Repeating rows of data.frame in dplyr [duplicate] - r

Question

This question already has answers here:
Repeat each row of data.frame the number of times specified in a column
(10 answers)
Closed 2 years ago.
I have a trouble with repeating rows of my real data using dplyr. There is already another post in here repeat-rows-of-a-data-frame but no solution for dplyr.
Here I just wonder how could be the solution for dplyr
but failed with error:
Error: wrong result size (16), expected 4 or 1
library(dplyr)
df <- data.frame(column = letters[1:4])
df_rep <- df%>%
mutate(column=rep(column,each=4))
Expected output
>df_rep
column
#a
#a
#a
#a
#b
#b
#b
#b
#*
#*
#*

Answer 1

Using the uncount function will solve this problem as well. The column count indicates how often a row should be repeated.
library(tidyverse)
df <- tibble(letters = letters[1:4])
df
# A tibble: 4 x 1
letters
<chr>
1 a
2 b
3 c
4 d
df %>%
mutate(count = c(2, 3, 2, 4)) %>%
uncount(count)
# A tibble: 11 x 1
letters
<chr>
1 a
2 a
3 b
4 b
5 b
6 c
7 c
8 d
9 d
10 d
11 d

Answer 2

I was looking for a similar (but slightly different) solution. Posting here in case it's useful to anyone else.
In my case, I needed a more general solution that allows each letter to be repeated an arbitrary number of times. Here's what I came up with:
library(tidyverse)
df <- data.frame(letters = letters[1:4])
df
> df
letters
1 a
2 b
3 c
4 d
Let's say I want 2 A's, 3 B's, 2 C's and 4 D's:
df %>%
mutate(count = c(2, 3, 2, 4)) %>%
group_by(letters) %>%
expand(count = seq(1:count))
# A tibble: 11 x 2
# Groups: letters [4]
letters count
<fctr> <int>
1 a 1
2 a 2
3 b 1
4 b 2
5 b 3
6 c 1
7 c 2
8 d 1
9 d 2
10 d 3
11 d 4
If you don't want to keep the count column:
df %>%
mutate(count = c(2, 3, 2, 4)) %>%
group_by(letters) %>%
expand(count = seq(1:count)) %>%
select(letters)
# A tibble: 11 x 1
# Groups: letters [4]
letters
<fctr>
1 a
2 a
3 b
4 b
5 b
6 c
7 c
8 d
9 d
10 d
11 d
If you want the count to reflect the number of times each letter is repeated:
df %>%
mutate(count = c(2, 3, 2, 4)) %>%
group_by(letters) %>%
expand(count = seq(1:count)) %>%
mutate(count = max(count))
# A tibble: 11 x 2
# Groups: letters [4]
letters count
<fctr> <dbl>
1 a 2
2 a 2
3 b 3
4 b 3
5 b 3
6 c 2
7 c 2
8 d 4
9 d 4
10 d 4
11 d 4

Answer 3

This is rife with peril if the data.frame has other columns (there, I said it!), but the do block will allow you to generate a derived data.frame within a dplyr pipe (though, ceci n'est pas un pipe):
library(dplyr)
df <- data.frame(column = letters[1:4], stringsAsFactors = FALSE)
df %>%
do( data.frame(column = rep(.$column, each = 4), stringsAsFactors = FALSE) )
# column
# 1 a
# 2 a
# 3 a
# 4 a
# 5 b
# 6 b
# 7 b
# 8 b
# 9 c
# 10 c
# 11 c
# 12 c
# 13 d
# 14 d
# 15 d
# 16 d
As #Frank suggested, a much better alternative could be
df %>% slice(rep(1:n(), each=4))

Answer 4

I did a quick benchmark to show that uncount() is a lot faster than expand()
# for the pipe
library(magrittr)
# create some test data
df_test <-
tibble::tibble(
letter = letters,
row_count = sample(1:10, size = 26, replace = TRUE)
)
# benchmark
bench <- microbenchmark::microbenchmark(
expand = df_test %>%
dplyr::group_by(letter) %>%
tidyr::expand(row_count = seq(1:row_count)),
uncount = df_test %>%
tidyr::uncount(row_count)
)
# plot the benchmark
ggplot2::autoplot(bench)