Higher-Order Functions in Package purrr

(Sections 14.3 - 14.4)

Packages

Make sure the following packages are attached:

library(tidyverse)
library(Lahman)

The purrr package is part of the tidyverse. It provides many higher-order functions.

Each higher-order function iterates over a given iterable data structure (vector, data frame, etc.) with the help of another given function.

`map()` and Variations

A Task

Suppose that we want to generate five vectors, each of which consists of a different given number of real numbers, randomly chosen between 0 and 1.

Solve With a Loop

vec <- c(5, 7, 8, 2, 9)
lst <- vector(mode = "list", length = length(vec))
for (i in seq_along(vec)) {
  lst[[i]] <- runif(vec[i])
}
lst

[[1]]
[1] 0.6469028 0.3942258 0.6185018 0.4768911 0.1360972

[[2]]
[1] 0.067384386 0.129152617 0.393117930 0.002582699 0.620205954 0.764414018
[7] 0.743835758

[[3]]
[1] 0.8261657 0.4227291 0.4092877 0.5396926 0.9607224 0.6535573 0.5467153
[8] 0.2660636

[[4]]
[1] 0.19679560 0.07787092

[[5]]
[1] 0.8183933 0.9424046 0.8842245 0.1658784 0.3551013 0.7480950 0.4509505
[8] 0.5558754 0.9640731

Solve with `map()`

how_many <- c(5, 7, 8, 2, 9)
lst <- 
  how_many %>%
  map(runif)
lst

[[1]]
[1] 0.6469028 0.3942258 0.6185018 0.4768911 0.1360972

[[2]]
[1] 0.067384386 0.129152617 0.393117930 0.002582699 0.620205954 0.764414018
[7] 0.743835758

[[3]]
[1] 0.8261657 0.4227291 0.4092877 0.5396926 0.9607224 0.6535573 0.5467153
[8] 0.2660636

[[4]]
[1] 0.19679560 0.07787092

[[5]]
[1] 0.8183933 0.9424046 0.8842245 0.1658784 0.3551013 0.7480950 0.4509505
[8] 0.5558754 0.9640731

How `map()` Works

map(.x, .f, ...)

In the template above:

.x can be a list or any atomic vector;
.f is a function that is to be applied to each element of .x.
... consists of other arguments that are supplied as arguments for the .f function.

The result is always a list.

More Control

What if we want the random real numbers of be between 4 and 8?

No problem!

lst <- 
  how_many %>%
  map(runif, min = 4, max = 8)

What is This Code Doing?

lower_bounds <- 1:3
lower_bounds %>%
  map(runif, n = 2, max = 8)

[[1]]
[1] 3.008575 5.964877

[[2]]
[1] 6.514459 6.282105

[[3]]
[1] 6.688118 7.431739

How It Works

lower_bounds <- 1:3
lower_bounds %>%
  map(runif, n = 2, max = 8)

the .x argument is lower_bounds;
the .f argument is runif();
the n=2 becomes the first argument of runif();
the max = 8 becomes the max argument of runif();
so R deduces that the min parameter of runif() must iterate over lower_bounds!

Vary Two or More Parameters of a Function

Use pmap() for this:

how_many <- c(3,1,4)
upper_bounds <- c(1, 5, 10)
list(how_many, upper_bounds) %>%
  pmap(runif, min = 0)

[[1]]
[1] 0.4142409 0.5140328 0.6190231

[[2]]
[1] 0.193564

[[3]]
[1] 3.0889976 7.4509632 3.6170952 0.2348365

How This Works

how_many <- c(3,1,4)
upper_bounds <- c(1, 5, 10)
list(how_many, upper_bounds) %>%
  pmap(runif, min = 0)

The first argument of pmap() is called .l. This is set to list(how_many, upper_bounds), and they supply values for two arguments of runif().
min = 0 Takes care of the min parameter of runif().
So R deduces that how_many covers the n parameter, and upper_bounds covers the max parameter.

Why Not Vary All Three Parameters?

how_many <- c(3,1,4)
lower_bounds <- c(-5, 0, 5)
upper_bounds <- c(0, 5, 10)
args <- list(how_many, lower_bounds, upper_bounds)
args %>%
  pmap(runif)

[[1]]
[1] -0.007434684 -2.358819144 -3.901037909

[[2]]
[1] 1.012372

[[3]]
[1] 5.382376 9.158435 8.451728 9.673688

Write Your Own Helper Function

r_letters <- function(n, upper) {
  if (upper ) {
    sample(LETTERS, size = n, replace = TRUE)
  } else {
    sample(letters, size = n, replace = TRUE)
  }
}

Try It Out

r_letters(5, upper = TRUE)

[1] "O" "G" "A" "X" "S"

r_letters(4, upper = FALSE)

[1] "p" "m" "n" "u"

Now Map!

# vary number of letters to pick
n <- c(3, 6, 9)
# vary the case (upper, lower)
upper <- c(TRUE, FALSE, TRUE)  
list(n, upper) %>% 
  pmap(r_letters)

[[1]]
[1] "R" "I" "Q"

[[2]]
[1] "m" "d" "e" "y" "m" "o"

[[3]]
[1] "A" "O" "M" "C" "J" "I" "P" "V" "W"

Map with an Anonymous Function

c(1, 3, 5) %>% 
  map(function(x) runif(3, min = 0, max = x))

[[1]]
[1] 0.8951945 0.7801631 0.8545849

[[2]]
[1] 0.6993537 2.7956469 2.8740788

[[3]]
[1] 4.959646 2.544537 1.022301

Mapping With Data Frames

.x can be any object that can be coerced to a list. A data frame will do:

data("m111survey", package = "bcscr")
number_na <-
  m111survey %>% 
  map(function(col) sum(is.na(col)))
number_na

$height
[1] 0

$ideal_ht
[1] 2

$sleep
[1] 0

$fastest
[1] 0

$weight_feel
[1] 0

$love_first
[1] 0

$extra_life
[1] 0

$seat
[1] 0

$GPA
[1] 1

$enough_Sleep
[1] 0

$sex
[1] 0

$diff.ideal.act.
[1] 2

Options for Other Types of Output

When the result can take on a form other than a list, it is possible to use variants of map() such as:

map_int()
map_dbl()
map_lgl()
map_chr()

