Statistics for Library and Information Services: A Primer for Using Open Source R Software for Accessibility and Visualization features seventeen chapters, organized into three parts. Each chapter illustrates how to use open source R, emphasizing the statistical model and its visual representation.

The book begins with Part I, Introduction to Statistics, which features four chapters. Chapter 1 discusses essential background information. It covers basic terminology and the basic installation of open source R. Chapter 2 develops the process of research and the research hypothesis, helping the student to identify their research question. Chapter 3 examines the types of data and collection methods so that students can determine the type of data they will need to analyze and the effect of that data on the choice of scales of measurement. Chapter 4 introduces the R interface and its command line.

Part II, Making Sense of Statistics, comprises ten chapters. Chapter 5 considers descriptive statistics, including central tendency, variation, shape of distributions, frequencies, and the standard deviation in R. Chapter 6 introduces bivariate analysis, often called one of the simplest forms of analysis that includes the analysis of two variables (denoted as X and Y). Chapter 7 introduces the concept of probability theory and its basic properties. By the end of the chapter, the student will be familiar with conditional and independence, and estimating probability using simulation. Chapter 8 discusses random variables and probability distributions, including discrete and continuous random variables, and both the mean and standard deviation of a random variable. Chapter 9 introduces the ideas of sampling, the central limit theorem, and sampling distributions for proportions.

Chapter 10 takes up confidence intervals in a population, point estimation, large-sample confidence interval in a population, and the population mean. Chapter 11 introduces hypothesis testing of the mean, testing a proportion p, testing with paired difference (dependent sample and independent sample), correlation and coefficient determination, and comparing two populations and treatments. Chapter 12 follows through with correlation and both linear and regression analysis. Chapter 13 explores inferences using the chi-square distribution, single factor ANOVA, the F test, multiple comparisons, and the Two Factor ANOVA. Chapter 14 introduces the student to Time series and predictive analysis.

Part III, Visualization in R, begins to delve more deeply into visualization. It contains three chapters. Chapter 15 discusses visualization analysis by examining the steps to produce basic visualization, including bar, line, and pie graphs. By the end of the chapter, the student will be able to employ R in order to analyze descriptive statistics distribution. Chapter 16 continues the discussion with display of visualizations by introducing a package called ggplot2 in order to produce more advanced visualization analysis. By the end of the chapter, the student will have clear methodologies and examples of designing more complex visualization. Chapter 17 ends the section by putting the design element and statistics analysis together to produce better visualization. The chapter discusses five major checklists that include supporting text, arrangement, colors, lines, and grabbing the user’s attention.