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Subset distinct/unique rows within a nested data frame

Source: R/nest_distinct.R
nest_distinct.Rd

nest_distinct() selects only unique/distinct rows in a nested data frame.

Usage

nest_distinct(.data, .nest_data, ..., .keep_all = FALSE)

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

...

Optional variables to use when determining uniqueness. If there are multiple rows for a given combination of inputs, only the first row will be preserved. If omitted, will use all variables.

.keep_all

If TRUE, keep all variables in .nest_data. If a combination of ... is not distinct, this keeps the first row of values.

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 a subset of the input but appear in the same order.

  • Columns are not modified if ... is empty or .keep_all is TRUE. Otherwise, nest_distinct() first calls dplyr::mutate() to create new columns within each object in .nest_data.

  • Groups are not modified.

  • Data frame attributes are preserved.

Details

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

Examples

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

gm_nest %>% nest_distinct(country_data, country)
#> # A tibble: 5 × 2
#>   continent country_data     
#>   <fct>     <list>           
#> 1 Asia      <tibble [33 × 1]>
#> 2 Europe    <tibble [30 × 1]>
#> 3 Africa    <tibble [52 × 1]>
#> 4 Americas  <tibble [25 × 1]>
#> 5 Oceania   <tibble [2 × 1]> 
gm_nest %>% nest_distinct(country_data, country, year)
#> # A tibble: 5 × 2
#>   continent country_data      
#>   <fct>     <list>            
#> 1 Asia      <tibble [396 × 2]>
#> 2 Europe    <tibble [360 × 2]>
#> 3 Africa    <tibble [624 × 2]>
#> 4 Americas  <tibble [300 × 2]>
#> 5 Oceania   <tibble [24 × 2]>