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2022 R bootcamp schedule

The bootcamp will be held August 20-21, so Day 1 is August 20 and Day 2 is August 21.

Unless otherwise noted, modules are about 75 minutes long: 40 minutes for presentation, 25 minutes for breakout and 10 minutes for discussion of solutions.

Day 1

  • Day 1 morning (8:30-12:15) (learning R)

    • Module 0: Introduction, what is R, starting R, why R? (Chris) (15 minutes)
    • Module 1: Basics of R, with RStudio (Chris presents)
      • R as a calculator
      • helpful shortcuts: tab-complete, up arrow, Ctrl-{up arrow}
      • vectors, indexing, and subset assignment
      • some basic functions; help()
      • vectorized calculations
      • basic R objects: vectors, dataframes, lists
      • basic graphics
      • breakout problems
    • Break (15 minutes)
    • Module 2: Managing R and your analyses (Chris presents)
      • managing R objects, the R workspace
      • using packages (installing, loading, namespaces)
      • the working directory
      • file reading/writing
      • Git, GitHub and version control
      • getting R help online
      • breakout problems
    • Module 3: Working with data (Chris presents) (45 minutes)
      • dataframes, lists, and matrices
      • attributes, missing values and factors
      • subsetting
      • strings
      • breakout problems

  • Lunch (on your own) (12:00-1:30)

  • Day 1 afternoon (1:30-5:00) (data processing and manipulation)

    • Finish Module 3 as needed
    • Module 4: Calculations (Alan presents)
      • vectorized calculations and efficiency
      • apply, lapply (map operations)
      • tabulation, stratified analyses,
      • merging/joining tables
      • breakout problems
    • Break (15 minutes)
    • Module 5: Programming in R (Chris presents)
      • writing your own functions, function arguments, functions as objects
      • basic scoping and environments
      • loops, if-else
      • breakout problems/homework

Day 2

  • Day 2 morning (9-12:45) (programming and data analysis)

    • Module 6: Data manipulation using the tidyverse (Corrine presents)
      • dplyr overview and piping
      • stratified analyses: groupwise operations and split-apply-combine using dplyr
      • reshaping and tidying data
      • breakout problems
    • Break (15 minutes)
    • Module 7: Graphics (Florica presents)
      • base R and ggplot2 overview
      • ggplot2 basics
      • using aesthetics to control plotting
      • exporting graphics (vector/raster formats)
      • breakout problems
    • Module 8: Data analysis (Chris presents)
      • regression, GLMs
      • smoothing
      • optimization
      • simulation, sample()
      • dates and times
      • breakout problems

  • Lunch (on your own) (12:45-2:00)

  • Day 2 afternoon (2:00-4:30) (more advanced topics)

    • Module 9: Workflows, coding practices, and project management (Chris presents) (60 minutes)
      • debugging, timing, memory use
      • scripting, source(), batch jobs
      • good coding practices
      • reproducible research
    • Break (20 minutes)
    • Module 10: Advanced topics morsels (Chris presents) (60 minutes)
      • object-oriented programming (S3, S4, R6)
      • computing on the language (using R to write and evaluate R code)
      • errors and try-catch
      • encodings
      • working with databases
      • parallel processing: future, parallel lapply, parallel for loops, RNG issues
    • Module 11: Wrapping up (Chris presents) (5 minutes)
      • R inconsistencies and different ways to do things
      • Where to learn more (campus and non-campus resources)