Data Visualization Using Stata – Code Horizons Seminar

See logistics, read the course description, see the outline of the class, and see example visualizations below.

Logistics

Next offering: August 8-10, 2026

• Remote seminar offered synchronously (with asynchronous participation allowed).

• Read more details and/or register for the seminar on the Code Horizons site

Course Description

 Understanding data and effectively presenting model results are challenges that data analysts face most every day. There is seldom a more effective solution than a well thought out visualization. Problems in the data are easily identified; complex effects are quickly summarized; effect sizes and variability are immediately clear. In this seminar, we will cover best practices for accurately representing data as well as many specific approaches to data exploration, model diagnostics, and model presentation.

 The primary focus is on the applied analyst’s “bread and butter” types of visualizations: those I suspect will be useful in most every research project. However, we also cover more advanced visualization methods.

 Topics covered range from exploratory data analysis techniques to methods for presenting complex model results. Applied exercises will help participants implement the techniques we cover in Stata. Additional template Stata code will be provided to workshop participants allowing everyone to reproduce all workshop examples.

 The seminar will use Stata. Stata is widely-used to clean, examine, model, and visualize data. The data and model visualization capabilities of Stata are impressive yet vastly underutilized by most users. This seminar will teach attendees about best data visualization practices generally—and specific ways to implement these using Stata.

Outline

 Day 1

• Why visualize data?

  • The art and science of effective data visualization

• Introduction to data visualization in Stata

  • Unique benefits of Stata for visualization

  • Common options universal to most graphs

  • Commonly used Stata tools

• Plots of univariate distributions

  • Histograms

  • Kernel density plots

    • Overlays for group comparisons

  • Box (and whisker) plots

  • Violin plots

• Transforming distributions

  • Visual tools and cautions

• Plotting parts of a whole, and amounts across groups

  • Pie charts (and many cautions)

    • Perceptual accuracy and choosing plots

    • Stacked bar charts; group comparisons

  • Stacked bar charts

  • Bar charts

  • Dot plots

  • Radar/spider plots

• Confidence intervals and standard errors

  • Visual tools for conveying uncertainty

• Balance plots

  • Observational data, experimental data, and causal inference matching methods

Day 2

• Plots of bivariate relationships

  • Scatterplots

    • Options for continuous and nominal variables

    • Scatterplot smoothing

      • Lowess

        • Incorporating covariates

      • Local polynomial smoothing

o   Heat plots

§  Correlation matrices as heat plots

• Plotting change over time

  • Slopegraphs

  • Ridgeline plots (AKA joyplots)

  • Area plots

• General data visualization rules and guidelines

  • Axis range rules

  • 3D graphics

  • Using color well

    • Nominal vs ordinal palettes

    • Color blindness-proofing your graphs

    • Figures that work in color or black and white

  • Fonts

  • Graphics file formats

  • Graph schemes in Stata

  • Confidence intervals and inferring statistical significance

• Maps

  • Map projection options; pros and cons

  • Choropleth maps

  • Area vs population issues in visualization

  • World, countries, states, and counties

Day 3

• Visualizing model results

  • Coefficient plots

    • Comparing across models and/or groups

·       Plots of model predictions

o   Continuous predictors vs nominal predictors

o   Adding distributional information to plots

§  Univariate and group-specific

o   Visualization with many groups

·       Marginal effects

o   Plots of effects

§  Summaries

o   Plots of group differences

·       Interaction effects

o   Nominal x nominal interactions

o   Nominal x continuous interactions

o   Continuous x continuous interactions

o   Nonlinear interaction effects

• Diagnosing, modeling, and visualizing nonlinearities

  • Scatterplot smoothing

  • Binned scatterplots

  • Continuous variables modeled as nominal

  • Ordinal predictors

  • Splines

·       Model diagnostics

o   Residuals

o   Influence

o   Added-variable plots