Summary

In this chapter, we learned all about getting data prepared for analysis so that you can start to run models. It starts with inputting external data in raw form, and we saw that there are several ways you can accomplish these available methods. You also learned how to generate your own data and two different ways you can use to join, or munge data together, one using SQL and the other using dplyr function.

We later proceeded to cover some basic data cleaning and data exploration techniques that are sometimes needed after your data is input, such as standardizing and transposing the data, changing the variables type, creating dummy variables, binning, and eliminating redundant data. You now know about the key R functions that are used to take a first glance at the contents of the data, as well as its structure.

We then covered the important concepts of analyzing missing values and outliers, and how to handle them.

We saw a few ways to decrease the number of variables to a manageable...