summarise based on multiple groups in R dplyr - r

Question

I have a large data frame that looks like this
library(tidyverse)
df <- tibble(id=c(1,1,2,2,2,3), counts=c(10,20,15,15,10,20), fruit=c("apple","banana","cherry","cherry","ananas","pear"))
df
#> # A tibble: 6 × 3
#> id counts fruit
#> <dbl> <dbl> <chr>
#> 1 1 10 apple
#> 2 1 20 banana
#> 3 2 15 cherry
#> 4 2 15 cherry
#> 5 2 10 ananas
#> 6 3 20 pear
Created on 2022-04-13 by the reprex package (v2.0.1)
For each id, I want to keep the fruit with the maximum counts and then I want to add the sum_counts of unique fruits per id in another column.
I want my data to look like this:
# A tibble: 3 × 4
id central_fruit fruits sum_counts
<dbl> <chr> <chr> <dbl>
1 1 banana banana, apple 30
2 2 cherry cherry, ananas 30
3 3 pear pear 20
This is what I have tried so far and I do not know why I fail miserably
library(tidyverse)
df <- tibble(id=c(1,1,2,2,2,3), counts=c(10,20,15,15,15,20), fruit=c("apple","banana","cherry","cherry","ananas","pear"))
df %>%
group_by(id,fruit) %>%
add_count(fruit) %>%
ungroup() %>%
group_by(id) %>%
summarise(central_fruit=fruit[which.max(counts)],
fruits = toString(sort(unique(fruit), decreasing = TRUE)),
sum_counts = sum(unique(counts)))
#> # A tibble: 3 × 4
#> id central_fruit fruits sum_counts
#> <dbl> <chr> <chr> <dbl>
#> 1 1 banana banana, apple 30
#> 2 2 cherry cherry, ananas 15
#> 3 3 pear pear 20
Created on 2022-04-13 by the reprex package (v2.0.1)

Answer 1

Here's a dplyr approach.
library(dplyr)
df <- tibble(id=c(1,1,2,2,2,3), counts=c(10,20,15,15,10,20), fruit=c("apple","banana","cherry","cherry","ananas","pear"))
df %>%
group_by(id) %>%
mutate(fruits = paste0(unique(fruit), collapse = ", "),
sum_counts = sum(unique(counts))) %>%
filter(counts == max(counts)) %>%
distinct() %>%
rename("central_fruit" = "fruit") %>%
select(-counts)
#> # A tibble: 3 × 4
#> # Groups: id [3]
#> id central_fruit fruits sum_counts
#> <dbl> <chr> <chr> <dbl>
#> 1 1 banana apple, banana 30
#> 2 2 cherry cherry, ananas 25
#> 3 3 pear pear 20
Created on 2022-04-13 by the reprex package (v2.0.1)

Answer 2

This should work:
df |>
group_by(id) |>
distinct(fruit, .keep_all = TRUE) |>
mutate(
is_central_fruit = counts == max(counts),
sum_counts = sum(counts),
fruits = paste(fruit, collapse = ", ")
) |>
filter(
is_central_fruit
) |>
select(
-is_central_fruit,
-counts,
central_fruit = fruit
)
# id central_fruit sum_counts fruits
# <dbl> <chr> <dbl> <chr>
# 1 1 banana 30 apple, banana
# 2 2 cherry 25 cherry, ananas
# 3 3 pear 20 pear
If you want to order the fruits column then I wouldn't store fruits as a character vector, but as a list of factors.

Answer 3

And another dplyr approach but preserving the fruits order (central_fruit is first in fruits):
df %>%
distinct() %>%
group_by(id) %>%
mutate(sum_counts = sum(counts)) %>%
arrange(id, desc(counts)) %>%
mutate(fruits = paste(fruit, collapse = ", ")) %>%
slice(1) %>%
select(id, central_fruit = fruit, fruits, sum_counts) %>%
ungroup()
This returns
# A tibble: 3 x 4
id central_fruit fruits sum_counts
<dbl> <chr> <chr> <dbl>
1 1 banana banana, apple 30
2 2 cherry cherry, ananas 25
3 3 pear pear 20