Example

number_na <-
  m111survey %>% 
  map_int(~ sum(is.na(.)))
number_na

         height        ideal_ht           sleep         fastest     weight_feel 
              0               2               0               0               0 
     love_first      extra_life            seat             GPA    enough_Sleep 
              0               0               0               1               0 
            sex diff.ideal.act. 
              0               2

Another Example

Here are the types of each variable in m111survey:

m111survey %>% 
  map_chr(typeof)

         height        ideal_ht           sleep         fastest     weight_feel 
       "double"        "double"        "double"       "integer"       "integer" 
     love_first      extra_life            seat             GPA    enough_Sleep 
      "integer"       "integer"       "integer"        "double"       "integer" 
            sex diff.ideal.act. 
      "integer"        "double"

Yet Another Example

Here is a statement of whether or not each variable is a factor:

m111survey %>% 
  map_lgl(is.factor)

         height        ideal_ht           sleep         fastest     weight_feel 
          FALSE           FALSE           FALSE           FALSE            TRUE 
     love_first      extra_life            seat             GPA    enough_Sleep 
           TRUE            TRUE            TRUE           FALSE            TRUE 
            sex diff.ideal.act. 
           TRUE           FALSE

When Output is a List of Data Frames

We would like to produce a graph of the number of home-runs hit by each of the top five home-run hitters in the Major Leagues, for each season since 1900.

The Data

library(Lahman)
?Batting

A Helper Function

top_hr_hitters <- function(x, n) {
  x %>% 
    arrange(desc(HR)) %>% 
    select(playerID, yearID, HR) %>% 
    head(n)
}

Batting %>% 
  filter(yearID == 1927) %>% 
  top_hr_hitters(n = 10)

    playerID yearID HR
1   ruthba01   1927 60
2  gehrilo01   1927 47
3  willicy01   1927 30
4  wilsoha01   1927 30
5  hornsro01   1927 26
6  terrybi01   1927 20
7  bottoji01   1927 19
8  hafeych01   1927 18
9  lazzeto01   1927 18
10 willike01   1927 17

Split the Data

We need a list in which each element is a data frame covering a single season.

dplyr’s group_split() is our friend:

lst <-
  Batting %>% 
  filter(yearID >= 1900) %>% 
  select(playerID, yearID, HR) %>% 
  group_by(yearID) %>% 
  group_split()
str(lst)

