Adding differences to a summary-table created without iteration - r

Question

Based on my first question found here about creating a summary table without iteration, ie. without using map, I made the following algorithm based on the formidable answers,
library(tidyverse)
sum_variables <- c("mpg", "hp", "disp")
# Create grouping var; ####
mtcars <- mtcars %>% mutate(
am_factor = case_when(
am == 0 ~ "Automatic",
TRUE ~ "Manual"
)
)
# Create summary table; ####
mtcars %>%
group_by(am_factor) %>%
summarise(
across(
all_of(sum_variables),
~ paste0(mean(.) %>% round(2), "(±", sd(.) %>% round(2), ")")
)
) %>% pivot_longer(
cols = -"am_factor"
) %>% pivot_wider(
names_from = "am_factor"
)
Which gives me the following output,
# A tibble: 3 x 3
name Automatic Manual
<chr> <chr> <chr>
1 mpg 17.15(±3.83) 24.39(±6.17)
2 hp 160.26(±53.91) 126.85(±84.06)
3 disp 290.38(±110.17) 143.53(±87.2)
Using paste0 here has the benefit of reducing the amount of codes needed in the algorithm, but complicates further additions to table. If I, for example, want to add differences to this table, my current solution is the following,
mtcars %>%
group_by(am_factor) %>%
summarise(
across(
all_of(sum_variables),
~ paste0(mean(.) %>% round(2), "(±", sd(.) %>% round(2), ")")
)
) %>% pivot_longer(
cols = -"am_factor"
) %>% pivot_wider(
names_from = "am_factor"
) %>% mutate(
difference = str_extract(Automatic, "[:digit:].?[:digit:]") %>% as.numeric() -
str_extract(Manual, "[:digit:].?[:digit:]") %>% as.numeric()
)
Which gives the desired output,
# A tibble: 3 × 4
name Automatic Manual difference
<chr> <chr> <chr> <dbl>
1 mpg 17.15(±3.83) 24.39(±6.17) -7
2 hp 160.26(±53.91) 126.85(±84.06) 34
3 disp 290.38(±110.17) 143.53(±87.2) 147
Although it works, it defeats the purpose of making a simple algorithm for the purpose.
How do I create a summary of my data in a simple manner? It has to be a tidyverse-solution, preferably without iteration.

Answer 1

This is not necessarily simpler or shorter but I would prefer to do the mathematical calculations on numbers directly rather than extracting them from strings.
library(dplyr)
library(tidyr)
mtcars %>%
group_by(am_factor) %>%
summarise(across(all_of(sum_variables), list(mean = mean,
sd = ~sprintf('%.2f (± %.2f)', mean(.), sd(.))))) %>%
pivot_longer(cols = -am_factor,
names_to = c('measure', '.value'),
names_sep = '_') %>%
group_by(measure) %>%
mutate(difference = -diff(mean)) %>%
ungroup %>%
select(-mean) %>%
pivot_wider(names_from = am_factor, values_from = sd)
# measure difference Automatic Manual
# <chr> <dbl> <chr> <chr>
#1 mpg -7.24 17.15 (± 3.83) 24.39 (± 6.17)
#2 hp 33.4 160.26 (± 53.91) 126.85 (± 84.06)
#3 disp 147. 290.38 (± 110.17) 143.53 (± 87.20)