Projects
Projects are useful for managing files in your work, whether they're data or analysis files. In this section, you'll learn how to create and use projects.
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Projects are useful for managing files in your work, whether they're data or analysis files. In this section, you'll learn how to create and use projects.
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RStudio projects are organizational tools that help you manage your analyses, including all related data and scripts. You can create a new project in several ways.
Starting from scratch isn't always the best approach. Sometimes, it's better to build upon someone else's code. If that code is available on GitHub or another platform hosting Git repositories, you can easily create a copy of that project on your local computer. All you need is a small tool called git. With git, you can clone projects containing code examples for all my data analytics courses. You'll find the git URLs below.
To use Git, you have two options: a client with a graphical user interface, like , or running Git from the command line, which I prefer. Either way, you'll need to install the latest version of Git on your computer. You can find all the downloads .
To run git from a command line, you can use your operating system's integrated terminal. On Windows, it's called the Command Line (cmd.exe), while on macOS and Linux, it's simply called the Terminal. RStudio also offers a built-in terminal—you'll find a "Terminal" tab right next to the console. When you click on this tab, you'll see your username and device name, followed by the current path the terminal is in:
Navigate to the directory where you want to store the example code for this course. If you're unfamiliar with terminal navigation, learn about the cd command, which stands for change directory. Choose a short path without spaces or special characters, including German umlauts (ä, ö, ü). For instance, I store all my R code in c:\rcode, creating subdirectories for each semester and project.
Once you're in your chosen directory, copy and paste the following command, then press ENTER. This will download a copy of the course's R project to your computer. To clone the repository with example code for my modules, you can use the following command :
After the command completed, you should have a new subdirectory with the name of the project (r:\code\data-analytics-code) within the directory where you ran the command.
To create a new empty project, click on File → New Project. A project wizard will open, offering you three options:
Create a project in a new directory
Create a project in an existing directory
Check out a project from a version control system
The third option is what we described earlier for GitHub, but other version control systems like SVN are also supported.
Choose option one or two, depending on whether you already have a folder containing files that should belong to your project.
If you create a new project from scratch (option 1), you'll be prompted to pick a project template. RStudio offers various templates that generate specific files and folders typically required for different project types. For a blank slate, simply select the "New Project" option.
In the next step, you'll need to enter a directory name and select a parent directory where this new directory will be created as a subdirectory. Select a location that's easy to remember and avoid using special characters, including German umlauts (ä, ö, ü). You'll also see an option to "Use renv with this project." I recommend checking this box if you plan to work on multiple projects on your computer. This creates a separate R environment within your project, allowing you to install packages specific to that project rather than in your global R installation. This approach is particularly useful if you need different versions of the same library for different projects.
Click "Create Project," and RStudio will open your new project.
