# Package guide {#package-guide}

This guide offers capsule descriptions of the best R packages for doing certain kinds of computational historical work. CRAN provides [task views](https://cran.r-project.org/web/views/) that list all of the packages available for a particular purpose, such as machine learning or time series analysis. You may also find those helpful if you are looking for a certain kind of package.

## The tidyverse

The [tidyverse](https://cran.r-project.org/package=tidyverse) package is an easy way of installing and accessing a set of packages that subscribe to a "tidy data" philosophy and that work together well. It is important to understand, though, what its constituent packages do. [dplyr](https://cran.r-project.org/package=dplyr) lets you manipulate tabular data, whether in memory or via a database connection. [tidyr](https://cran.r-project.org/package=tidyr) provides additional functions that let you reshape data to get it into a tidy format. Those two packages work with an updated class of data frame provided by the [tibble](https://cran.r-project.org/package=tibble) package. [ggplot2](https://cran.r-project.org/package=ggplot2) is offers a grammar of graphics approach to data visualization. [stringr](https://cran.r-project.org/package=stringr) provides fast and consistent functions for dealing with string data. [purrr](https://cran.r-project.org/package=purrr) adds functional programming facilities to R. [forcats](https://cran.r-project.org/package=forcats) lets you deal easily with factors, R's data type for categorical data. [broom](https://cran.r-project.org/package=broom) takes many different kinds of R objects and turns them into tidy data frames. [magrittr](https://cran.r-project.org/package=magrittr) provide the pipe function (`%>%`) much used by other R packages and in this book.