Hadley Wickham has announced to depreceate dplyr::do in favor of purrr:map. In a recent post, I have made use of do, so some commentators informed me about that. In this post, I will show use cases of map, specifically as a replacement of do. map is for lists; read more about lists here.
library(tidyverse)
library(broom)
We will use mtcars as a sample dataframe (boring, I know, but convenient).
data(mtcars)
Cor is a function that takes a dataframe as its input
As in the last post, assume we would like to conduct a correlation test. First, let’s start simple using cor.
mtcars %>%
select_if(is.numeric) %>%
select(1:3) %>% # to make it smaller
cor
#> mpg cyl disp
#> mpg 1.0000000 -0.8521620 -0.8475514
#> cyl -0.8521620 1.0000000 0.9020329
#> disp -0.8475514 0.9020329 1.0000000
Here’s no need for purrr:map. map is essentially a looping device, taking a list as input. However, cor does not takes lists as input. It takes a whole dataframe (consisting of many lists). That’s even more practical than a looping function such as map.
cor.test via do and via map
Now let’s see where map makes sense. Consider cor.test from the last post. cor.test does not accept a dataframe as input, hence the dplyr logic does not apply well. Instead we have to build a workaround using do:
mtcars %>%
do(cor.test(.$hp, .$cyl) %>% tidy)
#> estimate statistic p.value parameter conf.low conf.high
#> 1 0.8324475 8.228604 3.477861e-09 30 0.6816016 0.9154223
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
Here we apply the function cor.test to two columns. Applying functions to columns (ie., lists) works smoothly with map and friends:
mtcars %>%
select(hp) %>% # take out this line for iteration/loop
map(~cor.test(.x, mtcars$cyl) %>% tidy)
#> $hp
#> estimate statistic p.value parameter conf.low conf.high
#> 1 0.8324475 8.228604 3.477861e-09 30 0.6816016 0.9154223
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
map applies a function to a list element
So, what does map do? It applies a function .fun over all elements of a list .list:
map(.list, .fun)
.list must either be a list or a simple vector. mapp is convenient for iteration as a replacement of “for-loops”:
mtcars %>%
select(hp, cyl, mpg) %>% # only three for the sake of demonstration
map(~cor.test(.x, mtcars$cyl) %>% tidy)
#> $hp
#> estimate statistic p.value parameter conf.low conf.high
#> 1 0.8324475 8.228604 3.477861e-09 30 0.6816016 0.9154223
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
#>
#> $cyl
#> estimate statistic p.value parameter conf.low conf.high
#> 1 1 Inf 0 30 1 1
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
#>
#> $mpg
#> estimate statistic p.value parameter conf.low conf.high
#> 1 -0.852162 -8.919699 6.112687e-10 30 -0.9257694 -0.7163171
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
BTW, it would be nice to combine the tidy output elements ($hp, $cyl, $mpg) to a dataframe:
mtcars %>%
select(hp, cyl, mpg) %>% # only three for the sake of demonstration
map_df(~cor.test(.x, mtcars$cyl) %>% tidy)
#> estimate statistic p.value parameter conf.low conf.high
#> 1 0.8324475 8.228604 3.477861e-09 30 0.6816016 0.9154223
#> 2 1.0000000 Inf 0.000000e+00 30 1.0000000 1.0000000
#> 3 -0.8521620 -8.919699 6.112687e-10 30 -0.9257694 -0.7163171
#> method alternative
#> 1 Pearson's product-moment correlation two.sided
#> 2 Pearson's product-moment correlation two.sided
#> 3 Pearson's product-moment correlation two.sided
map_df maps the function (that’s what comes after “~”) to each list (ie., column) of mtcars. If possible, the resulting elements will be row-binded to a dataframe. To make the output of cor.test nice (ie., tidy) we again use tidy.
Extract elements from a list using map
Say, we are only interest in the p-value (OMG). How to extract each of the 3 p-values in our example?
mtcars %>%
select(hp, cyl, mpg) %>% # only three for the sake of demonstration
map(~cor.test(.x, mtcars$cyl) %>% tidy) %>%
map("p.value")
#> $hp
#> [1] 3.477861e-09
#>
#> $cyl
#> [1] 0
#>
#> $mpg
#> [1] 6.112687e-10
To extract several elements, say the p-value and r, we can use the [ operator:
mtcars %>%
select(hp, cyl, mpg) %>% # only three for the sake of demonstration
map(~cor.test(.x, mtcars$cyl) %>% tidy) %>%
map(`[`, c("p.value", "statistic"))
#> $hp
#> p.value statistic
#> 1 3.477861e-09 8.228604
#>
#> $cyl
#> p.value statistic
#> 1 0 Inf
#>
#> $mpg
#> p.value statistic
#> 1 6.112687e-10 -8.919699
[ is some kind of “extractor” function; it extracts elements, and returns a list or data frame:
mtcars["hp"] %>% head
#> hp
#> Mazda RX4 110
#> Mazda RX4 Wag 110
#> Datsun 710 93
#> Hornet 4 Drive 110
#> Hornet Sportabout 175
#> Valiant 105
mtcars["hp"] %>% head %>% str
#> 'data.frame': 6 obs. of 1 variable:
#> $ hp: num 110 110 93 110 175 105
x <- list(1, 2, 3)
x[1]
#> [[1]]
#> [1] 1
Maybe more convenient, there is a function called magrittr:extractor. It’s a wrapper aroung [:
library(magrittr)
mtcars %>%
select(hp, cyl, mpg) %>% # only three for the sake of demonstration
map(~cor.test(.x, mtcars$cyl) %>% tidy) %>%
map_df(extract, c("p.value", "statistic"))
#> p.value statistic
#> 1 3.477861e-09 8.228604
#> 2 0.000000e+00 Inf
#> 3 6.112687e-10 -8.919699