R Projects

R Projects are a super helpful feature of RStudio. They help you:

• Stay organized. R Projects help in organizing your work into self-contained directories (folders), where all related scripts, data, and outputs are stored together. This organization simplifies file management and makes it easier to locate and manage files associated with your analysis or project.

• Find the right files. When you open an R Project, RStudio automatically sets the working directory to the project’s directory. This is where RStudio “looks” for files. Because it’s always the Project folder, it can help avoid common issues with file paths.

• Be more reproducible. By encapsulating all the files for an analysis within a single project, R Projects enhance reproducibility. You can share the entire project directory with others, and they can replicate your environment and analysis without much hassle. This is particularly important for research and collaborative work where transparency is key – i.e., for publications!

Let’s go over how to create and use an R Project!

New R Project

Let’s make an R Project so we can stay organized in the next steps. Click the new R Project button at the top left of RStudio:

In the New Project Wizard, click “New Directory”:

Click “New Project”:

Type in a name for your new folder.

Store it somewhere easy to find, such as your Desktop:

You now have a new R Project folder on your Desktop!

Make sure you add any scripts or data files to this folder as you go through your Intro to R lessons, or work on a new project. This will make sure R is able to “find” your files.

Testing out your R Project

Let’s read in some data.

read_csv() needs an argument file =.

• file is the path to your file, in quotation marks
• can be path to a file on a website (URL)
• can be path in your local computer – absolute file path or relative file path

# Examples

dat <- read_csv(file = "www.someurl.com/table1.csv")

dat <- read_csv(file = "/Users/avahoffman/Downloads/Youth_Tobacco_Survey_YTS_Data.csv")

dat <- read_csv(file = "Youth_Tobacco_Survey_YTS_Data.csv")

If we aren’t reading from URL, and we are Reading from your computer.. What is the “path”?

When you set up an R Project, R looks for files in that folder.

Download the data file at http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv. Move downloaded files into the R Project folder.

Confirm the data is in the R Project folder.

If we add the Youth_Tobacco_Survey_YTS_Data.csv file to the R Project folder, we only need to use the file name for the file argument:

dat <- read_csv(file = "Youth_Tobacco_Survey_YTS_Data.csv")

NICE!

Why does this work?

When we create an R Project, we establish the working directory.

Working directory is a folder (directory) that RStudio assumes “you are working in”.

It’s where R looks for files.

The working directory is wherever the .Rproj file is.

TROUBLESHOOTING

If your R project directory and working directory do not match:

• Session > Set Working Directory > To Project Directory

If you are trying to knit your work, it might help to set the knit directory to the “Current Working Directory”:

You can also run the getwd() function to determine your working directory.

# Get the working directory
getwd()

You can also set the working directory manually with the setwd() function:

# set the working directory
setwd("/Users/avahoffman/Desktop")

here package

the here() function from the here package gives you the absolute path of the R Project file. It can be useful for being very explicit about what the path is.

library(here)
here()