list<tibble[,3]> [1:123] 
$ : tibble [195 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:195] "allenbo01" "baileha01" "barreji01" "barrysh01" ...
 ..$ yearID  : int [1:195] 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 ...
 ..$ HR      : int [1:195] 0 0 5 1 5 2 0 0 1 5 ...
$ : tibble [412 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:412] "anderjo01" "bakerbo01" "bakerbo01" "barreji01" ...
 ..$ yearID  : int [1:412] 1901 1901 1901 1901 1901 1901 1901 1901 1901 1901 ...
 ..$ HR      : int [1:412] 8 0 0 4 0 1 1 8 6 3 ...
$ : tibble [455 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:455] "adamsjo01" "adkindo01" "altroni01" "anderjo01" ...
 ..$ yearID  : int [1:455] 1902 1902 1902 1902 1902 1902 1902 1902 1902 1902 ...
 ..$ HR      : int [1:455] 0 0 0 4 0 2 0 3 4 3 ...
$ : tibble [400 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:400] "abbated01" "abbotfr01" "adkindo01" "altroni01" ...
 ..$ yearID  : int [1:400] 1903 1903 1903 1903 1903 1903 1903 1903 1903 1903 ...
 ..$ HR      : int [1:400] 1 1 0 0 0 0 2 0 0 0 ...
$ : tibble [406 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:406] "abbated01" "abbotfr01" "altroni01" "amesre01" ...
 ..$ yearID  : int [1:406] 1904 1904 1904 1904 1904 1904 1904 1904 1904 1904 ...
 ..$ HR      : int [1:406] 3 0 1 0 3 0 0 0 0 1 ...
$ : tibble [407 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:407] "abbated01" "abbotfr01" "ablesha01" "adamsri01" ...
 ..$ yearID  : int [1:407] 1905 1905 1905 1905 1905 1905 1905 1905 1905 1905 ...
 ..$ HR      : int [1:407] 3 0 0 0 0 0 0 1 0 2 ...
$ : tibble [437 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:437] "abstebi01" "adamsba01" "alperwh01" "altizda01" ...
 ..$ yearID  : int [1:437] 1906 1906 1906 1906 1906 1906 1906 1906 1906 1906 ...
 ..$ HR      : int [1:437] 0 0 3 1 0 0 3 0 2 2 ...
$ : tibble [447 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:447] "abbated01" "adamsba01" "alperwh01" "altizda01" ...
 ..$ yearID  : int [1:447] 1907 1907 1907 1907 1907 1907 1907 1907 1907 1907 ...
 ..$ HR      : int [1:447] 2 0 2 2 0 1 1 0 0 0 ...
$ : tibble [470 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:470] "abbated01" "alperwh01" "altizda01" "altizda01" ...
 ..$ yearID  : int [1:470] 1908 1908 1908 1908 1908 1908 1908 1908 1908 1908 ...
 ..$ HR      : int [1:470] 1 1 0 0 0 0 0 0 0 0 ...
$ : tibble [543 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:543] "abbated01" "ablesha01" "abstebi01" "adamsba01" ...
 ..$ yearID  : int [1:543] 1909 1909 1909 1909 1909 1909 1909 1909 1909 1909 ...
 ..$ HR      : int [1:543] 1 0 1 0 1 1 0 0 0 0 ...
$ : tibble [539 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:539] "abbated01" "abbated01" "abbotod01" "abstebi01" ...
 ..$ yearID  : int [1:539] 1910 1910 1910 1910 1910 1910 1910 1910 1910 1910 ...
 ..$ HR      : int [1:539] 0 0 0 0 0 0 0 0 0 0 ...
$ : tibble [567 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:567] "ablesha01" "adamsba01" "adamsbe01" "ainsmed01" ...
 ..$ yearID  : int [1:567] 1911 1911 1911 1911 1911 1911 1911 1911 1911 1911 ...
 ..$ HR      : int [1:567] 0 0 0 0 0 0 0 0 0 0 ...
$ : tibble [630 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:630] "adamsba01" "adamsbe01" "adamswi01" "aglerjo01" ...
 ..$ yearID  : int [1:630] 1912 1912 1912 1912 1912 1912 1912 1912 1912 1912 ...
 ..$ HR      : int [1:630] 0 0 0 0 0 0 0 2 0 1 ...
$ : tibble [616 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:616] "acostme01" "adamsba01" "adamswi01" "agnewsa01" ...
 ..$ yearID  : int [1:616] 1913 1913 1913 1913 1913 1913 1913 1913 1913 1913 ...
 ..$ HR      : int [1:616] 0 0 0 2 2 0 0 1 0 0 ...
$ : tibble [797 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:797] "acostme01" "adamsba01" "adamsda01" "adamska01" ...
 ..$ yearID  : int [1:797] 1914 1914 1914 1914 1914 1914 1914 1914 1914 1914 ...
 ..$ HR      : int [1:797] 0 1 1 0 0 0 0 0 0 0 ...
$ : tibble [796 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:796] "acostme01" "adamsba01" "adamsbe01" "adamsda01" ...
 ..$ yearID  : int [1:796] 1915 1915 1915 1915 1915 1915 1915 1915 1915 1915 ...
 ..$ HR      : int [1:796] 0 0 0 0 0 0 0 0 0 0 ...
$ : tibble [565 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:565] "acostme01" "adamsba01" "adamsbe01" "agnewsa01" ...
 ..$ yearID  : int [1:565] 1916 1916 1916 1916 1916 1916 1916 1916 1916 1916 ...
 ..$ HR      : int [1:565] 0 0 0 0 0 0 0 0 0 0 ...
$ : tibble [516 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:516] "adamsbe01" "agnewsa01" "ainsmed01" "aldrivi01" ...
 ..$ yearID  : int [1:516] 1917 1917 1917 1917 1917 1917 1917 1917 1917 1917 ...
 ..$ HR      : int [1:516] 1 0 0 0 1 0 0 0 0 0 ...
$ : tibble [506 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:506] "acostme01" "acostme01" "adamsba01" "adamsbe01" ...
 ..$ yearID  : int [1:506] 1918 1918 1918 1918 1918 1918 1918 1918 1918 1918 ...
 ..$ HR      : int [1:506] 0 0 0 0 0 0 0 0 0 0 ...
$ : tibble [534 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:534] "adamsba01" "adamsbe01" "adamswi01" "agnewsa01" ...
 ..$ yearID  : int [1:534] 1919 1919 1919 1919 1919 1919 1919 1919 1919 1919 ...
 ..$ HR      : int [1:534] 0 1 0 0 3 0 0 0 0 0 ...
$ : tibble [515 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:515] "acostjo01" "adamsba01" "ainsmed01" "alexape01" ...
 ..$ yearID  : int [1:515] 1920 1920 1920 1920 1920 1920 1920 1920 1920 1920 ...
 ..$ HR      : int [1:515] 0 1 1 1 0 0 1 0 1 0 ...
$ : tibble [520 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:520] "acostjo01" "adamsba01" "ainsmed01" "ainsmed01" ...
 ..$ yearID  : int [1:520] 1921 1921 1921 1921 1921 1921 1921 1921 1921 1921 ...
 ..$ HR      : int [1:520] 0 0 0 0 1 0 0 0 0 0 ...
$ : tibble [513 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:513] "acostjo01" "adamsba01" "adamssp01" "ainsmed01" ...
 ..$ yearID  : int [1:513] 1922 1922 1922 1922 1922 1922 1922 1922 1922 1922 ...
 ..$ HR      : int [1:513] 0 1 0 13 0 0 0 0 0 7 ...
$ : tibble [530 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:530] "abramge01" "adamsba01" "adamssp01" "adamssp02" ...
 ..$ yearID  : int [1:530] 1923 1923 1923 1923 1923 1923 1923 1923 1923 1923 ...
 ..$ HR      : int [1:530] 0 0 4 0 3 0 0 1 0 0 ...
$ : tibble [547 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:547] "adamsba01" "adamssp01" "ainsmed01" "aldrivi01" ...
 ..$ yearID  : int [1:547] 1924 1924 1924 1924 1924 1924 1924 1924 1924 1924 ...
 ..$ HR      : int [1:547] 0 1 0 0 1 0 0 0 1 0 ...
