nest_mutate | R Documentation |
nest_mutate()
adds new variables to and preserves existing ones within
the nested data frames in .nest_data
.
nest_transmute()
adds new variables to and drops existing ones from the
nested data frames in .nest_data
.
nest_mutate(.data, .nest_data, ...) nest_transmute(.data, .nest_data, ...)
.data |
A data frame, data frame extension (e.g., a tibble), or a lazy data frame (e.g., from dbplyr or dtplyr). |
.nest_data |
A list-column containing data frames |
... |
Name-value pairs. The name gives the name of the column in the output. The value can be:
|
nest_mutate()
and nest_transmute()
are largely wrappers for
dplyr::mutate()
and dplyr::transmute()
and maintain the functionality of
mutate()
and transmute()
within each nested data frame. For more
information on mutate()
or transmute()
, please refer to the documentation
in dplyr
.
An object of the same type as .data
. Each object in the column .nest_data
will also be of the same type as the input. Each object in .nest_data
has
the following properties:
For nest_mutate()
:
Columns from each object in .nest_data
will be preserved according to
the .keep
argument.
Existing columns that are modified by ...
will always be returned in
their original location.
New columns created through ...
will be placed according to the
.before
and .after
arguments.
For nest_transmute()
:
Columns created or modified through ...
will be returned in the order
specified by ...
.
Unmodified grouping columns will be placed at the front.
The number of rows is not affected.
Columns given the value NULL
will be removed.
Groups will be recomputed if a grouping variable is mutated.
Data frame attributes will be preserved.
Other single table verbs:
nest_arrange()
,
nest_filter()
,
nest_rename()
,
nest_select()
,
nest_slice()
,
nest_summarise()
gm_nest <- gapminder::gapminder %>% tidyr::nest(country_data = -continent) # add or modify columns: gm_nest %>% nest_mutate( country_data, lifeExp = NULL, gdp = gdpPercap * pop, pop = pop/1000000 ) # use dplyr::across() to apply transformation to multiple columns gm_nest %>% nest_mutate( country_data, across(c(lifeExp:gdpPercap), mean) ) # nest_transmute() drops unused columns when mutating: gm_nest %>% nest_transmute( country_data, country = country, year = year, pop = pop/1000000 )
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