Summarise Data using group_by across several columns

Question

Let's say I have a dataframe where all columns are factors:

set.seed(123)
gender <- sample(1:2,12,replace=T)
country <- c('FIN', 'FIN', 'EST', 'NIG','NIG','JAM', 'FIN', 'NIG', 'EST', 'NIG','NIG','JAM','FIN', 'FIN', 'EST', 'NIG','NIG','JAM', 'FIN', 'NIG', 'EST', 'NIG','NIG','JAM')
a <- sample(1:5,24,,replace=T)
b <- sample(1:5,24,,replace=T)
c <- sample(1:5,24,,replace=T)
d <- sample(1:5,24,,replace=T)
# Join the variables to create a data frame
df <- data.frame(gender,country,a,b,c,d)

df <- df %>% mutate_at(c("gender","country","a","b","c","d"), as.factor)

I want a table that will give a proportion percentage and n across every column a:d by Gender and Region so the end result would look something like this:

I've done this for a using the code (but I want to do this also for all the other columns b,c,d without replicating the code):

df %>% 
  group_by(gender,country,a) %>%
  summarise(totaln=n()) %>% 
  group_by(country,gender) %>% 
  mutate(percentage=totaln/sum(totaln)*100) 

so for a I get this:

   gender country a     totaln percentage
   <fct>  <fct>   <fct>  <int>      <dbl>
 1 1      EST     1          2       50  
 2 1      EST     3          1       25  
 3 1      EST     4          1       25  
 4 1      FIN     1          1       25  
 5 1      FIN     2          3       75  
 6 1      NIG     1          1       25  
 7 1      NIG     2          1       25  
 8 1      NIG     3          1       25  
 9 1      NIG     4          1       25  
10 2      FIN     1          1       50  
11 2      FIN     3          1       50  
12 2      JAM     1          2       50  
13 2      JAM     3          2       50  
14 2      NIG     3          1       16.7
15 2      NIG     4          1       16.7
16 2      NIG     5          4       66.7

Answer 1

What you probably first want to do is to transform your data in a tidy-format by using pivot_longer on a:d, and then group also by this variable, from there it is almost the same code as you wrote:

df_summary <- df %>% pivot_longer(c(a, b, c, d), names_to = "abcd") %>%
  group_by(gender, country, abcd, value) %>%
  summarise(totaln = n()) %>%
  group_by(country, gender, abcd) %>%
  mutate(percentag = totaln / sum(totaln) * 100)

df_summary 

#> # A tibble: 64 × 6
#> # Groups:   country, gender, abcd [24]
#>    gender country abcd  value totaln percentag
#>    <fct>  <fct>   <chr> <fct>  <int>     <dbl>
#>  1 1      EST     a     1          1        25
#>  2 1      EST     a     3          2        50
#>  3 1      EST     a     4          1        25
#>  4 1      EST     b     2          1        25
#>  5 1      EST     b     3          1        25
#>  6 1      EST     b     4          2        50
#>  7 1      EST     c     2          1        25
#>  8 1      EST     c     5          3        75
#>  9 1      EST     d     1          1        25
#> 10 1      EST     d     2          2        50
#> # … with 54 more rows

To create your desired output shape, you can do:

df_summary %>% 
  ungroup() %>% 
  mutate(summary = paste0(round(percentag), "% (n = ", totaln, ")")) %>% 
  select(gender, country, abcd, value, summary) %>% 
  pivot_wider(names_from = c(gender, country), values_from = summary, values_fill = "0")

^{Created on 2022-06-16 by the reprex package (v2.0.1)}

Answer 2

Two options. Both retain the 0 combinations, that can easily be filter ed out if needed.

1. Long format:

    df %>% tidyr::pivot_longer(-c(gender, country), names_to = "ltr", values_to = "val") %>% group_by(gender, country, ltr) %>% summarize( totaln = sum(as.numeric(table(val))), percentage = 100 * as.numeric(table(val)) / totaln ) %>% ungroup() # `summarise()` has grouped output by 'gender', 'country', 'ltr'. You can override using the `.groups` argument. # # A tibble: 120 x 5 # gender country ltr totaln percentage # <fct> <fct> <chr> <dbl> <dbl> # 1 1 EST a 4 50 # 2 1 EST a 4 0 # 3 1 EST a 4 25 # 4 1 EST a 4 25 # 5 1 EST a 4 0 # 6 1 EST b 4 25 # 7 1 EST b 4 50 # 8 1 EST b 4 25 # 9 1 EST b 4 0 # 10 1 EST b 4 0 # # ... with 110 more rows

2. Wide format:

    df %>% group_by(gender, country) %>% summarize(across(a:d, list( perc = ~ 100 * as.numeric(table(.)) / sum(as.numeric(table(.))), total = ~ as.numeric(table(.)), newval = ~ factor(names(table(.)), levels = levels(.)) ) )) %>% ungroup() # `summarise()` has grouped output by 'gender', 'country'. You can override using the `.groups` argument. # # A tibble: 30 x 14 # gender country a_perc a_total a_newval b_perc b_total b_newval c_perc c_total c_newval d_perc d_total d_newval # <fct> <fct> <dbl> <dbl> <fct> <dbl> <dbl> <fct> <dbl> <dbl> <fct> <dbl> <dbl> <fct> # 1 1 EST 50 2 1 25 1 1 75 3 1 0 0 1 # 2 1 EST 0 0 2 50 2 2 0 0 2 50 2 2 # 3 1 EST 25 1 3 25 1 3 0 0 3 25 1 3 # 4 1 EST 25 1 4 0 0 4 0 0 4 25 1 4 # 5 1 EST 0 0 5 0 0 5 25 1 5 0 0 5 # 6 1 FIN 25 1 1 25 1 1 25 1 1 0 0 1 # 7 1 FIN 75 3 2 50 2 2 25 1 2 50 2 2 # 8 1 FIN 0 0 3 0 0 3 25 1 3 25 1 3 # 9 1 FIN 0 0 4 0 0 4 25 1 4 25 1 4 # 10 1 FIN 0 0 5 25 1 5 0 0 5 0 0 5 # # ... with 20 more rows