Statistics
for Ecologists 2022. John Fieberg. An excellent R based overview of
most of what you’ll need for stats and analysis.
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.
Spatial Statistics for
Data Science: Theory and Practice with R 2023. Paula Moraga.
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.