They increase the power of R by improving existing base R functionalities, or by adding new ones. Reprex package is meant for.R packages are collections of functions and data sets developed by the community. If you’re asking for R help, reporting a bug, or requesting a new feature, you’re more likely to succeed if you include a good reproducible example, which is precisely what the Getting Started guide or, for more detailed examples, go straight to the These packages provide a comprehensive foundation for creating and using models of all types. Tidymodels packages, which largely replace the

Modeling with the tidyverse uses the collection of Paste() that makes it easier to combine data and strings. Piping operators (like %$% and %%) that can be useful in other places. It also provide a number of more specialised Purrr, which provides very consistent and natural methods for iterating on R objects, there are two additional tidyverse packages that help with general programming challenges:

dbplyr allows you to use remote database tables by converting dplyr code into SQL.ĭata.table backend by automatically translating to the equivalent, but usually much faster, data.table code.

There are also two packages that allow you to interface with different backends using the same dplyr syntax: You’ll need to pair DBI with a database specific backends likeĭplyr, there are five packages (includingįorcats) which are designed to work with specific types of data: Readr, for reading flat files, the tidyverse package installs a number of other packages for reading data: They are not loaded automatically with library(tidyverse), so you’ll need to load each one with its own call to library(). The tidyverse also includes many other packages with more specialised usage. R uses factors to handle categorical variables, variables that have a fixed and known set of possible values. Forcats provides a suite of useful tools that solve common problems with factors.

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