Replacing dplyr::do by purrr:map. Some considerations

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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

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