$ : tibble [557 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:557] "adamsba01" "adamsbo01" "adamssp01" "adamssp02" ...
 ..$ yearID  : int [1:557] 1925 1925 1925 1925 1925 1925 1925 1925 1925 1925 ...
 ..$ HR      : int [1:557] 0 0 2 0 1 2 0 0 0 0 ...
$ : tibble [526 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:526] "adamsba01" "adamssp01" "adamssp02" "aldrivi01" ...
 ..$ yearID  : int [1:526] 1926 1926 1926 1926 1926 1926 1926 1926 1926 1926 ...
 ..$ HR      : int [1:526] 0 0 0 0 0 0 0 0 0 0 ...
$ : tibble [541 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:541] "adamssp01" "adamssp02" "aldrivi01" "alexape01" ...
 ..$ yearID  : int [1:541] 1927 1927 1927 1927 1927 1927 1927 1927 1927 1927 ...
 ..$ HR      : int [1:541] 0 0 0 0 2 0 0 1 0 0 ...
$ : tibble [530 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:530] "adamssp01" "adkingr01" "aldrivi01" "alexape01" ...
 ..$ yearID  : int [1:530] 1928 1928 1928 1928 1928 1928 1928 1928 1928 1928 ...
 ..$ HR      : int [1:530] 0 0 1 1 1 0 0 0 1 0 ...
$ : tibble [530 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:530] "adamssp01" "adkingr01" "akersbi01" "alexada01" ...
 ..$ yearID  : int [1:530] 1929 1929 1929 1929 1929 1929 1929 1929 1929 1929 ...
 ..$ HR      : int [1:530] 0 0 1 25 0 6 0 0 0 0 ...
$ : tibble [531 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:531] "adamssp01" "akersbi01" "alexada01" "alexape01" ...
 ..$ yearID  : int [1:531] 1930 1930 1930 1930 1930 1930 1930 1930 1930 1930 ...
 ..$ HR      : int [1:531] 0 9 20 0 3 7 0 0 0 0 ...
$ : tibble [509 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:509] "adairji01" "adamsbo02" "adamssp01" "akersbi01" ...
 ..$ yearID  : int [1:509] 1931 1931 1931 1931 1931 1931 1931 1931 1931 1931 ...
 ..$ HR      : int [1:509] 0 0 1 0 3 5 0 0 0 0 ...
$ : tibble [527 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:527] "adamsbo02" "adamssp01" "akersbi01" "alexada01" ...
 ..$ yearID  : int [1:527] 1932 1932 1932 1932 1932 1932 1932 1932 1932 1932 ...
 ..$ HR      : int [1:527] 0 0 1 0 8 1 1 0 0 0 ...
$ : tibble [491 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:491] "adamssp01" "adamssp01" "alexada01" "allenet01" ...
 ..$ yearID  : int [1:491] 1933 1933 1933 1933 1933 1933 1933 1933 1933 1933 ...
 ..$ HR      : int [1:491] 0 1 5 0 0 1 0 0 0 6 ...
$ : tibble [521 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:521] "adamssp01" "allenet01" "allenjo02" "almadme01" ...
 ..$ yearID  : int [1:521] 1934 1934 1934 1934 1934 1934 1934 1934 1934 1934 ...
 ..$ HR      : int [1:521] 0 10 0 0 0 2 0 0 31 0 ...
$ : tibble [513 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:513] "allenet01" "allenjo02" "almadme01" "andreiv01" ...
 ..$ yearID  : int [1:513] 1935 1935 1935 1935 1935 1935 1935 1935 1935 1935 ...
 ..$ HR      : int [1:513] 8 1 3 0 1 0 19 0 0 0 ...
$ : tibble [511 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:511] "allenet01" "allenet01" "allenjo02" "almadme01" ...
 ..$ yearID  : int [1:511] 1936 1936 1936 1936 1936 1936 1936 1936 1936 1936 ...
 ..$ HR      : int [1:511] 1 3 0 1 0 0 0 0 6 0 ...
$ : tibble [526 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:526] "alexahu01" "allenbo03" "allenet01" "allenjo02" ...
 ..$ yearID  : int [1:526] 1937 1937 1937 1937 1937 1937 1937 1937 1937 1937 ...
 ..$ HR      : int [1:526] 0 0 0 0 1 4 0 0 0 0 ...
$ : tibble [530 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:530] "allenet01" "allenjo02" "almadme01" "almadme01" ...
 ..$ yearID  : int [1:530] 1938 1938 1938 1938 1938 1938 1938 1938 1938 1938 ...
 ..$ HR      : int [1:530] 0 1 1 3 0 0 0 0 0 4 ...
$ : tibble [579 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:579] "adamsbu01" "aderhmo01" "allenjo02" "almadme01" ...
 ..$ yearID  : int [1:579] 1939 1939 1939 1939 1939 1939 1939 1939 1939 1939 ...
 ..$ HR      : int [1:579] 0 1 0 1 0 0 0 0 0 0 ...
$ : tibble [541 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:541] "aderhmo01" "allenjo02" "anderre01" "andrena01" ...
 ..$ yearID  : int [1:541] 1940 1940 1940 1940 1940 1940 1940 1940 1940 1940 ...
 ..$ HR      : int [1:541] 0 0 0 0 0 0 0 0 0 0 ...
$ : tibble [582 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:582] "adamsac01" "aderhmo01" "albosed01" "alenoch01" ...
 ..$ yearID  : int [1:582] 1941 1941 1941 1941 1941 1941 1941 1941 1941 1941 ...
 ..$ HR      : int [1:582] 0 0 0 1 1 0 1 0 0 0 ...
$ : tibble [539 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:539] "abernte01" "abreujo01" "adamsac01" "adkinde01" ...
 ..$ yearID  : int [1:539] 1942 1942 1942 1942 1942 1942 1942 1942 1942 1942 ...
 ..$ HR      : int [1:539] 0 1 0 0 0 0 0 0 0 0 ...
$ : tibble [557 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:557] "abernte01" "adamsac01" "adamsbu01" "adamsbu01" ...
 ..$ yearID  : int [1:557] 1943 1943 1943 1943 1943 1943 1943 1943 1943 1943 ...
 ..$ HR      : int [1:557] 0 0 0 4 0 0 0 0 0 0 ...
$ : tibble [569 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:569] "abernte01" "adamsac01" "adamsbu01" "aderhmo01" ...
 ..$ yearID  : int [1:569] 1944 1944 1944 1944 1944 1944 1944 1944 1944 1944 ...
 ..$ HR      : int [1:569] 0 0 17 0 0 1 0 0 0 0 ...
$ : tibble [579 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:579] "adamsac01" "adamsbu01" "adamsbu01" "aderhmo01" ...
 ..$ yearID  : int [1:579] 1945 1945 1945 1945 1945 1945 1945 1945 1945 1945 ...
 ..$ HR      : int [1:579] 0 2 20 0 2 0 0 1 0 1 ...
$ : tibble [682 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:682] "abernwo01" "adamsac01" "adamsbo03" "adamsbu01" ...
 ..$ yearID  : int [1:682] 1946 1946 1946 1946 1946 1946 1946 1946 1946 1946 ...
 ..$ HR      : int [1:682] 0 0 4 5 0 0 0 2 0 0 ...
$ : tibble [582 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:582] "abernwo01" "aberscl01" "adamsbo03" "adamsbu01" ...
 ..$ yearID  : int [1:582] 1947 1947 1947 1947 1947 1947 1947 1947 1947 1947 ...
 ..$ HR      : int [1:582] 0 4 4 2 2 2 0 8 0 0 ...
$ : tibble [573 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:573] "aberscl01" "adamsbo03" "adamshe01" "anderan01" ...
 ..$ yearID  : int [1:573] 1948 1948 1948 1948 1948 1948 1948 1948 1948 1948 ...
 ..$ HR      : int [1:573] 1 1 0 1 0 0 0 5 2 0 ...
$ : tibble [573 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:573] "aberscl01" "abramca01" "adamsbo03" "adamshe01" ...
 ..$ yearID  : int [1:573] 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 ...
 ..$ HR      : int [1:573] 0 0 0 0 1 0 1 0 5 0 ...
