dplyr 0.4.0

New features

• add_rownames() turns row names into an explicit variable (#639).
• as_data_frame() efficiently coerces a list into a data frame (#749).
• bind_rows() and bind_cols() efficiently bind a list of data frames by
  row or column. combine() applies the same coercion rules to vectors
  (it works like c() or unlist() but is consistent with the bind_rows()
  rules).
• right_join() (include all rows in y, and matching rows in x) and
  full_join() (include all rows in x and y) complete the family of
  mutating joins (#96).
• group_indices() computes a unique integer id for each group (#771). It
  can be called on a grouped_df without any arguments or on a data frame
  with same arguments as group_by().

New vignettes

• vignette("data_frame") describes dplyr functions that make it easier
  and faster to create and coerce data frames. It subsumes the old memory
  vignette.
• vignette("two-table") describes how two-table verbs work in dplyr.

Minor improvements

• data_frame() (and as_data_frame() & tbl_df()) now explicitly
  forbid columns that are data frames or matrices (#775). All columns
  must be either a 1d atomic vector or a 1d list.

• do() uses lazyeval to correctly evaluate its arguments in the correct
  environment (#744), and new do_() is the SE equivalent of do() (#718).
  You can modify grouped data in place: this is probably a bad idea but it's
  sometimes convenient (#737). do() on grouped data tables now passes in all
  columns (not all columns except grouping vars) (#735, thanks to @kismsu).
  do() with database tables no longer potentially includes grouping
  variables twice (#673). Finally, do() gives more consistent outputs when
  there are no rows or no groups (#625).

• first() and last() preserve factors, dates and times (#509).

• Overhaul of single table verbs for data.table backend. They now all use
  a consistent (and simpler) code base. This ensures that (e.g.) n()
  now works in all verbs (#579).

• In *_join(), you can now name only those variables that are different between
  the two tables, e.g. inner_join(x, y, c("a", "b", "c" = "d")) (#682).
  If non-join colums are the same, dplyr will add .x and .y
  suffixes to distinguish the source (#655).

• mutate() handles complex vectors (#436) and forbids POSIXlt results
  (instead of crashing) (#670).

• select() now implements a more sophisticated algorithm so if you're
  doing multiples includes and excludes with and without names, you're more
  likely to get what you expect (#644). You'll also get a better error
  message if you supply an input that doesn't resolve to an integer
  column position (#643).

• Printing has recieved a number of small tweaks. All print() method methods
  invisibly return their input so you can interleave print() statements into a
  pipeline to see interim results. print() will column names of 0 row data
  frames (#652), and will never print more 20 rows (i.e.
  options(dplyr.print_max) is now 20), not 100 (#710). Row names are no
  never printed since no dplyr method is guaranteed to preserve them (#669).

  glimpse() prints the number of observations (#692)

  type_sum() gains a data frame method.

• summarise() handles list output columns (#832)

• slice() works for data tables (#717). Documentation clarifies that
  slice can't work with relational databases, and the examples show
  how to achieve the same results using filter() (#720).

• dplyr now requires RSQLite >= 1.0. This shouldn't affect your code
  in any way (except that RSQLite now doesn't need to be attached) but does
  simplify the internals (#622).

• Functions that need to combine multiple results into a single column
  (e.g. join(), bind_rows() and summarise()) are more careful about
  coercion.

  Joining factors with the same levels in the same order preserves the
  original levels (#675). Joining factors with non-identical levels
  generates a warning and coerces to character (#684). Joining a character
  to a factor (or vice versa) generates a warning and coerces to character.
  Avoid these warnings by ensuring your data is compatible before joining.

  rbind_list() will throw an error if you attempt to combine an integer and
  factor (#751). rbind()ing a column full of NAs is allowed and just
  collects the appropriate missing value for the column type being collected
  (#493).

  summarise() is more careful about NA, e.g. the decision on the result
  type will be delayed until the first non NA value is returned (#599).
  It will complain about loss of precision coercions, which can happen for
  expressions that return integers for some groups and a doubles for others
  (#599).

• A number of functions gained new or improved hybrid handlers: first(),
  last(), nth() (#626), lead() & lag() (#683), %in% (#126). That means
  when you use these functions in a dplyr verb, we handle them in C++, rather
  than calling back to R, and hence improving performance.

  Hybrid min_rank() correctly handles NaN values (#726). Hybrid
  implementation of nth() falls back to R evaluation when n is not
  a length one integer or numeric, e.g. when it's an expression (#734).

  Hybrid dense_rank(), min_rank(), cume_dist(), ntile(), row_number()
  and percent_rank() now preserve NAs (#774)

• filter returns its input when it has no rows or no columns (#782).

• Join functions keep attributes (e.g. time zone information) from the
  left argument for POSIXct and Date objects (#819), and only
  only warn once about each incompatibility (#798).

Bug fixes

• [.tbl_df correctly computes row names for 0-column data frames, avoiding
  problems with xtable (#656). [.grouped_df will silently drop grouping
  if you don't include the grouping columns (#733).
• data_frame() now acts correctly if the first argument is a vector to be
  recycled. (#680 thanks @jimhester)
• filter.data.table() works if the table has a variable called "V1" (#615).
• *_join() keeps columns in original order (#684).
  Joining a factor to a character vector doesn't segfault (#688).
  *_join functions can now deal with multiple encodings (#769),
  and correctly name results (#855).
• *_join.data.table() works when data.table isn't attached (#786).
• group_by() on a data table preserves original order of the rows (#623).
  group_by() supports variables with more than 39 characters thanks to
  a fix in lazyeval (#705). It gives meaninful error message when a variable
  is not found in the data frame (#716).
• grouped_df() requires vars to be a list of symbols (#665).
• min(.,na.rm = TRUE) works with Dates built on numeric vectors (#755)
• rename_() generic gets missing .dots argument (#708).
• row_number(), min_rank(), percent_rank(), dense_rank(), ntile() and
  cume_dist() handle data frames with 0 rows (#762). They all preserve
  missing values (#774). row_number() doesn't segfault when giving an external
  variable with the wrong number of variables (#781)
• group_indices handles the edge case when there are no variables (#867)