Consider the following example data
library(dplyr)
tmp <- mtcars %>%
group_by(cyl) %>%
summarise(mpg_sum = list(summary(mpg)))
We can use data.table
. Convert the 'data.frame' to 'data.table' (as.data.table(mtcars)
), grouped by 'cyl', we get the summary
of 'mpg' and convert it to list
library(data.table)
as.data.table(mtcars)[, as.list(summary(mpg)), by = cyl]
# cyl Min. 1st Qu. Median Mean 3rd Qu. Max.
#1: 6 17.8 18.65 19.7 19.74 21.00 21.4
#2: 4 21.4 22.80 26.0 26.66 30.40 33.9
#3: 8 10.4 14.40 15.2 15.10 16.25 19.2
Or using only dplyr
, after grouping by 'cyl', we use do
to do the same operation as above.
library(dplyr)
mtcars %>%
group_by(cyl) %>%
do(data.frame(as.list(summary(.$mpg)), check.names=FALSE) )
# cyl Min. 1st Qu. Median Mean 3rd Qu. Max.
#
#1 4 21.4 22.80 26.0 26.66 30.40 33.9
#2 6 17.8 18.65 19.7 19.74 21.00 21.4
#3 8 10.4 14.40 15.2 15.10 16.25 19.2
Or using purrr
library(purrr)
mtcars %>%
slice_rows("cyl") %>%
select(mpg) %>%
by_slice(dmap, summary, .collate= "cols")