CODEWITHSUNDEEP
rdplyrrenamepurrr
Emman
Emman

Reputation: 4201

In a tibble nesting tibbles (inside list-columns), how to update tibbles to rename columns with a common name?

Using purrr, I've summarized iris data into a new mutated list-column:

library(tidyverse)

my_tibble <-
  iris %>%
  nest(data = everything()) %>%
  mutate(summary_tbl = map(.x = data,
                           ~ .x %>%
                             group_by(Species) %>%
                             summarise(mean_by_cat = mean(Sepal.Length))))

my_tibble
#> # A tibble: 1 x 2
#>   data               summary_tbl     
#>   <list>             <list>          
#> 1 <tibble [150 x 5]> <tibble [3 x 2]>

Created on 2021-03-16 by the reprex package (v0.3.0)

If we unnest data or summary_tbl we can see that both contain a tibble, with some overlapping column names (in this case Species):

my_tibble %>%
  pull(data)

## [[1]]
## # A tibble: 150 x 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa 
## # ... with 140 more rows

my_tibble %>%
  pull(summary_tbl)

## [[1]]
## # A tibble: 3 x 2
##   Species    mean_by_cat
## * <fct>            <dbl>
## 1 setosa            5.01
## 2 versicolor        5.94
## 3 virginica         6.59

Is there an efficient way to rename a column name that appears inside any of my_tibble's list-columns? For example, if we define:

var_to_rename <- "Species"
new_name <- "my_grouping_var"

Then provided with my_tibble, var_to_rename, and new_name, how can we programmatically get the following?

my_tibble %>%
  pull(data)

## [[1]]
## # A tibble: 150 x 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width my_grouping_var
##           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa 
## # ... with 140 more rows

my_tibble %>%
  pull(summary_tbl)

## [[1]]
## # A tibble: 3 x 2
##   my_grouping_var  mean_by_cat
## * <fct>            <dbl>
## 1 setosa            5.01
## 2 versicolor        5.94
## 3 virginica         6.59

Clearly I could have renamed Species to my_grouping_var before the mutate part, but my question is aimed at renaming an existing tibble after the fact.

Upvotes: 1

Views: 94

Answers (2)

Dan Chaltiel
Dan Chaltiel

Reputation: 8523

If you want to use a function you can use rename() with the curly-curly operator ({{}}) and the colon-equal operator (:=):

foo = function(var_to_rename, new_name){
  my_tibble %>% 
    mutate(summary_tbl = map(summary_tbl, ~{
      rename(.x, {{new_name}}:={{var_to_rename}})
    }))
}
my_tibble2=foo("Species", "my_grouping_var")

my_tibble2 %>%
  pull(summary_tbl)
#> [[1]]
#> # A tibble: 3 x 2
#>   my_grouping_var mean_by_cat
#>   <fct>                 <dbl>
#> 1 setosa                 5.01
#> 2 versicolor             5.94
#> 3 virginica              6.59

Created on 2021-03-16 by the reprex package (v1.0.0)

More info on how to use dplyr's tidy-evaluation in functions on https://dplyr.tidyverse.org/articles/programming.html.

Upvotes: 3

Ronak Shah
Ronak Shah

Reputation: 389265

You can do :

library(dplyr)

my_tibble <- my_tibble %>%
                 mutate(across(.fns = ~.[[1]] %>% 
                     rename_with(~new_name, all_of(var_to_rename)) %>% list()))

my_tibble %>% pull(summary_tbl)

#[[1]]
# A tibble: 3 x 2
#  my_grouping_var mean_by_cat
#* <fct>                 <dbl>
#1 setosa                 5.01
#2 versicolor             5.94
#3 virginica              6.59

Upvotes: 2

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