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https://github.com/dgrtwo/widyr

Widen, process, and re-tidy a dataset
https://github.com/dgrtwo/widyr

Last synced: 3 months ago
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Widen, process, and re-tidy a dataset

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README

        

---
output: github_document
---

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

# widyr: Widen, process, and re-tidy a dataset

**Authors:** [Julia Silge](https://juliasilge.com/), [David Robinson](http://varianceexplained.org/)

**License:** [MIT](https://opensource.org/licenses/MIT)

[![R-CMD-check](https://github.com/juliasilge/widyr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/juliasilge/widyr/actions/workflows/R-CMD-check.yaml)
[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/widyr)](https://cran.r-project.org/package=widyr)
[![Codecov test coverage](https://codecov.io/gh/juliasilge/widyr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/juliasilge/widyr?branch=main)

This package wraps the pattern of un-tidying data into a wide matrix, performing some processing, then turning it back into a tidy form. This is useful for several mathematical operations such as co-occurrence counts, correlations, or clustering that are best done on a wide matrix.

## Installation

You can install the released version of widyr from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("widyr")
```

And the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("juliasilge/widyr")
```

## Towards a precise definition of "wide" data

The term "wide data" has gone out of fashion as being "imprecise" [(Wickham 2014)](http://vita.had.co.nz/papers/tidy-data.pdf), but I think with a proper definition the term could be entirely meaningful and useful.

A **wide** dataset is one or more matrices where:

* Each row is one **item**
* Each column is one **feature**
* Each value is one **observation**
* Each matrix is one **variable**

When would you want data to be wide rather than tidy? Notable examples include classification, clustering, correlation, factorization, or other operations that can take advantage of a matrix structure. In general, when you want to **compare between pairs of items** rather than compare between variables or between groups of observations, this is a useful structure.

The widyr package is based on the observation that during a tidy data analysis, you often want data to be wide only *temporarily*, before returning to a tidy structure for visualization and further analysis. widyr makes this easy through a set of `pairwise_` functions.

## Example: gapminder

Consider the gapminder dataset in the [gapminder package](https://CRAN.R-project.org/package=gapminder).

```{r}
library(dplyr)
library(gapminder)

gapminder
```

This tidy format (one-row-per-country-per-year) is very useful for grouping, summarizing, and filtering operations. But if we want to *compare* countries (for example, to find countries that are similar to each other), we would have to reshape this dataset. Note that here, each country is an **item**, while each year is the **feature**.

#### Pairwise operations

The widyr package offers `pairwise_` functions that operate on pairs of items within data. An example is `pairwise_dist`:

```{r}
library(widyr)

gapminder %>%
pairwise_dist(country, year, lifeExp)
```

This finds the Euclidean distance between the `lifeExp` value in each pair of countries. It knows which values to compare between countries with `year`, which is the feature column.

We could find the closest pairs of countries overall with `arrange()`:

```{r}
gapminder %>%
pairwise_dist(country, year, lifeExp) %>%
arrange(distance)
```

Notice that this includes duplicates (Germany/Belgium and Belgium/Germany). To avoid those (the upper triangle of the distance matrix), use `upper = FALSE`:

```{r}
gapminder %>%
pairwise_dist(country, year, lifeExp, upper = FALSE) %>%
arrange(distance)
```

In some analyses, we may be interested in correlation rather than distance of pairs. For this we would use `pairwise_cor`:

```{r}
gapminder %>%
pairwise_cor(country, year, lifeExp, upper = FALSE)
```

### Code of Conduct

This project is released with a [Contributor Code of Conduct](https://www.contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.