Some functions at the intersection of 'dplyr' and 'purrr' that formerly lived in 'purrr'.
purrrlyr contains some functions that lie at the intersection of purrr and dplyr. They have been removed from purrr in order to make the package lighter and because they have been replaced by other solutions in the tidyverse.
Please see Jenny Brian's webinar on row-oriented workflows for some alternative approaches.
CRAN maintenance release.
All data-frame based mappers have been moved to this package. These functions are not technically deprecated (so you can move to this package as easily as possible), but these functions are unlikely to be changed in the future (i.e. there will be no bug fixes) and are likely to go away in the near future, so we highly recommend updating to new approaches.
Mapping a function to each column of a data frame should now be
handled with the colwise mutating and summarising operations in
dplyr instead of
dmap(). These are the verbs with suffix
_if(), such as
summarise_if(). Note that this means the output of
conform to the requirements of dplyr operations: same length as
the input for mutating operations, and length 1 for summarising
Inovking a function row by row with the columns of a data frame
as arguments should be done with
pmap() followed by
dplyr::as_dataframe() instead of
Mapping rowwise slices of a data frame with
deprecated in favour of a combination of tidyverse functions.
tidyr::nest() to create a list-column containing
groupwise data frames. Then use
dplyr::mutate() to operate on
this list-column. Typically you will want to apply a function on
each element (nested data frame) of this list-column with