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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).
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