UO R Bootcamp

00 - Welcome

Wed, 20 Sep 2023 00:00:00 +0000

Welcome, everyone! We’re glad you’re here.

Slides

Useful books

Sun, 05 May 2019 00:00:00 +0100

Visualization

William Cleveland, The Elements of Graphing Data
Colin Ware, Information Visualization: Perception for Design
Stephen Kosslyn, Graph Design for Eye and Mind
Tamara Munzer, Visualization Analysis and Design
Nathan Yau
- Data Points
- Visualize This
Stephen Few, Now You See It: Simple Visualization Techniques for Quantitative Analysis
Alberto Cairo
- The Functional Art: An Introduction to Information Graphics and Visualization
- The Truthful Art: Data, Charts, and Maps for Communication
Katy Börner, Visual Insights: A Practical Guide to Making Sense of Data
Joel Katz, Designing Information: Human Factors and Common Sense in Information Design
Claus Wilke, Fundamentals of Data Visualization
Manuel Lima
- Visual Complexity: Mapping Patterns of Information
- The Book of Trees: Visualizing Branches of Knowledge
Edward Tufte

Maureen Stone, A Field Guide to Digital Color
Rudolf Arnheim, Visual Thinking
Scott McCloud, Understanding Comics: The Invisible Art

01 - Basics of R, RStudio, & R Markdown

Wed, 20 Sep 2023 00:00:00 +0000

Learning R can feel intimidating, and getting started is often the hardest part. Learning a few basics can go a long way and empower you to take the next step. And, even if you’ve been using R for a while, you can almost always learn something new and useful when going back over the fundamentals.

Slides

Useful readings

Sun, 05 May 2019 00:00:00 +0100

Papers

Brehmer M, Sedlmair M, Ingram S, Munzner T. Visualizing Dimensionally-reduced Data: Interviews with Analysts and a Characterization of Task Sequences. Proc. Beyond Time & Errors: Novel Evaluation Methods For Information Visualization (BELIV) 2014. p. 1–8.
Borland D, Taylor MR. Rainbow color map (still) considered harmful. IEEE computer graphics and applications. 2007 Mar;27(2):14–7.
Christensen DL, Baio J, Van Naarden Braun K, Bilder D, Charles J, Constantino JN, et al. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years–Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. MMWR Surveillance summaries : Morbidity and mortality weekly report Surveillance summaries / CDC. 2016 Apr 1;65(3):1–23.
Cleveland WS, McGill R. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association. 1984;79(387):531–54.
Cleveland WS, Diaconis P, McGill R. Variables on Scatterplots Look More Highly Correlated When the Scales Are Increased. Science; 1982;216(4550):1138–41.
Dörk M, Feng P, Collins C, Carpendale S. Critical InfoVis: exploring the politics of visualization. Proc. ‘CHI 2013.
Elliott, K. “39 studies about human perception in 30 minutes”; presented at OpenVis 2016.
Heer J, Bostock M. Crowdsourcing graphical perception: using mechanical turk to assess visualization design. ACM CHI ‘10; 2010. p. 203–12.
Kosara R. An Empire Built On Sand: Reexamining What We Think We Know About Visualization. in the Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization (BELIV ‘16) 2016. p. 162–8.
Krzywinski M, Altman N. Points of significance: error bars. Nat Meth. Nature Research; 2013 Oct;10(10):921–2.
Krzywinski M, Altman N. Visualizing samples with box plots. Nat Meth. 2014 Feb;11(2):119–20.
Shneiderman B. Research agenda: Visual overviews for exploratory search. Information Seeking Support Systems. 2008;11:4.
Sedlmair M, Meyer M, Munzner T. Design Study Methodology: Reflections from the Trenches and the Stacks. IEEE Transactions on Visualization and Computer Graphics. 2012 Dec;18(12):2431–40.
Sedlmair M, Tatu A, Munzner T, Tory M. A Taxonomy of Visual Cluster Separation Factors. Computer Graphics Forum. 2012;31(3pt4):1335–44.
Sedlmair M, Munzner T, Tory M. Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices. IEEE Transactions on Visualization and Computer Graphics. 2013 Dec;19(12):2634–43.
Weissgerber TL, Milic NM, Winham SJ, Garovic VD. Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biol. Public Library of Science; 2015 Apr;13(4):e1002128.

