# Introduction to R for Statistics and Data Science

July 6th - August 31st

Prerequisites:

• Basics of statistics. (Some familiarity with summary statistics, data visualization, and simple hypothesis tests.)
• Basics of programming. (Some prior experience writing code, in any language, at any level.)

# Hello!

Welcome to Adventures in R, we’re glad to have you on board!

On this site, you will find materials for a full, 8-week, college-level course focused on learning to use R for Data Science and Statistical Analysis.

This course was created by Dr. Kelly Bodwin. The materials shared here are free and open source, and may be shared and adapted under the Creative Commons license.

Looking for additional help with the materials, or even a full remote learning experience? Consider subscribing to the Course Patreon for Office Hours with Dr. Bodwin, weekly feedback on assignments, and an interactive class Discord server.

Last updated: 2020-07-01

# What to Expect

Each week, you will find three types of posted materials.

### Lessons

Sets of videos, readings, and activities covering a particular topic. Treat these as you would in-person lecture.

### Practice Puzzles

Short puzzle activities with a single final correct answer. Solve these to practice your new skills.

### Labs

Guided analyses of real data followed by open-ended questions. Devote time and focus to these to fully master the week’s skills.

# Weekly Course Materials

### Welcome: Information about the course and the Patreon

Learn a little more about this course and how it works.

### Week 1: Setup and First Visualizations

Get started with R, RStudio, and R Markdown; make your first data visualizations.

### Week 2: Workflow and Data Wrangling

Learn the basics of data wrangling with dplyr; good reproducibility habits.

### Week 3: Tidy Data; Factors, Strings, Dates

Learn the about tidy data and pivoting; packages for dealing with special variable types.

### Week 4: Writing Functions and Regular Expressions

Write your own R functions, and make use of regular expressions to analyze text data.

### Week 5: Functional Programming, GitHub, Packages

Learn mapping and iteration; practice package-based workflow and collaborating on GitHub.

### Week 6: Statistical Distributions; Simulation and Estimation

Simulate data from common distributions; explore statistical ideas.

### Week 7: Bootstrapping and Permutation

Implement resampling methods to make conclusions about data.

### Week 8: Regression and Hypothesis Tests

Learn to apply simple linear regression and basic hypothesis tests to data.