Learning R (and statistics)

Updated often. If you’re an enrolled students and would like to contribute content here, please open a thread on Piazza under “Misc” and say you’d like to share a useful link.

0.1 Getting help with R

START HERE, with fbriatte.org, where you can learn how to find answers to your burning R questions online.

Getting help with R
Stack overflow: topics are tagged, and “r” is a very popular tag on the site. To go directly to R-related topics, visit http://stackoverflow.com/questions/tagged/r.
Rstudio Community
Reddit: r/rstats, r/Rlanguage, r/rprogramming, r/RStudio
Github: github is a must for anyone in datascience and has many functions. For your purposes, searching for a particular package and looking into their “issues” page can be very helpful. E.g., here is the issues page for the rmarkdown package.

0.2 Free Online Books

“R Graphic Cookbook”
“R Markdown Cookbook”
“Introduction to Data Science: Data Wrangling and Visualization with R”, by Rafael Irizarry
“Introduction to Data Science: Statistics and Prediction Algorithms Through Case Studies”, by Refael Irizarry
“R for Data Science”, by Hadley Wickham & Garrett Grolemund
“YaRrr! The Pirate’s Guide to R”, by Nathaniel Phillips
“An introduction to Biostatistics using R”, by Glover & Mitchell
“Introductory Biostatistics with R”, by Dylan Childs, Bethan Hindle & Philip Warren
“Applied Biostats”, by Yaniv Brandvain.
“ggplot2: Elegant Graphics for Data Analysis (3e)”

0.3 Videos

Overview of R Markdown: a crash course
Code Like a Pro
Intro to Rstudio and Rstudio Cloud
Overview of R markdown
Rmarkdown with Rstudio
This youtube playlist - I curated this and it contains both R info and biostatistics in general.

0.4 Tutorials & Simulations

0.5 Web visualizations

CLT: https://www.zoology.ubc.ca/~whitlock/Kingfisher/CLT.htm

CLT: https://shiny.maths.nottingham.ac.uk/pmzdjc/YujingCLTApp/?showcase=0

Another one from CLT: https://shiny.maths.nottingham.ac.uk/pmzdjc/YujingCLTApp/?showcase=0

Sampling from the Normal: https://brandvain.shinyapps.io/standardnormal/

Sample means with a normal distribution: https://www.zoology.ubc.ca/~whitlock/Kingfisher/SamplingNormal.htm

Confidence intervals for the mean: https://www.zoology.ubc.ca/~whitlock/Kingfisher/CIMean.htm

This link has web apps for several topics covered in the course: https://shinyapps.science.psu.edu/

0.6 Freely Available (Biological) Datasets

I will keep adding stuff here, but here are some good places to search.

Dryad - lots of scientific papers make their datasets available here.
Zenodo - lots of scientific papers make their datasets available here.
National Library of Medicine Dataset Catalog - great resource from the NIH, points to datasets elsewher elike zenodo, dryad, figshare, etc.
re3data
FigShare - lots of scientific papers make their datasets available here.
Wold Population Review - not focused on biology but very cool nevertheless. There are subsections that have biological datasets though like the one focusing on health, for example.

1 Articles & Other Resources

Statistics for Biologists - Nature collection of articles highlighting important statistical issues that biologists should be aware of and provides practical advice to help them improve the rigor of their work.nature.com/collections/qghhqm
G*power: a tool to compute statistical power analyses for many different t tests, F tests, χ2 tests, z tests and some exact tests. G*Power can also be used to compute effect sizes and to display graphically the results of power analyses. www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower
The pwr package in R is a powerful tool for conducting power analysis. Power analysis is essential in determining the sample size needed to detect an effect of a given size with a certain level of confidence. It is widely used in experimental design and statistical hypothesis testing. data-wise.github.io/doe/appendix/r-packages/pwr.html
R markdown Gallery: Check out the range of outputs and formats you can create using R Markdown.