Map Function Replace Apply Family in Base R

Map function in tidyverse package is awesome.

Let us look at one example.

First of all, we split the dateset “mtcars”" into 3 data frames by variable “cyl”.

library(tidyverse)

## -- Attaching packages ------- tidyverse 1.2.1 --

## √ ggplot2 2.2.1     √ purrr   0.2.4
## √ tibble  1.3.4     √ dplyr   0.7.4
## √ tidyr   0.7.2     √ stringr 1.2.0
## √ readr   1.1.1     √ forcats 0.2.0

## -- Conflicts ---------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

mtcars %>% 
  split(.$cyl) %>%
  str()

## List of 3
##  $ 4:'data.frame':   11 obs. of  11 variables:
##   ..$ mpg : num [1:11] 22.8 24.4 22.8 32.4 30.4 33.9 21.5 27.3 26 30.4 ...
##   ..$ cyl : num [1:11] 4 4 4 4 4 4 4 4 4 4 ...
##   ..$ disp: num [1:11] 108 146.7 140.8 78.7 75.7 ...
##   ..$ hp  : num [1:11] 93 62 95 66 52 65 97 66 91 113 ...
##   ..$ drat: num [1:11] 3.85 3.69 3.92 4.08 4.93 4.22 3.7 4.08 4.43 3.77 ...
##   ..$ wt  : num [1:11] 2.32 3.19 3.15 2.2 1.61 ...
##   ..$ qsec: num [1:11] 18.6 20 22.9 19.5 18.5 ...
##   ..$ vs  : num [1:11] 1 1 1 1 1 1 1 1 0 1 ...
##   ..$ am  : num [1:11] 1 0 0 1 1 1 0 1 1 1 ...
##   ..$ gear: num [1:11] 4 4 4 4 4 4 3 4 5 5 ...
##   ..$ carb: num [1:11] 1 2 2 1 2 1 1 1 2 2 ...
##  $ 6:'data.frame':   7 obs. of  11 variables:
##   ..$ mpg : num [1:7] 21 21 21.4 18.1 19.2 17.8 19.7
##   ..$ cyl : num [1:7] 6 6 6 6 6 6 6
##   ..$ disp: num [1:7] 160 160 258 225 168 ...
##   ..$ hp  : num [1:7] 110 110 110 105 123 123 175
##   ..$ drat: num [1:7] 3.9 3.9 3.08 2.76 3.92 3.92 3.62
##   ..$ wt  : num [1:7] 2.62 2.88 3.21 3.46 3.44 ...
##   ..$ qsec: num [1:7] 16.5 17 19.4 20.2 18.3 ...
##   ..$ vs  : num [1:7] 0 0 1 1 1 1 0
##   ..$ am  : num [1:7] 1 1 0 0 0 0 1
##   ..$ gear: num [1:7] 4 4 3 3 4 4 5
##   ..$ carb: num [1:7] 4 4 1 1 4 4 6
##  $ 8:'data.frame':   14 obs. of  11 variables:
##   ..$ mpg : num [1:14] 18.7 14.3 16.4 17.3 15.2 10.4 10.4 14.7 15.5 15.2 ...
##   ..$ cyl : num [1:14] 8 8 8 8 8 8 8 8 8 8 ...
##   ..$ disp: num [1:14] 360 360 276 276 276 ...
##   ..$ hp  : num [1:14] 175 245 180 180 180 205 215 230 150 150 ...
##   ..$ drat: num [1:14] 3.15 3.21 3.07 3.07 3.07 2.93 3 3.23 2.76 3.15 ...
##   ..$ wt  : num [1:14] 3.44 3.57 4.07 3.73 3.78 ...
##   ..$ qsec: num [1:14] 17 15.8 17.4 17.6 18 ...
##   ..$ vs  : num [1:14] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ am  : num [1:14] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ gear: num [1:14] 3 3 3 3 3 3 3 3 3 3 ...
##   ..$ carb: num [1:14] 2 4 3 3 3 4 4 4 2 2 ...

Then, we want to make linear regression models to 3 data frames at the same time.

mtcars %>% 
  split(.$cyl) %>% 
  map(~lm(mpg ~ wt, data = .))

## $`4`
## 
## Call:
## lm(formula = mpg ~ wt, data = .)
## 
## Coefficients:
## (Intercept)           wt  
##      39.571       -5.647  
## 
## 
## $`6`
## 
## Call:
## lm(formula = mpg ~ wt, data = .)
## 
## Coefficients:
## (Intercept)           wt  
##       28.41        -2.78  
## 
## 
## $`8`
## 
## Call:
## lm(formula = mpg ~ wt, data = .)
## 
## Coefficients:
## (Intercept)           wt  
##      23.868       -2.192

Wonderful!

If you want the details of map function, please click this link http://r4ds.had.co.nz/iteration.html#the-map-functions.