Blog posts, etc.

The Why Axis’s “Mind the Gap: An Economic Chart Remake”, by Jon Schwabish
Notes on the Arabic FedEx logo.
Jenny Cham’s delightful “sketchnotes” from a 2014 seminar by the incomparable Tamara Munzner.
Randy Olson’s illustration of Small Multiples vs. Animated GIFs.
Visual.ly’s Color Emotion Guide
I am very interested in William Playfair and the history of data visualization:
- Atlas Obscura, The Scottish Scoundrel Who Changed How We See Data
- Lauren Klein, Repairing William Playfair at the MLA
- Georgia Tech’s Digital Humanities Lab, Repairing William Playfair
Neil Richards, When is a visualisation a call to action?
Carl Zimmer (yes, the Carl Zimmer) on genome graphs: As DNA reveals its secrets, scientists are assembling a new picture of humanity, with numerous examples of creative new ways to visualize genetic variation.
Ben Jones, “From Simple Tables to Multiple Vizzes”: an illustration of ten different ways to visualize the same table. What different stories pop out from the different visualizations?
Kristoffer Magnusson, How to tell when error bars correspond to a significant p-value
Kyle Walker, Should you visualize data with a map or a chart? Thoughts from teaching introductory geography
Lazaro Gamio (Washington Post), Election maps are telling you big lies about small things
Cartonerd, The NYT election map
vis4.net, Why we didn’t use a cartogram in the Brexit map
Benjamin Hennig, Cartogram Special – Brexit
Tom Pearson (Financial Times), Communicating with data — How the FT explained Brexit: “From early sketch to final dashboard, FT data visualisation experts explain the process”

Useful websites

Sun, 05 May 2019 00:00:00 +0100

Visualising Data’s “Resources” index
Matt Brehmer’s Monster List Of Resources (from UBC)
OHSU Communications Office (Graphic Design services, etc.)
HelpMeViz (crowdsourced data visualization help)

Blogs

References & Examples

R Graph Compendia/Catalogs:
A Compendium of Clean Graphs in R, by Eric-Jan Wagenmakers and Quentin F. Gronau (note: in “Base R”)
R Graph Catalog, by Joanna Zhao and Jennifer Bryan (note: in ggplot2)
Markdown Tutorial
Summer 2016 ggplot OHSU Data Jamboree
A useful tutorial on using the Seaborn Python library for visualizing distributions.
A Dramatic Tour through Python’s Data Visualization Landscape (including ggplot and Altair)
The Washington Post’s “demographic tug-of-war” visualization is a lovely illustration of how not to appropriately use color saturation and hue to encode information, and also has misleading axes (both in unit spacing and direction)!
The New York Times’ interactive “The 1,024 Ways Clinton or Trump Can Win the Election” is a great use of interactivity as a data exploration tool.
Also from the NYT, “Your Surgeon Is Probably a Republican, Your Psychiatrist Probably a Democrat” makes excellent use of a graphs and charts to tell a very clear story. Also note their detailed description at the end of the article of how the data were collected!
The Washington Post’s “One Hundred Years of Hurricanes” is a nice example of small-multiples cartography along with color.
The New York Times’ “What Good Marathons and Bad Investments Have in Common” illustrates a good use of contrast and preattentive processing (plus is a very interesting story!).
From Our World In Data, a nice example of using color and saturation to highlight certain information, in a terrifying plot about Life Expectancy vs. Health Expenditure Over Time.
Myriahedral map projections are interesting.
Tilegrams are a good alternative to a cartogram.
We do not recommend making bivariate chloropleth maps, but if you must, here’s a good tutorial on how to do it and what some of the considerations are.

