Random sampling in a list using different proportions by group

Question

I'm trying to do a random sampling by a group of variables using different proportions for each group.

For example, I want to sample iris dataset using 75% for setosa species, 80% for versicolor, and 70% for virginica.

sample_size<-data.frame(Species=c("setosa","versicolor","virginica"), prop=c(0.75,0.80,0.70))
iris2 <- merge(iris,sample_size, by="Species",all.x=TRUE)

# created a list
st <- split(iris2, iris2$Species)

set.seed(1234)

# Create the indexes: Sampling by segment using the proportions calculated above
st2 <- lapply(st, function(df) 
df <- sample(nrow(df), nrow(df)*df$prop))

# get the observations
st3 <- lapply(st, function(df,st2) 
df2 <- df[st2,])

I got correct the indexes for sampling:

$setosa
[1]  6 31 30 48 40 29  1 10 28 22 42 41 11 35 38 47 43  9 50  8 34 33  5  2 32 21 13 39 19 44 37 26 23 45  3 12 16
$versicolor
[1] 13 49 39 27 30 15 28 45 22 44 20 10 46  3 12 26 18  6 17 16 23 33 24 41  2  8  1 29 31  7 11 47 40 37 43 19 34 35 21  5
$virginica
[1]  4 16 33 44 22  7 24  9 38 49 13 45 35 39 48  5 42 50 17 10  1 43 21 30 15  8 34 36 25 23  3 29 27 40  2

But instead of getting the samples, I'm getting the entire population.

str(st3)
$ setosa    :'data.frame':  50 obs. of  6 variables:
..$ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
..$ Sepal.Length: num [1:50] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
..$ Sepal.Width : num [1:50] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
..$ Petal.Length: num [1:50] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
..$ Petal.Width : num [1:50] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
..$ prop        : num [1:50] 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 ...
$ versicolor:'data.frame':  50 obs. of  6 variables:
..$ Species     : Factor w/ 3 levels "setosa","versicolor",..: 2 2 2 2 2 2 2 2 2 2 ...
..$ Sepal.Length: num [1:50] 7 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 ...
..$ Sepal.Width : num [1:50] 3.2 3.2 3.1 2.3 2.8 2.8 3.3 2.4 2.9 2.7 ...
..$ Petal.Length: num [1:50] 4.7 4.5 4.9 4 4.6 4.5 4.7 3.3 4.6 3.9 ...
..$ Petal.Width : num [1:50] 1.4 1.5 1.5 1.3 1.5 1.3 1.6 1 1.3 1.4 ...
..$ prop        : num [1:50] 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 ...
$ virginica :'data.frame':  50 obs. of  6 variables:
..$ Species     : Factor w/ 3 levels "setosa","versicolor",..: 3 3 3 3 3 3 3 3 3 3 ...
..$ Sepal.Length: num [1:50] 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 ...
..$ Sepal.Width : num [1:50] 3.3 2.7 3 2.9 3 3 2.5 2.9 2.5 3.6 ...
..$ Petal.Length: num [1:50] 6 5.1 5.9 5.6 5.8 6.6 4.5 6.3 5.8 6.1 ...
..$ Petal.Width : num [1:50] 2.5 1.9 2.1 1.8 2.2 2.1 1.7 1.8 1.8 2.5 ...
..$ prop        : num [1:50] 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 ...

Any help is appreciated! Thanks in advance!

Answer 1

I split the data.frame into a list of data frames following: https://stackoverflow.com/a/18527515/3362993 then I ran dplyr::sample_frac on each list element.

library(dplyr)

data(iris)

props <- c(setosa = 0.75, versicolor = 0.8, virginica = 0.7)
iris <- split(iris, f = iris$Species)

res <- lapply(seq_along(props), function(x) sample_frac(iris[[x]], props[x]))    
res <- do.call("rbind", res)

table(res$Species)

---

setosa versicolor  virginica 
    38         40         35