 # Pipe Operator in R: How to use the pipe operator in R The pipe operator in R is an operation that helps you pass arguments or functions in sequence. If you are new to this and want to find out more, please check out our instructions below because we will show you what it is, its application, and how to apply it to your R program.

## Pipe operator in R

### What does the pipe operator do in R?

The pipe operator in R is part of thedplyr and ‘magrittr’ packages. Its function is to make you apply many arguments or functions in a data frame in the sequence. Using the pipe operator, your code will look simpler and easier to read. Let’s take a look at this example:

We will use the mtcars data frame, which is a built-in data frame in R.

mtcars

Now, if we want to summarize this table, usually, we would do the following:

summary(mtcars)

Output:

      mpg             cyl             disp             hp
Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0
1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5
Median :19.20   Median :6.000   Median :196.3   Median :123.0
Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7
3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0
Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0
drat             wt             qsec             vs
Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000
1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000
Median :3.695   Median :3.325   Median :17.71   Median :0.0000
Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375
3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000
Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000
am              gear            carb
Min.   :0.0000   Min.   :3.000   Min.   :1.000
1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000
Median :0.0000   Median :4.000   Median :2.000
Mean   :0.4062   Mean   :3.688   Mean   :2.812
3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000
Max.   :1.0000   Max.   :5.000   Max.   :8.000  

However, with the pipe operator, it would be:

data %&gt;% summary()

You might think, “But this is not simpler to read!”. That’s true if we have only one function. What if we want to perform multiple functions on a data frame?
In this new example, we will first try to get only the data about hp and mpg of the data set, then filter only the cars whose hp is more significant than 100, and finally, summarize that new data set. So, here is the normal code:

summary(filter(select(mtcars, mpg, hp), hp&gt;100))

Output:

     mpg              hp
Min.   :10.40   Min.   :105.0
1st Qu.:15.10   1st Qu.:118.0
Median :17.30   Median :175.0
Mean   :17.45   Mean   :174.2
3rd Qu.:19.45   3rd Qu.:210.0
Max.   :30.40   Max.   :335.0 

However, if we use pipe operator:

mtcars %>%
select(mpg, hp) %>%
filter(hp>100) %>%
summary()

As we can see, the code is much easier to read and understand.

### How to use pipe operator in R?

#### Install the library

The pipe operator is most commonly known in the ‘dplyr’ and ‘magrittr’ packages, so we have to install and load the packages before using the pipe operator. I will take the ‘dplyr’ packages as an example in this tutorial.

To install the ‘dplyr’ packages, simply type in:

install.packages("dplyr")

Next, load the package by typing in:

library("dplyr")

Now everything is set and ready to go.

#### Use the pipe operator

The pipe operator is defined as the "%>%" sign. You will have the name of the data frame first then the function later. Which function you want to use first will come first.

So for example:

library("dplyr")

# Load the built-in Orange function
Orange %>%

# Get the first 10 rows
slice(1:10) %>%

# Summarize the data
summary()

Output:

 Tree       age         circumference
3:0   Min.   : 118.0   Min.   : 30.00
1:7   1st Qu.: 484.0   1st Qu.: 60.75
5:0   Median : 664.0   Median : 99.00
2:3   Mean   : 772.1   Mean   : 91.00
4:0   3rd Qu.:1174.2   3rd Qu.:118.75
Max.   :1582.0   Max.   :145.00  

## Summary

In this tutorial, we helped you learn about the pipe operator in R and how to use it in your program. This is a very powerful tool that makes your code cleaner and more readable.

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Posted in R