Managing and Understanding Data

A key early component of any machine learning project involves managing and understanding data. Although this may not be as gratifying as building and deploying models—the stages in which you begin to see the fruits of your labor—it is unwise to ignore this important preparatory work.

Any learning algorithm is only as good as its training data, and in many cases, this data is complex, messy, and spread across multiple sources and formats. Due to this complexity, often the largest portion of effort invested in machine learning projects is spent on data preparation and exploration.

This chapter approaches data preparation in three ways. The first section discusses the basic data structures R uses to store data. You will become very familiar with these structures as you create and manipulate datasets. The second section is practical, as it covers several functions that are used for getting data in and out of R. In the third section, methods for understanding data are illustrated while exploring a real-world dataset.

By the end of this chapter, you will understand:

• How to use R’s basic data structures to store and manipulate values
• Simple functions to get data into R from common source formats
• Typical methods to understand and visualize complex data

The ways R handles data will dictate the ways you must work with data, so it is helpful to understand R’s data structures before jumping directly into data preparation. However, if you are already familiar with R programming, feel free to skip ahead to the section on data preprocessing.

All code files for this book can be found at https://github.com/PacktPublishing/Machine-Learning-with-R-Fourth-Edition