Data Visualization for Social and Policy Research
A Step-by-Step Approach Using R and Python

Teaches creative, effective visualization techniques for tables, time series, maps, text, and networks for beginners in R and Python.

Jose Manuel Magallanes Reyes (Author)

9781108494335, Cambridge University Press

Hardback, published 7 April 2022

292 pages
23.5 x 15.6 x 2 cm, 0.54 kg

''A picture is worth a thousand words.' Nowhere is this more the case than when presenting data to policymakers in order to guide decision-making. In this book, José Manuel Magallanes Reyes shows us how, drawing upon his extensive background in social science, public policy, data science, analytics, and teaching visualization to undergraduate and graduate students in political science and public policy.' Ed Lazowska, Professor, and Bill & Melinda Gates Chair Emeritus, Paul G. Allen School of Computer Science & Engineering, Founding Director of the eScience Institute, University of Washington

All social and policy researchers need to synthesize data into a visual representation. Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analysed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book's website.

1. Introduction
Part I. Get Started: 2. Data for plotting
3. Visualization basics
Part II. Visualizing Tabular Data: 4. Insights from ONE variable
5. Insights from TWO variables
6, Insights from THREE or more variables
Part III. Beyond Tabular Data: 7. Geospatial data
8. Data from the social network.

Subject Areas: Data capture & analysis [UNC], Ethical & social aspects of IT [UBJ], Politics & government [JP], Social research & statistics [JHBC], Research methods: general [GPS]