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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.
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:
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 items rather than compare between variables, 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)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(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 rowsThis tidy format (one-row-per-country-per-year) is very useful for grouping, summarizing, and filtering operations. But if we want to compare pairs of countries (for example, to find countries that are similar to each other), we would have to reshape this dataset. Note that here, country is the item, while year is the feature column.
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 rowsIn a single step, this finds the Euclidean distance between the
lifeExp value in each pair of countries, matching pairs
based on year. 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 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 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, sort = TRUE)## # A tibble: 10,011 × 3
## item1 item2 correlation
## <fct> <fct> <dbl>
## 1 Indonesia Mauritania 1.00
## 2 Morocco Senegal 1.00
## 3 Saudi Arabia West Bank and Gaza 1.00
## 4 Brazil France 0.999
## 5 Bahrain Reunion 0.999
## 6 Malaysia Sao Tome and Principe 0.999
## 7 Peru Syria 0.999
## 8 Bolivia Gambia 0.999
## 9 Indonesia Morocco 0.999
## 10 Libya Senegal 0.999
## # … with 10,001 more rowsThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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