Skip to main content

Installing R Studio on Windows and MAC Operating System

The Beginner’s Guide to Installing R Studio for Windows and MAC Operating System

We’ve installed R and can open the R interface to input code, but there are other ways to interface with R - and one of those ways is using R Studio. In this lesson, we’ll get R Studio installed on your computer.


Installing R on Windows and MAC Operating Systems.

What is R Studio?

R Studio is a graphical user interface for R, that allows you to write, edit and store code, generate, view and store plots, manage files, objects and data frames, and integrate with version control systems – to name a few of its functions. We will be exploring exactly what R Studio can do for you in future lessons, but for anybody just starting out with R coding, the visual nature of this program as an interface for R is a huge benefit.

Installing R Studio

Thankfully, installation of R Studio is fairly straightforward. First, you go to the R Studio download page. We want to download the R Studio Desktop version of the software, so click on the appropriate “Download” , under that heading and you will see a list of “Installers for supported platforms”.

 


The R Studio download page. Select R Studio Desktop

 


The various versions of R Studio are available for different operating systems

At this point the installation process diverges for Macs and Windows, so follow the instructions for the appropriate OS. Let’s begin with Windows.



Installing R Studio - Windows

For Windows, select the R Studio installer for the various Windows editions (10/11). This will initiate the download process.

When the download is complete, open this executable file to access the installation wizard. You may be presented with a security warning at this time - allow it to make changes to your computer.

Following this, the installation wizard will open. Following the defaults on each of the windows of the wizard is appropriate for installation. In brief, on the welcome screen, click next. If you want R Studio installed elsewhere, “Browse” through your file system. Otherwise, it will likely default to the “Program Files” folder - this is appropriate. Click next. On this final page, allow R Studio to create a Start menu shortcut. Click Install. R Studio is now being installed. Wait for this process to finish; R Studio is now installed on your computer. Click Finish.

Check that R Studio is working appropriately by opening it from your Start menu.

The installation wizard for R Studio installation

Completing installation of R Studio

R Studio is running!

Installing R Studio - Macs

For Macs, select the Macs OS X 11+ Studio installer (Mac OS X 11+ (64-bit)). This will initiate the download process. If your MAC version is older than this, you can further scroll down and find for your download link for your version.



When the download is complete, click on the downloaded file and it will begin to install. When this is finished, the applications window will open.

Drag the R Studio file into your Applications folder to complete installation for R Studio

Test the installation by opening your applications folder and opening the R Studio software.

R Studio is running!

Summary

In this lesson we installed R Studio, both for Macs and for Windows computers. Before moving on to the next lecture, click through the available menus and explore the software a bit. We will have an entire lesson dedicated to exploring R Studio, but having some familiarity beforehand will be helpful!

Thanks for Visiting and reading our Blog.

Comments

  1. Looking forward to working in R studio with you, under your guidance.

    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

What is Data? And What is Data Science Process?

The Beginner’s Guide to Data & Data Science Process About Data: In our First Video today we talked about Data and how the Cambridge English Dictionary and Wikipedia defines Data, then we looked on few forms of Data that are: Sequencing data   Population census data ( Here  is the US census website and  some tools to help you examine it , but if you aren’t from the US, I urge you to check out your home country’s census bureau (if available) and look at some of the data there!) Electronic medical records (EMR), other large databases Geographic information system (GIS) data (mapping) Image analysis and image extrapolation (A fun example you can play with is the  DeepDream software  that was originally designed to detect faces in an image, but has since moved on to more  artistic  pursuits.) Language and translations Website traffic Personal/Ad data (e.g.: Facebook, Netflix predictions, etc.) These data forms need a lot of preprocessin...

Efficient Data Manipulation with Loop Functions in R: A Deep Dive into apply and mapply

The Beginner’s Guide to Loop Functions in R: In addition to lapply and sapply , R also has apply and mapply , which are other loop functions that are commonly used for data manipulation and analysis. In this blog post, we'll explain what these functions are, how they work, and provide some practice material for beginners to intermediate level. apply:  Apply a Function to a Matrix or Array apply is a loop function in R that applies a function to either rows or columns of a matrix or array. Here's the basic syntax: apply(matrix/array, margin, function) The matrix/array argument is the matrix or array you want to apply the function to, and the margin argument specifies whether you want to apply the function to rows or columns. margin = 1 applies the function to rows, while margin = 2 applies the function to columns. The function argument is the function you want to apply. For example, let's say we have a matrix of numbers and we want to apply the sum function to each row:...

Optimization Example of Lexical Scoping in R: Exploring optim, optimize, and nlm Functions

The Beginner’s Guide to Optimization Example of Lexical Scoping in R: When it comes to optimization in R, lexical scoping can be a useful tool for optimizing complex functions that involve multiple variables. In this blog post, we will explore how lexical scoping can be used to optimize a function using the NLL (negative log-likelihood) function , and how the optim, optimize, and nlm functions can be used to perform optimization in R. Optimizing the NLL Function using Lexical Scoping The NLL function is a common function used in optimization problems. It is defined as the negative log of the likelihood function, which is used to estimate the parameters of a statistical model. In R, the NLL function can be defined using lexical scoping, which allows us to pass arguments to the function and access variables from within the function. Here is an example of how to define the NLL function using lexical scoping in R: nll <- function(data, parameters) {   # Define local variables ...