Getting used to R, RStudio, and R Markdown 2017. Chester Ismay. The basics.
R Programming for Data Science. 2016. Roger D. Peng. Provides a more detailed intro to basic R programming.
Exploratory Data Analysis with R. 2016. Roger D. Peng. Uses the tidyverse and ggplot2 for data exploration. Great introduction to these packages and how they can be made to sing together.
Efficient R Programming. 2016. Colin Gillespe and Robin Lovelace
Statistical Inference for Data Science. 2018. Brian Caffo. A wonderful book that is a companion to his Coursera course, but is open, and full of gret concepts and R examples.
Regression Models for Data Science in R. 2018. Brian Caffo. A wonderful primer on regression models. Incredibly thorough.
Advanced R. 2014. Great walkthrough of the details and guts of R. From novices to R wizards, you will learn things you never thought possible (or the actual reasoning behind that hacky stuff you’ve been doing for years).
Principles of Econometrics with R 2016. Constantin Colonescu. Yes, it’s econometrics, but there’s a lot here that’s very generalizable to biological data analysis in R as well.
STAT545 UBC Course by Jenny Bryan that covers many similar topics to us - probably better - and more!
A Tour of Time Series Analysis with R
ModernDive: An Introduction to Statistical and Data Sciences via R. 2017. Chester Ismay and Albert Y. Kim Nice intro stats book (for undergrads) using all R examples
Fundamentals of Data Visualization. 2018. Claus Wilke. A wonderful online collection of best principles and practices for data viz.
Forecasting: Principles and Practice. 2018. Rob Hyndman and George Athanasaopoules. A great intro to timeseries and forecasting in R.
Meta-Analysis in R 2019. Harrer, M., Cuijpers, P., Furukawa, T.A, & Ebert, D. D.
Using Git and Github with Rstudio
Git and Github in Rstudio
Happy with Git. 2006. Jenny Bryan. Introduction to Git and Github for her class. Very detailed and walks you through each step.
Adler, J. (2009) R in a Nutshell: A Desktop Quick Reference. O’Reilly Media. http://shop.oreilly.com/product/9780596801717.do
Silver, N. (2012) The Signal and the Noise. The Penguin Press.http://www.amazon.com/dp/B007V65R54/
Bolker, B. (2009) Ecological Models and Data in R. Princeton University Press. http://www.amazon.com/Ecological-Models-Data-Benjamin-Bolker/dp/0691125228
Matloff, N. (2011) The Art of R Programming: A Tour of Statistical Software Design. No Starch Press. http://nostarch.com/artofr.htm
Song, S. Qian (2009) Environmental and Ecological Statistics with R. Chapman and Hall/CRC Press, London. http://www.amazon.com/Environmental-Ecological-Statistics-Chapman-Applied-ebook/dp/B005H6YDPU
R Weekly A weekly newsletter
R bloggers R Blogger aggregator
RStudio Blog
Simply Statistics
Statistical modeling, causal inference, and social science: Andrew Gelman’s research group
R-statistics blog
Error Statistics Philosophy Great source of information on philosophy of statistics and data analysis
Dynamic Ecology Covers many topics in analysis and philosophy of data in addition to ecology
Quantum Forest A shoebox for scribbles on data analysis by Luis Apiolaza
Inundata from Karthik Ram of ROpenSci
ROpenSci
Xi’an’s Og
Civil Statistician Former census statistician
Citizen Statistician Various stats faculty
Not So Standard Deviations Listen to this! #Rcatladies
FiveThirtyEight Elections Podcast: How we use data analysis to forecast elections.
What’s the Point: Data in society. From FiveThirtyEight.
Karthik Ram
ROpenSci
Hilary Parker
#RCatLadies
Hadley Wickham
Jenny Bryan
STAT545
Roger D. Peng
Emily Robinson
Scott Chamberlain
R-Ladies Boston
Carly Strasser
The Logic of Scientific Discovery. 1934. Karl Popper.
The Methodology of Scientific Research Programs Collected works of Irme Lakatos
Against Method Paul Feyerabend’s provocative take on sciene in context.
For and Against Method Collected correspondence/dialogue of Imre Lakatos and Paul Feyerabend Causality Judea Pearl’s plea and proof of how to assess causal logic in data analysis.