R: sample with different sample sizes for groups

Question

I have a data frame with 2 grouping columns V1 and V2. I want to sample exactly n = 4 elements for each distinct value in V1 and make sure that a minimum of m = 1 of each distinct element in V2 is sampled.

library(tidyverse)
set.seed(1)
df = data.frame(
  V1 = c(rep("A",6), rep("B",6)),
  V2 = c("C","C","D","D","E","E","F","F","G","G","H","H"),
  V3 = rnorm(12)
)

df
   V1 V2         V3
1   A  C -0.6264538
2   A  C  0.1836433
3   A  D -0.8356286
4   A  D  1.5952808
5   A  E  0.3295078
6   A  E -0.8204684
7   B  F  0.4874291
8   B  F  0.7383247
9   B  G  0.5757814
10  B  G -0.3053884
11  B  H  1.5117812
12  B  H  0.3898432

My desired output is for example...

V1    V2        V3
1 A     C     -0.626
2 A     D     -0.836
3 A     E     -0.820
4 A     E      0.329
5 B     F      0.487
6 B     G      0.576
7 B     G     -0.305
8 B     H      0.390

I do not know how to generate this output. When I group by V1 and V2 I get n = 3 elements for each distinct value in V1.

df %>%
  group_by(V1,V2) %>%
  sample_n(1)

  V1    V2        V3
1 A     C     -0.626
2 A     D     -0.836
3 A     E     -0.820
4 B     F      0.487
5 B     G      0.576
6 B     H      0.390

The "splitstackshape" or "sampling" packages did not help.

Answer 1

Here is one approach:

library(dplyr)

nr <- 4
first_pass <- df %>% group_by(V1, V2) %>% sample_n(1) %>% ungroup

first_pass %>% 
  count(V1) %>% 
  mutate(n = nr - n) %>%
  left_join(df, by = 'V1') %>%
  group_by(V1) %>%
  sample_n(first(n)) %>%
  select(-n) %>%
  bind_rows(first_pass) %>%
  arrange(V1, V2)

#  V1    V2        V3
#  <chr> <chr>  <dbl>
#1 A     C      0.184
#2 A     D     -0.836
#3 A     E     -0.820
#4 A     E     -0.820
#5 B     F      0.487
#6 B     F      0.738
#7 B     G     -0.305
#8 B     H      0.390

The logic is to first randomly select 1 row for each V1 and V2 . We then calculate for each V1 how many more rows do we need to get nr rows and sample them randomly from each V1 and combine the final dataset.