Using `dplyr` in R, how would an expert analyst chain commands to filter a dataset for specific criteria, group it by a categorical variable, and then calculate the average of a numeric variable for each group?

An expert analyst leverages `dplyr` within R to perform complex data manipulations by chaining commands with the pipe operator, which ensures a logical, sequential flow of operations. The pipe operator, either `%>%` from the `magrittr` package (commonly loaded with `dplyr` as part of the `tidyverse`) or `|>` from base R, passes the result of the command on its left as the first argument to the command on its right. This creates a highly readable and intuitive data processing pipeline. To filter a dataset for specific criteria, the `filter()` verb is used. `filter()` operates by selecting rows from a data frame that meet one or more specified logical conditions. For instance, to retain only records where a categorical variable like `Department` is equal to "Marketing", one would specify `filter(Department == "Marketing")`. The double ....