Data Visualization for Social and Policy Research
Author: Jose Manuel Magallanes Reyes
Publisher : Cambridge University Press; New edition (April 7, 2022)
Language : English
Paperback : 292 pages
ISBN-10 : 1108714382
ISBN-13 : 9781108714389
Book Description
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 Author: 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.
下载地址:
Data Visualization for Social and Policy Research 9781108714389.rar (访问密码:1024)
Data Visualization for Social and Policy Research: A Step-by-Step Approach Using R and Python
未经允许不得转载:finelybook » Data Visualization for Social and Policy Research: A Step-by-Step Approach Using R and Python
相关推荐
- Real-World iOS by Tutorials: Professional App Development With Swift
- Core Data by Tutorials (Eighth Edition): Persisting iOS App Data with Core Data in Swift
- Learn Enough JavaScript to Be Dangerous: Write Programs, Publish Packages, and Develop Interactive Websites with JavaScript
- Mastering Python: Write powerful and efficient code using the full range of Python’s capabilities, 2nd Edition
- Augmented Reality Art: From an Emerging Technology to a Novel Creative Medium
- Mathematical Modeling and Soft Computing in Epidemiology