Installing And Setting Up R Environment On Windows

Introduction and Background

One day in the previous week, I heard my professor say something about “R language” and I said to myself, “What is this R language?”. Of course I had no idea about what that language was but I was a bit curious to know about it and to learn about it. Luckily, I was provided with a few concepts about R language on C# Corner’s R language category and later I Googled stuff to learn more about it. In this post, I am going to walk you through different things that I have learned during my learning time.

In this post, I will walk you through many steps to set up the environment for R programming. I will be covering the basic R environment set up and later I will talk about using Visual Studio as the default IDE for R programming, after all, “Who doesn’t like Visual Studio?!”.

What is R, anyways?

A good introduction to the R language and R environment is given by Mahesh on C# Corner, I would recommend that you read that one, What Is R Language.

R is a programming language, as well as an environment for computation. What sort of computation? Statistical computation is the major field for R. However, many fields are using R language for their purposes, for example, Data Scientists are using R language for many analysis that they have to perform on data sets. Data mining etc. come later in the picture.

I will point out a few of the major fields that R can be used in, later, but first a bit of the intro to R language would be useful.


Figure 1: R logo

R language was developed in the early 1990s for statistical computation purposes. It started all with small modules, then it gathered and gained much reputation from online communities and it became a very popular environment to use in many fields of computer-related sciences. It was developed using C and FORTRAN as the basic language for interpretation.

R is distributed under GNU license and allows modifications to be made and shared online. R was developed in C and FORTRAN. That is why, you can modify the source code and recompile the binaries to make it suitable for your own needs. You can do that, too!

Why learn R?

As already mentioned R is a programming language and environment for statistical programming and computation. That is why R is a very prominent language to be used in data science projects. Specifically, the R language is used instead of C, Java or even C++ in these cases because the packages in R are already ready to be used and in no time, you get to use the best of the data science in this environment.


Figure 2: A chart for benefits of learning R language

Even if you are not an open source developer, you would still find R language very much helpful. The usage of R ranges from field to field. As already depicted in the diagram above, this project can be used in many ways, in many fields and for many purposes. A few of the basics where, this can be used can be categorized as following:

1. Working with data science projects.
2. Building reports for data analysis.
3. Creating presentations with graphical representations of results.
4. Adding your own extensions to the projects.
5. Educational use.

I will write some articles about using R in many ways, for many projects and tasks in computer science, but I would like to leave that all to a later time. Until now, just know that if you are going to analyze large amount of data sets, process data mining and similar projects. Then your best bid would be R language instead of C++ or Java.

Another beauty of R language is that it can be coupled with many other famous programming languages and environments and frameworks. For example, R functions can be called from C, C++, Java, Python and from the .NET environment, easily. You would be using the R language to:

1. Process the data from CSV or TXT files.
2. Generate summary reports in the form of graphics or tables.
3. Use a special type of Markdown format to build the results.
4. Use multiple formats to extract the data results.