Superfluous columns returned by dplyr::summarise() function - r

Question

I am having some trouble with the new dplyr::summarise() function
Here is the data
df <- data.frame(id = factor(1:10),
group = factor(rep(letters[1:2],each = 5)),
w1 = rnorm(10),
w2 = rnorm(10),
w3 = rnorm(10),
dummy = as.character(LETTERS[1:10]),
stringsAsFactors = F)
Now I want to get means and standard deviations for the numeric variables only. So I ran the following code
df %>%
dplyr::select(id, group, w1:w3) %>%
group_by(group) %>%
dplyr::summarise(across(where(is.numeric), ~ mean(.x, na.rm = T), .names = "mean_{col}"),
across(where(is.numeric), ~ sd(.x, na.rm = T), .names = "sd_{col}"),
count = n())
Which gives me the following output
# A tibble: 2 x 11
# group mean_w1 mean_w2 mean_w3 sd_w1 sd_w2 sd_w3 sd_mean_w1 sd_mean_w2 sd_mean_w3 count
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
# a -0.399 0.152 -0.151 1.07 0.703 1.15 NA NA NA 5
# b 0.560 -0.107 -0.0439 1.18 0.612 0.862 NA NA NA 5
Now the columns starting with mean_ and sd_ are exactly what I want, but I'm also getting this set of sd_mean_ columns, I assume because it is trying to find the sd of the new mean_ columns.
How do I get the output without the superfluous columns?

Answer 1

The issue is when you go to second across the number of numeric columns have increased, so it applies sd function to the new columns as well. To avoid this apply multiple function in the same across using list().
library(dplyr)
df %>%
group_by(group) %>%
summarise(across(where(is.numeric), list(mean = ~mean(., na.rm = TRUE),
sd = ~sd(., na.rm = TRUE)),
.names = "{fn}_{col}"),
count = n())
# group mean_w1 sd_w1 mean_w2 sd_w2 mean_w3 sd_w3 count
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#1 a 0.0746 0.696 0.760 1.39 0.0530 1.29 5
#2 b 0.522 0.686 0.0979 0.566 -0.0133 1.12 5
Also, your attempt would work as expected if you don't select columns by their type :
df %>%
group_by(group) %>%
summarise(across(w1:w3, ~ mean(.x, na.rm = T), .names = "mean_{col}"),
across(w1:w3, ~ sd(.x, na.rm = T), .names = "sd_{col}"),
count = n())