Randomly sample data frame into 3 groups in R

Question

Objective: Randomly divide a data frame into 3 samples.

• one sample with 60% of the rows
• other two samples with 20% of the rows
• samples should not have duplicates of others (i.e. sample without replacement).

Here's a clunky solution:

allrows <- 1:nrow(mtcars)

set.seed(7)
trainrows <- sample(allrows, replace = F, size = 0.6*length(allrows))
test_cvrows <- allrows[-trainrows]
testrows <- sample(test_cvrows, replace=F, size = 0.5*length(test_cvrows))
cvrows <- test_cvrows[-which(test_cvrows %in% testrows)]

train <- mtcars[trainrows,]
test <- mtcars[testrows,]
cvr <- mtcars[cvrows,]

There must be something easier, perhaps in a package. dplyr has the sample_frac function, but that seems to target a single sample, not a split into multiple.

Close, but not quite the answer to this question: Random Sample with multiple probabilities in R

Answer 1

Do you need the partitioning to be exact? If not,

set.seed(7)
ss <- sample(1:3,size=nrow(mtcars),replace=TRUE,prob=c(0.6,0.2,0.2))
train <- mtcars[ss==1,]
test <- mtcars[ss==2,]
cvr <- mtcars[ss==3,]

should do it.

Or, as @Frank says in comments, you can split() the original data to keep them as elements of a list:

mycars <- setNames(split(mtcars,ss), c("train","test","cvr"))

Answer 2

Not the prettiest solution (especially for larger samples), but it works.

n = nrow(mtcars)
#use different rounding for differet sizes/proportions
times =rep(1:3,c(0.6*n,0.2*n,0.2*n))
ntimes = length(times)
if (ntimes < n)
    times = c(times,sample(1:3,n-ntimes,prob=c(0.6,0.2,0.2),replace=FALSE))
sets = sample(times)
df1 = mtcars[sets==1,]
df2 = mtcars[sets==2,]
df3 = mtcars[sets==3,]