Book Image

R Data Visualization Cookbook

By : Gohil
Book Image

R Data Visualization Cookbook

By: Gohil

Overview of this book

If you are a data journalist, academician, student or freelance designer who wants to learn about data visualization, this book is for you. Basic knowledge of R programming is expected.
Table of Contents (12 chapters)
11
Index

Writing a function in R

Most of the tasks in R are performed using functions. A function in R has the same utility as functions in Arithmetic.

Getting ready

In order to write a simple function in R, we must first open a new R script by navigating to File | New file.

How to do it…

We write a very simple function that accepts two values and adds them together. Copy and paste the code in the new blank R script:

add = function (x,y){
  x+y
}

How it works…

A function in R should be defined by function(). Once we define our function, we need to save it as a .r file. Note that the name of the file should be the same as the function; hence we save our function with name add.r.

In order to use the add() function in the R command window, we need to source the file by using the source() function as follows:

source('<your path>/add.R')

Now, we can type add(2,15) in the R command window. You get 17 printed as an output.

The function itself takes two arguments in our recipe but, in reality, it can take many arguments. Anything defined inside curly braces gets executed when we call add(). In our case, we request the user to input two variables, and the output is a simple sum.

See also

  • Functions can be helpful in performing repetitive tasks such as generating plots or perform complicated calculations. Felix Schönbrodt has implemented visually weighted watercolor plots in R using a function on his blog at http://www.nicebread.de/visually-weighted-watercolor-plots-new-variants-please-vote/.
  • We can generate similar plots simply by copying the function created by Felix in our R session and executing it. The plotting function created by Felix also provides users with different ways in which the R function's ability could be leveraged to perform repetitive tasks.