Use across() and c_across() {dplyr} functions!

how_to_across_dplyr.R

# https://dplyr.tidyverse.org/articles/rowwise.html 
``` r
library(tidyverse)

# how to use across() -------------------------------------------------------

# _ with summary functions ------------------------------------------------

# from cheetsheet
# summarise(mtcars, across(everything(), mean))

# traditionally, you can apply summary functions
# to vectors in a dataframe
mtcars %>% 
  as_tibble() %>%
  summarise(mean(mpg))
#> # A tibble: 1 × 1
#>   `mean(mpg)`
#>         <dbl>
#> 1        20.1
mtcars %>% 
  as_tibble() %>%
  group_by(vs) %>% 
  summarise(mean(mpg))
#> # A tibble: 2 × 2
#>      vs `mean(mpg)`
#>   <dbl>       <dbl>
#> 1     0        16.6
#> 2     1        24.6

# however, you can only apply it for only on
# variable at the time!
mtcars %>% 
  as_tibble() %>%
  summarise(mean(mpg),mean(cyl))
#> # A tibble: 1 × 2
#>   `mean(mpg)` `mean(cyl)`
#>         <dbl>       <dbl>
#> 1        20.1        6.19

# for this,
# you can use across() to apply
# a summary function to multiple columns
mtcars %>% 
  as_tibble() %>%
  summarise(across(.cols = c(mpg, cyl),.fns = mean))
#> # A tibble: 1 × 2
#>     mpg   cyl
#>   <dbl> <dbl>
#> 1  20.1  6.19
mtcars %>% 
  as_tibble() %>%
  group_by(vs) %>% 
  summarise(across(.cols = c(mpg, cyl),.fns = mean))
#> # A tibble: 2 × 3
#>      vs   mpg   cyl
#>   <dbl> <dbl> <dbl>
#> 1     0  16.6  7.44
#> 2     1  24.6  4.57
# use select helpers for it!
mtcars %>% 
  as_tibble() %>%
  group_by(vs) %>% 
  summarise(across(.cols = everything(),.fns = mean))
#> # A tibble: 2 × 11
#>      vs   mpg   cyl  disp    hp  drat    wt  qsec    am  gear  carb
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     0  16.6  7.44  307. 190.   3.39  3.69  16.7 0.333  3.56  3.61
#> 2     1  24.6  4.57  132.  91.4  3.86  2.61  19.3 0.5    3.86  1.79

# _ with vectorise functions ----------------------------------------------

# additionally

# if you want to apply one same 
# vectorize function 
# to multiple columns
# you require to apply it to each one
table4a %>% 
  janitor::clean_names() %>% 
  mutate(x1999 = as.character(x1999),
         x2000 = as.character(x2000))
#> # A tibble: 3 × 3
#>   country     x1999  x2000 
#>   <chr>       <chr>  <chr> 
#> 1 Afghanistan 745    2666  
#> 2 Brazil      37737  80488 
#> 3 China       212258 213766

# instead of repeating multiple lines
# for the same function,
# your could use:
table4a %>% 
  janitor::clean_names() %>% 
  mutate(across(.cols = -country,.fns = as.character))
#> # A tibble: 3 × 3
#>   country     x1999  x2000 
#>   <chr>       <chr>  <chr> 
#> 1 Afghanistan 745    2666  
#> 2 Brazil      37737  80488 
#> 3 China       212258 213766

# also...
# for mathematical operations
# you could use the ~.x notation like this:
table4a %>% 
  janitor::clean_names() %>% 
  mutate(x1999 = x1999*2,
         x2000 = x2000*2)
#> # A tibble: 3 × 3
#>   country      x1999  x2000
#>   <chr>        <dbl>  <dbl>
#> 1 Afghanistan   1490   5332
#> 2 Brazil       75474 160976
#> 3 China       424516 427532

# alternativelly,
# you can wrote less by using across
# also using select_helpers
# and avoid repeat variable names
table4a %>% 
  janitor::clean_names() %>% 
  mutate(across(.cols = -country,.fns = ~.x*2))
#> # A tibble: 3 × 3
#>   country      x1999  x2000
#>   <chr>        <dbl>  <dbl>
#> 1 Afghanistan   1490   5332
#> 2 Brazil       75474 160976
#> 3 China       424516 427532

# how to use c_across() -----------------------------------------------------

# _ with summary functions ------------------------------------------------

# from cheetsheet
# transmute(rowwise(UKgas), total = sum(c_across(1:2)))
table4a %>% 
  janitor::clean_names() %>%
  rowwise() %>% 
  mutate(sum = sum(c_across(cols = -country))) # warning: use `na.rm = T` to avoid missing results! 
#> # A tibble: 3 × 4
#> # Rowwise: 
#>   country      x1999  x2000    sum
#>   <chr>        <int>  <int>  <int>
#> 1 Afghanistan    745   2666   3411
#> 2 Brazil       37737  80488 118225
#> 3 China       212258 213766 426024

# and for applying rowwise
# multiple 
# summary functions
table4a %>% 
  janitor::clean_names() %>%
  rowwise() %>% 
  mutate(sum = sum(c_across(cols = -country)), # warning: use `na.rm = T` to avoid missing results! 
         sd = sd(c_across(cols = -country)))
#> # A tibble: 3 × 5
#> # Rowwise: 
#>   country      x1999  x2000    sum      sd
#>   <chr>        <int>  <int>  <int>   <dbl>
#> 1 Afghanistan    745   2666   3411   1376.
#> 2 Brazil       37737  80488 118225  40270.
#> 3 China       212258 213766 426024 122985.
```

<sup>Created on 2022-03-29 by the [reprex package](https://reprex.tidyverse.org) (v2.0.1)</sup>