Welcome to AP Stats in R!

This site is my attempt at providing a resource for former, current, and future teachers and students of AP Statistics to learn R.

Throughout the different tutorials, I will show you how to use R to do the skills taught in the AP Statistics curriculum. Eventually, I may also add some other content.

Organization

This site is organized according to the chapters of The Practice of Statistics, 4th Ed. by Daren S. Starnes, Dan Yates, and David S. Moore. I don’t use any actual content from the book, but I have organized it the same way in the hopes that that helps you understand how everything fits together.

Style

The tutorial documents will include explanations of how things work, but also blocks of code like the following:

#first we calculate the average
average = (1+2+3+4+5)/5
#then we print it out
average

I’ll do my best to break down these code segments so you know exactly what R is doing with different functions and operations.

About the Author

My name is Peter Moon; I am a former AP Statistics student, a former AP Statistics teacher, and a current PhD student in Mathematics Education at University of Maryland, College Park. If you are curious about the other work I do, feel free to check out my university website here

If you have questions, comments, or concerns about AP Stats in R, please feel free to reach out to me at my university email account, pmoon (at) umd (dot) edu.

Table of Contents/Progress Checklist

Each sub-section of a chapter has a tutorial document and also a downloadable RMarkdown (.rmd) file that you can open in R. If you do not have R installed, please see the tutorial here. Please install BOTH R and RStudio as it will make your life significantly easier!

  1. Chapter 1: Exploring Data
    1. Analyzing Categorical Data
      1. Conditional Counts & Total Counts (tutorial/rmd)
      2. Frequency Tables (tutorial/rmd)
      3. Pie Charts & Bar Charts (tutorial/rmd)
      4. Segmented Bar Charts <– I am here
    2. Graphical Displays of Quantitative Data
    3. Numerical Summaries of Quantitative Data
  2. Chapter 2: Modeling Distributions of Data
    1. Describing Location in a Distribution
    2. Normal Distributions
  3. Chapter 3: Describing Relationships
    1. Scatterplots & Correlation
    2. Least-Squares Regression
  4. Chapter 8: Confidence Intervals
    1. Estimating a Population Proportion
    2. Estimating a Population Mean
  5. Chapter 9: Significance Testing
    1. Testing a Population Proportion
    2. Testing a Population Mean
  6. Chapter 10: Comparing Two Populations or Groups
    1. Comparing Two Proportions
    2. Comparing Two Means
  7. Chapter 11: Chi-Square and Inference for Categorical Distributions
    1. Chi-Square Goodness-of-Fit Tests
    2. Inference for Relationships
  8. Chapter 12: More Regression Skills
    1. Inference for Linear Regression
    2. Transforming to Achieve Linearity