case or N %/% 2 resp. N %/% 2 +1 records in the uneven case. The code should be easily readable.

Answer 1

In the meantime, I found a possible solution myself. First I searched for "stratified" instead of "weighted" and changed the question title accordingly. Then, function slice_sample was found in package dplyr. It can be run with two optional arguments n and prop, so we can do:

Case 1:

df |> slice_sample(n = nrow(df) %/% 2, weight_by = a)

Case 2:

df |> slice_sample(prop=0.5, weight_by = a)