How can I keep old columns and rename new columns when using `mutate` with `across`

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When I mutate across data, the columns selected by .cols are replaced by the results of the mutation. How can I perform this operation whilst:

  • Keeping the columns selected by .cols in the output
  • Appropriately & automatically renaming the columns created by mutate?

For example:

require(dplyr)
#> Loading required package: dplyr
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
require(magrittr)
#> Loading required package: magrittr
set.seed(7337)

## Create arbitrary tibble
myTibble <- tibble(x = 1:10,
                   y = runif(10),
                   z = y * pi)

## I can mutate across these columns
mutate(myTibble, across(everything(), multiply_by, 2))
#> # A tibble: 10 x 3
#>        x       y      z
#>    <dbl>   <dbl>  <dbl>
#>  1     2 1.78    5.58  
#>  2     4 0.658   2.07  
#>  3     6 0.105   0.331 
#>  4     8 1.75    5.50  
#>  5    10 1.33    4.19  
#>  6    12 1.02    3.20  
#>  7    14 1.20    3.75  
#>  8    16 0.00794 0.0250
#>  9    18 0.108   0.340 
#> 10    20 1.74    5.45

## I can subsequently rename these columns
mutate(myTibble, across(everything(), multiply_by, 2)) %>% 
  rename_with(paste0, everything(), "_double")
#> # A tibble: 10 x 3
#>    x_double y_double z_double
#>       <dbl>    <dbl>    <dbl>
#>  1        2  1.78      5.58  
#>  2        4  0.658     2.07  
#>  3        6  0.105     0.331 
#>  4        8  1.75      5.50  
#>  5       10  1.33      4.19  
#>  6       12  1.02      3.20  
#>  7       14  1.20      3.75  
#>  8       16  0.00794   0.0250
#>  9       18  0.108     0.340 
#> 10       20  1.74      5.45

## But how can I achieve this (without the fuss of creating & joining an additional table):

# A tibble: 10 x 6
    # x      y     z           x_double y_double z_double
# <int>  <dbl> <dbl>        <dbl>    <dbl>    <dbl>
#   1     1 0.313  0.982      2    0.625    1.96 
  # 2     2 0.759  2.39       4    1.52     4.77 
  # 3     3 0.705  2.22       6    1.41     4.43 
  # 4     4 0.573  1.80       8    1.15     3.60 
  # 5     5 0.599  1.88      10    1.20     3.77 
  # 6     6 0.0548 0.172     12    0.110    0.344
  # 7     7 0.571  1.80      14    1.14     3.59 
  # 8     8 0.621  1.95      16    1.24     3.90 
  # 9     9 0.709  2.23      18    1.42     4.46 
  # 10    10 0.954  3.00     20    1.91     5.99

Created on 2021-09-16 by the reprex package (v2.0.1)

across dplyr rename r
1 Answers

Use the .names argument of across

across names its outputs using the argument .names, which is an argument passed to glue::glue(). This is a string in which "{.col}" and "{.fn}" are replaced by the names of your columns (specified by .cols) and functions (specified by .fns)

The default value for .names is NULL, which is equivalent to "{.col}". This means that every mutated column is assigned the same name its counterpart in .cols, which effectively 'overwrites' these columns in the output.

To produce your desired table you would need to do:

require(dplyr)
#> Loading required package: dplyr
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
require(magrittr)
#> Loading required package: magrittr
set.seed(7337)

## Create arbitrary tibble
myTibble <- tibble(x = 1:10,
                   y = runif(10),
                   z = y * pi)

mutate(myTibble, across(everything(), multiply_by, 2, .names = "{.col}_double"))
#> # A tibble: 10 x 6
#>        x       y      z x_double y_double z_double
#>    <int>   <dbl>  <dbl>    <dbl>    <dbl>    <dbl>
#>  1     1 0.889   2.79          2  1.78      5.58  
#>  2     2 0.329   1.03          4  0.658     2.07  
#>  3     3 0.0527  0.165         6  0.105     0.331 
#>  4     4 0.875   2.75          8  1.75      5.50  
#>  5     5 0.666   2.09         10  1.33      4.19  
#>  6     6 0.509   1.60         12  1.02      3.20  
#>  7     7 0.598   1.88         14  1.20      3.75  
#>  8     8 0.00397 0.0125       16  0.00794   0.0250
#>  9     9 0.0541  0.170        18  0.108     0.340 
#> 10    10 0.868   2.73         20  1.74      5.45

Created on 2021-09-16 by the reprex package (v2.0.1)

In this way, you can use across with .fns and .names to do quite a lot:

mutate(myTibble, across(everything(),
                        .fns = list(double = multiply_by, half = divide_by),
                        2,
                        .names = "{.col}_{.fn}"))
#> # A tibble: 10 x 9
#>        x       y      z x_double x_half y_double  y_half z_double  z_half
#>    <int>   <dbl>  <dbl>    <dbl>  <dbl>    <dbl>   <dbl>    <dbl>   <dbl>
#>  1     1 0.889   2.79          2    0.5  1.78    0.444     5.58   1.40   
#>  2     2 0.329   1.03          4    1    0.658   0.165     2.07   0.517  
#>  3     3 0.0527  0.165         6    1.5  0.105   0.0263    0.331  0.0827 
#>  4     4 0.875   2.75          8    2    1.75    0.437     5.50   1.37   
#>  5     5 0.666   2.09         10    2.5  1.33    0.333     4.19   1.05   
#>  6     6 0.509   1.60         12    3    1.02    0.255     3.20   0.800  
#>  7     7 0.598   1.88         14    3.5  1.20    0.299     3.75   0.939  
#>  8     8 0.00397 0.0125       16    4    0.00794 0.00199   0.0250 0.00624
#>  9     9 0.0541  0.170        18    4.5  0.108   0.0271    0.340  0.0850 
#> 10    10 0.868   2.73         20    5    1.74    0.434     5.45   1.36
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