Summary
We have been through a lot in this chapter. After all, the core of data wrangling is about the transformations that we covered here. Most of the work of analysts, data scientists, and developers involves filtering, reshaping, joining, and doing all kinds of data wrangling to get the data into the shape needed for the project.
We started by studying slicing and filtering, allowing us to zoom in on parts of a dataset and revisited the difference between both. Then, we moved on to show you how to group and summarize data, which turns out to be an important task, possibly one of the most used functions when munging data.
Replacing and filtering were the next two subjects. That section covered how to replace values and how to fill in missing data in our dataset. Once the data was cleaned of NAs, the subsequent step was ordering the data, making it easier to read and understand the information.
It is worth repeating that datasets have more information than what it may initially...