Resources & Tools

ColorBrewer
Adobe Kuler
Colorgorical
Photochrome
Lyra
Packages:
- janitor
- Vincent (Python-to-Vega generation library)
- mpld3, an implementation of matplotlib on top of d3.js.

02 - Data Types & Structures

Wed, 20 Sep 2023 00:00:00 +0000

Data comes in many different shapes and sizes, which means we need a way to represent different kinds of data in R in order to distinguish them. Today, we’re going to cover the different fundamental types of data in R and give you a feel for different ways that data can be structured and indexed.

Slides

03 - Functions, Packages, & Debugging

Wed, 20 Sep 2023 00:00:00 +0000

Functions are the “verbs” of R—they allow you to actually do interesting things with your data. We will cover the basics of how to use functions in R, how to get access to different functions by downloading packages, some general principles for what to do when you run into problems.

Slides

04 - Piping

Wed, 20 Sep 2023 00:00:00 +0000

Before we get started importing, wrangling, tidying, and visualizing data, we should talk about a powerful tool for chaining functions together: pipes.

Slides

05 - Introduction to the Tidyverse

Wed, 20 Sep 2023 00:00:00 +0000

So far we have been using functions in base R. Now we’re going to take a first look at the tidyverse—a widely used framework for doing the full spectrum of data analysis, from importing to cleaning, visualizing, and modelling data.

Slides

06 - Importing Data & Project-Oriented Workflows

Wed, 20 Sep 2023 00:00:00 +0000

The first step of any data analysis workflow is to get data into R. This isn’t always as straightforward as you might think, but, fortunately, there are some core functions that make this easy and efficient. Since we are starting at the beginning, we will also discuss the idea of a project-oriented workflow, which is a way to keep an organized and consistent process whenever you work with data in R that will also make your work reproducible and shareable. And the decisions you make right at the start of a data analysis project—even before importing your data—will have a lot of down-stream consequences.

Slides

07 - Data Wrangling with {dplyr}

Wed, 20 Sep 2023 00:00:00 +0000

When you are given data to analyze, it will almost always be in a format that makes it hard to create visualizations, perform modelling, and generate tables. In other words, most of the time, it will need to be wrangled into the correct format. The dplyr package has a very powerful set of functions for doing just this. Today we will be covering the core dplyr “verbs” that allow you to transform your data with optimal specificity and efficiency.

Slides

08 - Data Tidying with {tidyr}

Wed, 20 Sep 2023 00:00:00 +0000

The concept of tidy data is, as the name suggests, of primary importance in the tidyverse. This lesson will introduce you to the criteria of tidy data, why it’s important, and how to reshape your raw data into a tidy format using pivot_wider() and pivot_longer().

Slides

09 - Data Visualization with {ggplot2}

Wed, 20 Sep 2023 00:00:00 +0000

Data visualization is at the very core of science. In order to understand and glean insights from our data, we need different ways of representing it visually. R has an incredible capacity for creating all sorts of plots, charts, and tables, and today we will only scratch the surface. We will discuss the fundamentals of the powerful ggplot2 package and the “grammar of graphics” that underlies it.

Slides

10 - R Tips & Tricks

Wed, 20 Sep 2023 00:00:00 +0000

For this final topic, we will cover some tips and tricks that will help you become an R power user. This includes customizing how R Studio looks, introducing obscure (but helpful) functions, and some shameless self-promotion on the part of Cameron.

Slides

Prework

Fri, 11 Sep 2020 11:17:19 -0700

Hello! Welcome to the 6th Annual UO R Bootcamp. There are a few things we’d like you to do before we get started to make things run as smoothly as possible.

If possible, please try to complete this checklist before the first UO R Bootcamp session. Thank you!

