2 Introduction to R, RStudio, and Quarto

2.1 Learning Objectives

By the end of this week, you should be able to:

Install and open R, RStudio, and Quarto
Navigate the four-pane layout of RStudio
Create and run R scripts
Understand the differences between the console, script editor, and environment
Execute basic R operations and understand data types
Install and load R packages
Create and render a Quarto (.qmd) document to .pdf

2.2 Getting Started

R is a programming language designed for data analysis.
RStudio is an Integrated Development Environment (IDE) that makes working with R easier.
Quarto is a tool for creating reproducible documents that combine code and text.

2.3 Installing R, RStudio, and Quarto

Install R: https://cran.r-project.org/
Install RStudio: https://posit.co/download/rstudio-desktop/
Install Quarto: https://quarto.org/docs/get-started/

When you open RStudio, you’ll see four panes:

Console – runs code interactively
Source – write and save scripts or Quarto documents
Environment/History – view and manage objects
Files/Plots/Packages/Help/Viewer – navigation and visualization tools

2.4 Introduction to Quarto

Quarto allows you to create documents that include both text and executable R code.

2.4.1 Your First Quarto Document

In RStudio: File → New File → Quarto Document
Replace the header with:

---
title: "My First Quarto Document"
author: "Your Name"
format: pdf
---

Below the header, add:

x <- c(1, 2, 3, 4, 5)
mean(x)

[1] 3

Click Render to produce a PDF file.

2.4.2 In-Class Quarto Exercise

Create a new Quarto document with:
- A title, your name, and the date
- A short paragraph of text
- A code chunk that calculates the mean and standard deviation of a numeric vector
Render it to PDF and verify it works.

2.5 Basic R Concepts

2.5.1 Variables and Assignments

x <- 5
y <- 10
z <- x + y
z

[1] 15

2.5.2 Vectors and Functions

ages <- c(25, 30, 35, 40)
mean(ages)

[1] 32.5

sd(ages)

[1] 6.454972

2.5.3 Data Frames

name <- c("Alice", "Bob", "Charlie")
age <- c(25, 30, 35)
student_data <- data.frame(name, age)
student_data

     name age
1   Alice  25
2     Bob  30
3 Charlie  35

2.5.4 Inspecting Data

str(student_data)

'data.frame':   3 obs. of  2 variables:
 $ name: chr  "Alice" "Bob" "Charlie"
 $ age : num  25 30 35

summary(student_data)

     name                age      
 Length:3           Min.   :25.0  
 Class :character   1st Qu.:27.5  
 Mode  :character   Median :30.0  
                    Mean   :30.0  
                    3rd Qu.:32.5  
                    Max.   :35.0

head(student_data)

     name age
1   Alice  25
2     Bob  30
3 Charlie  35

2.5.5 Comments and Help

# This is a comment
?mean  # Help for the mean function

2.5.6 Using Scripts and Console

Write your code in the script editor and run lines with Ctrl+Enter (Cmd+Enter on Mac)
Save scripts with the .R extension
Use the Console for quick exploration

2.5.7 Installing and Loading Packages

install.packages("tidyverse")

2.5.8 In-Class R Exercises

Create a numeric vector of five numbers and calculate its mean, median, and standard deviation.
Create a data frame with three columns (name, age, and major) and print its structure.
Import a dataset from a URL using read.csv() and summarize it using summary().

my_vec <- c(10, 20, 30, 40, 50)
mean(my_vec)

[1] 30

median(my_vec)

[1] 30

sd(my_vec)

[1] 15.81139

df <- data.frame(
  name = c("Lily", "Mark", "Tom"),
  age = c(21, 22, 23),
  major = c("Biology", "Math", "History")
)
str(df)

'data.frame':   3 obs. of  3 variables:
 $ name : chr  "Lily" "Mark" "Tom"
 $ age  : num  21 22 23
 $ major: chr  "Biology" "Math" "History"

data <- read.csv("https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv")
summary(data)

    Month               X1958           X1959           X1960      
 Length:12          Min.   :310.0   Min.   :342.0   Min.   :390.0  
 Class :character   1st Qu.:339.2   1st Qu.:387.5   1st Qu.:418.5  
 Mode  :character   Median :360.5   Median :406.5   Median :461.0  
                    Mean   :381.0   Mean   :428.3   Mean   :476.2  
                    3rd Qu.:411.8   3rd Qu.:465.2   3rd Qu.:514.8  
                    Max.   :505.0   Max.   :559.0   Max.   :622.0

2.6 Homework Preview

Create a .qmd document that:
- Includes a title and your name
- Demonstrates at least three code chunks
- Shows basic statistics on a numeric vector
- Imports a dataset, inspects it with str() and summary(), and writes one paragraph summarizing your findings
Render to PDF and submit to Canvas.

