nplyr

nplyr: a grammar of (nested) data manipulation :bird:

https://github.com/jibarozzo/nplyr

Repository

nplyr: a grammar of (nested) data manipulation :bird:

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  message = FALSE,
  warning = FALSE
)
```

# nplyr 



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## Overview

`{nplyr}` is a grammar of nested data manipulation that allows users to perform [dplyr](https://dplyr.tidyverse.org/)-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](https://tidyr.tidyverse.org/) 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](https://dplyr.tidyverse.org). If you are new to dplyr, the best place to start is the [data transformation chapter](https://r4ds.had.co.nz/transform.html) 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](https://cran.r-project.org/package=devtools) or [remotes](https://cran.r-project.org/package=remotes) package:

```{r, eval=FALSE}
# install from CRAN
install.packages("nplyr")

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

## Usage

To get started, we'll create a nested column for the country data within each continent from the [gapminder](https://CRAN.R-project.org/package=gapminder) dataset.

```{r}
library(nplyr)

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

gm_nest
```

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

```{r}
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

# 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)
```

nplyr also supports grouped operations with `nest_group_by()`:

```{r}
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

# unnesting shows summarised tibbles for each continent
gm_nest_example %>%
  slice(2) %>%
  tidyr::unnest(country_data)
```

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](https://github.com/jibarozzo/nplyr/issues) in the package repository. 

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