Working with big data sets, tables or data frames may be overwhelming. The package dplyr provides a relatively short series of functions which allow for manipulation/transformation of tabular data such as data frames. It was created by Hadley Wickham (who has also written ggplot2) and colleagues. Here, we will learn how to efficiently use dplyr to simplify or modify data frames.
In this first chapter, we learn what dplyr is and how to install/activate it. We also learn how to use pipes.
Learn about: why dplyr?, install dplyr, cheat sheets, pipes, examples of data sets.
This section focuses on functions that help create, add, modify or transform cases/observations.
Learn about: summarize(), group_by(), filter(), slice(), arrange(), add_case(), add_rows(), ...:
This section focuses on functions that help create, add, modify and transform variables.
Learn about: add_column(), select(), pull(), bind_cols(), mutate(), transmute(), ...
This section focuses on functions that help combine whole tables or fragments of tables.
Learn about: bind_cols(), bind_rows(),