CODEWITHSUNDEEP
rtidyversepurrr
kputschko
kputschko

Reputation: 816

Adding a counting sequence to a nested dataframe in R using purrr

I have a list of nested data frames. I want to add a column of a sequential count to each nested dataframe.

This works in typical dataframe:

library(tidyverse)
mtcars %>% mutate(index = sequence(n()))

    mpg cyl  disp  hp drat    wt  qsec vs am gear carb index
1  21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4     1
2  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4     2
3  22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1     3
4  21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1     4
5  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2     5

But it does not work in a nested structure:

table <- 
tribble(~id, ~data, 1, mtcars) %>% 
mutate(data_index = map(data, ~mutate(.x, index = sequence(n()))))

table

# A tibble: 1 x 3
     id                   data             data_index
  <dbl>                 <list>                 <list>
1     1 <data.frame [32 x 11]> <data.frame [32 x 12]>

head(table$data_index[[1]])

   mpg cyl disp  hp drat    wt  qsec vs am gear carb index
1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4     1
2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4     1
3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1     1
4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1     1
5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2     1
6 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1     1

Any thoughts on how to overcome this?

Upvotes: 2

Views: 381

Answers (1)

www
www

Reputation: 39154

I modified your code a little bit. The key is n() returns the row number of the entire data frame, not mtcars. So I used nrow(.x) instead.

library(tidyverse)

dt <- tribble(~id, ~data, 1, mtcars) 

dt2 <- dt %>% 
  mutate(data_index = map(data, ~mutate(.x, index = sequence(nrow(.x)))))
head(dt2$data_index[[1]])
#    mpg cyl disp  hp drat    wt  qsec vs am gear carb index
# 1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4     1
# 2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4     2
# 3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1     3
# 4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1     4
# 5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2     5
# 6 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1     6

Upvotes: 3

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