Summary
In this chapter, we learned about the basics of managing data in R. We started by taking an in-depth look at the structures used for storing various types of data. The foundational R data structure is the vector, which is extended and combined into more complex data types, such as lists and data frames. The data frame is an R data structure that corresponds to the notion of a dataset having both features and examples. R provides functions for reading and writing data frames to spreadsheet-like tabular data files.
We then explored a real-world dataset containing the prices of used cars. We examined numeric variables using common summary statistics of center and spread, and visualized relationships between prices and odometer readings with a scatterplot. Next, we examined nominal variables using tables. In examining the used car data, we followed an exploratory process that can be used to understand any dataset. These skills will be required for the other projects throughout this book.
Now that we have spent some time understanding the basics of data management with R, you are ready to begin using machine learning to solve real-world problems. In the next chapter, we will tackle our first classification task using nearest neighbor methods. You may be surprised to discover that with just a few lines of R code, a machine can achieve human-like performance on a challenging medical diagnosis task.
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