Stratified sampling on factor

Question

I have a dataset of 1000 rows with the following structure:

     device geslacht leeftijd type1 type2
1       mob        0       53     C     3
2       tab        1       64     G     7
3        pc        1       50     G     7
4       tab        0       75     C     3
5       mob        1       54     G     7
6        pc        1       58     H     8
7        pc        1       57     A     1
8        pc        0       68     E     5
9        pc        0       66     G     7
10      mob        0       45     C     3
11      tab        1       77     E     5
12      mob        1       16     A     1

I would like to make a sample of 80 rows, composed of 10 rows with type1 = A, 10 rows with type1 = B, and so on. Is there anyone who can help he?

Answer 1

Here's how I would approach this using data.table

library(data.table)
indx <- setDT(df)[, .I[sample(.N, 10, replace = TRUE)], by = type1]$V1
df[indx]
#     device geslacht leeftijd type1 type2
#  1:    mob        0       45     C     3
#  2:    mob        0       53     C     3
#  3:    tab        0       75     C     3
#  4:    mob        0       53     C     3
#  5:    tab        0       75     C     3
#  6:    mob        0       45     C     3
#  7:    tab        0       75     C     3
#  8:    mob        0       53     C     3
#  9:    mob        0       53     C     3
# 10:    mob        0       53     C     3
# 11:    mob        1       54     G     7
#...

Or a simpler version would be

setDT(df)[, .SD[sample(.N, 10, replace = TRUE)], by = type1]

Basically we are sampling (with replacement- as you have less than 10 rows within each group) from the row indexes within each group of type1 and then subsetting the data by this index

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Similarly with dplyr you could do

library(dplyr)
df %>% 
  group_by(type1) %>%
  sample_n(10, replace = TRUE)