Select the row with the maximum value in each group based on multiple columns in R dplyr

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My data frame looks like this one

library(tidyverse)

df1 <- tibble(col1= c("apple","apple","banana","banana"), 
              col2 = c("appl","aple","banan","bananb"),
              count_col1=c(1,1,4,4), count_col2=c(3,4,1,1))
df1
#> # A tibble: 4 × 4
#>   col1   col2   count_col1 count_col2
#>   <chr>  <chr>       <dbl>      <dbl>
#> 1 apple  appl            1          3
#> 2 apple  aple            1          4
#> 3 banana banan           4          1
#> 4 banana bananb          4          1

Created on 2022-02-17 by the reprex package (v2.0.1)

I want to select after grouping_by col1 the row that has the maximum value based on count_col1 and count_col2.

I want my data to look like this

      col1   col2   count_col1 count_col2
      apple  aple            1          4
      banana banan           4          1
      banana bananb          4          1

for one column you can write something

df1 %>% 
  slice(which.max(count_col1))

but not for two

dplyr tidyverse r
1 Answers

We may get rowwise max of the 'count' columns with pmax, grouped by 'col1', filter the rows where the max value of 'Max' column is.

library(dplyr)
df1 %>% 
 mutate(Max = pmax(count_col1, count_col2) ) %>%
 group_by(col1) %>%
 filter(Max == max(Max)) %>%
 ungroup %>%
 select(-Max)

-output

# A tibble: 3 × 4
  col1   col2   count_col1 count_col2
  <chr>  <chr>       <dbl>      <dbl>
1 apple  aple            1          4
2 banana banan           4          1
3 banana bananb          4          1

We may also use slice_max

library(purrr)
df1 %>%
  group_by(col1) %>%
  slice_max(invoke(pmax, across(starts_with("count")))) %>%
  ungroup
# A tibble: 3 × 4
  col1   col2   count_col1 count_col2
  <chr>  <chr>       <dbl>      <dbl>
1 apple  aple            1          4
2 banana banan           4          1
3 banana bananb          4          1
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