A Deep Dive Into R Programming

Introduction

R has become the leading choice for data science professionals and statisticians. The popularity of R has increased substantially over the years when it comes to data analysis. R is a GNU project, which was initially developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and the source code for R software environment is written primarily in C and Fortran. The founders decided to name the programming language R, based on the first letter of their names. The language is both similar and different in many ways when compared to the language S, developed by Bell Labs. R is considered to be a different implementation of S. Most of the code written for S runs in R as well.

Defining R and its features might look pretty vague. Let’s start and get our hands dirty. This article is divided into two main sections,

Once this is completed, you will get an idea of setting up the R environment locally and it will help you get started with R programming. Let’s head to the first section of the article. Setting Up R Environment and R Tools in Visual Studio R tools for Visual Studio were released in March as a public preview release. This will help you to work with R programming in VS. However, in order to set up R Tools in Visual Studio, there is a prerequisite – R language engine should be installed in the local machine, or else we will get the error, shown below: In order to set up the environment, first, we will:

Hence, let’s install Microsoft R Open, which will install the R language engine in the local machine. You can download Microsoft R Open from here. Depending on the platform, you can chose to download the appropriate R Open executable. I have downloaded the Windows 7 Platform executable. Double click the exe and click next. Click Next. This will start installing Microsoft R Open. Specify the destination location or leave the default location as it is. Select the components required to be installed. Ideally, leave all of them checked. Click next. This will start the installation. Wait for the installation to complete. Click finish. This would complete the R language Engine installation in the local machine. Once completed, it will provide us the R console, where we can implement R Programming. Double click the icon and it opens up R console. We can test its functionality by using normal arithmetic operations. However, the console is less interactive. Hence, let’s spin up Visual Studio IDE and install R Tools for Visual Studio (RTVS), which is a highly flexible and a mature environment to implement R Programming. You can get the executable from here. Click the executable and run it. Close any Visual Studio session, that is active for a smooth installation. Click install. This will start the installation of R Tools in Visual Studio. The setup is completed. Let’s head to Visual Studio to check out the new addition. In the tab next to the test, we have the new tab for R Tools. Click data science settings so that the session opens in the data scientist profile. Click yes. This will reset the Visual Studio layout to the snapshot, shown below: We have the R Interactive Window on the left side, where we will be doing the programming part. The variable Explorer on the top right end is where we can analyze the loaded variables and import the data from an external data source. R Plot at the bottom right corner is used to display the graphical representations. We can switch back to the default Visual Studio Layout once, we are done with R Programming. Understand the Power of R - Analyze and derive a conclusion from data using R Let’s use R programming to dig into bulk data and derive the results for our specific queries. Here, I am using dummy student details, which are in CSV format as the input and I will try to derive the answers for the data-related questions. We will be entering the commands in R Interactive Window on the left side of the Window. First, we have to set the working directory, which can be done using setwd method. Now, let’s load the data into the R Tools environment in Visual Studio. We can use read CSV command to import the data from CSV. I have placed the R CSV file, which contains the student details, in the working directory. This file looks as shown below: Now, let’s load the CSV file into Visual Studio. We can read from other data sources like MS Excel as well.