Data Visualization in R

Brady West

Wednesday, August 15, 2018
12:00 - 1:30 PM CDT/ 1:00 - 2:30 PM EDT/ 10:00 - 11:30 AM PDT

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About This Course:

This AAPOR webinar will provide attendees with an overview of state-of-the-art approaches to visualizing survey data using the R software. The presentation will begin with a brief overview of importing survey data into R, and then focus on worked examples of generating various types of plots and graphical displays to effectively communicate key findings from surveys in a visual manner. The presentation will cover visualization tools in R for both weighted and unweighted survey data, and discuss the key conceptual points behind visual displays that incorporate survey weights. Resources for generating effective graphics in other popular statistical software packages will be provided as well. Attendees will receive web links to all of the data and R code used in the examples, enabling easy replication of the examples illustrated in the presentation.

 

Webinar Level:
Introductory
 

Learning Objectives:

   

• Understand key aspects of effective data visualization, including the role of sample design features
• Become familiar with critical packages in R for data visualization
• Apply the functions introduced to their own data sets and create visually effective graphics

About the Instructor:

 
Brady T. West is a Research Associate Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor (U-M) campus
He earned his PhD from the Michigan Program in Survey Methodology in 2011. Before that, he received an MA in Applied Statistics from the U-M Statistics Department in 2002, being recognized as an Outstanding First-year Applied Masters student, and a BS in Statistics with Highest Honors and Highest Distinction from the U-M Statistics Department in 2001. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, survey nonresponse, interviewer effects, and multilevel regression models for clustered and longitudinal data. He is the lead author of a book comparing different statistical software packages in terms of their mixed-effects modeling procedures (Linear Mixed Models: A Practical Guide using Statistical Software, Second Edition, Chapman Hall/CRC Press, 2014), and he is a co-author of a second book entitled Applied Survey Data Analysis (with Steven Heeringa and Pat Berglund), the second edition of which was published by Chapman Hill in June 2017.