From these questions - Random sample of rows from subset of an R dataframe & Sample random rows in dataframe I can easily see how to randomly sample (select) 'n' rows from a df, or 'n' rows that originate from a specific level of a factor within a df.
Here are some sample data:
df <- data.frame(matrix(rnorm(80), nrow=40))
df$color <- rep(c("blue", "red", "yellow", "pink"), each=10)
df[sample(nrow(df), 3), ] #samples 3 random rows from df, without replacement.
To e.g. just sample 3 random rows from 'pink' color - using library(kimisc):
library(kimisc)
sample.rows(subset(df, color == "pink"), 3)
or writing custom function:
sample.df <- function(df, n) df[sample(nrow(df), n), , drop = FALSE]
sample.df(subset(df, color == "pink"), 3)
However, I want to sample 3 (or n) random rows from each level of the factor. I.e. the new df would have 12 rows (3 from blue, 3 from red, 3 from yellow, 3 from pink). It's obviously possible to run this several times, create newdfs for each color, and then bind them together, but I am looking for a simpler solution.
You can assign a random ID to each element that has a particular factor level using ave. Then you can select all random IDs in a certain range.
rndid <- with(df, ave(X1, color, FUN=function(x) {sample.int(length(x))}))
df[rndid<=3,]
This has the advantage of preserving the original row order and row names if that's something you are interested in. Plus you can re-use the rndid vector to create subset of different lengths fairly easily.