Course notes

Modified

May 28, 2026

Annotating charts
The choice of where to place axis tick marks is a small detail that has a large effect on readability. Poorly chosen breaks can obscure patterns, force readers to…

Stylized text describing tidy data principles.

Data wrangling (I)
We’ll use the penguins dataset throughout this lesson to demonstrate {dplyr} operations. If you need a reminder of its structure:
 
Data wrangling (II)
Data for real projects rarely lives in a single table. A hospital database might have separate tables for patients, visits, diagnoses, and prescriptions. A course system…

Deep dive: coordinates + facets
A coordinate system combines the x and y position aesthetics to produce a two-dimensional location on the plot. It also determines how axes and panel backgrounds are drawn.…

Deep dive: layers (I)
Throughout this lesson we’ll use data on houses sold in Tompkins County, NY from 2022–24, scraped from Redfin.

Deep dive: layers (II)
We’ll continue using the Tompkins County home sales data from the previous lesson. Here is the full data preparation pipeline in a single pipeline:

Deep dive: stats + scales + guides
The World Bank publishes a rich and detailed set of socioeconomic indicators spanning several decades and dozens of topics. Here we focus on a few key indicators for the…

Deep dive: themes
Here is a chart made entirely with {ggplot2}, themed to match the Great British Bake Off:

Animated scatter plot showing GDP per capita on the x-axis (log scale) and life expectancy on the y-axis, with point size encoding population and color encoding country. Points move and grow as the year advances from 1952 to 2007, showing the overall upward trend in both income and longevity across all continents. Source: Animated bubble chart with R and gganimate

From static to motion: Animated charts
Animation adds a temporal or sequential dimension to a chart. The core question is whether that dimension carries meaningful information that a static chart cannot show.

First instance of the chart. Originally published in The Economist

Implementing accessibility
In early 2020, “flatten the curve” became one of the most widely shared data visualizations in history.

Diagram showing a user's browser on the left connected via arrows to an R process running locally on the right.

Interactive reporting with Shiny I
Shiny is a web application framework for R (and Python!) that allows you to build interactive web applications using R code. A Shiny app is a live, running R process: when a…
 
Interactive reporting with Shiny II
As apps grow, it is common to compute the same filtered dataset in multiple render*() calls:
 
Interactive reporting with Shiny III
Much of the interface provided by Shiny is based on Bootstrap — a widely used HTML/CSS/JavaScript framework. Shiny widgets, page layouts, and default styling all come from…

Interactivity in charts
The foundational principle of interactive data visualization comes from Ben Shneiderman’s 1996 paper “The eyes have it”:

A qualitative color palette showing distinct hues for unordered categories.

Optimizing color spaces
Color in data visualization serves distinct purposes. Using color that matches its intended purpose makes charts easier to read; mismatched color usage introduces confusion.

A Fox News chart showing top tax rates with a truncated $y$-axis that exaggerates the difference.

Qualities of great visualizations
In his book The Truthful Art, Alberto Cairo argues that a great visualization must satisfy five interconnected qualities:

A 3D pie chart with many categories, hard to read.

Telling the story with data
Data communication is not just about presenting facts. It is about telling a story — a sequence of observations presented in an order that creates understanding and, often…

The grammar of graphics
This page is a summary of A Layered Grammar of Graphics by Hadley Wickham. I strongly encourage you to read the original article in conjunction with this summary.

John Snow's 1854 map of cholera deaths in London's Soho district, showing death locations as black bars and water pump locations as dots. Deaths cluster tightly around the Broad Street pump.

Visualizing spatial data (I)
Data visualization and cartography have been intertwined since at least 1854, when Dr. John Snow mapped cholera deaths in London’s Soho district:

Diagram showing the seven simple feature geometry types: Point, Linestring, Polygon, Multipoint, Multilinestring, Multipolygon, and Geometry Collection.

Visualizing spatial data II
Raster maps provide a photographic background but give no access to the underlying geography. For complete control over the geographic elements of your geospatial…

Source: YouGov

Visualizing survey data
A poll is a survey or inquiry into public opinion conducted by interviewing a random sample of people.
 
Welcome to INFO 3312/5312
INFO 3312/5312 - Data Communication organizes the material around three interrelated questions.
 
Wrap-up: Where to go from here
The course learning objectives for INFO 3312/5312:
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