● Materials tested over three years with several cohorts of students at different levels (UG and PGT), based on experiences teaching these materials to professional crime analysts, and developed by researchers with over 20 year experience teaching crime mapping. ● Very practical and embedded integration of criminological, spatial statistics, and cartographic concepts with the focus placed on lay understanding and development of intuition for ‘professional/applied research’ practice rather than in mathematical formulation and proof. ● Moves away from heavy US focus of competing alternatives. Datasets and examples used come from a variety of national contexts (including the US) which should broaden its appeal.

This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages. This volume will help users master the fundamentals of the R programming language, providing tutorials in each chapter that lay out research questions and hypotheses centering around a real criminal justice dataset, such as data from the National Survey on Drug Use and Health, National Crime Victimization Survey, Youth Risk Behavior Surveillance System, The Monitoring the Future Study, and The National Youth Survey. Users will also learn how to manipulate common sources of agency data, such as calls-for-service (CFS) data. The end of each chapter includes exercises that reinforce the R tutorial examples, designed to help master the software as well as to provide practice on statistical concepts, data analysis, and interpretation of results. The text can be used as a stand-alone guide to learning R or it can be used as a companion guide to an introductory statistics textbook, such as Basic Statistics in Criminal Justice (2020).

This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages. This volume will help users master the fundamentals of the R programming language, providing tutorials in each chapter that lay out research questions and hypotheses centering around a real criminal justice dataset, such as data from the National Survey on Drug Use and Health, National Crime Victimization Survey, Youth Risk Behavior Surveillance System, The Monitoring the Future Study, and The National Youth Survey. Users will also learn how to manipulate common sources of agency data, such as calls-for-service (CFS) data. The end of each chapter includes exercises that reinforce the R tutorial examples, designed to help master the software as well as to provide practice on statistical concepts, data analysis, and interpretation of results. The text can be used as a stand-alone guide to learning R or it can be used as a companion guide to an introductory statistics textbook, such as Basic Statistics in Criminal Justice (2020).