Setting Up R and RStudio

Author

Bastola

Published

March 18, 2025

Introduction

This lab tutorial will guide you through installing R and RStudio on your computer, managing packages, and loading data into R. This setup is essential for performing all kinds of data analysis tasks in R.

What are R and RStudio?

R is a programming language designed for statistical computing and graphics, widely used in data analysis, statistical modeling, and research. RStudio is an integrated development environment (IDE) that facilitates the use of R by providing a user-friendly interface for scripting, debugging, and visualization.

Importance in Statistics Education

R and RStudio are critical tools in statistics education for several reasons:

Statistical Analysis: They provide a robust environment for performing and learning a wide range of statistical analyses.
Reproducible Research: Tools like knitr and R Markdown in RStudio promote reproducibility, allowing for seamless integration of analysis and reporting.
Interactive Learning: The interactive nature of RStudio helps in visualizing data and statistical concepts more effectively.

Installing R and RStudio

On Windows

Download R:
- Visit CRAN and download the latest version of R for Windows.
- Run the downloaded .exe file and follow the installation instructions.
Download RStudio:
- Go to the RStudio Download page and download the free version of RStudio Desktop for Windows.
- Install it by running the downloaded .exe file.

On Mac

Download R:
- Navigate to CRAN and choose the version for macOS.
- Open the downloaded .pkg file to start the installation.
Download RStudio:
- Visit RStudio Download page and select RStudio for macOS.
- Open the downloaded .dmg file and drag RStudio to your Applications folder.

RMarkdown

RMarkdown is a powerful tool for combining narrative text and executable R code into a single document, which can then be rendered into various formats including HTML, PDF, and Word documents. It’s especially valuable in academic, scientific, and data analysis settings for creating reproducible data analysis scripts and reports.

Understanding R Markdown Documents

R Markdown allows you to integrate code and text in the same document, facilitating reproducible research and interactive data analysis reports. Here’s a quick overview:

Managing .Rmd Files and Setting the Working Directory

Downloading and Organizing .Rmd Files

To effectively manage your course materials in RStudio, follow these steps:

Download .Rmd File:
- Navigate to MyClasses and locate the desired .Rmd file for your course.
- Download the file to your local machine.
Create a Course Folder:
- Create a new folder named math216 in a suitable location on your computer or within your RStudio account.
Organize Files:
- Move the downloaded .Rmd file into the math216 folder to keep your course materials organized.

Text Portions

Text in R Markdown is written using standard markdown syntax. This allows you to add headers, lists, links, and other styling features to create dynamic reports.

Text Formatting in R Markdown

Markdown is a lightweight markup language that allows you to add formatting elements using plain text. Here’s how you can use different Markdown syntax to format text within your R Markdown documents.

Headers

Create headers by prefixing the text with one or more hash symbols (#). The number of # symbols before the text determines the level of the header.

# Header 1
## Header 2
### Header 3

Lists

You can create bulleted or numbered lists:

Unordered Lists (bullets):

- Item 1
- Item 2
  - Subitem 1
  - Subitem 2

Ordered Lists:

1. First item
2. Second item
   1. Subitem 1
   2. Subitem 2

Links

Include hyperlinks using the format [link text](URL):

[Visit Canvas](https://salisbury.instructure.com/courses/66117)

Emphasis

Italicize or bold text using asterisks or underscores:

Italic: *Italic* or _Italic_
Bold: **Bold** or __Bold__

Images

Embed images using the format ![Alt text](image_URL "Optional Title"):

![Example Image](path_to_image.jpg "This is an image")

Blockquotes

Create blockquotes with >:

> This is a blockquote.

Code

Display inline code with backticks and code blocks with triple backticks:

Inline code: `code`
Code block:

\```
This is a code block
\```

Horizontal Rules

Insert a horizontal line with three or more hyphens:

---

Combining Markdown with R Code

Markdown can be interleaved with executable R code chunks:

# Calculate the sum of numbers from 1 to 10
sum(1:10)

[1] 55

R Code Chunk Options

Before diving into data loading, let’s understand important R chunk options:

eval: Use eval=FALSE to prevent code from running but still display it.
echo: With echo=FALSE, the code is run but not displayed in the document.
include: include=FALSE runs the code and hides both the code and output from the final output.

Example to demonstrate:

# This code will run, and both code and output will appear in the document.
summary(cars)

     speed           dist       
 Min.   : 4.0   Min.   :  2.00  
 1st Qu.:12.0   1st Qu.: 26.00  
 Median :15.0   Median : 36.00  
 Mean   :15.4   Mean   : 42.98  
 3rd Qu.:19.0   3rd Qu.: 56.00  
 Max.   :25.0   Max.   :120.00

Managing Packages

R packages extend R’s capabilities, offering tools for additional statistical techniques, data manipulation, and graphical outputs. Knowing how to install and manage these packages is essential for leveraging R’s full potential in practical and academic applications.

# To install a package
# install.packages("tidyverse")  # uncomment to install

# To load the package
library(tidyverse)

Setting the Working Directory

It’s crucial to set the working directory in R to the folder where your course files are stored. This can be done in several ways:

Using RStudio:
- In RStudio, you can set the working directory through the menu by navigating to Session > Set Working Directory > Choose Directory... and selecting your math313 folder.
Using R Script:
- You can also set the working directory directly in an R script using the setwd() function. Place the following line of code at the beginning of your script:
```
setwd("~/math313")
```
Replace "~/math313" with the actual path to your course folder.
Using R Console:
- You can manually set the working directory in the R console with the same command:
```
setwd("~/math313")
```

Loading Data

Here’s how you can load a CSV file into R:

# Assuming the CSV file is in your working directory and is named 'data.csv'
data <- read.csv("data.csv")

# Viewing the first few rows of the dataset
head(data)