Modify a function in order to pre-cast the input into a wide matrix format, perform the function, and then re-tidy (e.g. melt) the output into a tidy table.
Usage
widely(.f, sort = FALSE, sparse = FALSE, maximum_size = 1e+07)
widely_(.f, sort = FALSE, sparse = FALSE, maximum_size = 1e+07)Arguments
- .f
Function being wrapped
- sort
Whether to sort in descending order of
value- sparse
Whether to cast to a sparse matrix
- maximum_size
To prevent crashing, a maximum size of a non-sparse matrix to be created. Set to NULL to allow any size matrix.
Value
Returns a function that takes at least four arguments:
- tbl
A table
- row
Name of column to use as rows in wide matrix
- column
Name of column to use as columns in wide matrix
- value
Name of column to use as values in wide matrix
- ...
Arguments passed on to inner function
widely creates a function that takes those columns as
bare names, widely_ a function that takes them as strings.
Examples
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
gapminder %>%
widely(dist)(country, year, lifeExp)
#> # A tibble: 10,011 × 3
#> item1 item2 value
#> <fct> <fct> <dbl>
#> 1 Afghanistan Albania 107.
#> 2 Afghanistan Algeria 76.8
#> 3 Afghanistan Angola 4.65
#> 4 Afghanistan Argentina 110.
#> 5 Afghanistan Australia 129.
#> 6 Afghanistan Austria 124.
#> 7 Afghanistan Bahrain 98.1
#> 8 Afghanistan Bangladesh 45.3
#> 9 Afghanistan Belgium 125.
#> 10 Afghanistan Benin 39.3
#> # … with 10,001 more rows
# can perform within groups
closest_continent <- gapminder %>%
group_by(continent) %>%
widely(dist)(country, year, lifeExp)
closest_continent
#> # A tibble: 2,590 × 4
#> # Groups: continent [5]
#> continent item1 item2 value
#> <fct> <fct> <fct> <dbl>
#> 1 Asia Afghanistan Bahrain 98.1
#> 2 Asia Afghanistan Bangladesh 45.3
#> 3 Asia Afghanistan Cambodia 41.8
#> 4 Asia Afghanistan China 86.2
#> 5 Asia Afghanistan Hong Kong, China 125.
#> 6 Asia Afghanistan India 56.1
#> 7 Asia Afghanistan Indonesia 62.3
#> 8 Asia Afghanistan Iran 74.5
#> 9 Asia Afghanistan Iraq 66.9
#> 10 Asia Afghanistan Israel 125.
#> # … with 2,580 more rows
# for example, find the closest pair in each
closest_continent %>%
top_n(1, -value)
#> # A tibble: 5 × 4
#> # Groups: continent [5]
#> continent item1 item2 value
#> <fct> <fct> <fct> <dbl>
#> 1 Asia Jordan West Bank and Gaza 2.94
#> 2 Europe Belgium Germany 1.08
#> 3 Africa Comoros Mauritania 2.01
#> 4 Americas Honduras Peru 3.95
#> 5 Oceania Australia New Zealand 3.55