Calculate proportion of several binary variables by another variable - r

Question

I have data with several binary variables, and I want to calculate the proportion of each one, by another variable.
Example
I survey people and ask them: Please mark which of the following fruits you like (can mark more than one choice): ☐ Banana ☐ Apple ☐ Orange ☐ Strawberry ☐ Peach
Each person who checked the box gets 1 in the data, and when leaving blank it's denoted as 0. The data looks like that:
library(dplyr)
set.seed(2021)
my_df <-
matrix(rbinom(n = 100, size = 1, prob = runif(1)), ncol = 5) %>%
as.data.frame() %>%
cbind(1:20, ., sample(c("male", "female"), size = 20, replace = T)) %>%
setNames(c("person_id", "banana", "apple", "orange", "strawberry", "peach", "gender"))
my_df
#> person_id banana apple orange strawberry peach gender
#> 1 1 1 1 1 0 0 female
#> 2 2 1 0 0 0 1 female
#> 3 3 0 0 1 0 1 female
#> 4 4 1 1 0 1 0 female
#> 5 5 1 1 1 0 0 male
#> 6 6 1 1 1 0 1 female
#> 7 7 0 1 0 1 1 male
#> 8 8 1 1 0 0 0 male
#> 9 9 1 1 1 0 0 female
#> 10 10 0 0 0 0 0 male
#> 11 11 1 1 1 1 1 male
#> 12 12 1 1 0 0 1 male
#> 13 13 1 1 0 1 0 male
#> 14 14 1 1 0 0 0 male
#> 15 15 0 0 0 0 1 male
#> 16 16 0 1 0 0 1 male
#> 17 17 1 0 0 0 1 male
#> 18 18 1 1 1 1 1 male
#> 19 19 0 0 1 1 1 female
#> 20 20 0 0 0 0 0 female
Created on 2021-02-01 by the reprex package (v0.3.0)
I want to get the proportion for each fruit, split by gender. From this answer I learned how to do it for one variable (for example, banana):
my_df %>%
group_by(gender) %>%
summarise(n_of_observations = n(), prop = sum(banana == 1)/n())
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 2 x 3
## gender n_of_observations prop
## <chr> <int> <dbl>
## 1 female 10 0.6
## 2 male 10 0.4
But how can I get such a table for all fruits?
Desired output:
## fruit gender prop
## <chr> <chr> <dbl>
## 1 banana female 0.6
## 2 banana male 0.4
## 3 apple female 0.4
## 4 apple male 0.3
## 5 orange female 0.3
## 6 orange male 0.1
## 7 strawberry female 0.4
## 8 strawberry male 0.4
## 9 peach female 0.3
## 10 peach male 0.6
I'm looking for a dplyr solution, if possible. Thanks a lot!

Answer 1

You can use across to summarize multiple variables at once:
my_df %>%
group_by(gender) %>%
summarise(across(banana:peach, list(n = ~length(.x), prop = ~sum(.x == 1) / n())))
# A tibble: 2 x 11
gender banana_n banana_prop apple_n apple_prop orange_n orange_prop strawberry_n strawberry_prop peach_n peach_prop
<chr> <int> <dbl> <int> <dbl> <int> <dbl> <int> <dbl> <int> <dbl>
1 female 8 0.625 8 0.5 8 0.625 8 0.25 8 0.5
2 male 12 0.667 12 0.75 12 0.25 12 0.333 12 0.583
Note that the first argument of across specifies the variables you want to summarize. Here, I wrote banana:peach meaning all columns between banana and peach.

Answer 2

You can use tidyr to pivot your data first and then summarize it:
library(tidyr)
tidyr::pivot_longer(my_df, banana:peach,
names_to = "fruit") %>%
dplyr::group_by(gender, fruit) %>%
dplyr::summarize(prop = sum(value) / n())
gender fruit prop
<chr> <chr> <dbl>
1 female apple 0.5
2 female banana 0.625
3 female orange 0.625
4 female peach 0.5
5 female strawberry 0.25
6 male apple 0.75
7 male banana 0.667
8 male orange 0.25
9 male peach 0.583
10 male strawberry 0.333
You can pipe it to arrange if you want to sort by fruit. You can also add the number of observations in the summarize function with n = n().