$ : tibble [577 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:577] "aberal01" "abramca01" "adamsbo03" "adamshe01" ...
 ..$ yearID  : int [1:577] 1950 1950 1950 1950 1950 1950 1950 1950 1950 1950 ...
 ..$ HR      : int [1:577] 0 0 3 0 8 0 0 0 0 0 ...
$ : tibble [616 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:616] "abramca01" "adamsbo03" "adcocjo01" "addisbo01" ...
 ..$ yearID  : int [1:616] 1951 1951 1951 1951 1951 1951 1951 1951 1951 1951 ...
 ..$ HR      : int [1:616] 3 5 10 1 0 7 4 2 10 0 ...
$ : tibble [632 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:632] "abernbi01" "abramca01" "abramca01" "adamsbo03" ...
 ..$ yearID  : int [1:632] 1952 1952 1952 1952 1952 1952 1952 1952 1952 1952 ...
 ..$ HR      : int [1:632] 0 0 2 6 13 1 0 0 0 1 ...
$ : tibble [586 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:586] "aberal01" "aberal01" "abramca01" "adamsbo03" ...
 ..$ yearID  : int [1:586] 1953 1953 1953 1953 1953 1953 1953 1953 1953 1953 ...
 ..$ HR      : int [1:586] 0 0 15 8 18 0 0 0 0 1 ...
$ : tibble [576 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:576] "aaronha01" "aberal01" "abramca01" "abramca01" ...
 ..$ yearID  : int [1:576] 1954 1954 1954 1954 1954 1954 1954 1954 1954 1954 ...
 ..$ HR      : int [1:576] 13 0 0 6 3 23 11 3 4 0 ...
$ : tibble [655 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:655] "aaronha01" "aberal01" "abernte02" "abramca01" ...
 ..$ yearID  : int [1:655] 1955 1955 1955 1955 1955 1955 1955 1955 1955 1955 ...
 ..$ HR      : int [1:655] 27 0 0 6 2 0 15 0 0 0 ...
$ : tibble [621 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:621] "aaronha01" "aberal01" "abernte02" "abramca01" ...
 ..$ yearID  : int [1:621] 1956 1956 1956 1956 1956 1956 1956 1956 1956 1956 ...
 ..$ HR      : int [1:621] 26 0 0 0 0 0 38 0 0 16 ...
$ : tibble [615 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:615] "aaronha01" "aberal01" "aberal01" "abernte02" ...
 ..$ yearID  : int [1:615] 1957 1957 1957 1957 1957 1957 1957 1957 1957 1957 ...
 ..$ HR      : int [1:615] 44 0 0 0 0 1 12 0 0 0 ...
$ : tibble [638 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:638] "aaronha01" "ackerto01" "adairje01" "adamsbo03" ...
 ..$ yearID  : int [1:638] 1958 1958 1958 1958 1958 1958 1958 1958 1958 1958 ...
 ..$ HR      : int [1:638] 30 0 0 0 19 0 0 0 4 0 ...
$ : tibble [632 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:632] "aaronha01" "ackerto01" "adairje01" "adamsbo03" ...
 ..$ yearID  : int [1:632] 1959 1959 1959 1959 1959 1959 1959 1959 1959 1959 ...
 ..$ HR      : int [1:632] 39 0 0 0 25 0 30 10 12 0 ...
$ : tibble [637 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:637] "aaronha01" "abernte02" "adairje01" "adcocjo01" ...
 ..$ yearID  : int [1:637] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ...
 ..$ HR      : int [1:637] 40 0 1 25 0 15 8 0 13 0 ...
$ : tibble [698 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:698] "aaronha01" "adairje01" "adcocjo01" "aguirha01" ...
 ..$ yearID  : int [1:698] 1961 1961 1961 1961 1961 1961 1961 1961 1961 1961 ...
 ..$ HR      : int [1:698] 34 9 35 0 0 29 18 6 27 3 ...
$ : tibble [760 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:760] "aaronha01" "aaronto01" "adairje01" "adcocjo01" ...
 ..$ yearID  : int [1:760] 1962 1962 1962 1962 1962 1962 1962 1962 1962 1962 ...
 ..$ HR      : int [1:760] 45 8 11 29 0 0 12 0 29 25 ...
$ : tibble [752 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:752] "aaronha01" "aaronto01" "abernte02" "acklefr01" ...
 ..$ yearID  : int [1:752] 1963 1963 1963 1963 1963 1963 1963 1963 1963 1963 ...
 ..$ HR      : int [1:752] 44 1 0 0 6 13 0 1 0 9 ...
$ : tibble [754 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:754] "aaronha01" "abernte02" "acklefr01" "adairje01" ...
 ..$ yearID  : int [1:754] 1964 1964 1964 1964 1964 1964 1964 1964 1964 1964 ...
 ..$ HR      : int [1:754] 24 0 0 9 21 0 0 0 0 6 ...
$ : tibble [751 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:751] "aaronha01" "aaronto01" "abernte02" "adairje01" ...
 ..$ yearID  : int [1:751] 1965 1965 1965 1965 1965 1965 1965 1965 1965 1965 ...
 ..$ HR      : int [1:751] 32 0 0 7 14 0 0 0 0 0 ...
$ : tibble [774 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:774] "aaronha01" "abernte02" "abernte02" "adairje01" ...
 ..$ yearID  : int [1:774] 1966 1966 1966 1966 1966 1966 1966 1966 1966 1966 ...
 ..$ HR      : int [1:774] 44 0 0 0 4 18 0 22 0 0 ...
$ : tibble [786 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:786] "aaronha01" "abernte02" "adairje01" "adairje01" ...
 ..$ yearID  : int [1:786] 1967 1967 1967 1967 1967 1967 1967 1967 1967 1967 ...
 ..$ HR      : int [1:786] 39 0 0 3 0 1 14 0 0 0 ...
$ : tibble [715 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:715] "aaronha01" "aaronto01" "abernte02" "adairje01" ...
 ..$ yearID  : int [1:715] 1968 1968 1968 1968 1968 1968 1968 1968 1968 1968 ...
 ..$ HR      : int [1:715] 29 1 0 2 0 0 5 0 0 2 ...
$ : tibble [932 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:932] "aaronha01" "aaronto01" "abernte02" "adairje01" ...
 ..$ yearID  : int [1:932] 1969 1969 1969 1969 1969 1969 1969 1969 1969 1969 ...
 ..$ HR      : int [1:932] 44 1 0 5 0 0 26 0 0 0 ...
$ : tibble [919 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:919] "aaronha01" "aaronto01" "abernte02" "abernte02" ...
 ..$ yearID  : int [1:919] 1970 1970 1970 1970 1970 1970 1970 1970 1970 1970 ...
 ..$ HR      : int [1:919] 38 2 0 0 0 0 0 24 0 0 ...
$ : tibble [883 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:883] "aaronha01" "aaronto01" "abernte02" "acosted01" ...
 ..$ yearID  : int [1:883] 1971 1971 1971 1971 1971 1971 1971 1971 1971 1971 ...
 ..$ HR      : int [1:883] 47 0 0 0 14 0 0 4 23 1 ...
$ : tibble [889 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:889] "aaronha01" "abernte02" "acostcy01" "acosted01" ...
 ..$ yearID  : int [1:889] 1972 1972 1972 1972 1972 1972 1972 1972 1972 1972 ...
 ..$ HR      : int [1:889] 34 0 0 0 0 13 0 0 0 9 ...
$ : tibble [892 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:892] "aaronha01" "abbotgl01" "acostcy01" "adamsmi02" ...
 ..$ yearID  : int [1:892] 1973 1973 1973 1973 1973 1973 1973 1973 1973 1973 ...
 ..$ HR      : int [1:892] 40 0 0 3 8 3 0 0 0 0 ...
$ : tibble [914 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:914] "aaronha01" "abbotgl01" "acostcy01" "akerja01" ...
 ..$ yearID  : int [1:914] 1974 1974 1974 1974 1974 1974 1974 1974 1974 1974 ...
