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

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

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.