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.
In versions of dplyr
0.3 and later, this works just fine:
df %>% group_by(color) %>% sample_n(size = 3)
dplyr
(version <= 0.2)I set out to answer this using dplyr, assuming that this would work:
df %.% group_by(color) %.% sample_n(size = 3)
But it turns out that in 0.2 the sample_n.grouped_df
S3 method exists but isn't registered in the NAMESPACE file, so it's never dispatched. Instead, I had to do this:
df %.% group_by(color) %.% dplyr:::sample_n.grouped_df(size = 3)
Source: local data frame [12 x 3]
Groups: color
X1 X2 color
8 0.66152710 -0.7767473 blue
1 -0.70293752 -0.2372700 blue
2 -0.46691793 -0.4382669 blue
32 -0.47547565 -1.0179842 pink
31 -0.15254540 -0.6149726 pink
39 0.08135292 -0.2141423 pink
15 0.47721644 -1.5033192 red
16 1.26160230 1.1202527 red
12 -2.18431919 0.2370912 red
24 0.10493757 1.4065835 yellow
21 -0.03950873 -1.1582658 yellow
28 -2.15872261 -1.5499822 yellow
Presumably this will be fixed in a future update.