These are methods for dplyr's group_by()
and ungroup()
generics.
Grouping is translated to the either keyby
and by
argument of
[.data.table
depending on the value of the arrange
argument.
# S3 method for dtplyr_step group_by(.data, ..., .add = FALSE, add = deprecated(), arrange = TRUE) # S3 method for dtplyr_step ungroup(.data, ...)
.data | |
---|---|
... | In |
.add, add | When This argument was previously called |
arrange | If |
library(dplyr, warn.conflicts = FALSE) dt <- lazy_dt(mtcars) # group_by() is usually translated to `keyby` so that the groups # are ordered in the output dt %>% group_by(cyl) %>% summarise(mpg = mean(mpg))#> Source: local data table [3 x 2] #> Call: `_DT10`[, .(mpg = mean(mpg)), keyby = .(cyl)] #> #> cyl mpg #> <dbl> <dbl> #> 1 4 26.7 #> 2 6 19.7 #> 3 8 15.1 #> #> # Use as.data.table()/as.data.frame()/as_tibble() to access results# use `arrange = FALSE` to instead use `by` so the original order # or groups is preserved dt %>% group_by(cyl, arrange = FALSE) %>% summarise(mpg = mean(mpg))#> Source: local data table [3 x 2] #> Call: `_DT10`[, .(mpg = mean(mpg)), by = .(cyl)] #> #> cyl mpg #> <dbl> <dbl> #> 1 6 19.7 #> 2 4 26.7 #> 3 8 15.1 #> #> # Use as.data.table()/as.data.frame()/as_tibble() to access results