-
Notifications
You must be signed in to change notification settings - Fork 29
/
README.Rmd
113 lines (77 loc) · 4.36 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{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/)<br/>
**License:** [MIT](https://opensource.org/licenses/MIT)
<!-- badges: start -->
[](https://github.com/juliasilge/widyr/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=widyr)
[](https://app.codecov.io/gh/juliasilge/widyr?branch=main)
<!-- badges: end -->
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.