Stratified Sampling in R with dplyr

stratify.r

# Uses a subset of the Iris data set with different proportions of the Species factor
set.seed(42)
iris_subset <- iris[c(1:50, 51:80, 101:120), ]

stratified_sample <- iris_subset %>%
  group_by(Species) %>%
  mutate(num_rows=n()) %>%
  sample_frac(0.4, weight=num_rows) %>%
  ungroup

# These results should be equal
table(iris_subset$Species) / nrow(iris_subset)
table(stratified_sample$Species) / nrow(stratified_sample)

# Success!
#     setosa versicolor  virginica 
#       0.5        0.3        0.2 

This is nice, introduced sample_frac to me thanks!

However, I might not have entirely understood now having read the docs ... why do we need to have the num_rows and weight by them? The docs say subset_frac honours any grouping so I would have thought this also achieves a stratified sample:

stratified_sample <- iris_subset %>%
  group_by(Species) %>%
  sample_frac(0.4) %>%
  ungroup

Indeed, I think sample_frac will by definition see the same weight in each group so that the weight has no effect after grouping?

I have the same question as above-- any responses on this?