 ..$ HR      : int [1:914] 20 0 0 0 0 0 0 0 32 0 ...
$ : tibble [907 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:907] "aaronha01" "abbotgl01" "acostcy01" "adamsgl01" ...
 ..$ yearID  : int [1:907] 1975 1975 1975 1975 1975 1975 1975 1975 1975 1975 ...
 ..$ HR      : int [1:907] 12 0 0 4 0 0 0 0 12 0 ...
$ : tibble [886 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:886] "aaronha01" "abbotgl01" "adamsgl01" "adamsmi02" ...
 ..$ yearID  : int [1:886] 1976 1976 1976 1976 1976 1976 1976 1976 1976 1976 ...
 ..$ HR      : int [1:886] 10 0 0 0 0 0 0 0 2 0 ...
$ : tibble [984 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:984] "aasedo01" "abbotgl01" "adamsbo04" "adamsgl01" ...
 ..$ yearID  : int [1:984] 1977 1977 1977 1977 1977 1977 1977 1977 1977 1977 ...
 ..$ HR      : int [1:984] 0 0 2 6 0 0 0 1 0 5 ...
$ : tibble [960 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:960] "aasedo01" "abbotgl01" "adamsgl01" "adamsmi02" ...
 ..$ yearID  : int [1:960] 1978 1978 1978 1978 1978 1978 1978 1978 1978 1978 ...
 ..$ HR      : int [1:960] 0 0 7 0 0 0 10 17 0 0 ...
$ : tibble [961 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:961] "aasedo01" "abbotgl01" "adamsgl01" "aikenwi01" ...
 ..$ yearID  : int [1:961] 1979 1979 1979 1979 1979 1979 1979 1979 1979 1979 ...
 ..$ HR      : int [1:961] 0 0 8 21 2 0 15 0 0 3 ...
$ : tibble [950 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:950] "aasedo01" "abbotgl01" "adamsgl01" "aguaylu01" ...
 ..$ yearID  : int [1:950] 1980 1980 1980 1980 1980 1980 1980 1980 1980 1980 ...
 ..$ HR      : int [1:950] 0 0 6 1 20 0 0 5 0 0 ...
$ : tibble [944 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:944] "aasedo01" "abbotgl01" "adamsgl01" "agostju01" ...
 ..$ yearID  : int [1:944] 1981 1981 1981 1981 1981 1981 1981 1981 1981 1981 ...
 ..$ HR      : int [1:944] 0 0 2 0 1 17 0 0 1 0 ...
$ : tibble [992 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:992] "aasedo01" "adamsgl01" "adamsri02" "agostju01" ...
 ..$ yearID  : int [1:992] 1982 1982 1982 1982 1982 1982 1982 1982 1982 1982 ...
 ..$ HR      : int [1:992] 0 1 0 0 3 17 0 6 0 4 ...
$ : tibble [1,006 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1006] "abbotgl01" "abbotgl01" "ackerji01" "adamsri02" ...
 ..$ yearID  : int [1:1006] 1983 1983 1983 1983 1983 1983 1983 1983 1983 1983 ...
 ..$ HR      : int [1:1006] 0 0 0 2 0 0 0 23 0 0 ...
$ : tibble [984 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:984] "aasedo01" "abbotgl01" "ackerji01" "agostju01" ...
 ..$ yearID  : int [1:984] 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 ...
 ..$ HR      : int [1:984] 0 0 0 0 3 11 0 2 0 0 ...
$ : tibble [998 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:998] "aasedo01" "abregjo01" "ackerji01" "adamsri02" ...
 ..$ yearID  : int [1:998] 1985 1985 1985 1985 1985 1985 1985 1985 1985 1985 ...
 ..$ HR      : int [1:998] 0 0 0 2 0 6 0 1 0 0 ...
$ : tibble [1,017 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1017] "aasedo01" "ackerji01" "ackerji01" "adducji01" ...
 ..$ yearID  : int [1:1017] 1986 1986 1986 1986 1986 1986 1986 1986 1986 1986 ...
 ..$ HR      : int [1:1017] 0 0 0 0 0 0 4 2 0 2 ...
$ : tibble [1,048 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1048] "aasedo01" "abnersh01" "ackerji01" "afenitr01" ...
 ..$ yearID  : int [1:1048] 1987 1987 1987 1987 1987 1987 1987 1987 1987 1987 ...
 ..$ HR      : int [1:1048] 0 2 0 0 0 12 1 0 9 0 ...
$ : tibble [1,035 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1035] "aasedo01" "abnersh01" "ackerji01" "adducji01" ...
 ..$ yearID  : int [1:1035] 1988 1988 1988 1988 1988 1988 1988 1988 1988 1988 ...
 ..$ HR      : int [1:1035] 0 2 0 1 0 3 3 0 0 3 ...
$ : tibble [1,073 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1073] "aasedo01" "abbotji01" "abnersh01" "ackerji01" ...
 ..$ yearID  : int [1:1073] 1989 1989 1989 1989 1989 1989 1989 1989 1989 1989 ...
 ..$ HR      : int [1:1073] 0 0 2 0 0 0 0 1 0 0 ...
$ : tibble [1,115 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1115] "aasedo01" "abbotji01" "abbotpa01" "abnersh01" ...
 ..$ yearID  : int [1:1115] 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
 ..$ HR      : int [1:1115] 0 0 0 1 0 0 0 0 0 0 ...
$ : tibble [1,086 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1086] "abbotji01" "abbotky01" "abbotpa01" "abnersh01" ...
 ..$ yearID  : int [1:1086] 1991 1991 1991 1991 1991 1991 1991 1991 1991 1991 ...
 ..$ HR      : int [1:1086] 0 0 0 1 2 0 0 0 0 0 ...
$ : tibble [1,066 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1066] "abbotji01" "abbotky01" "abbotpa01" "abnersh01" ...
 ..$ yearID  : int [1:1066] 1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 ...
 ..$ HR      : int [1:1066] 0 0 0 1 0 0 0 0 0 0 ...
$ : tibble [1,180 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1180] "abbotji01" "abbotku01" "abbotpa01" "agostju01" ...
 ..$ yearID  : int [1:1180] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
 ..$ HR      : int [1:1180] 0 3 0 0 0 10 0 0 0 3 ...
$ : tibble [1,030 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1030] "abbotji01" "abbotku01" "acrema01" "aguilri01" ...
 ..$ yearID  : int [1:1030] 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 ...
 ..$ HR      : int [1:1030] 0 9 0 0 4 5 8 14 22 0 ...
$ : tibble [1,253 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1253] "abbotji01" "abbotji01" "abbotku01" "abbotky01" ...
 ..$ yearID  : int [1:1253] 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 ...
 ..$ HR      : int [1:1253] 0 0 17 0 0 0 0 0 0 0 ...
$ : tibble [1,253 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1253] "abbotji01" "abbotku01" "abbotky01" "abreubo01" ...
 ..$ yearID  : int [1:1253] 1996 1996 1996 1996 1996 1996 1996 1996 1996 1996 ...
 ..$ HR      : int [1:1253] 0 8 0 0 0 0 0 0 0 0 ...
$ : tibble [1,236 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1236] "abbotje01" "abbotku01" "abreubo01" "aceveju01" ...
 ..$ yearID  : int [1:1236] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 ...
 ..$ HR      : int [1:1236] 1 6 3 0 0 0 0 0 0 0 ...
$ : tibble [1,322 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr [1:1322] "abbotje01" "abbotji01" "abbotku01" "abbotku01" ...
 ..$ yearID  : int [1:1322] 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 ...
 ..$ HR      : int [1:1322] 12 0 2 3 0 17 0 0 0 0 ...
 [list output truncated]
@ ptype: tibble [0 × 3] (S3: tbl_df/tbl/data.frame)
 ..$ playerID: chr(0) 
 ..$ yearID  : int(0) 
 ..$ HR      : int(0)

