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Subset columns in nested data frames using their names and types

Source: R/nest_select.R
nest_select.Rd

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, ...)

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.

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.

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.

See also

Other single table verbs: nest_arrange(), nest_filter(), nest_mutate(), nest_rename(), nest_slice(), nest_summarise()

Examples

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

gm_nest %>% nest_select(country_data, country, year, pop)
#> # A tibble: 5 × 2
#>   continent country_data      
#>   <fct>     <list>            
#> 1 Asia      <tibble [396 × 3]>
#> 2 Europe    <tibble [360 × 3]>
#> 3 Africa    <tibble [624 × 3]>
#> 4 Americas  <tibble [300 × 3]>
#> 5 Oceania   <tibble [24 × 3]> 
gm_nest %>% nest_select(country_data, dplyr::where(is.numeric))
#> # A tibble: 5 × 2
#>   continent country_data      
#>   <fct>     <list>            
#> 1 Asia      <tibble [396 × 4]>
#> 2 Europe    <tibble [360 × 4]>
#> 3 Africa    <tibble [624 × 4]>
#> 4 Americas  <tibble [300 × 4]>
#> 5 Oceania   <tibble [24 × 4]>