Magrittr

{magrittr} is a package that allows us to perform more operations within a timeline to make our code more efficient to write and to read. Its main implementation is the pipe (%>%) which we already discussed in Dplyr. However, it also offers as a number of aliases to make operators available in our pipelines.

`.` and `

Before we get into the aliases offered by {magrittr}, we should realize that we can do without these aliases by using dots (.) and backticks (`). Here, the dot indicates the data in its current form (i.e. after all previous operations have been applied) and the backticks are put around the operator (e.g. +). The operator is then treated as a function (i.e. parentheses are required).

For instance, take the below data frame:

# Create example data
vec_fib <- c(1, 1, 2, 3, 5, 8, 13, 21, 34, 55)

If we want to subtract 3 from all numbers, multiply by 1.5, and then set all negative values to NA, we could do the following:

# First, load magrittr
pacman::p_load("magrittr")

# Start pipeline with vector
vec_fib %>%
    # Subtract 3
    `-`(3) %>%
    # Multiply by 1.5
    `*`(1.5) %>%
    # Set all negative values to NA
    ifelse(. < 0, NA, .)

 [1]   NA   NA   NA  0.0  3.0  7.5 15.0 27.0 46.5 78.0

Aliases

However, by using {magrittr} allows us functions instead of these operators that improve readability of the code and are easier to use, which are called the aliases. Using those aliase, we would write the above code as:

# Start piepline with vector
vec_fib %>%
    # Subtract 3
    subtract(3) %>%
    # Multiply by 1.5
    multiply_by(1.5) %>%
    # Set all negative values to NA
    # Here, the alias is perhaps not as useful, as we use . < 0 in a function. The aliases are mostly useful as first function in each piece of the pipeline
    ifelse(is_less_than(., 0), NA, .)

 [1]   NA   NA   NA  0.0  3.0  7.5 15.0 27.0 46.5 78.0

The available aliases (also available here or in the help function of each alias in R) are:

Description	Symbol
extract2	`[[`
inset	`[<-`
inset2	`[[<-`
use_series	`$`
add	`+`
subtract	`-`
multiply_by	`*`
raise_to_power	`^`
multiply_by_matrix	`%*%`
divide_by	`/`
divide_by_int	`%/%`
mod	`%%`
is_in	`%in%`
and	`&`
or	`\|`
equals	`==`
is_greater_than	`>`
is_weakly_greater_than	`>=`
is_less_than	`<`
is_weakly_less_than	`<=`
not (n’est pas)	`!`
set_colnames	`colnames<-`
set_rownames	`rownames<-`
set_names	`names<-`
set_class	`class<-`
set_attributes	`attributes<-`
set_attr	`attr<-`

`|>`

Besides the pipe implemented by {magrittr}, R also offers a native pipe: |>. Instead of existing data being called by the dot (.), you can use a low dash (_). Although in general the pipes function the same, there are some differences in what they can do and how they are used. The tidyverse has a more elaborate explanation on this topic here and more details are also available here and here.

Other pipes

The %>% pipe is not the only pipe {magrittr} offers us.

`%T>%`

The Tee pipe returns the left-hand side of the value instead of the right-hand side. In other words, it returns the input into the function instead of the output of the function. This is helpful when we are only interested in the side-effects of a function, instead of its main output (e.g. printing in console).

Imagine we are interested in only the description of a .csv file, but not actually loading it into our global environment. In that case, I could use the read_csv() function from {readr} with the Tee pipe:

# Load readr
pacman::p_load("readr")

# Get information on .csv file available online
"https://drive.google.com/uc?id=1zO8ekHWx9U7mrbx_0Hoxxu6od7uxJqWw&export=download" %T>% 
    # Print only data information
    read_csv()

Rows: 100 Columns: 12
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (10): Customer Id, First Name, Last Name, Company, City, Country, Phone...
dbl   (1): Index
date  (1): Subscription Date

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

[1] "https://drive.google.com/uc?id=1zO8ekHWx9U7mrbx_0Hoxxu6od7uxJqWw&export=download"

`%$%`

The exposition pipe gives the names of the data to the next function, which is especially useful if the function does not have a data argument, such as table():

# Just piping iris into table will not work (commented out to prevent error)
# iris %>% table(Species)

# Using an exposition pipe, it will work
iris %$% table(Species)

Species
    setosa versicolor  virginica 
        50         50         50

$<>$

The assignment pipe is a shorthand pipe for a pipeline that assigns the final value back into the object that was used as the start of the pipeline (i.e. it is short for x <- x %>% ...). For example:

# Starting value
x <- 5; y <- 5

# With the assignment operator
x <- x %>%
    # Divide by 2
    divide_by(2)

# With the assignment pipe
y %<>%
    # Divide by 2
    divide_by(2)

# Check whether results are equal
x == y

[1] TRUE

Exercises

1. Use aliases to change values

From the starwars dataset (available in {dplyr}, ), extract the column height and divide by 100.

Answer

# Load dplyr for data
pacman::p_load("dplyr")

# Start pipe with starwars data
starwars %>%
    # Extract column height (unnamed)
    extract2("height") %>%
    # Divide by 100
    divide_by(100)

 [1] 1.72 1.67 0.96 2.02 1.50 1.78 1.65 0.97 1.83 1.82 1.88 1.80 2.28 1.80 1.73
[16] 1.75 1.70 1.80 0.66 1.70 1.83 2.00 1.90 1.77 1.75 1.80 1.50   NA 0.88 1.60
[31] 1.93 1.91 1.70 1.85 1.96 2.24 2.06 1.83 1.37 1.12 1.83 1.63 1.75 1.80 1.78
[46] 0.79 0.94 1.22 1.63 1.88 1.98 1.96 1.71 1.84 1.88 2.64 1.88 1.96 1.85 1.57
[61] 1.83 1.83 1.70 1.66 1.65 1.93 1.91 1.83 1.68 1.98 2.29 2.13 1.67 0.96 1.93
[76] 1.91 1.78 2.16 2.34 1.88 1.78 2.06   NA   NA   NA   NA   NA

2. Use different pipes to meddle with starwars

Now, reassign a frequency table of species to the object name starwars of individuals of at least 2 meters tall using the pipe operators from {magrittr}.