Part 2. Basic methods

In part 1, we explored the R environment and discussed how to input data from a wide variety of sources, combine and transform it, and prepare it for further analyses. Once your data has been input and cleaned up, the next step is typically to explore each variable one at a time. This provides you with information about the distribution of each variable, which is useful in understanding the characteristics of the sample, identifying unexpected or problematic values, and selecting appropriate statistical methods. Next, a subset of variables is typically studied two at a time. This step can help you to uncover basic relationships among variables, and is a useful first step in developing more complex models.

Part 2 focuses on graphical and statistical techniques for obtaining basic information about data. Chapter 6 describes methods for visualizing the distribution of individual variables. For categorical variables, this includes bar plots, pie charts, and the newer fan plot. For numeric variables, this includes histograms, density plots, box plots, dot plots, and the less well-known violin plot. Each type of graph is useful for understanding the distribution of a single variable.

Chapter 7 describes statistical methods for summarizing individual variables and bivariate relationships. This chapter starts with coverage of descriptive statistics for numerical data based on the dataset as a whole, and on subgroups of interest. Next, the use of frequency tables and cross-tabulations for summarizing categorical data is described. The chapter ends with coverage of basic inferential methods for understanding relationships between two variables at a time, including bivariate correlations, chi-square tests, t-tests, and nonparametric methods.

When you have finished part 2, you will be able to use basic graphical and statistical methods available in R to describe your data, explore group differences, and identify significant relationships among variables.