Rowwise function behaving abnormally

[jaymicro]

I am attempting to perform a rowwise mean calculation across multiple columns. The function below works fine

df <- tibble(x = runif(6), y = runif(6), z = runif(6))
df %>% rowwise() %>% mutate(m = mean(c(x, y, z)))

However, when I use a colon to specify the columns to perform the mean function R will return the outputs from the first column.

df %>% rowwise() %>% mutate(m = mean(c(x:z)))

---

Below is the reprex for the issue

library(tidyverse)
#> Warning: package 'tidyverse' was built under R version 4.4.1
#> Warning: package 'ggplot2' was built under R version 4.4.1
#> Warning: package 'tibble' was built under R version 4.4.1
#> Warning: package 'tidyr' was built under R version 4.4.1
#> Warning: package 'readr' was built under R version 4.4.1
#> Warning: package 'purrr' was built under R version 4.4.1
#> Warning: package 'dplyr' was built under R version 4.4.1
#> Warning: package 'stringr' was built under R version 4.4.1
#> Warning: package 'forcats' was built under R version 4.4.1
#> Warning: package 'lubridate' was built under R version 4.4.1
set.seed(111)
df <- tibble(x = runif(6), y = runif(6), z = runif(6))
df %>% rowwise() %>% mutate(m = mean(c(x, y, z)))
#> # A tibble: 6 × 4
#> # Rowwise: 
#>       x      y      z     m
#>   <dbl>  <dbl>  <dbl> <dbl>
#> 1 0.593 0.0107 0.0671 0.224
#> 2 0.726 0.532  0.0475 0.435
#> 3 0.370 0.432  0.156  0.320
#> 4 0.515 0.0937 0.446  0.352
#> 5 0.378 0.556  0.171  0.368
#> 6 0.418 0.590  0.967  0.658
df %>% rowwise() %>% mutate(m = mean(c(x:z)))
#> # A tibble: 6 × 4
#> # Rowwise: 
#>       x      y      z     m
#>   <dbl>  <dbl>  <dbl> <dbl>
#> 1 0.593 0.0107 0.0671 0.593
#> 2 0.726 0.532  0.0475 0.726
#> 3 0.370 0.432  0.156  0.370
#> 4 0.515 0.0937 0.446  0.515
#> 5 0.378 0.556  0.171  0.378
#> 6 0.418 0.590  0.967  0.418

^{Created on 2024-10-10 with reprex v2.1.1}

Standard output and standard error

-- nothing to show --

Session info

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
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#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  English_Canada.utf8
#>  ctype    English_Canada.utf8
#>  tz       America/Vancouver
#>  date     2024-10-10
#>  pandoc   3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
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#> ──────────────────────────────────────────────────────────────────────────────

[joranE]

I believe the intent is for people to use c_across in those circumstances when you want the tidyselect semantics:

df %>% rowwise() %>% mutate(m = mean(c_across(x:z)))

[jaymicro]

That makes sense. Thank you!