How do I get column names when mutating across columns?

[johrpan]

I would like to compute one value per row into an additional column. This is the code I'm using:

table <- table %>%
    rowwise() %>%
    mutate(result = compute_result(c_across()))

My compute_result function gets passed a vector of values. However, I need to get access to the column names from within that function. How would I do that without resorting to manually looping around?

[jrkrideau]

I think we need a reproducible example (reprex)

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[johrpan]

---

Is this what you mean?

library(tidyverse)

compute_result <- function(x) {
    # I get a vector with numbers here.
    # So this works as expected:
    sum(x)

    # Instead, I would like to get a vector with named values to (e.g.) do
    # something with `names(x)`.
}

mtcars %>%
    rowwise() %>%
    mutate(result = compute_result(c_across())) %>%
    print()
#> # A tibble: 32 x 12
#> # Rowwise: 
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb result
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4   329.
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4   330.
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1   260.
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1   426.
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2   590.
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1   386.
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4   657.
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2   271.
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2   300.
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4   350.
#> # … with 22 more rows

^{Created on 2021-06-23 by the reprex package (v2.0.0)}

[nirgrahamuk]

library(tidyverse)

head(mtcars,2) %>% rowwise() %>% mutate(result = list(c_across())) %>% pull(result)
mtcarsnames <- names(mtcars)
head(mtcars,2) %>% mutate(rn=row_number()) %>% group_by(rn) %>% nest(all_of(mtcarsnames)) %>% pull(data)
#or even 
head(mtcars,2) %>% rowwise() %>% group_split()

it seems you are angling for something like the second and third ones , so you can feed a data.frame to your function.

[arthur.t]

I personally find across kind of unintuitive and difficult to debug. nest is more intuitive. It will create a list of data frames with the contents.

iris %>% 
  nest(data = Sepal.Length:Petal.Width) %>% 
  mutate(result = map(data, function(x) colnames(x))) %>% 
  unnest(data)

Obviously in this example, the function is just getting the column names, but you can put your own function there.