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<h1 class="title toc-ignore">Further Resources</h1>
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<p> </p>
<div id="learning-r-and-statistics-resources" class="section level3">
<h3>Learning R and statistics resources</h3>
<p> </p>
<p>Here are some further resources for learning R, statistics and
statistics with R. All of these resources are free and open source. This
is by no means a comprehensive list so feel free to add your own by
issuing a pull request on GitHub or <a href="People.html">contact me</a>
directly.</p>
<p>You can also find additional resources on the <a
href="https://cran.r-project.org/other-docs.html">R-Project website</a>
and also this <a
href="https://github.com/rstudio-education/rstats-ed">GitHub
repository</a>.</p>
<p> </p>
<div id="general-r-resources" class="section level4">
<h4>General R resources</h4>
<ul>
<li><p><a
href="https://cran.r-project.org/other-docs.html">R-Project</a>: User
contributed documentation</p></li>
<li><p><a href="https://journal.r-project.org/">The R Journal</a>:
Journal of the R project for statistical computing</p></li>
<li><p><a href="http://swirlstats.com/">Swirl</a>: An R package that
teaches you R from within R</p></li>
<li><p><a
href="https://www.rstudio.com/resources/cheatsheets/">RStudio’s
printable cheatsheets</a></p></li>
<li><p><a href="http://rseek.org/">Rseek</a>: A custom Google search for
R-related sites</p></li>
</ul>
<p> </p>
</div>
<div id="free-statistics-books-and-resources" class="section level4">
<h4>Free Statistics books and resources</h4>
<p> </p>
<ul>
<li><p><a href="https://learningstatisticswithr.com/book/">Learning
statistics with R</a>: A fantastic book written by Danielle Navarro
which comprehensively takes you through the basics up to more
complicated statistical approaches.</p></li>
<li><p><a href="https://bookdown.org/rdpeng/exdata/">Exploratory Data
Analysis with R</a>: Covers the essential exploratory techniques for
summarising data. Written by Roger D. Peng.</p></li>
<li><p><a href="https://www.huber.embl.de/msmb/index.html">Modern
Statistics for Modern Biology</a>: A really nice book with an
interesting take on learning and using statistics by Susan Holmes and
Wolfgang Huber.</p></li>
<li><p><a
href="https://crumplab.github.io/statistics/index.html">Answering
questions with data</a>: Aimed at Psychology students but a good read
for other students. Mostly lays out statistics in a more traditional way
but pretty comprehensive. By Matthew J. C. Crump.</p></li>
<li><p><a href="https://daviddalpiaz.github.io/appliedstats/">Applied
Statistics with R</a>: A comprehensive statistics book which also
contains specific chapters covering a range of statistical concepts by
David Dalpiaz.</p></li>
<li><p><a href="https://bookdown.org/roback/bookdown-BeyondMLR/">Beyond
Multiple Linear Regression</a>: Covering generalised linear models and
multilevel models in R but also has a good introduction to linear
models. Written by Paul Roback and Julie Legler.</p></li>
<li><p><a href="https://m-clark.github.io/mixed-models-with-R/">Mixed
Models with R</a>: A short introductory book on using and fitting mixed
models in R by Michael Clark.</p></li>
<li><p><a href="https://r4ds.had.co.nz/">R for Data Science</a>: A
tidyverse centric book for learning R and data science written by Hadley
Wickham.</p></li>
<li><p><a href="https://moderndive.com/">Statistical Inference via Data
Science</a>: Takes you from data wrangling through to inferential
statistics using the tidyverse by Chester Ismay and Albert Y.
Kim.</p></li>
</ul>
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