Slicing and filtering
When you have a table as large as the dataset we are working with, it is very hard to look at all the observations one by one. Look how many rows and columns this dataset has:
# Dataset dimensions dim(df) [1] 32561 15
The dim() function shows the number of rows first, then the number of columns, or variables. It’s easy to see that it would take us too much time – not to mention that it is not productive as well – to look at 32,561 observations. Therefore, the tasks of slicing and filtering play a major role, acting like a magnifying glass for us to zoom in on specific parts of the data.
These tasks can sound like they’re the same, but there is a slight difference between them.
Slicing
Slicing means cutting and displaying a slice, a piece, of the dataset. A good application of this task is when we need to look at the errors of a model. In this case, it is possible to take only the observations where...
