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Topic General/Theory

Instructors

Affiliated with Miami Ohio

Prof. John Bailer

Bailer is a statistics professor at Miami University in Oxford, Ohio and President of the ISI (2019-2021). His broad interests in applying statistics has led to his appointment as an affiliate member of the Departments of Biology, Media, Journalism and Film, Sociology and Gerontology and the Institute for the Environment and Sustainability. He received a PhD in Biostatistics from the University of North Carolina. He was a staff fellow at the National Institute of Environmental Health Sciences before joining the faculty at Miami University. He co-founded the Stats+Stories podcast and contributes to the Statisticians React to the News blog.

Assoc. Prof. Thomas Fisher

Tom Fisher is an Associate Professor in the Department of Statistics at Miami University in Oxford, Ohio, and is an ISI Elected Member. He received his PhD in Mathematical Sciences from Clemson University and a B.S. in Computer Science from the University of Maryland, Baltimore County. His research interest includes the application of statistical models to the environmental sciences and the development of statistical techniques for time series. He has been an R user for over 15 years and has been teaching with the tidyverse for about 5 years.

Course description

In this short course, you will learn to read and reshape a dataset from the World Bank, filter observations and select variables to produce an analysis data set and finally to produce figures. Customizing, enhancing and annotating data visualizations follows. R software with be used with functions included the tidyverse suite of packages being highlighted (dplyr, tidyr and ggplot2 in particular).

Target audience

General audience. Some exposure to using R is recommended although not strictly required.

Syllabus

This workshop will be comprised of 4 parts:

  1. Moving from an ‘ugly’ spreadsheet to a beautiful data set
  2. Exploring the characteristics of an attractive data display
  3. Understanding a conceptual model for statistical graphics
  4. Generating graphical insights from data

Upon completion of this short course, participants will be able to

  1. reshape raw data in a tidy data format using pivot_* tools
  2. filter observations and select variables to produce an analysis data set
  3. generate and annotate a figure from an analysis data set
  4. add layers based on data frames to a plot

Required software

R (tidyverse package+), RStudio: Directions for installing R, RStudio and required packages will be provided in advance of the workshop.