resources.Rmd

---
title: "Resources, Blogs, and More"
---

### R

[Statistics for Ecologists](https://fw8051statistics4ecologists.netlify.app/) 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](https://ismayc.github.io/rbasics-book/index.html) 2017. Chester Ismay. The basics.  
[R Programming for Data Science](https://bookdown.org/rdpeng/rprogdatascience/). 2016. Roger D. Peng. Provides a more detailed intro to basic R programming.  
[Exploratory Data Analysis with R](https://bookdown.org/rdpeng/exdata/). 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](https://bookdown.org/csgillespie/efficientR/). 2016.  Colin Gillespe and Robin Lovelace\
[Statistical Inference for Data Science](https://leanpub.com/LittleInferenceBook/read). 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](https://leanpub.com/regmods/read). 2018. Brian Caffo. A wonderful primer on regression models. Incredibly thorough.\
[Advanced R](http://adv-r.had.co.nz/). 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](https://bookdown.org/ccolonescu/RPoE4/) 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](http://stat545.com/) UBC Course by Jenny Bryan that covers many similar topics to us - probably better - and more!  
[A Tour of Time Series Analysis with R](http://tts.smac-group.com/)  
[ModernDive: An Introduction to Statistical and Data Sciences via R](https://ismayc.github.io/moderndiver-book/index.html). 2017. Chester Ismay and Albert Y. Kim  Nice intro stats book (for undergrads) using all R examples  
[Fundamentals of Data Visualization](http://serialmentor.com/dataviz/). 2018. Claus Wilke. A wonderful online collection of best principles and practices for data viz.  
[Forecasting: Principles and Practice](http://otexts.org/fpp2/). 2018. Rob Hyndman and George Athanasaopoules. A great intro to timeseries and forecasting in R.  
[Meta-Analysis in R](https://bookdown.org/MathiasHarrer/Doing_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](http://paulamoraga.com/book-spatial/) 2023. Paula Moraga.  



### Github
[Using Git and Github with Rstudio](http://www.molecularecologist.com/2013/11/using-github-with-r-and-rstudio/)\
[Git and Github in Rstudio](http://www.datasurg.net/2015/07/13/rstudio-and-github/)\
[Happy with Git](http://happygitwithr.com/index.html). 2006. Jenny Bryan. Introduction to Git and Github for her class. Very detailed and walks you through each step.  

### Offline Books

Adler, J. (2009) <strong>R in a Nutshell: A Desktop Quick Reference.</strong> O'Reilly Media. <a href="http://shop.oreilly.com/product/9780596801717.do">http://shop.oreilly.com/product/9780596801717.do</a>

Silver, N. (2012) <strong>The Signal and the Noise. </strong>The Penguin Press.<a href="http://www.amazon.com/dp/B007V65R54/">http://www.amazon.com/dp/B007V65R54/</a>

Bolker, B. (2009) <strong>Ecological Models and Data in R.</strong> Princeton University Press. <a href="http://www.amazon.com/Ecological-Models-Data-Benjamin-Bolker/dp/0691125228">http://www.amazon.com/Ecological-Models-Data-Benjamin-Bolker/dp/0691125228</a>

Matloff, N. (2011) <strong>The Art of R Programming: A Tour of Statistical Software Design.</strong> No Starch Press. http://nostarch.com/artofr.htm

Song, S. Qian (2009) <strong>Environmental and Ecological Statistics with R</strong>. Chapman and Hall/CRC Press, London. <a href="http://www.amazon.com/Environmental-Ecological-Statistics-Chapman-Applied-ebook/dp/B005H6YDPU">http://www.amazon.com/Environmental-Ecological-Statistics-Chapman-Applied-ebook/dp/B005H6YDPU</a>


