nplyr

Author: Mark Rieke
License: MIT

Overview

{nplyr} is a grammar of nested data manipulation that allows users to perform dplyr-like manipulations on data frames nested within a list-col of another data frame. Most dplyr verbs have nested equivalents in nplyr. A (non-exhaustive) list of examples:

• nest_mutate() is the nested equivalent of mutate()
• nest_select() is the nested equivalent of select()
• nest_filter() is the nested equivalent of filter()
• nest_summarise() is the nested equivalent of summarise()
• nest_group_by() is the nested equivalent of group_by()

As of version 0.2.0, nplyr also supports nested versions of some tidyr functions:

• nest_drop_na() is the nested equivalent of drop_na()
• nest_extract() is the nested equivalent of extract()
• nest_fill() is the nested equivalent of fill()
• nest_replace_na() is the nested equivalent of replace_na()
• nest_separate() is the nested equivalent of separate()
• nest_unite() is the nested equivalent of unite()

nplyr is largely a wrapper for dplyr. For the most up-to-date information on dplyr please visit dplyr’s website. If you are new to dplyr, the best place to start is the data transformation chapter in R for data science.

Installation

You can install the released version of nplyr from CRAN or the development version from github with the devtools or remotes package:

# install from CRAN
install.packages("nplyr")

# install from github
devtools::install_github("markjrieke/nplyr")

Usage

To get started, we’ll create a nested column for the country data within each continent from the gapminder dataset.

library(nplyr)

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

gm_nest
#> # A tibble: 6 × 2
#>   continent country_data        
#>   <fct>     <list>              
#> 1 Asia      <tibble [578 × 5]>  
#> 2 Europe    <tibble [1,302 × 5]>
#> 3 Africa    <tibble [637 × 5]>  
#> 4 Americas  <tibble [470 × 5]>  
#> 5 FSU       <tibble [139 × 5]>  
#> 6 Oceania   <tibble [187 × 5]>

dplyr can perform operations on the top-level data frame, but with nplyr, we can perform operations on the nested data frames:

gm_nest_example <- 
  gm_nest %>%
  nest_filter(country_data, year == max(year)) %>%
  nest_mutate(country_data, pop_millions = pop/1000000)

# each nested tibble is now filtered to the most recent year
gm_nest_example
#> # A tibble: 6 × 2
#>   continent country_data     
#>   <fct>     <list>           
#> 1 Asia      <tibble [43 × 6]>
#> 2 Europe    <tibble [34 × 6]>
#> 3 Africa    <tibble [53 × 6]>
#> 4 Americas  <tibble [33 × 6]>
#> 5 FSU       <tibble [9 × 6]> 
#> 6 Oceania   <tibble [11 × 6]>

# if we unnest, we can see that a new column for pop_millions has been added
gm_nest_example %>%
  slice_head(n = 1) %>%
  tidyr::unnest(country_data)
#> # A tibble: 43 × 7
#>    continent country           year lifeExp        pop gdpPercap pop_millions
#>    <fct>     <fct>            <int>   <dbl>      <int>     <dbl>        <dbl>
#>  1 Asia      Afghanistan       2007    43.8   31889923      975.       31.9  
#>  2 Asia      Azerbaijan        2007    67.5    8017309     7709.        8.02 
#>  3 Asia      Bahrain           2007    75.6     708573    29796.        0.709
#>  4 Asia      Bangladesh        2007    64.1  150448339     1391.      150.   
#>  5 Asia      Bhutan            2007    65.6    2327849     4745.        2.33 
#>  6 Asia      Brunei            2007    77.1     386511    48015.        0.387
#>  7 Asia      Cambodia          2007    59.7   14131858     1714.       14.1  
#>  8 Asia      China             2007    73.0 1318683096     4959.     1319.   
#>  9 Asia      Hong Kong, China  2007    82.2    6980412    39725.        6.98 
#> 10 Asia      India             2007    64.7 1110396331     2452.     1110.   
#> # … with 33 more rows

nplyr also supports grouped operations with nest_group_by():

gm_nest_example <- 
  gm_nest %>%
  nest_group_by(country_data, year) %>%
  nest_summarise(
    country_data, 
    n = n(),
    lifeExp = median(lifeExp),
    pop = median(pop),
    gdpPercap = median(gdpPercap)
  )

gm_nest_example
#> # A tibble: 6 × 2
#>   continent country_data     
#>   <fct>     <list>           
#> 1 Asia      <tibble [58 × 5]>
#> 2 Europe    <tibble [58 × 5]>
#> 3 Africa    <tibble [13 × 5]>
#> 4 Americas  <tibble [57 × 5]>
#> 5 FSU       <tibble [44 × 5]>
#> 6 Oceania   <tibble [56 × 5]>

# unnesting shows summarised tibbles for each continent
gm_nest_example %>%
  slice(2) %>%
  tidyr::unnest(country_data)
#> # A tibble: 58 × 6
#>    continent  year     n lifeExp      pop gdpPercap
#>    <fct>     <int> <int>   <dbl>    <dbl>     <dbl>
#>  1 Europe     1950    22    65.8 7408264      6343.
#>  2 Europe     1951    18    65.7 7165515      6509.
#>  3 Europe     1952    31    65.9 7124673      5210.
#>  4 Europe     1953    17    67.3 7346100      6774.
#>  5 Europe     1954    17    68.0 7423300      7046.
#>  6 Europe     1955    17    68.5 7499400      7817.
#>  7 Europe     1956    17    68.5 7575800      8224.
#>  8 Europe     1957    31    67.5 7363802      6093.
#>  9 Europe     1958    18    69.6 8308052.     8833.
#> 10 Europe     1959    18    69.6 8379664.     9088.
#> # … with 48 more rows

More examples can be found in the package vignettes and function documentation.

Bug reports/feature requests

If you notice a bug, want to request a new feature, or have recommendations on improving documentation, please open an issue in the package repository.