Stat 341
Computational Bayesian Statistics
Spring 2021

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[RStudio] [calendar] [test info] [resources] [from class] [homework]

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Materials From Class

Here is where you can find things used, handed out, or mentioned in class.

Dope Sheets

To find things related to specific class days, to to the daily dope sheet

Tutorials

Plotting with ggformula

• Whole Thing

• Part B (NHANES) – This will give you a chance to try things with a different data set and lots of specific direction.

• Part C (more practice) – This will let you explore on your own and introduce a few more “extras”.

• Alternative (only use if hosted tutorial isn’t working for some reason): Run in RStudio console. Copy and paste one of the commands below – the one for the part you want to do.

  learnr::run_tutorial('PlottingBasics2020A', package = 'StatTutor')
  learnr::run_tutorial('PlottingBasics2020B', package = 'StatTutor')
  learnr::run_tutorial('PlottingBasics2020C', package = 'StatTutor')

Links

• article about how and why BBC uses R and ggplot2 to create production-quality data visualizations
  • bbplot cookbook – learn how to use the package

CalvinBayes

I’m also assembling an R package called CalvinBayes. The package includes data sets from a book by Kruschke and some utilties to make our lives easier.

This may be expanded over the course of the semester. If you install locally, be sure to update the package periodically.

RStudio

• Calvin’s RStudio server
• First look at R
• Mathematics in R Markdown
  • “Handout”
  • A short video by Professor Scofield
• RStudio Getting Started Videos by Nick Horton at Amherst. (Note: you will need to use the Calvin RStudio address rather than the one for Amherst students.)
• RStudio’s web site
  • Cheat Sheets
  • More about R Markdown

Additional Resources

I’ve assembled a list of resources related to Bayesian inference here.