🧭 Descriptive Analytics

Introduction

We will explore visualizing of different kinds of data in this set of modules.

What Are the Parts of a Data Viz?

How to pick a Data Viz?

Most Data Visualizations use one or more of the following geometric attributes or aesthetics. These geometric aesthetics are used to represent qualitative or quantitative variables from your data.

What does that mean? We can think of simple visualizations as combinations of these aesthetics. Some examples:

Aesthetic #1	Aesthetic #2	Shape	Chart Picture
Position X = Quant Variable	Position Y = Quant Variable	Points/Circles with Fixed Size and/or Line
Position X = Qual Variable	Position Y = Count of Qual Variable	Columns
Position X = Qual Variable	Position Y = Qual Variable	Rectangles, with area proportional to joint (X,Y) count
Position X = Qualitative Variable	Position Y = Rank Ordered Quant Variable	Box + Whisker, Box length proportional to Inter-Quartile Range, whisker-length proportional to upper and lower quartile resp.
Position X = Quant Variable	Position Y = Quant Variable + Quant Variable	Lines + Area between Lines

In the following set of Modules we will see how different geometries lend themselves to making charts that are meaningful in a Business context.

References

1. https://awagaman.people.amherst.edu/stat230/Stat230CodeCompilationExampleCodeUsingNHANES.pdf

2. Kyle W. Brown, R-Gallery-Book. <https://www.kyle-w-brown.com/R-Gallery/

3. Descriptive Analytics @ University of Cincinnatti Business Analytics http://uc-r.github.io/descriptive

4. Data Viz Glossary. https://observablehq.com/embed/@a10k/data-visualization-glossary?cell=*