Magrittr: Pipe Operators

Tidyverse: Magrittr

library(magrittr)
library(dplyr)
library(tidyr)
library(DT)
library(data.table)

Magrittr is the source of pipe operators in R. Although the most commonly used pipe operator, %>% is now available in dplyr, it also has other interesting pipe options.

%>% Operator

To begin testing Magrittr, we import a Heart Disease dataset exported from Kaggle.

heart <- read.csv("https://raw.githubusercontent.com/mkivenson/Data-Acquisition-and-Management/master/TidyVerse/heart.csv") %>% 
  rename(age = 1)

datatable(heart)

Let’s say that we want to perform the following data wrangling techniques on the dataset:

Select only the age, sex, trestbps, and chol variables.
Filter for younger patients with an age of less than or equal to 50.
Filter for female patients.
Gather trestbps and chol columns into rows
Sort by age

Using dpylr, tidyr, and data.table alone (without piping operators), this code would look something like this:

data <- select(heart, age, sex, trestbps, chol)
data <- filter(data, (age <= 50) & (sex == 0))
data <- gather(data, "metric", "value", 3:4)
data <- arrange(data, age)
datatable(data)

If we instead use the magrittr %>% pipe operator, we can do this. It is the same result but somewhat cleaner.

data <- heart %>% 
  select(age, sex, trestbps, chol) %>%
  filter((age <= 50) & (sex == 0)) %>%
  gather("metric", "value", 3:4) %>%
  arrange(age)

datatable(data)

%<>% Operator

What if we wanted to modify the mtcars dataframe directly, rather than creating a new dataframe (or writing heart <- heart)? We can just use the %<>% operator instead of %>%:

heart %<>% 
  select(age, sex, trestbps, chol) %>%
  filter((age <= 50) & (sex == 0)) %>%
  gather("metric", "value", 3:4) %>%
  arrange(age)

datatable(heart)

Additional Examples by Fernando Figueres

%T>% Operator

Some functions, such as plot() will terminate a string of piped arguments. To avoid this, we can use the %T>% operator. If for example, we would like to display a plot and summary statistics, we can do as follows.

heart %>% 
  select(age,value) %T>%
  plot() %>% 
  summary()

##       age            value      
##  Min.   :34.00   Min.   : 94.0  
##  1st Qu.:41.00   1st Qu.:120.5  
##  Median :44.00   Median :141.5  
##  Mean   :43.52   Mean   :175.8  
##  3rd Qu.:46.00   3rd Qu.:219.8  
##  Max.   :50.00   Max.   :341.0

%$% Operator

In some cases, we may need to refer to specific variables while working with piped arguments. The %$% pipe allows us to access specific varibles without breaking our argument string.

In the following example we’ll calculate the correlation between two variables of the heart data set.

heart %$% 
  cor(age,value)

## [1] 0.09042612