2.7 Next Steps

You now know how to run R scripts and render Quarto documents.
Next week, you’ll learn how to create data visualizations using ggplot2.

--- title: "Introduction to R, RStudio, and Quarto" --- ## Learning Objectives By the end of this week, you should be able to: - Install and open R, RStudio, and Quarto - Navigate the four-pane layout of RStudio - Create and run R scripts - Understand the differences between the console, script editor, and environment - Execute basic R operations and understand data types - Install and load R packages - Create and render a Quarto (`.qmd`) document to `.pdf` ------------------------------------------------------------------------ ## Getting Started R is a programming language designed for data analysis.\ RStudio is an Integrated Development Environment (IDE) that makes working with R easier.\ Quarto is a tool for creating reproducible documents that combine code and text. ------------------------------------------------------------------------ ## Installing R, RStudio, and Quarto 1. Install R: <https://cran.r-project.org/> 2. Install RStudio: <https://posit.co/download/rstudio-desktop/> 3. Install Quarto: <https://quarto.org/docs/get-started/> When you open RStudio, you’ll see four panes: - Console – runs code interactively - Source – write and save scripts or Quarto documents - Environment/History – view and manage objects - Files/Plots/Packages/Help/Viewer – navigation and visualization tools ------------------------------------------------------------------------ ## Introduction to Quarto Quarto allows you to create documents that include both text and executable R code. ### Your First Quarto Document 1. In RStudio: File → New File → Quarto Document 2. Replace the header with: ``` --- title: "My First Quarto Document" author: "Your Name" format: pdf --- ``` 3. Below the header, add: ```{r} x <- c(1, 2, 3, 4, 5) mean(x) ``` 4. Click Render to produce a PDF file. ------------------------------------------------------------------------ ### In-Class Quarto Exercise - Create a new Quarto document with: - A title, your name, and the date - A short paragraph of text - A code chunk that calculates the mean and standard deviation of a numeric vector - Render it to PDF and verify it works. ------------------------------------------------------------------------ ## Basic R Concepts ### Variables and Assignments ```{r} x <- 5 y <- 10 z <- x + y z ``` ### Vectors and Functions ```{r} ages <- c(25, 30, 35, 40) mean(ages) sd(ages) ``` ### Data Frames ```{r} name <- c("Alice", "Bob", "Charlie") age <- c(25, 30, 35) student_data <- data.frame(name, age) student_data ``` ### Inspecting Data ```{r} str(student_data) summary(student_data) head(student_data) ``` ### Comments and Help ```{r} # This is a comment ?mean # Help for the mean function ``` ------------------------------------------------------------------------ ### Using Scripts and Console - Write your code in the script editor and run lines with Ctrl+Enter (Cmd+Enter on Mac) - Save scripts with the `.R` extension - Use the Console for quick exploration ------------------------------------------------------------------------ ### Installing and Loading Packages ```{r eval=FALSE} install.packages("tidyverse") ``` ------------------------------------------------------------------------ ### In-Class R Exercises 1. Create a numeric vector of five numbers and calculate its mean, median, and standard deviation. 2. Create a data frame with three columns (name, age, and major) and print its structure. 3. Import a dataset from a URL using `read.csv()` and summarize it using `summary()`. ```{r} my_vec <- c(10, 20, 30, 40, 50) mean(my_vec) median(my_vec) sd(my_vec) df <- data.frame( name = c("Lily", "Mark", "Tom"), age = c(21, 22, 23), major = c("Biology", "Math", "History") ) str(df) data <- read.csv("https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv") summary(data) ``` ------------------------------------------------------------------------ ## Homework Preview - Create a `.qmd` document that: - Includes a title and your name - Demonstrates at least three code chunks - Shows basic statistics on a numeric vector - Imports a dataset, inspects it with `str()` and `summary()`, and writes one paragraph summarizing your findings - Render to PDF and submit to Canvas. ------------------------------------------------------------------------ ## Next Steps You now know how to run R scripts and render Quarto documents.\ Next week, you’ll learn how to create data visualizations using `ggplot2`.