A Criminologist's Guide to R
Crime by the Numbers
1st Edition
Published on 15. December 2022
432 pages
978-1-000-62894-4 (ISBN)
Description
A Criminologist's Guide to R: Crime by the Numbers introduces the programming language R and covers the necessary skills to conduct quantitative research in criminology. By the end of this book, a person without any prior programming experience can take raw crime data, be able to clean it, visualize the data, present it using R Markdown, and change it to a format ready for analysis. A Criminologist's Guide to R focuses on skills specifically for criminology such as spatial joins, mapping, and scraping data from PDFs, however any social scientist looking for an introduction to R for data analysis will find this useful.
Key Features:
Introduction to RStudio including how to change user preference settings.
Basic data exploration and cleaning - subsetting, loading data, regular expressions, aggregating data.
Graphing with ggplot2.
How to make maps (hotspot maps, choropleth maps, interactive maps).
Webscraping and PDF scraping.
Project management - how to prepare for a project, how to decide which projects to do, best ways to collaborate with people, how to store your code (using git), and how to test your code.
Key Features:
Introduction to RStudio including how to change user preference settings.
Basic data exploration and cleaning - subsetting, loading data, regular expressions, aggregating data.
Graphing with ggplot2.
How to make maps (hotspot maps, choropleth maps, interactive maps).
Webscraping and PDF scraping.
Project management - how to prepare for a project, how to decide which projects to do, best ways to collaborate with people, how to store your code (using git), and how to test your code.
Reviews / Votes
"While many introductory R books use heterogeneous examples, here, the author did a great job introducing the R programming language using examples from criminology in a homogenous way. This book also offers a valuable compendium of crime related datasets for those already familiar programming in R. Specially, for graduate students, researchers, and data scientists, that wish to conduct more complex analyses on these types of data."- Enrique Garcia-Ceja, Tecnologico de Monterrey, Mexico, Technometrics, November 2023.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
2 Tables, black and white; 4 Line drawings, color; 45 Line drawings, black and white; 29 Halftones, color; 89 Halftones, black and white; 33 Illustrations, color; 134 Illustrations, black and white
ISBN-13
978-1-000-62894-4 (9781000628944)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
12/2022
1st Edition
Chapman & Hall/CRC
€129.23
Shipment within 10-20 days
Person
Jacob Kaplan is the Chief Data Scientist of the Research on Policing Reform and Accountability (RoPRA), a multi-disciplinary, multi-institutional team of social scientists studying the feasibility and efficacy of policing reform, with a focus on statistically rigorous research and practical applications. His current appointment is at the Princeton School of Public and International Affairs. He holds a PhD and a master's degree in criminology from the University of Pennsylvania and a bachelor's degree in criminal justice from California State University, Sacramento. He is the author of several R packages that make it easier to work with data, including fastDummies and asciiSetupReader. He is also the author of books on the two primary criminal justice data sets: the FBI's Uniform Crime Reporting (UCR) Program Data, and the FBI's National Incident Based Reporting System (NIBRS) data.
Content
1 A soup to nuts project example 2 Introduction to R and Rstudio 3 Data types and structures 4 Reading and writing Data 5 Mise en place 6 Collaboration 7 R Markdown 8 Testing your code 9 Git 10 Subsetting: Making big things small 11 Exploratory data analysis 12 Regular Expressions 13 Reshaping data 14 Graphing with ggplot2 15 More graphing with ggplot2 16 Hotspot maps 17 Choropleth maps 18 Interactive maps 19 Webscraping with rvest 20 Functions 21 For Loops 22 Scraping tables from PDFs 23 More scraping tables from PDFs 24 Geocoding
