Purrr - tips and tricks

This code have been lightly revised to make sure it works as of 2018-12-18.

Purrr tips and tricks

If you like me started by only using map() and its cousins (map_df, map_dbl, etc) you are missing out a lot of what purrr have to offer! With the advent of #purrrresolution on twitter I’ll throw my 2 cents in in form of my bag of tips and tricks (which I’ll update in the future).

First we load the packages:

library(repurrrsive) # datasets used in some of the examples.

loading files

Multiple files can be read and combined at once using map_df and read_cvs.

files <- c("2015.cvs", "2016.cvs", "2017.cvs")
map_df(files, read_csv)

Combine with list.files to create magic1.

files <- list.files("../open-data/", pattern = "^2017", full.names = TRUE)
full <- map_df(files, read_csv)

combine if you forget *_df the first time around.

If you like me sometimes forget to end my map() with my desired out put. A last resort is to manually combine it in a second line if you don’t want to replace map() with map_df() (which is properly the better advice, but can be handy in a pinch).

X <- map(1:10000, ~ data.frame(x = .x))
X <- bind_rows(X)

name shortcut in map

provide “TEXT” to extract the element named “TEXT”. Follow 3 lines are equivalent.

map(got_chars, function(x) x[["name"]]) 
map(got_chars, ~ .x[["name"]])
map(got_chars, "name")

works the same with indexes.2

map(got_chars, function(x) x[[1]]) 
map(got_chars, ~ .x[[1]])
map(got_chars, 1)

use {} inside map

If you don’t know how to write the proper anonymous function or you want some counter in your map(), you can use {} to construct your anonymous function.

Here is a simple toy example that shows that you can write multiple lines inside map.

map(1:3, ~ {
  h <- .x + 2
  g <- .x - 2
  h + g
map(1:3, ~ {

This can be very handy if you want to be a responsible (websraping) pirate3.

s_GET <- safely(GET)

pb <- progress_estimated(length(target_urls))
map(target_urls, ~{
}) -> httr_raw_responses

discard, keep and compact

discard() and keep() will provide very valuable since they help you filter your list/vector based on certain predictors.

They can be useful in cases of webcraping where certain lines are to be ignored.

url <- "http://www.imdb.com/chart/boxoffice"

read_html(url) %>%
  html_nodes('tr') %>%
  html_text() %>%
  str_replace_all("\n +", " ") %>%
  trimws() %>%
  keep(~ str_extract(.x, ".$") %in% 0:9) %>%
  discard(~ as.numeric(str_extract(.x, ".$")) > 5)

Where we here scrape Top Box Office (US) from IMDb.com and we use keep() to keeps all lines that end in a integer and discards() to discards all lines where the integer is more then 5.

compact() is a handy wrapper that removed all elements that are NULL.

safely + compact

If you have a function that sometimes throws an error, warning or for whatever reason isn’t entirely stable, you can use the wonder of safely() and compact(). safely() is a function that takes a function f() and returns a function safe_f() that returns a list with the elements result and error where result is the output of f() if it is able to run, and NULL otherwise. This means that we can create a function that will always work!

unstable_function <- function() {

safe_function <- safely(unstable_function)

map(data, ~ safe_function(.x)) %>%
  map("result") %>%

combining this with compact which removes all NULL values thus returning only the successful calls.


purrr includes an little group of functions called reduce() (with its cousins reduce_right(), reduce2() and reduce2_right()) which iteratively combines from the left (right for reduce_right()) making

reduce(list(x1, x2, x3), f)
f(f(x1, x2), x3)


This example4 comes from Colin Fay shows how to use reduce().

regex_build <- function(list){
    reduce(list, ~ paste(.x, .y, sep = "|"))

## [1] "a|b|c|d|e"

This example by Jason Becker5 shows how to easier label data using reduce_right.

# Load a directory of .csv files that has each of the lookup tables
lookups <- map(dir('data/lookups'), read.csv, stringsAsFactors = FALSE)
# Alternatively if you have a single lookup table with code_type as your
# data attribute you're looking up
# lookups <- split(lookups, code_type)
lookups$real_data <- read.csv('data/real_data.csv', 
                              stringsAsFactors = FALSE)
real_data <- reduce_right(lookups, left_join)


I find that subsetting list can be a hassle more often then not. But pluck() have really helped to alleviate those problems quite a bit.

list(A = list("a1","a2"), 
     B = list("b1", "b2"),
     C = list("c1", "c2"),
     D = list("d1", "d2", "d3")) %>% 

head_while, tail_while

purrr includes the twins head_while and tail_while which will gives you all the elements that satisfy the condition intill the first time it doesn’t.

X <- sample(1:100)

# This
p <- function(X) !(X >= 10)
X[seq(Position(p, X) - 1)]

# is the same as this
head_while(X, ~ .x >= 10)


if you need to do some simulation studies rerun could prove very useful. It takes 2 arguments. .n is the number of times to run, and ... is the expression that have to be rerun.

rerun(.n = 10, rnorm(10)) %>%
  map_df(~ tibble(mean = mean(.x),
                  sd = sd(.x),
                  median = median(.x)))


This little wonder of a function composes multiple functions to be applied in order from right to left.

This toy examples show how it works:

sample(x = 1:6, size =  50, replace = TRUE) %>%
  table %>% 
  sort %>%

dice1 <- function(n) sample(size = n, x = 1:6, replace = TRUE)
dice_rank <- compose(names, sort, table, dice1)

A more informative is found here6:

tidy_lm <- compose(tidy, lm)
tidy_lm(Sepal.Length ~ Species, data = iris)
## # A tibble: 3 x 5
##   term              estimate std.error statistic   p.value
##   <chr>                <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)           5.01    0.0728     68.8  1.13e-113
## 2 Speciesversicolor     0.93    0.103       9.03 8.77e- 16
## 3 Speciesvirginica      1.58    0.103      15.4  2.21e- 32


imap() is a handy little wrapper that acts as the indexed map(). Thus making it shorthand for map2(x, names(x), ...) when x have named and map2(x, seq_along(x), ...) when it doesn’t have names.

imap_dbl(sample(10), ~ {
  cat("draw nr", .y, "is", .x, "\n")

or it could be used in conjunction with rerun() to easily add id to each sample.

rerun(.n = 10, rnorm(10)) %>%
  imap_dfr(~ tibble(run = .y, 
                    mean = mean(.x),
                    sd = sd(.x),
                    median = median(.x)))
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