Authors: Julia Silge, David
Robinson
License:
MIT
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.
You can install the released version of widyr from CRAN with:
install.packages("widyr")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("juliasilge/widyr")The term “wide data” has gone out of fashion as being “imprecise” (Wickham 2014), 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.
Consider the gapminder dataset in the gapminder package.
library(dplyr)
library(gapminder)
gapminder
#> # A tibble: 1,704 × 6
#> country continent year lifeExp pop gdpPercap
#> <fct> <fct> <int> <dbl> <int> <dbl>
#> 1 Afghanistan Asia 1952 28.8 8425333 779.
#> 2 Afghanistan Asia 1957 30.3 9240934 821.
#> 3 Afghanistan Asia 1962 32.0 10267083 853.
#> 4 Afghanistan Asia 1967 34.0 11537966 836.
#> 5 Afghanistan Asia 1972 36.1 13079460 740.
#> 6 Afghanistan Asia 1977 38.4 14880372 786.
#> 7 Afghanistan Asia 1982 39.9 12881816 978.
#> 8 Afghanistan Asia 1987 40.8 13867957 852.
#> 9 Afghanistan Asia 1992 41.7 16317921 649.
#> 10 Afghanistan Asia 1997 41.8 22227415 635.
#> # … with 1,694 more rows
#> # ℹ Use `print(n = ...)` to see more rowsThis 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.
The widyr package offers pairwise_ functions that operate on pairs of
items within data. An example is pairwise_dist:
library(widyr)
gapminder %>%
pairwise_dist(country, year, lifeExp)
#> # A tibble: 20,022 × 3
#> item1 item2 distance
#> <fct> <fct> <dbl>
#> 1 Albania Afghanistan 107.
#> 2 Algeria Afghanistan 76.8
#> 3 Angola Afghanistan 4.65
#> 4 Argentina Afghanistan 110.
#> 5 Australia Afghanistan 129.
#> 6 Austria Afghanistan 124.
#> 7 Bahrain Afghanistan 98.1
#> 8 Bangladesh Afghanistan 45.3
#> 9 Belgium Afghanistan 125.
#> 10 Benin Afghanistan 39.3
#> # … with 20,012 more rows
#> # ℹ Use `print(n = ...)` to see more rowsThis 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():
gapminder %>%
pairwise_dist(country, year, lifeExp) %>%
arrange(distance)
#> # A tibble: 20,022 × 3
#> item1 item2 distance
#> <fct> <fct> <dbl>
#> 1 Germany Belgium 1.08
#> 2 Belgium Germany 1.08
#> 3 United Kingdom New Zealand 1.51
#> 4 New Zealand United Kingdom 1.51
#> 5 Norway Netherlands 1.56
#> 6 Netherlands Norway 1.56
#> 7 Italy Israel 1.66
#> 8 Israel Italy 1.66
#> 9 Finland Austria 1.94
#> 10 Austria Finland 1.94
#> # … with 20,012 more rows
#> # ℹ Use `print(n = ...)` to see more rowsNotice that this includes duplicates (Germany/Belgium and
Belgium/Germany). To avoid those (the upper triangle of the distance
matrix), use upper = FALSE:
gapminder %>%
pairwise_dist(country, year, lifeExp, upper = FALSE) %>%
arrange(distance)
#> # A tibble: 10,011 × 3
#> item1 item2 distance
#> <fct> <fct> <dbl>
#> 1 Belgium Germany 1.08
#> 2 New Zealand United Kingdom 1.51
#> 3 Netherlands Norway 1.56
#> 4 Israel Italy 1.66
#> 5 Austria Finland 1.94
#> 6 Belgium United Kingdom 1.95
#> 7 Iceland Sweden 2.01
#> 8 Comoros Mauritania 2.01
#> 9 Belgium United States 2.09
#> 10 Germany Ireland 2.10
#> # … with 10,001 more rows
#> # ℹ Use `print(n = ...)` to see more rowsIn some analyses, we may be interested in correlation rather than
distance of pairs. For this we would use pairwise_cor:
gapminder %>%
pairwise_cor(country, year, lifeExp, upper = FALSE)
#> # A tibble: 10,011 × 3
#> item1 item2 correlation
#> <fct> <fct> <dbl>
#> 1 Afghanistan Albania 0.966
#> 2 Afghanistan Algeria 0.987
#> 3 Albania Algeria 0.953
#> 4 Afghanistan Angola 0.986
#> 5 Albania Angola 0.976
#> 6 Algeria Angola 0.952
#> 7 Afghanistan Argentina 0.971
#> 8 Albania Argentina 0.949
#> 9 Algeria Argentina 0.991
#> 10 Angola Argentina 0.936
#> # … with 10,001 more rows
#> # ℹ Use `print(n = ...)` to see more rowsThis project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.