You could see that the output for this code is the same as the previous one. Output for the code with the pipe operator The output for this code will be the same as the previous one. Here, the pipe operator is assigning each functional output as an argument to the next one, and so on. Let us see how we can use the pipe operator to make this code more readable. This code though looks difficult to read and the user might get confused while reading it. We filtered the data first, then grouped it by mean value, and finally summarized it. Here in this example, if you see, multiple functions are used to filter, group, and summarize the data from the mtcars dataset. What is the Basic Use of the Pipe Operator?Īs it is evident, the pipe operator allows us to assign an argument to a given function and is used in most of the nested functional arguments where the result of one function is an argument for the other function, see the example below for nested functions: We will study a few of those ways through examples. There are different ways in which we can use the pipe operator in R programming. Usage of this operator increases, readability, efficiency, and simplicity of your code when you have nested functions in your code loop. It is generally denoted by symbol %>% in R Programming. The pipe operator is a special operational function available under the magrittr and dplyr package (basically developed under magrittr), which allows us to pass the result of one function/argument to the other one in sequence. To deal with this, or I should say to simplify your code both in terms of readability and efficiency, we have a pipe operator in R programming. However, when it comes to readability, the complex codes might be difficult to read especially when you have nested code within the code (huge ambiguity). Efficiency will always be achieved by using the control statements and functions in your code. The efficiency and readability are the two important aspects any programmer lives their life for.