A Grammar of Data Manipulation • dplyr
As well as these single-table verbs, dplyr also provides a variety of two-table verbs, which you can learn about in vignette("two-table"). If you are new to dplyr, the best place to start is the data …
Introduction to dplyr • dplyr - tidyverse
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 …
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 …
Keep or drop columns using their names and types — select • dplyr
Examples Here we show the usage for the basic selection operators. See the specific help pages to learn about helpers like starts_with(). The selection language can be used in functions like …
Summarise each group down to one row — summarise • dplyr
In addition, a message informs you of that choice, unless the result is ungrouped, the option "dplyr.summarise.inform" is set to FALSE, or when summarise() is called from a function in a …
Create, modify, and delete columns — mutate • dplyr
Methods This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments …
Package index • dplyr
Vector functions Unlike other dplyr functions, these functions work on individual vectors, not data frames.
Keep rows that match a condition — filter • dplyr - tidyverse
It can be applied to both grouped and ungrouped data (see group_by() and ungroup()). However, dplyr is not yet smart enough to optimise the filtering operation on grouped datasets that do …
Compute lagged or leading values — lead-lag • dplyr
Find the "previous" (lag()) or "next" (lead()) values in a vector. Useful for comparing values behind of or ahead of the current values.
Apply a function (or functions) across multiple columns — across • …
Timing of evaluation R code in dplyr verbs is generally evaluated once per group. Inside across() however, code is evaluated once for each combination of columns and groups. If the …