- Tips on writing Rmd reports
- Tips on writing peer reviews
- A page of example datasets.
- How to use
rstanon the cluster
- How to check if
rstanis installed correctly
- A description of how to use git to get the course material.
- A primer on linear algebra
- Some notes on how the slides are made from Rmd.
- Stan documentation
- RStan documentation
- Example models in Stan: each contains a Stan program, code for simulating data, real data, and model output and diagnostics
- Vignette on stanfit objects
- Brief guide to Stan’s warnings
- Rewrite of Kruschke’s models using brms (and tidyverse), by A. Solomon Kurz.
- Rewrite of Applied longitudinal data analysis using brms (and tidyverse) by A. Solomon Kurz
If you are looking for more reference or reading material, these are also good:
Logan, M. 2010. Biostatistical Design and Analysis Using R. Wiley-Blackwell. A fairly comprehensive book that covers how to use R to do many of the topics in Quinn and Keough.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis, 2nd edition (free web version of the in-process 3rd edition) A more comprehensive reference to ggplot2 than the chapter of R for data science. Also see the documentation.
Wilke, Claus O. Fundamentals of Data Visualization.. O’Reilly Publishers. (free web version) How to think about visualization (with source code for plots available!).
Haddock, S. and C. Dunn. 2011. Practical Computing for Biologists. Sinauer and Associates.
Miscellaneous R tips
- knitr chunk options for control of Rmarkdown code chunks
- Formulae in R and, in more detail, General linear models in R FAQ
- How to print the source code
for functions that don’t show it to you when you type their names. (tldr;
showMethods(fun); getMethod(fun, c(x='class1', y='class2')))
- Strongly recommended global configuration:
- Hands-On Programming with R, by Garrett Grolemund
- linuxcommand.org and bashguide
- Software Carpentry
- Reproducible Research by Karl Broman: talk and course
- Karl Broman’s excellent short tutorials on rmarkdown, git/github, make, perl, and more.
- a visual introduction to git
- Bioinformatics Data Skills by Vince Buffalo
- Jenny Bryan’s stat 545: Data wrangling, exploration, and analysis with R
- Yaniv Brandvain’s Applied Biostats course