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Book Reviews

Submission + - Book Review: Statistical Analysis with R

lilsquirr3l writes: Statistical Analysis with R by John M. Quick is a fun beginner's guide for anyone who wants to start using R Project for Statistical Computing. It covers the fundamentals required to understand how R works and provide the basic functions for users to start using R on their own. The author assumes that the user has no prior background on any programming language or even statistics. Hence, this book is suitable for anyone who is thinking of using R but has no idea where to start.

Efficient learning of R requires the user to personally try it on their own with example dataset or a small dataset of their own work. This is typically not available in most online tutorials of R. When John Quick started publishing online tutorials on his R Tutorial Series blog with example datasets, I find it very helpful, as it was very easy to follow. In this book, he did a great job making learning R enjoyable by using a story on the wars between the three kingdoms in China. It unintentionally encourages the user to be more engaged in the learning process. He maintained the clarity and quality of his tutorials in this book.

In the first three chapters, the author covers the fundamentals of R and the basic requirement for the user to start using R. Chapter 4 through 6 is when the user actually gets to import the provided dataset into R and start exploring and analyze their data. In these chapters, the user learns how to obtain descriptive statistics for their dataset, regression analysis and model comparisons. The author focuses on the different functions that can be used rather than elaborating on the statistical analysis. While this might not be very enlightening for someone looking to learn more about statistics, I find it less overwhelming for someone who simply wants to learn how to use R. This is especially important since users from different fields have different sets of statistical tools.

One important aspect that I like in this book is that the author clearly explains all the arguments required of the function. This is very helpful for users who has no background in programming language and often finds it challenging to distinguish between user input argument and one that tells the function what to look for. Additionally, the author provides screenshots of the output from R, helping user to check that they are getting the expected output. The explanation of the output is definitely useful as different statistical software provides their output differently. The author also acknowledged some common errors that can be encountered when executing a function, helping the user to learn how to correct their functions.

Chapter 7 provides a good review chapter as it encourages the user to run all the analysis taught in previous chapters using different subset of the initial dataset. R is a program that is versatile, allowing different users to call up functions by their own preference. When applicable, the author provides alternatives and clearly clarifies it. I find this very useful as sometimes I come across the same function but written differently. For a new user, this is confusing, as one is unclear if the difference is important to note or if it is a reflection of user preferences. Readers of this book will not encounter this problem.

Chapter 8 and 9, the two most fun chapters, cover the powerful capability of R to produce graphical representations of datasets. By the end of chapter 9, the author provides enough information for the user to start exploring R on their own. The last chapter is the one chapter that users will find themselves referring back most frequently. It provides all the different ways of obtaining help for R.

The organization of each section in this book makes it easy for the user to understand each of the function the author introduces. The author first describe what information is required, provides the step by step R code and output, reiterate what the user just did and then explain the requirements for the function along with alternatives, if applicable. However, the book was written in a continuous fashion, expecting user to start from the beginning to the end like learning it in a class. Chapters 4 through 7 are based on one dataset. This book is not meant to be a quick reference, unless the user is simply looking up a line of function they had forgotten.

As a biologist who has some background in R and does a lot of statistical analysis on a regular basis, this book became too repetitive after a while. The author makes the user repeat the functions with a different variable after explaining it with one variable. It is great for beginners as it helps them to learn by repeating but I tend to skip over most of it once I learned how to do it from the explicit explanations.

Overall, I highly recommend this book for beginners, especially if you have no background on programming language. It is very easy to follow and the presented role to guide the Shu kingdom of ancient China makes it enjoyable. All the R functions introduced in this book covers the basic functions that are commonly used in R. When completed, users will find that these are the functions that they use almost every time they have a new dataset to explore. For someone who knows the basics of R and is looking to learn more about using it for robust statistical analysis, the guides in this book might be too simple.
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Book Review: Statistical Analysis with R

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