Material from monthly meetings of the R Club in Biology at SDU.
The format for presentations is completely flexible. The only rule is that presentations should be limited to 10 minutes (shorter is fine too), unless a longer time-slot is arranged ahead of time (which is perfectly feasable).
Don't worry about repeating material that was previously covered. The universe of statistics and programming is too vast, and our meetings too short and infrequent, for repetition to be a problem.
If your presentation involves written materials (scripts, slides, etc.), it would be great if they could be archived in this repo. R Markdown documents are an ideal way to host such material, because they allow R scripts, their output, and any associated content to be rendered into a single, nicely-formatted HTML document. See a template for an R Markdown presentation here, and the resulting markdown document here. Note that it's perfectly fine if you don't have the time or desire to use R Markdown.
- Frederik: Hierarchical cluster analysis with
dendextend - Lio: To be dead or not to be dead: ImageJ and R
- Johannes: Labelling individual points in a plot using
identify() - Patrick: Pattern-matching using regular expressions [link]
- Iris: a simulation problem
- Patrick: working with spatial rasters [link]
- Rita: Let’s Map! How to make a map in R [link]
- Patrick: checking assumptions of glm models [link]
- Simeon: model(comparison) in R [link]
- Brian: update on potential courses/training/workshops
- Gesa: plotting confidence intervals for glms [link]
- Patrick: intro to managing projects with GitHub
- Owen: cluster analysis [link]
- Gesa: intro to Rmarkdown
- Iain: VSCode as an alternative to RStudio
- Iris: intro to ggplot2 (#2)
- Gesa: using RStudio Projects
- Lio: text-mining with the tm package (see also vignette by Ingo Feinerer)
- Iain: vectorizing functions
- Rita: intro to the taxize package
- Patrick: working with factors using forcats [link]
- Gesa: managing naming conflicts with conflicted [link]
- Iris: introduction to ggplot2 (#1)
- Patrick: getting help with a problem on StackOverflow [link]
- Brian: nifty but forgettable functions in base R [link]
- Stina: auto-formatting problems in Excel
This list is intended to serve as inspiration for future presenters, but many of the categories may be too broad for a single presentation. For example, instead of presenting on "model validation" broadly, one might present on "model validation for logistic regression models".
- model validation / checking assumptions
- model comparison (e.g. AIC)
- model types
- general linear models
- type I vs. II vs. III sums of squares
- generalized additive models
- non-linear models (e.g. NLS)
- mixed models / random effects
- phylogenetic models
- non-parameteric tests
- dealing with missing data (e.g. imputation)
- manually optimizing likelihood functions
- Bayesian methods
Resources: Quick-R Statistics
- color palettes [cheat sheet, blog post, colorspace package]
- animation [gganimate, animation, gallery of animations for statistics]
- putting multiple plots on one page
Resources: Fundamentals of Data Visualization, R Graphics Cookbook, Quick-R Advanced Graphs
- tidy data principles [paper by Hadley Wickham]
- tidyverse packages
- working with dates/times
- regular expressions [R doc]
Resources: Quick-R Data Management
- Git / GitHub
- Rmarkdown [Intro by RStudio]
- using R in the cloud
Resources: Cran Task View: Reproducible Research
- making maps
- working with shapefiles
- packages rgeos, rgdal, raster, sp, sf, ...
Resources: CRAN Task View: Analysis of Spatial Data, Geocomputation with R by Lovelace et al.
- writing custom functions
- control flow functions (if, for, while, ifelse, switch) [R doc, Advanced-R chapter]
- debugging code
- code style [Google's style guide, Advanced-R chapter]
- non-standard evaluation [Advanced-R chapter]
- scraping data off the web [rvest package]
- bioinformatics applications
- image processing
- parallel processing
- writing R packages [post by Hilary Parker, R Packages by Hadley Wickham]
- Advanced R by Hadley Whickham
- Quick-R by DataCamp
- R for Data Science by Grolemund & Wickham
- R Programming for Data Science by Roger Peng
- Coding Club Tutorials
- RStudio Cheat Sheets
- Fundamentals of Data Visualization by Claus Wilke
- UBC Stat545 course materials by Jenny Bryan et al.