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Introduction to dplyr

Start here if this is your first time using dplyr. You’ll learn the basic philosophy, the most important data manipulation verbs, and the pipe, %>%, which allows you to combine multiple verbs together to solve real problems.

Grouped data

To unlock the full potential of dplyr, you need to understand how each verb interacts with grouping. This vignette shows you how to manipulate grouping, how each verb changes its behaviour when working with grouped data, and how you can access data about the “current” group from within a verb.

Two-table verbs

Most dplyr verbs work with a single data set, but most data analyses involve multiple datasets. This vignette introduces you to the dplyr verbs that work with more one than data set, and introduces to the mutating joins, filtering joins, and the set operations.

dplyr <-> base R

How does dplyr compare to base R? This vignette describes the main differences in philosophy, and shows the base R code most closely equivalent to each dplyr verb.

Automate

Column-wise operations

Learn how to easily repeat the same operation across multiple columns using across().

Row-wise operations

In R, it’s usually easier to do something for each column than for each row. In this vignette you will learn how to use the rowwise() function to perform operations by row. Along the way, you’ll learn about list-columns, and see how you might perform simulations and modelling within dplyr verbs.

Programming with dplyr

Most dplyr verbs use “tidy evaluation”, a special type of non-standard evaluation. In this vignette, you’ll learn the two basic forms, data masking and tidy selection, and how you can program with them using either functions or for loops.

Other

Window functions

Window functions are a useful family of functions that work with vectors (returning an output the same size as the input), and combine naturally with mutate() and filter().

dplyr compatibility

A guide for package authors who need to work with multiple versions of dplyr.