rowwise_df_tidiers {broom} | R Documentation |
Rowwise tidiers are deprecated and will be removed from an upcoming version
of broom. We strongly recommend moving to a nest-map-unnest
workflow
over a rowwise-do
workflow. See the vignettes for examples.
## S3 method for class 'rowwise_df' tidy(x, object, ...) ## S3 method for class 'rowwise_df' tidy_(x, object, ...) ## S3 method for class 'rowwise_df' augment(x, object, ...) ## S3 method for class 'rowwise_df' augment_(x, object, ...) ## S3 method for class 'rowwise_df' glance(x, object, ...) ## S3 method for class 'rowwise_df' glance_(x, object, ...) ## S3 method for class 'tbl_df' tidy(x, ...) ## S3 method for class 'tbl_df' augment(x, ...) ## S3 method for class 'tbl_df' glance(x, ...)
x |
a rowwise_df |
object |
the column name of the column containing the models to be tidied. For tidy, augment, and glance it should be the bare name; for _ methods it should be quoted. |
... |
additional arguments to pass on to the respective tidying method |
These tidy
, augment
and glance
methods are for
performing tidying on each row of a rowwise data frame created by dplyr's
group_by
and do
operations. They first group a rowwise data
frame based on all columns that are not lists, then perform the tidying
operation on the specified column. This greatly shortens a common idiom
of extracting tidy/augment/glance outputs after a do statement.
Note that this functionality is not currently implemented for data.tables, since the result of the do operation is difficult to distinguish from a regular data.table.
A "grouped_df"
, where the non-list columns of the
original are used as grouping columns alongside the tidied outputs.
library(dplyr) regressions <- mtcars %>% group_by(cyl) %>% do(mod = lm(mpg ~ wt, .)) regressions regressions %>% tidy(mod) regressions %>% augment(mod) regressions %>% glance(mod) # we can provide additional arguments to the tidying function regressions %>% tidy(mod, conf.int = TRUE) # we can also include the original dataset as a "data" argument # to augment: regressions <- mtcars %>% group_by(cyl) %>% do(mod = lm(mpg ~ wt, .), original = (.)) # this allows all the original columns to be included: regressions %>% augment(mod) # doesn't include all original regressions %>% augment(mod, data = original) # includes all original