Tidyverse: summarising values from different columns into a single cell-CodePudding

I have the following dataset:

df <- structure(list(var = c("a", "a", "a", "a", "a", "a", "a", "a", 
"a", "a", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b"), 
    beta_2 = c(-0.0441739987111475, -0.237256549142376, -0.167105040977351, 
    -0.140660549127359, -0.0623609020878716, -0.279740636040755, 
    -0.0211523654970921, 0.135368375550385, -0.0612770247281429, 
    -0.13183964102725, 0.363736380163624, -0.0134490092107583, 
    -0.0179957210095045, -0.00897746346470879, -0.0588242539401108, 
    -0.0571976057977875, -0.0290052449275881, 0.263181562031473, 
    0.00398338217426211, 0.0945495450635497), beta_3 = c(8.54560737016843e-05, 
    -0.0375859675101865, -0.0334219898732454, 0.0332275634691021, 
    6.41499442849741e-05, -0.0200724300602369, 8.046644459034e-05, 
    0.0626880671346749, 0.066218613897726, 0.0101268565262127, 
    0.44671567722757, 0.180543425234781, 0.526177616390516, 0.281245231195401, 
    -0.0362628519010746, 0.0609803646123324, 0.104137160504616, 
    0.804375133555955, 0.211218123083386, 0.824756942938928), 
    beta_4 = c(-8.50289708803184e-06, 0.0376601781861706, 0.104418586040791, 
    -0.0949557776511923, 2.11896613386966e-05, 0.0969765824620132, 
    4.95280289930771e-06, -0.0967836292162074, -0.132623370126544, 
    0.0579395551175153, -0.140392004360494, 0.00950912868877355, 
    -0.388317615535003, -0.0282634228070272, 0.0547116932731301, 
    0.0119441792873249, -0.0413015877795695, -0.720387490330028, 
    -0.0321860166581817, -0.627489324697221)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -20L))

df
# # A tibble: 20 × 4
#   var     beta_2    beta_3      beta_4
#   <chr>   <dbl>     <dbl>       <dbl>
# 1 a      -0.0442    0.0000855  -0.00000850
# 2 a      -0.237    -0.0376      0.0377    
# 3 a      -0.167    -0.0334      0.104     
# 4 a      -0.141     0.0332     -0.0950    
# 5 a      -0.0624    0.0000641   0.0000212 
# ...

I would like to summarise each beta_ column grouped by var so that I have the mean of beta_2, beta_3 and beta_4 in a single cell.

I can do it with the following code:

df %>% 
  pivot_longer(!var) %>% 
  group_by(var, name) %>% 
  summarise(mean_beta = mean(value) %>% round(2), .groups = "drop") %>% 
  aggregate(mean_beta ~ var, ., function(x) paste0(x, collapse = ", ")) %>% 
  as_tibble()

# # A tibble: 2 × 2
#   var   mean_beta        
#   <chr> <chr>            
# 1 a     -0.1, 0.01, 0    
# 2 b     0.05, 0.34, -0.19

I'm looking for a more straightforward, tidyverse-only solution. I have tried using map inside summarise but couldn't get what I wanted. Any idea?

CodePudding user response：

You may do the following -

library(dplyr)

df %>%
  group_by(var) %>%
  summarise(mean_beta = cur_data() %>%
                          summarise(across(.fns = 
                          ~.x %>% mean(na.rm = TRUE) %>% round(2))) %>%
              unlist() %>% toString())

#  var   mean_beta        
#  <chr> <chr>            
#1 a     -0.1, 0.01, 0    
#2 b     0.05, 0.34, -0.19

cur_data() provides the sub-data within each group as dataframe that can be summarised for each column and concatenated together.

CodePudding user response：

Another possible solution:

library(tidyverse)

df %>% 
 group_by(var) %>% 
 summarise(across(everything(), mean)) %>% 
 {bind_cols(var=.$var, mean_betas=apply(., 1, \(x) str_c(x[-1], collapse = ", ")))}

#> # A tibble: 2 × 2
#>   var   mean_betas                              
#>   <chr> <chr>                                   
#> 1 a     "-0.10101983, 0.008141079, -0.002735024"
#> 2 b     " 0.05400016, 0.340388682, -0.190217246"