A Criminologist's Guide to R

This book introduces the programming language R and covers the necessary skills to conduct quantitative research in criminology. By the end, 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.

Auteur

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.

Texte du rabat

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.

Contenu

Chapter 1 A soup to nuts project example

Chapter 2 Introduction to R and Rstudio

Chapter 3 Data types and structures

Chapter 4 Reading and writing Data

Chapter 5 Mise en place

Chapter 6 Collaboration

Chapter 7 R Markdown

Chapter 8 Testing your code

Chapter 9 Git

Chapter 10 Subsetting: Making big things small

Chapter 11 Exploratory data analysis

Chapter 12 Regular Expressions

Chapter 13 Reshaping data

Chapter 14 Graphing with ggplot2

Chapter 15 More graphing with ggplot2

Chapter 16 Hotspot maps

Chapter 17 Choropleth maps

Chapter 18 Interactive maps

Chapter 19 Webscraping with rvest

Chapter 20 Functions

Chapter 21 For Loops

Chapter 22 Scraping tables from PDFs

Chapter 23 More scraping tables from PDFs

Chapter 24 Geocoding