RStudio

Luc Clair
University of Winnipeg | ECON/GECON 3201

Preliminaries

Software

To run R code and use R Studio, you need to install the following software

R (the programming language)
RStudio (the interface to work with R)

Installing R

Download R: https://cran.r-project.org

Windows Instructions	Mac Instructions
Click “Download R for Windows” Click “base” Click the link: “Download R x.x.x for Windows” Open the downloaded `.exe` file Follow the installation prompts (accept defaults)	Click “Download R for macOS” Choose the `.pkg` file that matches your OS version (usually the top one) Download and open the `.pkg` installer Follow the prompts to install

Installing RStudio

Download RStudio: https://posit.co/download/rstudio-desktop/

Windows Instructions	Mac Instructions
Click “Download RStudio for Windows” Open the downloaded `.exe` file Follow the installation prompts	Click “Download RStudio for macOS” Open the downloaded `.dmg` file Drag the RStudio icon into the Applications folder

Introduction

Why RStudio?

RStudio is an integrated development environment (IDE) for R
- Provides a user-friendly interface for writing code
- Visualizing data
- Managing projects
- Producing reproducible reports (Quarto, R Markdown)

It streamlines the process of statistical computing and data analysis, making it especially useful for research, teaching, and applied econometrics

IDE for R

An IDE is a software application that provides a complete set of tools for writing, running, and debugging code in one place

RStudio combines script editing, console, workspace viewer, and plotting in one interface

Enables seamless development, testing, and output visualization

Projects for Organization

Use RStudio Projects to organize coursework, datasets, scripts, and outputs in isolated environments

Promotes reproducibility and reduces working directory errors

Script Editor with Syntax Highlighting

Write and run R scripts (.R), R Markdown (.Rmd), and Quarto documents

Features like auto-completion, function tool-tips, and real-time diagnostics boost coding efficiency

Getting Started with RStudio

First Look

Open RStudio

Opening a Source Document

E.g., R script

Opening a Source Document (cont.)

RStudio Interface

RStudio Interface

Source (Top-Left): Code scripts, R Markdown, Quarto

RStudio Interface (cont.)

Console (Bottom-Left): Where code runs

RStudio Interface (cont.)

Environment/History (Top-Right): View objects, data, function history

RStudio Interface (cont.)

Files/Plots/Packages/Help (Bottom-Right): View plots, install packages, browse files, and search help files

Writing and Running R Code

Use .R scripts to save and re-use code

Writing and Running R Code (cont.)

Use Ctrl+Enter (or Cmd on Mac) to run a line of code

Writing and Running R Code (cont.)

Alternatively, you can press the button at the top of the source window

Running Multiple lines of Code

Highlight lines of code you want to run, then use Ctrl+Enter or press the button

If you want to run entire code script, press at the top of the source window.

Workflow and RStudio Projects

Workflow

Step-by-step process you follow to complete a data analysis from raw data to final results

It includes data loading, cleaning, modeling, and reporting
A good workflow is organized, repeatable, and transparent
Reduces errors
Makes work reproducible and easier to follow

Workflow Example

Create a project folder/repository/directory
Load and inspect data
Clean data
Estimate an econometric model
Run robustness checks
Interpret and report results
Export results

RStudio Projects

RStudio projects create a work environment within RStudio

Essentially, they create a folder containing all project documents, including code scripts, data, images, plots, and text files

Opening an .Rproj file opens all files for the project the project and automatically sets the working directory to the project directory

Benefits of RStudio Projects

Keeps everything organized
- Each project has its own folder with scripts, data, and results

Reproducible workflow
- Opens with the same settings, file paths, and environment every time
Avoids Setwd() errors
- Automatically sets the working directory to the project folder

Benefits of RStudio Projects (cont.)

Manages multiple analyses cleanly
- Lets you separate class assignments, research papers, or datasets into distinct, self-contained units

Supports version control (Git)
- Built-in Git integration makes it easy to track changes and collaborate on code
Loads your workspace consistently
- Automatically restores your open files, history, and environment (optional)

Create RStudio Project

To Create a new RStudio project press the button

Create RStudio Project (cont.)

Choose whether you want to create a new folder for your project directory, convert an existing folder into a project directory, or link the project folder to a git repository (more on this next lecture)

Create RStudio Project (cont.)

Select the type of project you want to create

Create RStudio Project (cont.)

Choose the location for your project directory and make sure you check the box Create git repository

Create RStudio Project (cont.)

Your new project directory has been created

Note the .Rproj file created

Using R in RStudio

Using R in RStudio

For this course, the main purpose of using RStudio is to facilitate the use of R statistical software

Most often, we will write R code in an R script

We can then run code from our script, which shows up in the console as seen above

Both the code and the results will be displayed in the console

Variables, Datasets, and Functions

Throughout our analyses, we will store data and results as variables

We may also group related variables into datasets, or import datasets from external sources

Additionally, we can create functions to automate repeated tasks and simplify our code

All saved variables, datasets, and functions will appear in the Environment tab in RStudio, allowing us to view and manage the objects currently in memory

Variables, Datasets, and Functions (cont.)

Inspecting Datasets

To view your dataset in R, simply type View(data), where data is your dataset

Inspecting Datasets (cont.)

Other useful commands for inspecting data include head(), summary(), str()

Ideal for inspecting data sets, matrices, and model outputs

Installing and Loading R Packages

R is a statistical software built on user written commands/functions

Users write functions and group them in packages, which are then made available to all users

To use a function, you must install the necessary package(s) using install.packages("package_name") and load it into your session using library("package_name")

Many of the functions that we will use in this course are available in the base and stats packages, which load automatically with every R session

Installing and Loading R Packages (cont.)

To view your available packages and which ones are loaded into a session, click the Packages tab in the bottom right pane

Installing and Loading R Packages (cont.)

One can also load a package in RStudio by checking the box next to the package name in the Packages tab

Plotting and Output

R is powerful tool for creating plots and graphics

RStudio organizes plots under the Plots tab in the bottom right pane

Allows for easy saving and exporting

Plotting and Output (cont.)

E.g., plotting the probability density of a standard normal variable

Publishing

Quarto

Assignments for this course will be written in Quarto

Quarto is an open-source tool designed for creating dynamic documents, reports, presentations, and websites

It integrates with various programming languages and provides a unified framework for combining code, narrative text, and data visualization

More on Quarto in future lectures

Quarto (cont.)

To open a Quarto document, click the button in the top-left corner and select “Quarto Document…”

Quarto (cont.)

Select the format for your document, HTML, PDF, or Word and click Create

Quarto (cont.)

You can now edit your Quarto document

Rendering a Quarto Document

To view the rendered document click the button and save the file to your desired location
The rendered document shows up under the Viewer tab in the bottom-right pane

Summary

Summary

All-in-one workspace

Encourages clean, reproducible code
Tools for data, models, graphics, reporting
A key skill for econometrics, data science, and research