Join the UO Psychology Slack. The slack channel will help us help you troubleshoot any issues you have with your code during the camp and to answer any questions you have. It is also the best place to ask questions outside of the scheduled bootcamp hours. This is the department’s multipurpose slack workspace, and we’ll be using the channel #rbootcamp. You should have received an email invitation to join the UO Psychology workspace. If you haven’t received an invitation, send Sarah an email at sdimakis@uoregon.edu.
Download and install both R and RStudio. R is free and, for the purposes of the bootcamp, you will only need the free version of RStudio. If you have any trouble downloading or opening R or RStudio, please let one of the UO R Bootcamp team members know, and we will do our best to help you solve the issue.
Download the exercises for the UO R Bootcamp. We will be working through the materials and exercises together during the bootcamp.

Looking forward to seeing you all soon. Hopefully we’ll have you loving R in no time!

-The UO R Bootcamp Team

Resources

Fri, 11 Sep 2020 11:17:19 -0700

Below is a curated list of resources we’ve found to be particularly useful and accessible. If you come across other great resources that you think you should be on this list, please let us know!

Books

R for Data Science—thorough and accessible, includes exercises and community-contributed solutions
Modern Dive—great for complete beginners, more focused on stats and modelling
The Tidyverse Style Guide—for getting into the habit of using best practices early on
Big Book of R—a collection of pretty much any R-related book that’s out there (categorized by topic and searchable)

Websites

RStudio Education’s learning resources and blog—a veritable treasure trove!
RStudio cheatsheets—worth printing ones you commonly use
tidyverse.org—links to learning resources, package documentation, and a great blog to stay up to date
learnr4free—a searchable site for all sorts of learning resources

Interactive tutorials

RBootcamp—a free online course about the basics of the tidyverse
RStudio primers—these, along with other tutorials, can also be run in the Tutorial pane in the RStudio IDE
Teacup Giraffes—more stats focused, with incredible artwork and adorable tiny giraffes 🦒

Online communities

R for Data Science Online Learning Community—super friendly and welcoming to R users at all levels
#rstats Twitter—also very friendly and inclusive (despite being Twitter). Good advice on this here
Tidy Tuesday—a weekly community-based data viz challenge on Twitter. Great for hands-on, self-directed practice!
RStudio Community—great for asking for help from knowledgable experts

University of Oregon

5-course Educational Data Science Specialization offered by UO College of Education. See course websites here and info sheet below. Cannot recommend highly enough! ⭐ ⭐ ⭐ ⭐ ⭐
UO Psych Data Analysis Sequence (PSY611, PSY612, PSY613), led by Sara Weston and Elliot Berkman. See course websites here—you will learn a TON.
UO Libraries Data Services Department offers free one-on-one support for R and statistics, as well as free R workshops. Every Friday at noon, they also host Coffee + Data && Code in the DREAM Lab, providing an opportunity to learn from your peers and share your latest coding challenges and victories. ☕
SlackRs workspace—use this to ask for help from other UO community members when you’re stuck on something or need some advice.
Each other! As grad students, one of the best resources you have access to is your peers. You can read more of Brendan Cullen’s thoughts about this here. When in doubt, ask for help—and pay it forward if/whenever you can. ❤️

UO R Bootcamp

00 - Welcome

Slides

Useful books

Visualization

Related Topics

01 - Basics of R, RStudio, & R Markdown

Slides

Further Reading

Useful readings

Papers

Blog posts, etc.

Useful websites

Blogs

References & Examples

Resources & Tools

02 - Data Types & Structures

Slides

Further Reading

03 - Functions, Packages, & Debugging

Slides

Further Reading

04 - Piping

Slides

Further Reading

05 - Introduction to the Tidyverse

Slides

Further Reading

06 - Importing Data & Project-Oriented Workflows

Slides

Further Reading

07 - Data Wrangling with {dplyr}

Slides

Further Reading

08 - Data Tidying with {tidyr}

Slides

Further Reading

09 - Data Visualization with {ggplot2}

Slides

Further Reading

10 - R Tips & Tricks

Slides

Further Reading

Prework

Resources

Books

Websites

Interactive tutorials

Online communities

University of Oregon