README.md

In daranzolin/pmapply: Apply a function across pairs of vectors

pmapply

The goal of pmapply is to apply a pair-wise function over a series of vectors.

Installation

You can install the released version of pmapply from GitHub with:

remotes::install_github("daranzolin/pmapply")

Example: What keys should I use to join my tables?

Suppose you have several tables with varying keys. Some may match, some may partially match, and some may not match at all. Rather than rerunning sum(key1 %in% key2), sum(key2 %in% key3), sum(key1 %in% key3) ad infinitum, use pmapply:

library(pmapply)

generate_keys <- function(n = 1200) {
  a <- do.call(paste0, replicate(5, sample(LETTERS, n, TRUE), FALSE))
  paste0(a, sprintf("%04d", sample(9999, n, TRUE)), sample(LETTERS, n, TRUE))
}
keys <- generate_keys()
l <- list(
  k1 = sample(keys, 1000),
  k2 = sample(keys, 1000),
  k3 = sample(keys, 1000),
  k4 = sample(keys, 1000)
)

si <- function(x, y) sum(x %in% y)
m <- pmapply(l, si)
m
#>      k1   k2   k3   k4
#> k1 1000  833  835  835
#> k2  833 1000  837  835
#> k3  835  837 1000  841
#> k4  835  835  841 1000

The resulting matrix shows the total intersections between each combination.

Get the minimum and maximum values with get_min_max_combos:

get_minmax_combos(m)
#> $max_combo
#> [1] "k4 - k3"
#> 
#> $max_value
#> [1] 841
#> 
#> $min_combo
#> [1] "k2 - k1"
#> 
#> $min_value
#> [1] 833

Or plot the heatmap with combo_heatmap:

combo_heatmap(m)

Example 2: Correlation Matrices

(cormat <- pmapply(iris[,c(1:4)], function(x, y) cor(x, y)))
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length    1.0000000  -0.1175698    0.8717538   0.8179411
#> Sepal.Width    -0.1175698   1.0000000   -0.4284401  -0.3661259
#> Petal.Length    0.8717538  -0.4284401    1.0000000   0.9628654
#> Petal.Width     0.8179411  -0.3661259    0.9628654   1.0000000
combo_heatmap(cormat)

daranzolin/pmapply documentation built on Nov. 4, 2019, 9:39 a.m.