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Other purrr functions [program]

(Builds on: purrr basics)

In this reading, you’ll learn about two more map variants, map_dfr() and map_dfc(). Then, you’ll learn about walk(), as well as some useful purrr functions that work with functions that return either TRUE or FALSE.

The purrr package contains more functions that we can cover. The purrr cheatsheet is a great way to see an overview of the package or find a helpful function when you encounter a new type of iteration problem.

map functions that output tibbles

You already know how to choose the appropriate map function ending to produce an atomic vector or list. There are also map function variants that produce a single tibble.

With these map functions, the assembly line worker creates a tibble for each input element, and the output conveyor belt ends up with a collection of tibbles. The worker then combines all the small tibbles into a single, larger tibble.

There are multiple ways to combine smaller tibbles into a larger tibble. purrr provides two options:

There are _dfr and _dfc variants of pmap() and map2() as well.

_dfr

You will often use map_dfr() when reading in data. The following code reads in several very simple csv files that contain the names of different dinosaur genera.

read_csv("purrr-extras/file_001.csv")

## # A tibble: 1 x 2
##      id genus        
##   <dbl> <chr>        
## 1     1 Hoplitosaurus

read_csv("purrr-extras/file_002.csv")

## # A tibble: 1 x 2
##      id genus        
##   <dbl> <chr>        
## 1     2 Herrerasaurus

read_csv("purrr-extras/file_003.csv")

## # A tibble: 1 x 2
##      id genus      
##   <dbl> <chr>      
## 1     3 Coelophysis

read_csv() produces a tibble, and so we can use map_dfr() to map over all three file names and bind the resulting individual tibbles into a single tibble.

files <- str_glue("purrr-extras/file_00{1:3}.csv")
files

## purrr-extras/file_001.csv
## purrr-extras/file_002.csv
## purrr-extras/file_003.csv

files %>% 
  map_dfr(read_csv)

## # A tibble: 3 x 2
##      id genus        
##   <dbl> <chr>        
## 1     1 Hoplitosaurus
## 2     2 Herrerasaurus
## 3     3 Coelophysis

The result is a tibble with three rows and two columns, because map_dfr() aligns the columns of the individual tibbles by name.

The individual tibbles can have different numbers of rows or columns. map_dfr() creates a column for each unique column name.

read_csv("purrr-extras/file_004.csv")

## # A tibble: 2 x 3
##      id genus         start_period 
##   <dbl> <chr>         <chr>        
## 1     4 Dilophosaurus Sinemurian   
## 2     5 Segisaurus    Pliensbachian

c(files, "purrr-extras/file_004.csv") %>% 
  map_dfr(read_csv)

## # A tibble: 5 x 3
##      id genus         start_period 
##   <dbl> <chr>         <chr>        
## 1     1 Hoplitosaurus <NA>         
## 2     2 Herrerasaurus <NA>         
## 3     3 Coelophysis   <NA>         
## 4     4 Dilophosaurus Sinemurian   
## 5     5 Segisaurus    Pliensbachian

If some of the individual tibbles lack a column that others have, map_dfr() fills in with NA values.

_dfc

map_dfc() is typically less useful than map_dfr(), because it relies on row position to stack the tibbles side-by-side. Row position is prone to error, and it will often be difficult to check if the data in each row is aligned correctly. However, if you have data with variables in different places and are positive the rows are aligned, map_dfc() may be appropriate.

Unfortunately, even if the individual tibbles contain a unique identifier for each row, map_dfc() doesn’t use the identifiers to verify that the rows are aligned correctly, nor does it combine identically named columns.

read_csv("purrr-extras/file_005.csv")

## # A tibble: 1 x 3
##      id diet      start_period
##   <dbl> <chr>     <chr>       
## 1     1 herbivore Barremian

c("purrr-extras/file_001.csv", "purrr-extras/file_005.csv") %>% 
  map_dfc(read_csv)

## # A tibble: 1 x 5
##      id genus           id1 diet      start_period
##   <dbl> <chr>         <dbl> <chr>     <chr>       
## 1     1 Hoplitosaurus     1 herbivore Barremian

Instead, you end up with a duplicated column.

Therefore, if you have a unique identifier for each row, it is much better to use the identifier to join them together.

left_join(
  read_csv("purrr-extras/file_001.csv"),
  read_csv("purrr-extras/file_005.csv"),
  by = "id"
)

## # A tibble: 1 x 4
##      id genus         diet      start_period
##   <dbl> <chr>         <chr>     <chr>       
## 1     1 Hoplitosaurus herbivore Barremian

Finally, because map_dfc() combines tibbles by row position, the tibbles can have different numbers of columns, but must have the same number of rows.

The following files have different numbers of rows, and so map_dfc() produces an error.

read_csv("purrr-extras/file_006.csv")

## # A tibble: 2 x 2
##   diet      start_period
##   <chr>     <chr>       
## 1 herbivore Barremian   
## 2 carnivore Albian

c("purrr-extras/file_001.csv", "purrr-extras/file_006.csv") %>% 
  map_dfc(read_csv)

## Error: Argument 2 must be length 1, not 2

Walk

This short section discusses walk(). The walk functions work similarly to the map functions, but you use them when you’re interested in applying a function that performs an action instead of producing data (e.g., print()).

Predicate functions

purrr has some useful functions that work with predicate functions, which are functions that return a single TRUE or FALSE. For example, keep() and discard() iterate over a vector and keep or discard only those elements for which the predicate function returns TRUE.