tidyverse/dplyr v0.2.0

dplyr 0.2

on GitHub

Piping

dplyr now imports %>% from magrittr (#330). 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 recieves the LHS by using 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:

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

Thanks to @smbache for the excellent magrittr package. dplyr only provides %>% from magrittr, but it contains many other useful functions. To use them, load magrittr explicitly: 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 also deprecated chain() to encourage a single style of dplyr usage: please use %>% instead.

Do

do() has been completely overhauled. There are now two ways to use it, either with multiple named arguments or a single unnamed arguments. group_by() + do() is equivalent to plyr::dlply, except it always returns a data frame.

If you use named arguments, each argument becomes a list-variable in the output. A list-variable can contain any arbitrary R object so it's particularly well suited for storing models.

library(dplyr)
models <- mtcars %>% group_by(cyl) %>% do(lm = lm(mpg ~ wt, data = .))
models %>% summarise(rsq = summary(lm)$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.

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 5 seconds and lets you know (approximately) how much longer the job will take to complete.

New verbs

dplyr 0.2 adds three new verbs:

• 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.
• sample_n() randomly samples a fixed number of rows from a tbl;
  sample_frac() randomly samples a fixed fraction of rows. Only works
  for local data frames and data tables (#202).
• summarise_each() and mutate_each() make it easy to apply one or more
  functions to multiple columns in a tbl (#178).

Minor improvements

• If you load plyr after dplyr, you'll get a message suggesting that you
  load plyr first (#347).
• as.tbl_cube() gains a method for matrices (#359, @paulstaab)
• compute() gains temporary argument so you can control whether the
  results are temporary or permanent (#382, @cpsievert)
• group_by() now defaults to add = FALSE so that it sets the grouping
  variables rather than adding to the existing list. I think this is how
  most people expected group_by to work anyway, so it's unlikely to
  cause problems (#385).
• Support for MonetDB tables with src_monetdb()
  (#8, thanks to @hannesmuehleisen).
• New vignettes:
  • memory vignette which discusses how dplyr minimises memory usage
    for local data frames (#198).
  • new-sql-backend vignette which discusses how to add a new
    SQL backend/source to dplyr.
• changes() output more clearly distinguishes which columns were added or
  deleted.
• explain() is now generic.
• dplyr is more careful when setting the keys of data tables, so it never
  accidentally modifies an object that it doesn't own. It also avoids
  unnecessary key setting which negatively affected performance.
  (#193, #255).
• print() methods for tbl_df, tbl_dt and tbl_sql gain n argument to
  control the number of rows printed (#362). They also works better when you have
  columns containing lists of complex objects.
• row_number() can be called without arguments, in which case it returns
  the same as 1:n() (#303).
• "comment" attribute is allowed (white listed) as well as names (#346).
• hybrid versions of min, max, mean, var, sd and sum
  handle the na.rm argument (#168). This should yield substantial
  performance improvements for those functions.
• Special case for call to arrange() on a grouped data frame with no arguments. (#369)

Bug fixes

• Code adapted to Rcpp > 0.11.1
• internal DataDots class protects against missing variables in verbs (#314),
  including the case where ... is missing. (#338)
• all.equal.data.frame from base is no longer bypassed. we now have
  all.equal.tbl_df and all.equal.tbl_dt methods (#332).
• arrange() correctly handles NA in numeric vectors (#331) and 0 row
  data frames (#289).
• copy_to.src_mysql() now works on windows (#323)
• *_join() doesn't reorder column names (#324).
• rbind_all() is stricter and only accepts list of data frames (#288)
• rbind_* propagates time zone information for POSIXct columns (#298).
• rbind_* is less strict about type promotion. The numeric Collecter allows
  collection of integer and logical vectors. The integer Collecter also collects
  logical values (#321).
• internal sum correctly handles integer (under/over)flow (#308).
• summarise() checks consistency of outputs (#300) and drops names
  attribute of output columns (#357).
• join functions throw error instead of crashing when there are no common
  variables between the data frames, and also give a better error message when
  only one data frame has a by variable (#371).
• top_n() returns n rows instead of n - 1 (@leondutoit, #367).
• SQL translation always evaluates subsetting operators ($, [, [[)
  locally. (#318).
• select() now renames variables in remote sql tbls (#317) and implicitly adds
  grouping variables (#170).
• internal grouped_df_impl function errors if there are no variables to group by (#398).
• n_distinct did not treat NA correctly in the numeric case #384.
• Some compiler warnings triggered by -Wall or -pedantic have been eliminated.
• group_by only creates one group for NA (#401).
• Hybrid evaluator did not evaluate expression in correct environment (#403).