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Scientific Coordination

Verena Kunz

Administrative Coordination

Noemi Hartung

Visualizing Categorical Data with Hammock Plots

About
Location:
Mannheim, B6 4-5
General Topics
Data Analysis
Course Level
Format
Software used
Duration
Language
Fees
Students: free
Academics: free
Commercial: free
 
Keywords
Additional links
Lecturer(s): Matthias Schonlau

About the lecturer - Matthias Schonlau

Course description

Visualizing data is especially useful to get a “feel” for the data during exploratory data analysis. Familiar tools include scatterplots and scatterplot matrices, bar charts, and pie charts. It is hard to look at more than one variable at a time when categorical data are involved. The hammock plot is one such solution for categorical variables or a mix of categorical and continuous variables. I will introduce the hammock plot and give a variety of short examples where I found them useful. We will then all try it out in Stata using an example data set. You are welcome to also bring your own data set.


Target group

Participants will find the course useful if:
  • they work with data.
  • they like graphs.
  • they want to explore data rather than “just” running a model.


  • Learning objectives

    By the end of the course participants will:
  • be able to explore data graphically using hammock plots.
  • discuss parallel coordinate plots.


  • Prerequisites

  • Familiarity with Stata, Python, or R
  • Note: Instructor will demonstrate in Stata. A nearly completed Python implementation will be downloadable.  The plot is also available in R, and you can install the corresponding R package ggparallel.  The look and feel are slightly different in the R package.
  •  
    Software and hardware requirements
    For students:
    Please bring a laptop or other device with Stata (or Python, or R) installed. You need access to the internet or pre-install
  • Stata: “ssc install hammock”.  Stata 17 required (Stata 16 will likely work if you open the ado file and change all occurrences of “version 17” to “version 16”.)
  • Python: Python download instructions will be given during the course.
  • R:   ggparallel  


  • Schedule

    Recommended readings