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<h1 class="title toc-ignore">Resources, Blogs, and More</h1>
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<div id="r" class="section level3">
<h3>R</h3>
<p><a href="https://fw8051statistics4ecologists.netlify.app/">Statistics
for Ecologists</a> 2022. John Fieberg. An excellent R based overview of
most of what you’ll need for stats and analysis.<br />
<a href="https://ismayc.github.io/rbasics-book/index.html">Getting used
to R, RStudio, and R Markdown</a> 2017. Chester Ismay. The basics.<br />
<a href="https://bookdown.org/rdpeng/rprogdatascience/">R Programming
for Data Science</a>. 2016. Roger D. Peng. Provides a more detailed
intro to basic R programming.<br />
<a href="https://bookdown.org/rdpeng/exdata/">Exploratory Data Analysis
with R</a>. 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.<br />
<a href="https://bookdown.org/csgillespie/efficientR/">Efficient R
Programming</a>. 2016. Colin Gillespe and Robin Lovelace<br />
<a href="https://leanpub.com/LittleInferenceBook/read">Statistical
Inference for Data Science</a>. 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.<br />
<a href="https://leanpub.com/regmods/read">Regression Models for Data
Science in R</a>. 2018. Brian Caffo. A wonderful primer on regression
models. Incredibly thorough.<br />
<a href="http://adv-r.had.co.nz/">Advanced R</a>. 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).<br />
<a href="https://bookdown.org/ccolonescu/RPoE4/">Principles of
Econometrics with R</a> 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.<br />
<a href="http://stat545.com/">STAT545</a> UBC Course by Jenny Bryan that
covers many similar topics to us - probably better - and more!<br />
<a href="http://tts.smac-group.com/">A Tour of Time Series Analysis with
R</a><br />
<a
href="https://ismayc.github.io/moderndiver-book/index.html">ModernDive:
An Introduction to Statistical and Data Sciences via R</a>. 2017.
Chester Ismay and Albert Y. Kim Nice intro stats book (for undergrads)
using all R examples<br />
<a href="http://serialmentor.com/dataviz/">Fundamentals of Data
Visualization</a>. 2018. Claus Wilke. A wonderful online collection of
best principles and practices for data viz.<br />
<a href="http://otexts.org/fpp2/">Forecasting: Principles and
Practice</a>. 2018. Rob Hyndman and George Athanasaopoules. A great
intro to timeseries and forecasting in R.<br />
<a
href="https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/">Meta-Analysis
in R</a> 2019. Harrer, M., Cuijpers, P., Furukawa, T.A, & Ebert, D.
D.<br />
<a href="http://paulamoraga.com/book-spatial/">Spatial Statistics for
Data Science: Theory and Practice with R</a> 2023. Paula Moraga.</p>
</div>
<div id="github" class="section level3">
<h3>Github</h3>
<p><a
href="http://www.molecularecologist.com/2013/11/using-github-with-r-and-rstudio/">Using
Git and Github with Rstudio</a><br />
<a href="http://www.datasurg.net/2015/07/13/rstudio-and-github/">Git and
Github in Rstudio</a><br />
<a href="http://happygitwithr.com/index.html">Happy with Git</a>. 2006.
