Data manipulation within groups
Carry out data manipulation within specified groups.
group_dt(.data, by = NULL, ...) rowwise_dt(.data, ...)
.data |
A data.frame |
by |
Variables to group by,unquoted name of grouping variable of list of unquoted names of grouping variables. |
... |
Any data manipulation arguments that could be implemented on a data.frame. |
If you want to use summarise_dt and mutate_dt in
group_dt, it is better to use the "by" parameter in those functions,
that would be much faster because you don't have to use .SD (which takes
extra time to copy).
data.table
https://stackoverflow.com/questions/36802385/use-by-each-row-for-data-table
iris %>% group_dt(by = Species,slice_dt(1:2))
iris %>% group_dt(Species,filter_dt(Sepal.Length == max(Sepal.Length)))
iris %>% group_dt(Species,summarise_dt(new = max(Sepal.Length)))
# you can pipe in the `group_dt`
iris %>% group_dt(Species,
mutate_dt(max= max(Sepal.Length)) %>%
summarise_dt(sum=sum(Sepal.Length)))
# for users familiar with data.table, you can work on .SD directly
# following codes get the first and last row from each group
iris %>%
group_dt(
by = Species,
rbind(.SD[1],.SD[.N])
)
#' # for summarise_dt, you can use "by" to calculate within the group
mtcars %>%
summarise_dt(
disp = mean(disp),
hp = mean(hp),
by = cyl
)
# but you could also, of course, use group_dt
mtcars %>%
group_dt(by =.(vs,am),
summarise_dt(avg = mean(mpg)))
# and list of variables could also be used
mtcars %>%
group_dt(by =list(vs,am),
summarise_dt(avg = mean(mpg)))
# examples for `rowwise_dt`
df <- data.table(x = 1:2, y = 3:4, z = 4:5)
df %>% mutate_dt(m = mean(c(x, y, z)))
df %>% rowwise_dt(
mutate_dt(m = mean(c(x, y, z)))
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