R: Tidying and summarising a paired comparison dataset in the tidyverse style - r

Question

I have a dataset with features {a,b,c...} belonging to a pair of players taken form the set {a, b, c}. Each row represents the outcome of a matchup, columns name_1, name_2 represent player names, and all other columns a1, a2, b1, b2, c1, c2, etc.. represent numeric features corresponding to the player in the matchup.
Below is the example of a dataset:
set.seed(17)
df <- tibble(
name_1 = sample(letters[1:3], length(letters), replace = TRUE),
name_2 = sample(letters[1:3], length(letters), replace = TRUE),
a1 = rnorm(length(letters)),
a2 = rnorm(length(letters)),
b1 = rnorm(length(letters)),
b2 = rnorm(length(letters)),
c1 = rnorm(length(letters)),
c2 = rnorm(length(letters))) %>%
filter(!(name_1 == name_2))
What I need is to find a summary statistic for each feature grouped by player. The trouble is that the same player, for example, a, can be located sometimes under name_1, sometimes under name_2, hence his features can be located at feature1 or feature2.
Here is my feeble attempt to do this for one player (namely, a) and one feature (namely, a):
df %>%
mutate(feature_a_joined = case_when(df$name_1 == "a" ~ a1,
df$name_2 == "a" ~ a2)) %>%
summarise(mean = mean(feature_a_joined, na.rm = TRUE))
I am fairly new to R, but the examples that I`ve seen in multiple vignettes refer to more standard datasets. Is there an efficient way to make a summary for each player and each variable?
Update
My expected result would be something like this:
# A tibble: 3 x 4
player feature_a_mean feature_b_mean feature_c_mean
<chr> <dbl> <dbl> <dbl>
1 a -0.330 2.38 0.960
2 b -0.482 1.30 0.207
3 c -0.482 -0.477 -1.71

Answer 1

We can use map. Get the unique column names ('un1') from the data. Loop over those (map), apply the OP's code with case_when and get the mean
library(dplyr)
library(purrr)
library(stringr)
un1 <- unique(str_remove(names(df)[-(1:2)], "\\d+"))
map_dfc(un1, ~
df %>%
summarise(!! str_c('mean_', .x) :=
mean(case_when(name_1 == .x ~ !! rlang::sym(str_c(.x, '1')),
name_2 == .x ~ !! rlang::sym(str_c(.x, '2'))),
na.rm = TRUE)))
-output
# A tibble: 1 x 3
# mean_a mean_b mean_c
# <dbl> <dbl> <dbl>
#1 -0.00673 0.186 -0.0632
Update
Based on the OP's expected output (assuming the output values are placeholders), we reshape the multiple blocks of columns to 'long' format with pivot_longer, do a group by to get the summarise across columns 'a' to 'c'
library(tidyr)
df %>%
pivot_longer(everything(), names_to = c('.value', 'grp'),
names_sep= '(?<=[a-z])_?(?=[0-9])') %>%
group_by(player = name) %>%
summarise(across(a:c, mean, na.rm = TRUE), .groups = 'drop')
-output
# A tibble: 3 x 4
# player a b c
# <chr> <dbl> <dbl> <dbl>
#1 a -0.00673 0.197 0.126
#2 b -0.0455 0.186 -0.138
#3 c -0.118 -0.468 -0.0632