Webinar Series: Data Visualization and Display Using R Commander
Session 1: Tues February 21st | Session 2: Friday Feb 24th | Session 3: Tuesday February 28th | Session 4: Fri March 3rd
This webinar series “An Introduction to Data Visualization and Display using R Commander” provides an overview of visualization using the R language’s superb graphics tools. R is a free, open-source language and environment for statistical computing and graphics, with an extensive collection of features for data visualization. We will use both R’s native graphing capabilities and the tools in ggplot2, an R package that is easily installed. Our graphic user interface is a (free) Canadian product from McMaster University called “R Commander.” This is an SPSS-like menu-driven GUI that allows access to much of the power of R graphics without the need for programming in R. R Commander’s menu commands create and display the R code which you need. In most cases you will use this code directly to create data visualizations. Occasionally we will make some simple modifications or even write a line of new code. We will run R and R Commander within the highly-regarded web browser-like interface “R Studio.”
This series introduces users to the basic principles of graphing data, visualizing data, and effectively displaying data in documents and dashboards. The webinar series will be divided into four 2-hour sessions. Homework activities will be provided for practice between sessions.
Prior required knowledge
No previous experience with R or computer programming is required
By the end of this webinar series, participants will be able to:
- Utilize basic non-technical design principles of effective data visualization and data display based on the work of Stephen Few.
- Compare and contrast continuous and categorical variables, and understand the difference between categorical variables and ordinal variables.
- Download and install a version of R for either Windows or Macintosh.
- Navigate within the main windows of the RStudio user interface; import and export data; download and install R packages from the very large R repository, including R Commander and some of its required graphics libraries/packages.
- Use R Commander to display data consisting of one, two, three, and more variables. Graphical visualizations of data will include generating graphs and plots commonly used for data visualization and presentation, for example scatter plots, histograms and bar plots. More complex visualizations may include heat maps, correlation matrices, 3-dimensional plots, and a variety of more complex plots involving multiple variables
- Demonstrate and utilize R Commander’s tools for converting continuous variables to “binned” categorical variables, and use these to create complex graph structures where required.
- Use R Studio to create interactive graphs and “Markdown” displays and dashboards.
Session 1: Getting started with R, RStudio and R commander
- Getting started – installing R, R Studio, and R Commander
- Loading and installing packages
- Displaying textual output and errors while using R Commander
- Graphics view in R Studio
- Course practice dataset(s)
- The RStudio interface
- R Commander and its associated graphics packages
- Importing data from Excel/CSV into R; Viewing and editing data
- Binning continuous variables
- R Graphics – an Introduction
- Overview of base R graphics via R commander
- Introduction to ggplot graphics as implemented in R commander
- Printing, saving and exporting your work using R commander
- Intersession homework activities
Session 2: Principles of Visualization | Basic visualization/graphing in R
- Review of homework
- Introduction to pre-attentive attributes and their relevance to visualization of data
- Approaches to data with one variable
- Approaches to two variables – mixed continuous/categorical variable or all continuous
- Approaches to three variables – mixed continuous/categorical or all continuous
- Homework activities
Session 3: Beyond three variables | Telling Stories with Data
- Review of homework
- Approaches to four variables – one categorical (or binned)
- Approaches to five variables – two must be categorical, and one of these must be binary
- Trellis plots, heat maps, cluster dendrograms
- Brief Intro: R as a tool for GIS displays
- Homework activities
Session 4: Data Display
- Review of homework
- Dynamic Data Display
- Native R capabilities
- Internet examples
- Introduction to “Plotly” in R Studio
- Data Display: Telling stories with data
- Principles of effective data display
- Table Lenses and similar displays
- Dashboarding in R - Introduction to “Markdown”
- Using Markdown to create documents and dashboards
- Web Display of Data using R: a very brief introduction to the possibilities
- Overview: R’s “Shiny” package for web display of data
- Summary Review and final questions
Webinar-based course format
The interactive webinar software will provide remote access for students to view the instructor's screen, listen to the lecture in real time, and ask questions. The instructor will provide lecture slides (PowerPoint) for pre-reading prior to the start of the webinar. Data sets will be provided both for practice and for a course project.
Students can download the R software package for use on their computers through this site: https://www.rstudio.com/products/rstudio/download/
Course texts and pre-webinar reading
There are two highly recommended texts for this course:
- Few, Stephen. Now You See It: Simple Visualization Techniques for Quantitative Analysis, Oakland, Analytics Press, 2009.
- Chang, Winston. R Graphics Cookbook, Beijing, O’Reilly, 2013.
A third highly recommended text by Stephen Few is Information Dashboard Design, though we will not be making use of this book during the course.
The above listed texts are not a requirement of this course. However, they are all wonderful resources, and anyone who will be doing visualization of data and/or presentation of data in reports or briefing notes would benefit immensely from these resources.
It will be very helpful to have R, R Studio, R Commander, and a variety of R Commander “Plug-Ins” installed prior to class. Instructions for these installations along with brief reading suggestions from the two texts listed above (for those who have decided to purchase them or otherwise have access) will be provided prior to the start of the course.
Larry Frisch, MD MPH, is a Clinical Associate Professor at the University of British Columbia (UBC), School of Population and Public Health (SPPH). He received his MD from Harvard Medical School and his MPH from the University of Washington. Larry trained initially as a paediatrician and holds U.S. board certification in both Paediatrics and in Preventive Medicine and Public Health. Prior to returning to Canada, he held the John S. and Doris M. Andrews Chair in Community Health at Northeastern Ohio Universities College of Medicine. He has also held faculty positions at the University of Hawaii (pediatrics) and the University of Kansas (preventive medicine). In addition to numerous other teaching activities outside BC, Larry developed and has taught “Epidemiological Statistics” within the Population Health Data Analysis program offered by the University of Victoria in partnership with Population Data BC and teaches SPPH 302, “Topics in Health Care Informatics” at UBC.
- Regular rate: $265
- Student Rate: $165
For more information or to register contact:
Education and Training Lead: Ann Greenwood - firstname.lastname@example.org