All the Data

df <-
  lst %>% 
  ## make the list of data frames:
  map(top_hr_hitters, n = 5) %>% 
  ## purrr::list_rbind() makes one df from the list:
  list_rbind()

All at Once (With Chaining)!

Expand for Code

Batting %>%
  filter(yearID >= 1900) %>% 
  group_by(yearID) %>% 
  group_split() %>% 
  map(top_hr_hitters, n = 5) %>% 
  list_rbind() %>% 
  ggplot(aes(x = yearID, y = HR)) +
  geom_point(alpha = 0.5) +
  labs(x = NULL, y = "home runs")

`walk()` and Variations

`walk()`

walk() is similar to map().
But is used when we are interested in producing side-effects.
It applies its .f argument to each element of .x is was given,
but also returns the .x in case we want to pipe it into some other function.

A Silly Application

We will re-write an old function without using a for-loop:

pattern <- function(char = "*", n = 5) {
  line_length <- c(1:n, (n-1):1)
  the_line <- function(char, number) {
    cat(rep(char, times = number), "\n", sep = "")
  }
  line_length %>% walk(the_line, char = char)
}

Try It Out

pattern(char = "a", n = 4)

a
aa
aaa
aaaa
aaa
aa
a

Another Application: Saving Many Graphs

We would like to save plots of all numerical variables from the data frame m111survey, and also print summaries of them to the Console.

First we create a directory to hold the plots:

if (!dir.exists("plots")) dir.create("plots")

Names of the Variables We Want

Next, we get the names of the numerical variables in m111survey:

num_names <-
  m111survey %>% 
  keep(is.numeric) %>%   # purrr::keep()
  names()

The Variables Themselves

num_vars <-
  m111survey %>% 
  keep(is.numeric)

purrr::keep() keeps the columns that satisfy the given condition.

