Intermediate Statistics with R

Reviewed by Wei Wei, Professor, Metropolitan State University on 2/23/24

Comprehensiveness rating: 4 see less

The text covered both analysis of variance and regression, the two major areas in statistics, along with some categorical analysis method, Chi-square tests. The author introduced each chapter in detailed approaches. The author even wrote their own R package for some of the analysis. This text did not cover the nonparametric analysis area, which is helpful for analyzing small data. It will also be helpful if the author can offer more exercise questions at the end of each chapter.

Content Accuracy rating: 5

The text was written in very good quality. I did not find any bias on any of the analysis or explanations.

Relevance/Longevity rating: 5

The content is very relavant. The author used most up-to-date R functions and packages. Some of the datasets used in the examples are relatively out-dated, but those data work well to help the readers to understand the materials.

Clarity rating: 5

The text is very clearly written. All the R code and output are provided. The author did very good job of explaining the models and results. The author structured each chapter really well, in terms of model validation, data visualization, statistical analysis, and explanation of the results.

Consistency rating: 5

The entire text is structured consistently. All the formats of the R code and output are consistent throughout the text.

Modularity rating: 4

Adding subtitles to each section in each chapter can better guide the readers through the materials. Overall, the text has good modularity with dividing the complicated materials into smaller sections.

Organization/Structure/Flow rating: 5

The text flows well with the consistent organization for each chapter: introducing the concepts, validating the model , analyzing data with visualizations, and interpreting the results.

Interface rating: 5

There is no significant interface issues throughout the text. Some of the hand-written illustrations/pictures can be in better quality. But those are very minor issues and there are not many of those types of illustrations.

Grammatical Errors rating: 5

Very good quality.

Cultural Relevance rating: 5

All the examples used in this text are free of culturally sensitive issues.

Comments

This is a very well written text. Enjoyed reading it through. One comment: the author used one of their own R package, catstats, that the readers will need to install it from their GitHub repository. Hope the author will keep this package updated so the readers can continue accessing to it.

Reviewed by Eric Smith, Professor, Virginia Tech on 3/6/22

Comprehensiveness rating: 5 see less

The text covers material for a one-semester long class on applied statistics. I teach a course on biological statistics and it covers most of the areas that I teach in my course. Some topics I spend some time on are slightly covered – ANCOVA, randomized blocks, mixed models. Some material on logistic regression might be useful. However, I can easily add more material using my notes and examples. I found the material to be current, accurate and free of errors. There is also a nice set of case students for the students to look at to get a sense of what real data is like. I tend to add two things to my course - first I make a number of "how do I" examples with screenshots to help students navigate R and Rstudio. Second I have students read or try to reproduce analyses in publications so they can get a sense of "good" and "bad" analyses. The case studies at the end of the text more or less do this as well so I do not see this as a problem with the text.

Content Accuracy rating: 5

The writing is good quality and I did not find any errors or coding problems. I like the summary at the end of each chapter about highlights and coding.

Relevance/Longevity rating: 4

The book is up to date and the material is quite relevant and current. It will have to be updated regularly as it is based on R (which changes regularly). The inclusion of tidyverse is both good and bad in that some students struggle with this topic.

Clarity rating: 5

The authors have done a good job at explaining statistical analysis and the tools that are available to evaluate data. It would be nice for the authors to add some comments in the coding to help students recall what is being done although again this something easy for me to add. Comments might, for example, help explain some of the ggplot coding. I have found that coding in R is the greatest issue in my course so have numerous examples for them to follow.

Consistency rating: 5

The text is consistent in terminology and the chapters have a similar structure. Again, I like the summaries at the end of each chapter.

Modularity rating: 5

The text is free of significant interface issues, including navigation problems, distortion of images/charts, and any other display features that may distract or confuse the reader. It is quite easy to find topics and link to specific chapters. There might be some minor issues moving chapters around as the examples use code and some of the explanations are in other chapters. This is a rather minor problem though.

Organization/Structure/Flow rating: 5

I have a slightly different ordering of topics in my course however, it would not be a problem to teach most of the chapters in a different order although there are some obvious chapters that you want to teach in sequence (one-way ANOVA before two-way ANOVA or SLR before MLR). I tend to teach simple regression and correlation before ANOVA so that I can better explain the estimates that are produced in the ANOVA summary. Again, this is not a problem as the SLR chapter is relatively independent of the other chapters and can be taught earlier. Since the general structure of the text is quite sound, I can easily supplement the text with additional examples and material. The case studies section is especially useful.

Interface rating: 5

The authors used bookdown to prepare the text. I believe (once the first edition is done) this makes it easy to update, produces a consistent set of graphics and helps with navigation. To update should also be relatively easy.

Grammatical Errors rating: 5

The book has been through a number of editions so the grammar/spelling etc is good.

Cultural Relevance rating: 5

I did not find any issues.

Comments

I look forward to using this as the main text the next time I teach my class. It is a better text than many of the others that involve a first course in statistics using the R package.

Reviewed by Evidence Matangi, Assistant Professor, Taylor University on 11/15/21

Comprehensiveness rating: 4 see less

R introduction is concise, data sets introduction is clear with objectives formulating the the questions to answer from the data.
Plots for categorical variables would compliment those for quantitative variables. Grammar of graphics is condensed, more would be better for an introductory textbook. Data wrangling needs to talk about the 5 verbs and also briefly talk about piping. The ASA statement on p-values would be a great addition to this textbook. Briefly introduce loops in R.

Content Accuracy rating: 5

There is some missing information on page 97 on second bullet point for SS_Total.
page 154, Discussion section needs to be distinct as you comment on it earlier for reference.

Relevance/Longevity rating: 5

The content is so up-to-date and captivating addressing interesting problems to solve using data.

Clarity rating: 4

There is need to be intentional in introducing the R packages and its associated jargon. Distinguish between random and fixed effects and also highlight other multiple comparison tests. For example, introduce functions such as favstats and their normal associatives for the five number summary.

Consistency rating: 5

The flow from chapter to chapter and within chapters is very consistent, leading to the practice problems. The structure of the practice problems is very consistent throughout the textbook.

Modularity rating: 5

This is a typical set-up for a Statistics textbook. However, there is need to delve deeply on modern statistics topics such as bootstrap resampling and confidence intervals.

Organization/Structure/Flow rating: 5

Great organization of the topics.

Interface rating: 5

the outputs from R and their associated visuals add more value to the theoretical aspects.

Grammatical Errors rating: 5

great communication, it is very clear.

Cultural Relevance rating: 5

Awesome text.

Comments

This is a good textbook for intermediate modern statistics. Thank you for this open resource for equality in access to education.