R is a free open access programming tool that is used in Academia and industry to manage very large datasets, carry out statistical analyses and produce professional-looking figures. It’s highly flexible, meaning you can adjust anything you can dream of to your liking. It does require learning how to code, however.
If you:
1) Have never used R before, don’t know what it is, but are curious
2) Have never used R before, have heard that it’s useful to archaeologists, but don’t know where to start
3) Have used R a little bit before, got frustrated and are scared to go back, but think that you should
4) Have used R quite a bit, but want to learn about new applications
5) Are an R expert and want to help others become proficient in it
This club is for you!
We’ll be meeting once a week for 1-1.5 hours, starting on Monday the 26thof August, 2019. The first few weeks will be mostly for beginners. I will give demonstrations on how to get started with using R. You’ll learn about the R language and the basic functions you need to do statistical calculations and make figures. After the introductory sessions, we will start practicing using some of the most useful applications of R for archaeologists (or should I say Rchaeologists?). There will be a topic for each week (such as: making scatter plots, doing principal component analysis, making a graph with 8 panels). At the start of the session, I’ll show you on the screen how to set up the scripts and then you’ll get a chance to try it out for yourself. You can either use your own dataset from your project or a practice dataset that I’ll provide. The topics for each week will be chosen as we go, so if you have any suggestions for what you would like to be covered, let me know, and I will make sure to include it!
I’ll use the mailing list and this website to keep you updated about what we’re doing, so if you’re not on the list yet, send me an email at petra.vaiglova@wustl.edu, and I’ll add you.
The most useful textbook that I found for learning both statistics and R is called Discovering Statistics with R by Field, Miles and Field (2012). The writing is very accessible (very funny at times), and it’s a good resource to have when trying to decide how you are going to do your statistical analyses/visual presentation of data. https://www.discoveringstatistics.com/books/discovering-statistics-using-r/.
FALL 2019 SCHEDULE
Meeting this semester will take place on Wednesdays from 2:00-3:30 in McMillan Hall 101 and on Wednesdays at 2:00-3:30pm.
Semester plan:
Week 1 (August 26): Introduction to R language, dataframes, vectors, arguments, importing data
Week 2 (September 4): Working with dataframes (pulling out data, using subset function, levels), doing simple calculations (mean, standard deviation, t.tests)
Week 3 (September 11): Making scatter plots
Week 4 (September 18): Making more complex scatter plots, annotating figures
Week 5 (September 25): Ggplot
Week 6 (October 2): Graphing means and standard deviations, more tips for bi-variate plotting.
**No meeting on October 9 or October 16**
Week 7 (October 23): Adjusting graphical scripts, preparing figures for publication
Week 8 (October 30): Summary tables (long way and short way)
Week 9 (November 6): Using google to look up new functions and troubleshoot errors
Week 10 (November 13): Introduction to multi-variate analyses
Week 11 (November 20): Principal Component Analysis
Week 12 (December 4): Principal Coordinate Analysis
*Principal component analysis*
In a few weeks’ time, we will be using R to do multi-variate analysis (including, but not limited to, principal component analysis). Phil McElmurray will be leading these sessions, drawing on his previous experience with this type of analysis. If you have any suggestions for types of datasets you’d like to use to practice on, or if there is a graph in the literature you would like to reproduce (if the complete dataset is available online), you can email him at pmcelmurray@wustl.edu, and he will be happy to incorporate your suggestions.
SUMMER 2020 SCHEDULE
Meetings will take place every two weeks, starting on May 6th at 2pm. If you are interested, please email me and I will share a Zoom link with you.
May 6: Introduction to R language, dataframes, vectors, arguments, importing data