Editing Statistics and Statistical Programming (Fall 2020) (section)

=== Textbook, readings, and resources ===

This class will use a freely-licensed textbook:

* Diez, David M., Christopher D. Barr, and Mine Çetinkaya-Rundel. 2019. [https://www.openintro.org/book/os/ ''OpenIntro Statistics'']. 4th edition. OpenIntro, Inc.

The texbook (in any format) is required for the course. You can [https://www.openintro.org/go?id=os4&referrer=/book/os/index.php download it] at no cost and purchase hard copy versions in either [https://www.openintro.org/go?id=os4_color_pb&referrer=/book/os/index.php full color ($60)] or in [https://www.openintro.org/go?id=os4_bw_pb&referrer=/book/os/index.php black and white ($20)]. The B&W version is very affordable and I strongly recommend buying a hard copy for the purposes of the course and subsequent reference use. The book is excellent and has been adopted widely. It has also developed a large online community of students and teachers who have shared other resources. Lecture slides, videos, notes, and more are all freely licensed (many through the website and others elsewhere).

I will also assigning several chapters from the following:

* Reinhart, Alex. 2015. ''Statistics Done Wrong: The Woefully Complete Guide''. SF, CA: No Starch Press. ([https://search.library.northwestern.edu/primo-explore/fulldisplay?docid=01NWU_ALMA51732460650002441&context=L&vid=NULVNEW&search_scope=NWU&tab=default_tab&lang=en_US Safari online via NU libraries])

This book provides a readable conceptual introduction to some common failures in statistical analysis that you should learn to recognize and avoid. It was also written by a Ph.D. student. You have access to an electronic copy via the NU library (you'll need to sign-in and/or use the NU VPN to access it), but you may find it helpful to purchase as well.

A few other books may be useful resources while you're learning to analyze, visualize, and interpret statistical data with R. I will share some advice about these during the first class meeting:

* Healy, Kieran. 2019. ''Data Visualization: A Practical Introduction''. Princeton, NJ: Princeton UP. ([https://kieranhealy.org/publications/dataviz/ via Healy's website])
* Teetor, Paul. 2011. ''R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics''. 1 edition. Sebastopol, CA: O’Reilly Media. ([http://proquest.safaribooksonline.com/9780596809287 Safari Proquest/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]; [https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_1?ie=UTF8&qid=1482802812&sr=8-1&keywords=r+cookbook Amazon])
* Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. ([https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Various Sources]; [https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Amazon])
* Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources])
* Wickham, Hadly and Grolemund, Garret. 2017. ''R for Data Science''. Sebastopol, CA: O'Reilly. ([https://r4ds.had.co.nz/ Online version]).

There are also some invaluable non-textbook resources:

* [ftp://cran.r-project.org/pub/R/doc/contrib/Baggott-refcard-v2.pdf Baggott's R Reference Card v2] — Print this out. Take it with you everywhere and look at it dozens of times a day. You will learn the language faster!
* [https://stackoverflow.com/questions/tagged/r StackOverflow R Tag] — Somebody already had your question about how to do ''X'' in R. They asked it, and several people have answered it, on StackOverflow. Learning to read this effectively will take time but as build up some basic familiarity with R and with StackOverflow, it will get easier. I promise.
* [http://rseek.org/ Rseek] — Rseek is a modified version of Google that just searches R websites online. Sometimes, R is hard to search because R is a common letter. This has become much easier over time as R has become more popular, but it can still be an issue sometimes and Rseek is a good solution.
* [https://ggplot2.tidyverse.org/ ggplot2 documentation] — ggplot is a powerful data visualization package for R that I recommend highly. The documentation is indispensable for learning how to use it.
* [https://depts.washington.edu/acelab/proj/Rstats/index.html Statistical Analysis and Reporting in R] — A set of resources created and distributed by Jacob Wobbrock (University of Washington, School of Information) in conjunction with a MOOC he teaches. Contains cheatsheets, code snippets, and data to help execute commonly encountered statistical procedures in R.
* [https://www.datacamp.com DataCamp] offers introductory R courses. Northwestern usually has some free accounts that get passed out via Research Data Services each quarter. Apparently, if you are taking or teaching relevant coursework, instructors can [https://www.datacamp.com/groups/education request] free access to DataCamp for their courses from DataCamp. If folks are interested in this, I can reach out.

Computing resources:
* If you are planning to analyze large-scale data (i.e., data that won't fit in memory on your laptop) then you will want to sign up for a research allocation on Quest, which is Northwestern's high-performance computing cluster. Instructions on how to do that are [[Statistics_and_Statistical_Programming_(Spring_2019)/Quest_at_Northwestern|here]].