In the following article, we’re talking about RStudio projects, what they are, why you should use them, and how you can use them. We’ve also discussed several best practices regarding RStudio projects so you can work with them quickly and efficiently. Let’s get started.
What is RStudio?
RStudio is an IDE (Integrated Development Environment) for R, one of the most vital programming languages in data analysis. It has a console, an editor, as well as many tools for debugging, plotting, and managing the workspace. It has both open source and commercial editions available in the market, and you can use it with Mac, Linux, and Windows Operating Systems. It has an online version too which you can access through your browser.
It is a development environment, and it helps you in using R for statistical computing. To use RStudio, you should be familiar with R, the programming language. Here’s a detailed tutorial on R, if you’re interested.
Why Use Rstudio Projects?
There’s a common mistake many analysts make while working with RStudio. The conventional method of setting work directories is to use setwd(). The problem with the traditional approach is that it creates an absolute file path as the input and sets the same as the working directory. To access your data analysis, you must use getwd().
Having an absolute file path makes your working directory very vulnerable to link breaks. Your directory’s link can break easily in this method, which makes sharing this project with others very tricky. For example, if you move the list to just a sub-folder, its link will break. We can say that having an absolute file path makes it nearly impossible for you to share your work with others.
Moreover, as a data analyst, you’d have to work in teams, with other professionals. So, you’d have to share your work with others and collaborate.
Learn more: R Tutorial for Beginners: Become an Expert in R Programming
Instead of following the orthodox path of using setwd(), you can create RStudio projects and eliminate all the troubles. Rstudio projects make the file path relative, instead of absolute, which helps in keeping the file path secure. When you create RStudio projects, they add the.Rproj extension to your file.
With this extension, whenever RStudio runs through this file, its working directory points to the folder where it’s saved. This means that even when you move your project file to a sub-folder or another location, it would remain accessible.
You should only follow this method for creating and saving your RStudio sessions. Not only will it help you in making your file accessible, but it’ll also help you in sharing with others. You wouldn’t have to worry about the file path getting broken as you would have with the former approach, i.e. when you’d use setwd().
Avoiding the traditional approach might seem daunting to you, but don’t worry. This method is better in many ways as we’ve established earlier. Now that you know why you should use RStudio projects, we should now discuss how you can use them.
How to Create RStudio Projects
To create an RStudio project, you’d first have to use the ‘Create Project’ option, which you can access through the global toolbar by selecting the Projects menu there.
After you select the ‘Create Project’ option, RStudio creates a project file with the .Rproj extension within the working directory. It also creates a hidden list under the name .Rproj, user where it stores all the temporary files related to the project such as .gitignore. Then, it loads the project into RStudio and displays its name in the toolbar.
Once you create your project, you should only use files present in that directory unless your project requires using an Internet-based tool (calling an API or performing web-scraping). You can create RStudio projects within an existing list, or in a new record. Let’s now move on to how you can use these projects:
How to Work with RStudio Projects
It would be best if you always begin your work by opening the .Rproj file, and open other data after that, which is best practice. To open an .Rproj file, you can open RStudio and use the ‘Open Project’ option present in the Projects menu in the toolbar. When you’d select that command, you’d see a list of your created projects, from which you can choose the one you want to work on.
RStudio creates a new R session when you open a project. It also loads the .RData file present in the working directory (if the project requires it) along with an .Rhistory file in its History pane. RStudio also restores all the related settings (splitter positions, active tabs, etc.) to where they were when you had closed the project in the last session. As you would’ve noticed by now, using RStudio projects is easily better than using the traditional method.
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It would be best if you considered opening the .Rproj file as the initialization of your entire task. It ensures that your working directory works smoothly and efficiently. It also helps you in avoiding any errors related to your workflow.
Read: 6 Interesting R Project Ideas For Beginners.
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How to Structure your Project Directory
Apart from using RStudio projects, here is a brief guide on how to structure your project directory for efficient management and handling.
First, you should have a Data subfolder where you save all the files you have to read into R to perform the required visualization or analysis. In other words, this folder is for storing all the source files.
In this folder, you should store all the R scripts and all the files with extensions .Rmd and .R. It can have the following subfolders:
This is where you store all the files with .Rmd and .R extensions (also called RMarkdown files)
This is where you store all the custom functions you had created. It is optional.
This is where you store all the original R scripts for your project. This folder would come in handy when you’d have multiple analysis files to use in one project.
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In this folder, you should store all the files you create in your projects such as HTML, plots, and exports. This folder has many advantages too. First, it helps others find out where the results of your code are. Second, it helps in separating all the source files and the data you worked on.
Also Read: 8 Astonishing Data Science Projects in R For Beginners
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We hope you liked this guide on RStudio projects. If you want to learn more about R, the programming language, and RStudio, then we recommend heading to the upGrad Blog, where you’ll find many valuable resources, guides, and articles.
On the other hand, if you want to get a more thorough learning experience, then you should get a data science course.
What is RStudio and why do we use it to build projects?
RStudio is an Integrated Development Environment or IDE for R language. It is one of the most popular IDEs for R and provides a console, editor, tools for debugging, plotting, and managing the workspace. One of the main reasons for its popularity is that it has two editions available in the market- open source and commercial which makes it convenient for the user to build projects using it without paying anything. On top of that, you can even use it without downloading the start-up using its browser version.
What should be the structure of one’s project directory?
Apart from making projects, it is essential how you structure your project directory for efficient handling and readability of the user. The following is the ideal structure in which you must keep your files: The first folder should be the Data folder which will hold all the source files of your project. Next, the script folder will contain all the R scripts and the files with extensions .Rmd and .R. This folder will further have the following subfolders. Files folder will hold all the files with extensions such as .Rmd and .R. These files are also known as Rmarkdown files. Functions folder is optional. If you have created any custom function, you can store its file in this folder. Analysis folder comes to be useful when you have a lot of analysis files to be used in one single project. You can store the original R scripts in this folder.
What are the applications of the R language?
R is a popular language and is widely used in multiple domains. If you have a statistical background, it can even be a lot easier than Python for you. Some of the applications of the R language are listed below: R is very popular in the finance domain since it provides an advanced statistical suite to carry all the financial tasks. Just like Finance, Banking systems also use R language for risk analysis like credit risk modelling. Other domains like healthcare and social media also use R for multiple purposes.