Jenny Bryan. Introduction to Git and Github for her class. Very detailed
and walks you through each step.</p>
</div>
<div id="offline-books" class="section level3">
<h3>Offline Books</h3>
<p>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></p>
<p>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></p>
<p>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></p>
<p>Matloff, N. (2011) <strong>The Art of R Programming: A Tour of
Statistical Software Design.</strong> No Starch Press. <a
href="http://nostarch.com/artofr.htm"
class="uri">http://nostarch.com/artofr.htm</a></p>
<p>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></p>
</div>
<div id="blogs" class="section level3">
<h3>Blogs</h3>
<p><a href="https://rweekly.org/">R Weekly</a> A weekly newsletter<br />
<a href="http://www.r-bloggers.com/">R bloggers</a> R Blogger
aggregator<br />
<a href="https://blog.rstudio.org/">RStudio Blog</a><br />
<a href="http://simplystatistics.org/">Simply Statistics</a><br />
<a href="http://www.stat.columbia.edu/~gelman/blog/">Statistical
modeling, causal inference, and social science</a>: Andrew Gelman’s
research group<br />
<a href="http://www.r-statistics.com/">R-statistics blog</a><br />
<a href="http://errorstatistics.com/">Error Statistics
Philosophy</a> Great source of information on philosophy of statistics
and data analysis<br />
<a href="https://dynamicecology.wordpress.com/">Dynamic Ecology</a>
Covers many topics in analysis and philosophy of data in addition to
ecology<br />
<a href="http://www.quantumforest.com/">Quantum Forest</a> A shoebox for
scribbles on data analysis by Luis Apiolaza<br />
<a href="http://inundata.org/">Inundata</a> from Karthik Ram of
ROpenSci<br />
<a href="https://ropensci.org/blog/">ROpenSci</a><br />
<a href="http://xianblog.wordpress.com/">Xi’an’s Og</a><br />
<a href="http://civilstat.com/">Civil Statistician</a> Former census
statistician<br />
<a href="http://citizen-statistician.org/">Citizen Statistician</a>
Various stats faculty</p>
</div>
<div id="visualization-blogs" class="section level3">
<h3>Visualization Blogs</h3>
<p><a href="http://ilovecharts.tumblr.com/">I love charts</a></p>
</div>
<div id="podcasts" class="section level3">
<h3>Podcasts</h3>
<p><a href="https://soundcloud.com/nssd-podcast">Not So Standard
Deviations</a> Listen to this! #Rcatladies<br />
<a
href="http://fivethirtyeight.com/tag/fivethirtyeight-podcasts/">FiveThirtyEight
Elections Podcast</a>: How we use data analysis to forecast
elections.<br />
<a
href="http://fivethirtyeight.com/tag/fivethirtyeight-podcasts/">What’s
the Point</a>: Data in society. From FiveThirtyEight.</p>
</div>
<div id="twitter-feeds" class="section level3">
<h3>Twitter Feeds</h3>
<p><a href="https://twitter.com/_inundata">Karthik Ram</a><br />
<a href="https://twitter.com/ropensci">ROpenSci</a><br />
<a href="https://twitter.com/hspter">Hilary Parker</a><br />
<a href="https://twitter.com/rcatladies">#RCatLadies</a><br />
<a href="https://twitter.com/hadleywickham">Hadley Wickham</a><br />
<a href="https://twitter.com/JennyBryan">Jenny Bryan</a><br />
<a href="https://twitter.com/STAT545">STAT545</a><br />
<a href="https://twitter.com/rdpeng">Roger D. Peng</a><br />
<a href="https://twitter.com/robinson_es">Emily Robinson</a><br />
<a href="https://twitter.com/sckottie">Scott Chamberlain</a><br />
<a href="https://twitter.com/RLadiesBoston">R-Ladies Boston</a><br />
<a href="https://twitter.com/carlystrasser">Carly Strasser</a></p>
</div>
<div id="philosophy" class="section level3">
<h3>Philosophy</h3>
<p><a
href="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">The
Logic of Scientific Discovery</a>. 1934. Karl Popper.<br />
<a
href="https://www.amazon.com/Methodology-Scientific-Research-Programmes-Philosophical/dp/0521280311/ref=sr_1_5?ie=UTF8&qid=1474044943&sr=8-5&keywords=lakatos">The
Methodology of Scientific Research Programs</a> Collected works of Irme
Lakatos<br />
<a
href="https://www.amazon.com/Against-Method-Paul-Feyerabend/dp/1844674428/ref=sr_1_1?ie=UTF8&qid=1474045065&sr=8-1&keywords=Against+Method">Against
Method</a> Paul Feyerabend’s provocative take on sciene in
context.<br />
<a
href="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">For
and Against Method</a> Collected correspondence/dialogue of Imre Lakatos
and Paul Feyerabend <a
href="https://www.amazon.com/Causality-Reasoning-Inference-Judea-Pearl/dp/052189560X/ref=sr_1_1?ie=UTF8&qid=1474045137&sr=8-1&keywords=causality">Causality</a>
Judea Pearl’s plea and proof of how to assess causal logic in data
analysis.</p>
</div>
<div id="data-science" class="section level3">
<h3>Data Science</h3>
<p><a
href="https://dynamicecology.wordpress.com/2012/11/28/why-and-how-to-do-statistics-its-probably-not-why-and-how-you-think/">Why,
and how, to do statistics (it’s probably not why and how you think)</a>
<a
href="https://decisionstats.com/2016/08/23/how-do-i-shift-to-a-data-science-career/">How
do I shift to a career in Data Science?</a></p>
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