Getting to Know Your Data

Abstract

Now that you are familiar with creating data in different formats in R, we can start to discuss one of the most important steps of the data analysis process—transforming your data into what Hadley Wickham (Wickham, Journal of Statistical Software, 59(10), 1–23, 2014) calls tidy data. Fittingly, many useful functions for data tidying are in a set of packages called the tidyverse. Data tidying is a very important step that will ensure your data are in the format you need to conduct your analyses. It includes steps such as viewing data types; viewing, editing, and adding both variable labels and value labels; formatting classes; and recoding and creating new variables. Learning basic techniques to determine, for example, how different variables in your dataset are stored or whether a variable has too many missing cases can be extremely useful when you are planning what you can feasibly analyze and how to do it. In pretty much any research project involving data analysis, you can expect that your data will require some level of manipulation. We rarely receive data that are perfectly clean and set up for our purpose! Fortunately, R offers a great deal of flexibility in how to accomplish these tasks. In this section, we will walk through some examples of common data transformations you may need to perform in your own analysis while at the same time practicing the concept of levels of measurement using data from the National Crime Victimization Survey.

Key Terms

Project

A self-contained working directory.

Nominal variables

Categorical, unordered variables.

Ordinal variables

Categorical, ordered variables.

Interval/ratio variables

Numeric variables with equal intervals between values; they are functionally the same, yet ratio-level variables have a true zero.