How to select the rows with maximum values in each group with dplyr? [duplicate]

Answer 1

Try this:

result <- df %>% 
             group_by(A, B) %>%
             filter(value == max(value)) %>%
             arrange(A,B,C)

Seems to work:

identical(
  as.data.frame(result),
  ddply(df, .(A, B), function(x) x[which.max(x$value),])
)
#[1] TRUE

As pointed out in the comments, slice may be preferred here as per @RoyalITS' answer below if you strictly only want 1 row per group. This answer will return multiple rows if there are multiple with an identical maximum value.

Answer 2

You can use top_n

df %>% group_by(A, B) %>% top_n(n=1)

This will rank by the last column (value) and return the top n=1 rows.

Currently, you can't change the this default without causing an error (See https://github.com/hadley/dplyr/issues/426)

Answer 3

For me, it helped to count the number of values per group. Copy the count table into a new object. Then filter for the max of the group based on the first grouping characteristic. For example:

count_table  <- df %>%
                group_by(A, B) %>%
                count() %>%
                arrange(A, desc(n))

count_table %>% 
    group_by(A) %>%
    filter(n == max(n))

or

count_table %>% 
    group_by(A) %>%
    top_n(1, n)

Answer 4

More generally, I think you might want to get "top" of the rows that are sorted within a given group.

For the case of where a single value is max'd out, you have essentially sorted by only one column. However, it's often useful to hierarchically sort by multiple columns (for example: a date column and a time-of-day column).

# Answering the question of getting row with max "value".
df %>% 
  # Within each grouping of A and B values.
  group_by( A, B) %>% 
  # Sort rows in descending order by "value" column.
  arrange( desc(value) ) %>% 
  # Pick the top 1 value
  slice(1) %>% 
  # Remember to ungroup in case you want to do further work without grouping.
  ungroup()

# Answering an extension of the question of 
# getting row with the max value of the lowest "C".
df %>% 
  # Within each grouping of A and B values.
  group_by( A, B) %>% 
  # Sort rows in ascending order by C, and then within that by 
  # descending order by "value" column.
  arrange( C, desc(value) ) %>% 
  # Pick the one top row based on the sort
  slice(1) %>% 
  # Remember to ungroup in case you want to do further work without grouping.
  ungroup()

Answer 5

This more verbose solution provides greater control on what happens in case of duplicate maximum value (in this example, it will take one of the corresponding rows randomly)

library(dplyr)
df %>% group_by(A, B) %>%
  mutate(the_rank  = rank(-value, ties.method = "random")) %>%
  filter(the_rank == 1) %>% select(-the_rank)

Answer 6

df %>% group_by(A,B) %>% slice(which.max(value))