### Blogs
[R Weekly](https://rweekly.org/) A weekly newsletter\
[R bloggers](http://www.r-bloggers.com/) R Blogger aggregator\
[RStudio Blog](https://blog.rstudio.org/)\
[Simply Statistics](http://simplystatistics.org/)\
[Statistical modeling, causal inference, and social science](http://www.stat.columbia.edu/~gelman/blog/): Andrew Gelman's research group\
[R-statistics blog](http://www.r-statistics.com/)\
[Error Statistics Philosophy](http://errorstatistics.com/)\ Great source of information on philosophy of statistics and data analysis\
[Dynamic Ecology](https://dynamicecology.wordpress.com/) Covers many topics in analysis and philosophy of data in addition to ecology\
[Quantum Forest ](http://www.quantumforest.com/) A shoebox for scribbles on data analysis by Luis Apiolaza  
[Inundata](http://inundata.org/) from Karthik Ram of ROpenSci  
[ROpenSci](https://ropensci.org/blog/)\
[Xi'an's Og](http://xianblog.wordpress.com/)\
[Civil Statistician](http://civilstat.com/) Former census statistician\
[Citizen Statistician](http://citizen-statistician.org/) Various stats faculty

### Visualization Blogs  
[I love charts](http://ilovecharts.tumblr.com/)

### Podcasts
[Not So Standard Deviations](https://soundcloud.com/nssd-podcast) Listen to this! #Rcatladies  
[FiveThirtyEight Elections Podcast](http://fivethirtyeight.com/tag/fivethirtyeight-podcasts/): How we use data analysis to forecast elections.  
[What's the Point](http://fivethirtyeight.com/tag/fivethirtyeight-podcasts/): Data in society. From FiveThirtyEight.  

### Twitter Feeds
[Karthik Ram](https://twitter.com/_inundata)\
[ROpenSci](https://twitter.com/ropensci)\
[Hilary Parker](https://twitter.com/hspter)\
[#RCatLadies](https://twitter.com/rcatladies)\
[Hadley Wickham](https://twitter.com/hadleywickham)\
[Jenny Bryan](https://twitter.com/JennyBryan)\
[STAT545](https://twitter.com/STAT545)\
[Roger D. Peng](https://twitter.com/rdpeng)\
[Emily Robinson](https://twitter.com/robinson_es)\
[Scott Chamberlain](https://twitter.com/sckottie)\
[R-Ladies Boston](https://twitter.com/RLadiesBoston)\
[Carly Strasser](https://twitter.com/carlystrasser)  

### Philosophy
[The Logic of Scientific Discovery](https://www.amazon.com/Logic-Scientific-Discovery-Routledge-Classics/dp/0415278449/ref=sr_1_2?ie=UTF8&qid=1474044878&sr=8-2&keywords=karl+popper). 1934. Karl Popper.\
[The Methodology of Scientific Research Programs](https://www.amazon.com/Methodology-Scientific-Research-Programmes-Philosophical/dp/0521280311/ref=sr_1_5?ie=UTF8&qid=1474044943&sr=8-5&keywords=lakatos) Collected works of Irme Lakatos\
[Against Method](https://www.amazon.com/Against-Method-Paul-Feyerabend/dp/1844674428/ref=sr_1_1?ie=UTF8&qid=1474045065&sr=8-1&keywords=Against+Method) Paul Feyerabend's provocative take on sciene in context. \
[For and Against Method](https://www.amazon.com/Against-Method-Scientific-Lakatos-Feyerabend-Correspondence/dp/0226467759/ref=sr_1_2?ie=UTF8&qid=1474044943&sr=8-2&keywords=lakatos)  Collected correspondence/dialogue of Imre Lakatos and Paul Feyerabend\ 
[Causality](https://www.amazon.com/Causality-Reasoning-Inference-Judea-Pearl/dp/052189560X/ref=sr_1_1?ie=UTF8&qid=1474045137&sr=8-1&keywords=causality) Judea Pearl's plea and proof of how to assess causal logic in data analysis.

### Data Science
[Why, and how, to do statistics (it’s probably not why and how you think)](https://dynamicecology.wordpress.com/2012/11/28/why-and-how-to-do-statistics-its-probably-not-why-and-how-you-think/)
[How do I shift to a career in Data Science?](https://decisionstats.com/2016/08/23/how-do-i-shift-to-a-data-science-career/)