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RStudio is the most popular IDE (Integrated Development Environment) for R. It's free, powerful, and designed specifically for statistical analysis and research. If you'll be using R for this course, this is where you'll spend most of your time.

What is RStudio?

RStudio is not the R language itself—it's a program that makes writing R code much easier. Think of it like this: R is the engine, and RStudio is the car with the steering wheel, dashboard, and comfortable seats. You could drive the engine directly (by running R in a terminal), but RStudio makes the experience much more pleasant.

Installing RStudio

Before installing RStudio, you need to install R itself. Here's the order:

1. Install R first: Download from cran.r-project.org
2. Then install RStudio: Download from posit.co/download/rstudio-desktop

The RStudio Interface

When you first open RStudio, you'll see a window divided into four panels. Hover over each numbered region below to learn what it does:

Essential Keyboard Shortcuts

Learning these shortcuts will dramatically speed up your workflow:

Your First R Script

Let's write a simple script to make sure everything is working. In RStudio:

1. Go to File > New File > R Script (or press Ctrl+Shift+N)
2. Type the following code in the Source Editor:

# My first R script
# This is a comment - R ignores lines starting with #

# Print a message
print("Hello, RStudio!")

# Create some data
x <- c(1, 2, 3, 4, 5)
y <- x * 2

# Calculate the mean
mean(y)

# Create a simple plot
plot(x, y, main = "My First Plot")

> print("Hello, RStudio!")
[1] "Hello, RStudio!"

> x <- c(1, 2, 3, 4, 5)
> y <- x * 2
> mean(y)
[1] 6

> plot(x, y, main = "My First Plot")
# [Plot appears in the Plots panel showing a scatter plot
#  with points at (1,2), (2,4), (3,6), (4,8), (5,10)]

3. Place your cursor on the first line and press Ctrl+Enter repeatedly to run each line
4. Watch the output appear in the Console, and the plot appear in the Plots panel

Setting Up Your Project Folder

Before diving into data analysis, it's essential to organize your work properly. A well-structured project folder makes your research reproducible and helps you (and others) find files easily.

Recommended Folder Structure

For any research project, I recommend creating a folder structure like this:

You can create this structure using RStudio's Files panel (bottom-right) or by running these commands in the Console:

# Create project folder structure
dir.create("data/raw", recursive = TRUE)
dir.create("data/processed", recursive = TRUE)
dir.create("code")
dir.create("output/figures", recursive = TRUE)
dir.create("output/tables", recursive = TRUE)

Setting Your Working Directory

The working directory is the folder R uses as its starting point for reading and writing files. When you run read.csv("data.csv"), R looks for data.csv in the working directory.

To check your current working directory:

# Where is R looking for files?
getwd()

To change your working directory to your project folder:

# Set working directory (use YOUR path)
setwd("~/Documents/my_project")

# Windows example:
setwd("C:/Users/yourname/Documents/my_project")

# Mac example:
setwd("/Users/yourname/Documents/my_project")

# Method 1: Use the RStudio menu
# Session → Set Working Directory → Choose Directory...

# Method 2: Use the Files panel
# Navigate to your project folder in the Files panel (bottom-right)
# Click "More" (gear icon) → "Set As Working Directory"

# Method 3: Use RStudio Projects (Recommended!)
# File → New Project → creates a .Rproj file
# Double-click the .Rproj file to open RStudio with the correct directory

Practice: Download Real Data

Let's practice by downloading real economic data. Our World in Data provides excellent, well-documented datasets perfect for economics research. We'll use this data source throughout the course.

1. Visit Our World in Data Datasets on GitHub
2. Or download directly: GDP per capita (Maddison Project Database)
3. Save the file to your data/raw/ folder

Alternatively, download it directly with R:

# Download GDP per capita data from Our World in Data
url <- "https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/GDP%20per%20capita%20(Maddison%20Project%20Database%202020)/GDP%20per%20capita%20(Maddison%20Project%20Database%202020).csv"

# Download and save to data/raw folder
download.file(url, destfile = "data/raw/gdp_per_capita.csv")

Exploring Data in RStudio

Once you have data in your project, RStudio makes it easy to explore:

1. Files Panel: Navigate to your data/raw/ folder and click on the CSV file to preview it
2. Load and View: Use R code to load and inspect the data:

# Load the data
gdp_data <- read.csv("data/raw/gdp_per_capita.csv")

# Quick look at the data
head(gdp_data)

# Structure of the data
str(gdp_data)

# Open in spreadsheet viewer (click the data frame in Environment panel too!)
View(gdp_data)

Cloud Alternative: Posit Cloud

If you don't want to install anything, you can use Posit Cloud (formerly RStudio Cloud) in your browser. It's the same RStudio interface, running in the cloud.

Try Posit Cloud (Free) →

Video Tutorial