Welcome to the course! Today, we will lay the groundwork for our journey into data science using R.

• Course Overview
• The R Environment & RStudio
• Basic R Syntax & Data Types
• Introduction to R Markdown

• Stay up to date with the course outline (I will update regularly)
• Come to class prepared and on time
• Ask questions if you start getting lost
  • In class
  • Tutor OH
  • My OH

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

The “Data Science Lifecycle”

• Ask: Define the problem and form a hypothesis.
• Wrangle: Get, clean, and prepare data.
• Explore: Conduct exploratory data analysis (EDA) and visualize the data.
• Model: Build predictive or descriptive models.
• Communicate: Share findings and insights.

R is a powerful, open-source programming language and environment specifically designed for statistical computing and graphics.

• Rich Ecosystem: Thousands of packages for every data task imaginable.

• Visualization: Unrivaled capabilities with packages like ggplot2.

• Statistical Power: Built by statisticians, for statisticians.

• Reproducibility: Excellent tools for creating dynamic reports and dashboards.

• Python is also commonly used. It is more general use than R.

RStudio is an integrated development environment (IDE) that makes working with R much easier and more productive. It consists of four main panes:

• Console: Where you run R code directly.
• Source: Where you write and save your R scripts and R Markdown files.
• Environment/History: Where you see the variables and functions you have created.
• Files/Plots/Packages/Help/Viewer: A multi-tab pane for file management, viewing plots, managing packages, and more.

Let’s start with the basics. R’s syntax is simple and intuitive for a programming language.

Assignment Operator

Use <- to assign a value to a variable. The = sign also works, but <- is the conventional R style.

# Assign a value to x
x <- 10

# Print the value of x
print(x)

## [1] 10

Let’s see this in action. When you “knit” this document, the code below will be executed and the output will be displayed.

# Assign a value to a variable
my_number <- 42
my_text <- "Hello, R!"

# Print the variables
paste("my_number is:", my_number)

## [1] "my_number is: 42"

paste("my_text is:", my_text)

## [1] "my_text is: Hello, R!"

You can use R for basic arithmetic operations.

• Addition: +
• Subtraction: -
• Multiplication: *
• Division: /
• Exponentiation: ^ or **

5 * 3 + 2

## [1] 17

The two main numeric data types are integers and doubles (real numbers).

• Integer: Whole numbers, specified with L (e.g., 10L).
• Double (Numeric): Decimal numbers, the default numeric type.
• Note: class() allows you to see the data type of your variable.

class(10)

## [1] "numeric"

class(10L)

## [1] "integer"

Text data is stored as a character string. You can use either single quotes (') or double quotes (").

my_string <- "Data Science is fun!"
print(my_string)

## [1] "Data Science is fun!"

Logical values are either TRUE or FALSE. You can also use T and F as shorthand.

5 > 3

## [1] TRUE

5 == 5

## [1] TRUE

R Markdown is a powerful tool for creating dynamic, reproducible documents that combine R code and its output with narrative text.

• Narrative Text: Use Markdown syntax for headings, lists, and bold text.
• Code Chunks: Write R code in special blocks called “chunks.”
• Output: The results of your code, including tables and plots, are automatically embedded.

An R Markdown file (.Rmd) has three main components:

1. The YAML Header

title: "My First R Markdown Document"
author: "Your Name"
date: "2025-08-04"
output: html_document

This specifies document metadata and the output format.

2. The Narrative Text

This is just regular text using Markdown syntax.

# A main heading This is some **bold** text.

3. The Code Chunks

Code chunks are enclosed in backticks and curly braces:

```r
# Your R code goes here
summary(cars)

When you knit an R Markdown file, the code is executed and the output is inserted into the final document.

# Imagine this is an R Markdown chunk
my_vector <- c(1, 2, 3, 4, 5)
mean(my_vector)

## [1] 3

In RStudio, the “Knit” button is what brings it all together. It executes your R code and renders the final document to your desired format (e.g., HTML, PDF, Word).

• It’s the key to reproducibility.
• It ensures your analysis and the report are always in sync.

Today’s Summary

• We discussed what Data Science is.
• We introduced R and RStudio.
• We learned basic R syntax for assignment and arithmetic.
• We explored fundamental data types: numbers, strings, and logicals.
• We had a first look at the power of R Markdown.

Looking Ahead to Day 2

• Next class, we’ll dive deeper into R Markdown and introduce version control with Git and GitHub, which are essential for collaboration.

• Set up R, Rstudio

• Work on the day1_activity

• Set up Git, GitHub

• Ask me questions if you run into issues