Skip to main content

Installing R on Windows and MAC Operating System

The Beginner’s Guide to Installing R on Windows and MAC OS

Hello and Welcome to next part of our first course in The Data Science Specialization.

After getting familiar with what is Data science, Data, Data Science process and knowing what actually is Data Scientist, we move towards the next part of getting familiar with the tools that will be needed during our Data science specialization.





First, let’s remind ourselves exactly what R is and why we might want to use it.

R is both a programming language and an environment, focused mainly on statistical analysis and graphics. It will be one of the main tools you use in this and following courses.

R is downloaded from the Comprehensive R Archive Network, or CRAN, and while this might be your first brush with it, we will be returning to CRAN time and time again, when we install packages - so keep an eye out!

Why should you use R?

Outside of this course, you may be asking yourself - why should I use R?

The reasons for using R are myriad, but some big ones are:

1) Its popularity

R is quickly becoming the standard language for statistical analysis. This makes R a great language to learn as the more popular a software is, the quicker new functionality is developed, the more powerful it becomes, and the better the support there is! Additionally, as you can see in the graph below, knowing R is one of the top languages asked for in data scientist job postings!

 

2) Its cost

FREE!

This one is pretty self-explanatory - every aspect of R is free to use, unlike some other stats packages you may have heard of (eg: SAS, SPSS), so there is no cost barrier to using R!

3) Its extensive functionality

R is a very versatile language - we’ve talked about its use in stats and in graphing, but its use can be expanded to many different functions - from making websites, making maps using GIS data, analysing language… and even making automated lectures and videos! For whatever task you have in mind, there is often a package available for download that does exactly that!

4) Its community

And the reason that the functionality of R is so extensive is the community that has been built around R. Individuals have come together to make “packages” that add to the functionality of R - and more are being developed every day!

Particularly for people just getting started out with R, its community is a huge benefit - due to its popularity, there are multiple forums that have pages and pages dedicated to solving R problems. We talked about this in the Getting Help lesson; these forums are great both for finding other people who have had the same problem as you, and posting your own new problems.

Installing R

Now that we’ve spent some time looking at the benefits of R, it is time to install it! We’ll go over installation for both Windows and Mac below, but know that these are general guidelines and small details are likely to change subsequent to the making of this lecture - use this as a scaffold.

For both Windows and Mac machines, we start at the CRAN homepage: https://cran.r-project.org/

Installation - for Windows

If you are on a Windows computer, follow the link Download R for Windows, and follow the directions there - if this is your first time installing R, go to the base distribution and click on the link at the top of the page that should say something like “Download R [version number] for Windows.” This will download an executable file for installation.




The download page for R on Windows

Open the executable, and if prompted by a security warning, allow it to run. Select the language you prefer during installation and agree to the licensing information. You will next be prompted for a destination location - this will likely be defaulted to Program Files, in a subfolder called R, followed by another directory of the version number. Unless you have any issues with this, the default location is perfect.

The install wizard for installing R

You will then be prompted to select which components should be installed. Unless you are running short on memory, installing all of the components is desireable. Next you’ll be asked about startup options, and again, the defaults are fine for this. You will then be asked where Setup should place shortcuts - this is completely up to you, you can allow it to add the program to the start menu, or you can click the box at the bottom that says to not create a start menu link. Finally, you will be asked whether you want a desktop or Quick Launch icon - up to you! I do not recommend changing the defaults for the registry entries though.

After this window, the installation should begin. Test that the installation worked by opening R for the first time!



The R terminal running!

Installation - for Macs

If you are on a Mac computer, follow the link Download R for (Mac) OS X. There you can find the various R versions for download. Note: If your Mac is older than OS X 11 (“Big Surr”), you will need to follow the directions on this page for downloading older versions of R that are compatible with those operating systems.


Click on the link to the most recent version of R, which will download a .pkg file.

Downloading the .pkg file for Macs

Open the pkg file and follow the prompts as provided by the installer. First, click continue on the welcome page and again on the important information window page. Next you will be presented with the software license agreement, again, continue. Next you may be asked to select a destination for R, either available to all users or to a specific disk - select whichever you feel is best suited to your setup. Finally, you will be at the “Standard Install” page; R selects a default directory and if you are happy with that location, go ahead and click install. At this point, you may be prompted to type in the admin password, do so, and the install will begin!

Installation of R on a Mac

Once the installation is finished, go to your Applications and find R. Test that the installation worked by opening R for the first time!


Summary

In this lesson we first looked at what R is and why we might want to use it. We then focused on the installation process for R on both Windows and Mac computers. Before moving on to the next lecture, be sure that you have R installed properly. See you in the next lecture!


Labels:

Comments

  1. Anonymous3 March 2023 at 23:46

    Just installed R successfully by following your guidelines.
    Thanks for this content.

    ReplyDelete

Post a Comment

Type your comment here.
However, Comments for this blog are held for moderation before they are published to blog.
Thanks!

Popular posts from this blog

Debugging Your R Code: Indications and Best Practices

The Beginner’s Guide to Debugging Tools: As with any programming language, it's important to debug your code in R to ensure it is functioning correctly. Here are some indications that there may be something wrong with your R code, along with examples of common mistakes that can cause these issues: Error messages:   If R encounters an error in your code, it will often provide an error message indicating the source of the problem. For example, if you forget to close a parenthesis, you may get an error message like "Error: unexpected ')' in 'my_function'". Here, R is indicating that there is a syntax error in your function. Unexpected output:  If the output of your code is unexpected or doesn't match your expectations, there may be an issue with your code. For example, if you are trying to calculate the mean of a vector of numbers, but the output is much higher or lower than expected, there may be an issue with the code you used to calculate the mean. L...
Post a Comment
Read more

Getting Started with R Programming

The Beginner’s Guide to R Programming. I'm very excited to start R Programming and I hope you are too. This is the second course in the Data Science Specialization and it focuses on the nuts and bolts of using R as a programming language. The recommended background for this course is the course The Data Scientist's Toolbox . It is possible to take this class concurrently with that class but you may have to read ahead in the prerequisite class to get the relevant background for this class. For a complete set of course dependencies in the Data Science Specialization please see the course dependency chart , that has been posted on our blogpost. The primary way to interact with me and the other students in this course is through the discussion forums which in our case are comments section under the lectures, social media and blogpost . Here, you can start new threads by asking questions or you can respond to other people's questions. If you have a question about any aspect...
Post a Comment
Read more

Mastering R Programming: Best Coding Practices for Readable and Maintainable Code

The Beginner’s Guide to Coding Standards: When it comes to programming, writing code that is easy to read and maintain is just as important as writing code that works. This is especially true in R programming, where it's common to work with large datasets and complex statistical analyses. In this blog post, we'll go over some coding standards that you should follow when writing R code to ensure that your code is easy to read and maintain . Indenting One of the most important coding standards to follow is to use consistent indenting. Indenting makes your code more readable by visually indicating the structure of your code. In R programming, it's common to use two spaces for each level of indentation. For example: if (x > y) {   z <- x + y } else {   z <- x - y } Column Margins Another important coding standard is to use consistent column margins. This means that you should avoid writing code that extends beyond a certain number of characters (often 80 or 100). Th...
Post a Comment
Read more