Basic Statistics Using R for Crime Analysis

Reviewed by Colin Wasiloff, Professor, Trine University on 9/20/24

Comprehensiveness rating: 4 see less

Choi's "Basic Statistics Using R for Crime Analysis" offers a comprehensive review of basic statistical procedures to analyze crime data through use of the R statistical programming language (R-Studio). The text begins with a user-friendly section on the install of base R and R-Studio and then progresses into the statistical analysis of 2012 General Social Survey (GSS) data. Topics range from basic descriptive statistics, graphical analysis, and inferential statistics. The inferential statistics covered include ANOVA and Simple Linear Regression (single output factor).

Content Accuracy rating: 5

Choi's latest textbook offers an accurate and up to date view of R and popular packages available for R-Studio. A thorough review concluded the accuracy of both the R-code and its relevancy to this criminal justice topic. This recent publication is noted to be written for future crime analysts who may be studying at the undergraduate level, however I would offer that this text is appropriate and value-add for certificate and graduate level students as well. The code and lessons are built such that data from any era may be imported and analyzed- a signature feature of this text.

Relevance/Longevity rating: 5

Choi's "Basic Statistics Using R for Crime Analysis" is a fantastic primer to non-programmers and programmers alike. The approachable nature of this text allows for any level of R student to gain immediate and relevant experience in both the coding language and the subject matter itself. As an added bonus, the text makes use of the popular "Haven" package which allows users of other statistical packages such as Minitab or SPSS to analyze their datasets interchangeably; a bonus for experienced statisticians and analysts looking to make the jump to the open-source R code.

Clarity rating: 4

Choi's writing style and accompanying R-code is well received and clear. The learning outcomes of each chapter are clear and the R code is presented with annotations to ensure the reader is able to easily follow along (bonus points for the youtube video link on how to set the working directory; a common and painful failure mode for new R users). One improvement Choi may consider for future versions is to include the visual outputs of the R-code such as histograms, box plots, etc. generated throughout the text.

Consistency rating: 5

Choi's "Basic Statistics Using R for Crime Analysis" is written with consistent and modern R package code and is presented in a clear and incrementally building fashion. Students will likely have the most success following the text's chapters sequentially, unless they have prior experience in R. If that is the case, this text becomes and invaluable desktop guide for basic statistical operations within R for any subject matter.

Modularity rating: 5

Based on the structure of the text, Choi's "Basic Statistics Using R for Crime Analysis" may be best suited for modular use for those with an existing familiarity to R programming or statistical computing. This text can easily operate as a standalone desktop reference for basic statistical analysis as well as common R functions such as importing, tidying, and exporting data. This text may present a slight challenge for those without a basic statistical understanding or prior training; this may be also used as a companion/addition for a intro to statistics course in some cases.

Organization/Structure/Flow rating: 5

Choi's text presents in logical, clear, and building fashion. R code blocks are appropriately shaded in gray to guide the reader. Section headers are well worded and easily identifiable by their bold and red font.

Interface rating: 5

As reviewed the text was a single .pdf file suitable for browsing on a screen or printing. There were no interface issues detected.

Grammatical Errors rating: 5

No grammatical issues were detected.

Cultural Relevance rating: 5

No culturally insensitive or offensive material was detected in this text. GSS data is coded (0, 1, 2, ...) which can be used instead of renamed variables if desired.

Comments

Excellent book from Dr. Choi.

Reviewed by Dr.Rosie Kallie, Associate Professor of Instruction, University of Texas at Arlington on 9/15/24

Comprehensiveness rating: 4 see less

This OER textbook is a cohesive yet concise book that introduces key topics that you would expect to see in an applied introductory statistics course: Graphs, Descriptive Summary Statistics, Null Hypothesis Significance Testing, Analysis of Variance (ANOVA), Correlation, and Linear Regression. In addition to an appropriate scope and sequence, the book has the added feature of being an “easy read.” The concepts are presented in a way that can be grasped by a broad audience, even by readers who do not have a background in criminology. The book is not intended as a theoretical text with derivations of each topic.
Readers are also introduced to a powerful, free, open-source statistics analysis tool called R. Whether a novice or an experienced computer coder, the numeric examples are made more accessible to all. The author not only specifies the location of pre-coded R libraries, they also show examples of how to use the libraries’ built-in functions in problem solving.
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Content Accuracy rating: 5

No errors were found in the presentation of concepts.

Relevance/Longevity rating: 4

Data-driven decision making, accessible tech tools (e.g. R), and crime topics are all hot topics in today’s American society. With the strong interweaving of different crime analysis topics in each chapter, future updates may be time-consuming.

Clarity rating: 5

With its clearly written, approachable prose, the book can be used for self-study, for review, for a quick reference book, or as a companion to a required statistics book.

Consistency rating: 5

A key observation is with the author’s use of Null Hypothesis Significance Testing (NHST). Once the NHST framework is introduced, it is repeated and adapted appropriately for different statistics concepts.

Modularity rating: 4

Once the chapter on ANOVA is presented, then the book’s readers can creatively select chapters to research. NHST and ANOVA form the foundations for the remaining chapters.

Organization/Structure/Flow rating: 5

The topics are presented in an order such that knowledge of topics builds upon each other.

Interface rating: 4

The outline view of the text (pdf) enables easy navigation throughout the textbook. Also, the active links to the data sets are very easy to navigate. Use the back arrow to return to the textbook.

Grammatical Errors rating: 4

The book’s content is written clearly. When read aloud, only a few places may benefit from a different word or phrase.

Cultural Relevance rating: 4

Crime statistics were analyzed for a specific cultural group. It could prove interesting to compare similar statistics for other cultural groups.

Comments

Suggestions to strengthen the book:
Include screenshots of R results and graphs to enable user to efficiently compare their results to the author’s results.
Add links to OER exercises, case studies, videos at the end of each chapter, to encourage further study.
Provide brief explanation for arguments to built-in functions (e.g. ggplot )