Defined methods for dplyr generic single table functions. Most of them
preserve 'keyed_df' class and 'keys' attribute (excluding summarise
with
scoped variants, distinct
and do
which remove them). Also these methods
modify rows in keys according to the rows modification in reference
data frame (if any).
# S3 method for keyed_df
select(.data, ...)# S3 method for keyed_df
rename(.data, ...)
# S3 method for keyed_df
mutate(.data, ...)
# S3 method for keyed_df
transmute(.data, ...)
# S3 method for keyed_df
summarise(.data, ...)
# S3 method for keyed_df
group_by(.data, ...)
# S3 method for keyed_df
ungroup(x, ...)
# S3 method for keyed_df
rowwise(data, ...)
# S3 method for keyed_df
distinct(.data, ..., .keep_all = FALSE)
# S3 method for keyed_df
do(.data, ...)
# S3 method for keyed_df
arrange(.data, ..., .by_group = FALSE)
# S3 method for keyed_df
filter(.data, ...)
# S3 method for keyed_df
slice(.data, ...)
A keyed object.
Appropriate arguments for functions.
Parameter for dplyr::distinct.
Parameter for dplyr::arrange.
dplyr::transmute()
is supported implicitly with dplyr::mutate()
support.
dplyr::rowwise()
is not supposed to be generic in dplyr
. Use
rowwise.keyed_df
directly.
All scoped variants of present functions are also supported.
Two-table verbs
mtcars %>% key_by(vs, am) %>% dplyr::mutate(gear = 1)
Run the code above in your browser using DataLab