nest_select: Subset columns in nested data frames using their names and types

Description

nest_select() selects (and optionally renames) variables in nested data frames, using a concise mini-language that makes it easy to refer to variables based on their name (e.g., a:f selects all columns from a on the left to f on the right). You can also use predicate functions like is.numeric to select variables based on their properties.

Usage

nest_select(.data, .nest_data, ...)

Value

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:

Rows are not affect.
Output columns are a subset of input columns, potentially with a different order. Columns will be renamed if new_name = old_name form is used.
Data frame attributes are preserved.
Groups are maintained; you can't select off grouping variables.

Arguments

.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
...: One or more unquoted expressions separated by commas. Variable names can be used if they were positions in the data frame, so expressions like x:y can be used to select a range of variables.

Details

nest_select() is largely a wrapper for dplyr::select() and maintains the functionality of select() within each nested data frame. For more information on select(), please refer to the documentation in dplyr.

Examples

Run this code

gm_nest <- gapminder::gapminder %>% tidyr::nest(country_data = -continent)

gm_nest %>% nest_select(country_data, country, year, pop)
gm_nest %>% nest_select(country_data, where(is.numeric))