generalR

These are instructional materials to help people learn to use R. You can download the files that go with these materials from the generalR data repository on GitHub. If you would like to contribute to the project or flag any issues that you think need to be addressed, please feel free to file a pull request or start an issue on the generalR GitHub repository. The overall philosophy is to give learners the minimum that they need to get going and to teach them the material in a way that most R users learned the material (i.e. not from a book but by hacking their way to success).

  1. Introduction
    • Philosophy behind these instructional materials
    • Why R?
    • Introduction to R
    • Installing R, RStudio, and tidyverse
    • Getting settled in RStudio
  2. Lyme disease: Session 1
    • Learning to analyze data is empowering
    • R is expressive
    • R isn't just for statistics
    • Figuring things out
  3. Lyme disease: Session 2
    • Plotting continuous data vs continuous data
    • Manipulating aesthetics
    • Mapping data to aesthetics
  4. Lyme disease: Session 3
    • The `+` operator
    • Tracking down the source of errors
  5. Lyme disease: Session 4
    • What is a (tidy) data frame?
    • Understanding the structure of our data frame
    • Data types
    • Counting data
    • Summarizing data frames
  6. Lyme disease: Session 5
    • Raw data should stay raw
    • Heat maps
    • Simplifying our data frame with `filter`
    • Map maps
    • Taking stock
  7. Weather data: Session 6
    • Finding inspiration
    • Specifying variable types with `read_csv`
    • `selecting`-ing columns from a data frame
    • Renaming columns in a data frame
  8. Weather data: Session 7
    • Detecting problems in data
    • Fixing data problems
    • Rinse, repeat
  9. Weather data: Session 8
    • Creating columns
    • Keeping our code DRY
    • Writing our own functions
  10. Weather data: Session 9
    • Grouping and summarizing data
    • Working with dates
    • Thinking about and visualizing our data
    • DRY revisited
    • Rinse, repeat
  11. Weather data: Session 10
    • Critiquing visualizations
    • Highlighting data: factors revisited
    • Setting labels in a visualization
    • Themes
  12. IPEDS: Session 11
    • Developing questions and finding data
    • Getting familiar with and focusing on data
    • Working with spreadsheets
    • Joining data frames together
  13. IPEDS: Session 12
    • Rinse, repeat to answer new questions
    • Plotting continuous data against categorical data
    • Using `factor` to order categorical variables
    • Thinking of our data as paired and redefining tidy
  14. IPEDS: Session 13
    • Generalizing an analysis
    • Making a data frame tidy
    • Plotting groups of data
    • Faceting data visualizations
  15. IPEDS: Session 14
    • Thinking through questions
    • Finding text with R
    • Adjusting the shape of a data frame
    • More sophisticated text searches
    • Modifying text based on patterns
  16. IPEDS: Session 15
    • Thinking through problems
    • Combining data frames
    • DRYing code with functions
    • File operations in R
    • DRYing code with maps

Pat Schloss © 2022