I’m very excited to announce dplyr 0.2. It has three big features:

  • improved piping courtesy of the magrittr package

  • a vastly more useful implementation of do()

  • five new verbs: sample_n(), sample_frac(), summarise_each(), mutate_each and glimpse().

These features are described in more detail below. To learn more about the 35 new minor improvements and bug fixes, please read the full release notes .

Improved piping#

dplyr now imports %>% from the magrittr package by Stefan Milton Bache . I recommend that you use this instead of %.% because it is easier to type (since you can hold down the shift key) and is more flexible. With you %>%, you can control which argument on the RHS receives the LHS with the pronoun .. This makes %>% more useful with base R functions because they don’t always take the data frame as the first argument. For example you could pipe mtcars to xtabs() with:

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mtcars %>% xtabs( ~ cyl + vs, data = .)

dplyr only exports %>% from magrittr, but magrittr contains many other useful functions. To use them, load magrittr explicitly with library(magrittr). For more details, see vignette("magrittr"). %.% will be deprecated in a future version of dplyr, but it won’t happen for a while. I’ve deprecated chain() to encourage a single style of dplyr usage: please use %>% instead.

Do#

do() has been completely overhauled, and group_by() + do() is now equivalent in power to plyr::dlply(). There are two ways to use do(), either with multiple named arguments or a single unnamed arguments. If you use named arguments, each argument becomes a list-variable in the output. A list-variable can contain any arbitrary R object which makes this form of do() useful for storing models:

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library(dplyr)
models %>% group_by(cyl) %>% do(model = lm(mpg ~ wt, data = .))
models %>% summarise(rsq = summary(model)$r.squared)

If you use an unnamed argument, the result should be a data frame. This allows you to apply arbitrary functions to each group.

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mtcars %>% group_by(cyl) %>% do(head(., 1))

Note the use of the pronoun . to refer to the data in the current group. do() also has an automatic progress bar. It appears if the computation takes longer than 2 seconds and estimates how long the job will take to complete.

New verbs#

sample_n() randomly samples a fixed number of rows from a tbl; sample_frac() randomly samples a fixed fraction of rows. They currently only work for local data frames and data tables. summarise_each() and mutate_each() make it easy to apply one or more functions to multiple columns in a tbl. These works for all srcs that summarise() and mutate() work for. glimpse() makes it possible to see all the columns in a tbl, displaying as much data for each variable as can be fit on a single line.