r-bootcamp-fall-2023

2023 R bootcamp schedule

The bootcamp will be held August 19-20, so Day 1 is Saturday, August 19 and Day 2 is Saturday, August 20.

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 presents) (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 (Chris 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 (Will 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 (Chris 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 (Austin 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)

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