A Helper Function

save_graph <- function(var, varname) {
  p <-
    ggplot(data = NULL, aes(x = var)) +
    geom_density(fill = "burlywood") +
    labs(title = paste0(
      "Density plot for ",
      varname, ".")
    )
  ggsave(filename = paste0(
    "plots/density_", varname, ".png"),
    plot = p, device = "png"
  )
}

Try it out!

save_graph(m111survey$fastest, "fastest")

Another Helper Function

make_summary <- function(x, varname) {
  five <- fivenum(x, na.rm = TRUE)
  list(
    variable = varname,
       min = five[1],
       Q1 = five[2],
       median = five[3],
       Q3 = five[4],
       max = five[5]
  )
}

Try it out:

make_summary(x = m111survey$fastest, varname = "fastest")

The Results

$variable
[1] "fastest"

$min
[1] 60

$Q1
[1] 90.5

$median
[1] 102

$Q3
[1] 119.5

$max
[1] 190

Go For It!

list(num_vars, num_names) %>% 
  pwalk(save_graph) %>%  
  pmap_dfr(make_summary)

# A tibble: 6 × 6
  variable          min    Q1 median     Q3   max
  <chr>           <dbl> <dbl>  <dbl>  <dbl> <dbl>
1 height           51    65    68     71.8     79
2 ideal_ht         54    67    68     75       90
3 sleep             2     5     7      7       10
4 fastest          60    90.5 102    120.     190
5 GPA               1.9   2.9   3.22   3.56     4
6 diff.ideal.act.  -4     0     2      3       18

Check the `plots` Directory

It should contain these files:

density_diff.ideal.act.png
density_fastest.png
density.GPA.png
density_height.png
density_ideal_ht.png
density_sleep.png

Other purr Higher-Order Functions

`keep()`

keep() is similar to dplyr’s filter(), but whereas filter() chooses rows of a data frame based on a given conditions, keep() chooses the elements of the input list or vector .x based on a condition named .p.

Example

# keep the numbers that are 1 more than a multiple of 3
1:20 %>% 
  keep(.p = ~ . %% 3 == 1)

[1]  1  4  7 10 13 16 19

Another Example

# keep the factors in m111survey
m111survey %>% 
  keep(is.factor) %>% 
  str()

'data.frame':   71 obs. of  6 variables:
 $ weight_feel : Factor w/ 3 levels "1_underweight",..: 1 2 2 1 1 3 2 2 2 3 ...
 $ love_first  : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
 $ extra_life  : Factor w/ 2 levels "no","yes": 2 2 1 1 2 1 2 2 2 1 ...
 $ seat        : Factor w/ 3 levels "1_front","2_middle",..: 1 2 2 1 3 1 1 3 3 2 ...
 $ enough_Sleep: Factor w/ 2 levels "no","yes": 1 1 1 1 1 2 1 2 1 2 ...
 $ sex         : Factor w/ 2 levels "female","male": 2 2 1 1 2 2 2 2 1 1 ...

`discard()`

discard(.x,, . p = condition) is equivalent to keep(.x, .p = !condition). Thus:

# discard numbers that are 1 more than a multiple of 3
1:20 %>% 
  discard(.p = ~ . %% 3 == 1)

 [1]  2  3  5  6  8  9 11 12 14 15 17 18 20

Another Example

# discard the factors in m111survey
m111survey %>% 
  discard(is.factor) %>% 
  str()

'data.frame':   71 obs. of  6 variables:
 $ height         : num  76 74 64 62 72 70.8 70 79 59 67 ...
 $ ideal_ht       : num  78 76 NA 65 72 NA 72 76 61 67 ...
 $ sleep          : num  9.5 7 9 7 8 10 4 6 7 7 ...
 $ fastest        : int  119 110 85 100 95 100 85 160 90 90 ...
 $ GPA            : num  3.56 2.5 3.8 3.5 3.2 3.1 3.68 2.7 2.8 NA ...
 $ diff.ideal.act.: num  2 2 NA 3 0 NA 2 -3 2 0 ...

`reduce()`

Given a vector .x and a function .f that takes two inputs, reduce() does the following:

applies f to elements 1 and 2 of .x, getting a result;
applies f to the result and to element 3 of .x, getting another result;
applies f to this new result and to element 4 of .x, getting yet another result …
… and so on until all of the elements of .x have been exhausted.
then reduce() returns the final result in the above series of operations.

Example

Suppose that you want to add up the elements of the vector:

vec <- c(3, 1, 4, 6)

Of course you could just use:

sum(vec)

But we will use reduce().

The Idea

add the 3 and 1 of (the first two elements of vec), getting 4;
then add 4 to 4, the third element of vec, getting 8;
then add 8 to 6, the final element of vec, getting 14;
then return 14.

Implement the Idea

vec %>%
  reduce(.f = sum)

[1] 14

Another Example: Intersection

A common application of reduce is to take an operation that is defined on only two items and extend it to operate on any number of items.

Consider the function intersect():

vec1 <- c(3, 4, 5, 6)
vec2 <- c(4, 6, 8, -4)
intersect(vec1, vec2)

[1] 4 6

Limitation of `intersect()`

You cannot intersect three or more vectors at once:

intersect(vec1, vec2, c(4, 7, 9))

Error in base::intersect(x, y, ...): unused argument (c(4, 7, 9))

`reduce()` to the Rescue!

lst <- list(
  c("Akash", "Bipan", "Chandra", "Devadatta", "Raj"),
  c("Raj", "Vikram", "Sita", "Akash", "Chandra"),
  c("Akash", "Raj", "Chandra", "Bipan", "Lila"),
  c("Akash", "Vikram", "Devadatta", "Raj", "Lila")
)
lst %>% 
  reduce(intersect)

[1] "Akash" "Raj"

Higher-Order Functions in Package purrr

Packages

map() and Variations

A Task

Solve With a Loop

Solve with map()

How map() Works

More Control

What is This Code Doing?

How It Works

Vary Two or More Parameters of a Function

How This Works

Why Not Vary All Three Parameters?

Write Your Own Helper Function

Try It Out

Now Map!

Map with an Anonymous Function

Mapping With Data Frames

Options for Other Types of Output

Example

Another Example

Yet Another Example

When Output is a List of Data Frames

The Data

A Helper Function

Split the Data

All the Data

All at Once (With Chaining)!

walk() and Variations

walk()

A Silly Application

Try It Out

Another Application: Saving Many Graphs

Names of the Variables We Want

The Variables Themselves

A Helper Function

Another Helper Function

The Results

Go For It!

Check the plots Directory

Other purr Higher-Order Functions

keep()

Example

Another Example

discard()

Another Example

reduce()

Example

The Idea

Implement the Idea

Another Example: Intersection

Limitation of intersect()

reduce() to the Rescue!

`map()` and Variations

Solve with `map()`

How `map()` Works

`walk()` and Variations

`walk()`

Check the `plots` Directory

`keep()`

`discard()`

`reduce()`

Limitation of `intersect()`

`reduce()` to the Rescue!