Summary

Transformations are the core of data wrangling. Datasets are almost like living organisms that change and evolve during the wrangling process, being shaped by the transformations, which, by the way, are driven by the analysis requirements.

In this chapter, we learned about the main transformations for data wrangling in R. We started with slicing and filtering, two great functions for zooming in to a piece of the dataset for deeper analysis. Then we moved on to grouping and summarizing, the dynamic duo of the transformations, where one gathers the data into groups and the other summarizes the essence of the group in a single number or statistic. Replacing and filling was the next section, where we learned about solutions to replace values such as ? with NA, followed by functions to fill NA values with the mean for numeric variables and with the most frequent value for categorical variables.

The section about arranging data covered the use of the order() function to order...