dplyr 1.0.0

Breaking changes

• bind_cols() no longer converts to a tibble, returns a data frame if the input is a data frame.

• bind_rows(), *_join(), summarise() and mutate() use vctrs coercion
  rules. There are two main user facing changes:

  • Combining factor and character vectors silently creates a character
    vector; previously it created a character vector with a warning.

  • Combining multiple factors creates a factor with combined levels;
    previously it created a character vector with a warning.

• bind_rows() and other functions use vctrs name repair, see ?vctrs::vec_as_names.

• all.equal.tbl_df() removed.

  • Data frames, tibbles and grouped data frames are no longer considered equal, even if the data is the same.

  • Equality checks for data frames no longer ignore row order or groupings.

  • expect_equal() uses all.equal() internally. When comparing data frames, tests that used to pass may now fail.

• distinct() keeps the original column order.

• distinct() on missing columns now raises an error, it has been a compatibility warning for a long time.

• group_modify() puts the grouping variable to the front.

• n() and row_number() can no longer be called directly when dplyr is not loaded,
  and this now generates an error: dplyr::mutate(mtcars, x = n()).

  Fix by prefixing with dplyr:: as in dplyr::mutate(mtcars, x = dplyr::n())

• The old data format for grouped_df is no longer supported. This may affect you if you have serialized grouped data frames to disk, e.g. with saveRDS() or when using knitr caching.

• lead() and lag() are stricter about their inputs.

• Extending data frames requires that the extra class or classes are added first, not last.
  Having the exta class at the end causes some vctrs operations to fail with a mesage like:

  Input must be a vector, not a `<data.frame/...>` object
  

• right_join() no longer sorts the rows of the resulting tibble according to the order of the RHS by argument in tibble y.

New features

• The cur_ functions (cur_data(), cur_group(), cur_group_id(),
  cur_group_rows()) provide a full set of options to you access information
  about the "current" group in dplyr verbs. They are inspired by
  data.table's .SD, .GRP, .BY, and .I.

• The rows_ functions (rows_insert(), rows_update(), rows_upsert(), rows_patch(), rows_delete()) provide a new API to insert and delete rows from a second data frame or table. Support for updating mutable backends is planned (#4654).

• mutate() and summarise() create multiple columns from a single expression
  if you return a data frame (#2326).

• select() and rename() use the latest version of the tidyselect interface.
  Practically, this means that you can now combine selections using Boolean
  logic (i.e. !, & and |), and use predicate functions with where()
  (e.g. where(is.character)) to select variables by type (#4680). It also makes
  it possible to use select() and rename() to repair data frames with
  duplicated names (#4615) and prevents you from accidentally introducing
  duplicate names (#4643). This also means that dplyr now re-exports any_of()
  and all_of() (#5036).

• slice() gains a new set of helpers:

  • slice_head() and slice_tail() select the first and last rows, like
    head() and tail(), but return n rows per group.

  • slice_sample() randomly selects rows, taking over from sample_frac()
    and sample_n().

  • slice_min() and slice_max() select the rows with the minimum or
    maximum values of a variable, taking over from the confusing top_n().

• summarise() can create summaries of greater than length 1 if you use a
  summary function that returns multiple values.

• summarise() gains a .groups= argument to control the grouping structure.

• New relocate() verb makes it easy to move columns around within a data
  frame (#4598).

• New rename_with() is designed specifically for the purpose of renaming
  selected columns with a function (#4771).

• ungroup() can now selectively remove grouping variables (#3760).

• pull() can now return named vectors by specifying an additional column name
  (@ilarischeinin, #4102).

Experimental features

• mutate() (for data frames only), gains experimental new arguments
  .before and .after that allow you to control where the new columns are
  placed (#2047).

• mutate() (for data frames only), gains an experimental new argument
  called .keep that allows you to control which variables are kept from
  the input .data. .keep = "all" is the default; it keeps all variables.
  .keep = "none" retains no input variables (except for grouping keys),
  so behaves like transmute(). .keep = "unused" keeps only variables
  not used to make new columns. .keep = "used" keeps only the input variables
  used to create new columns; it's useful for double checking your work (#3721).

• New, experimental, with_groups() makes it easy to temporarily group or
  ungroup (#4711).

across()

• New function across() that can be used inside summarise(), mutate(),
  and other verbs to apply a function (or a set of functions) to a selection of
  columns. See vignette("colwise") for more details.

• New function c_across() that can be used inside summarise() and mutate()
  in row-wise data frames to easily (e.g.) compute a row-wise mean of all
  numeric variables. See vignette("rowwise") for more details.

rowwise()

• rowwise() is no longer questioning; we now understand that it's an
  important tool when you don't have vectorised code. It now also allows you to
  specify additional variables that should be preserved in the output when
  summarising (#4723). The rowwise-ness is preserved by all operations;
  you need to explicit drop it with as_tibble() or group_by().

• New, experimental, nest_by(). It has the same interface as group_by(),
  but returns a rowwise data frame of grouping keys, supplemental with a
  list-column of data frames containing the rest of the data.

vctrs

• The implementation of all dplyr verbs have been changed to use primitives
  provided by the vctrs package. This makes it easier to add support for
  new types of vector, radically simplifies the implementation, and makes
  all dplyr verbs more consistent.

• The place where you are mostly likely to be impacted by the coercion
  changes is when working with factors in joins or grouped mutates:
  now when combining factors with different levels, dplyr creates a new
  factor with the union of the levels. This matches base R more closely,
  and while perhaps strictly less correct, is much more convenient.

• dplyr dropped its two heaviest dependencies: Rcpp and BH. This should make
  it considerably easier and faster to build from source.

• The implementation of all verbs has been carefully thought through. This
  mostly makes implementation simpler but should hopefully increase consistency,
  and also makes it easier to adapt to dplyr to new data structures in the
  new future. Pragmatically, the biggest difference for most people will be
  that each verb documents its return value in terms of rows, columns, groups,
  and data frame attributes.

• Row names are now preserved when working with data frames.

Grouping

• group_by() uses hashing from the vctrs package.

• Grouped data frames now have names<-, [[<-, [<- and $<- methods that
  re-generate the underlying grouping. Note that modifying grouping variables
  in multiple steps (i.e. df$grp1 <- 1; df$grp2 <- 1) will be inefficient
  since the data frame will be regrouped after each modification.

• [.grouped_df now regroups to respect any grouping columns that have
  been removed (#4708).

• mutate() and summarise() can now modify grouping variables (#4709).

• group_modify() works with additional arguments (@billdenney and @cderv, #4509)

• group_by() does not create an arbitrary NA group when grouping by factors
  with drop = TRUE (#4460).

Lifecycle changes

• All deprecations now use the lifecycle,
  that means by default you'll only see a deprecation warning once per session,
  and you can control with options(lifecycle_verbosity = x) where
  x is one of NULL, "quiet", "warning", and "error".

Removed

• id(), deprecated in dplyr 0.5.0, is now defunct.

• failwith(), deprecated in dplyr 0.7.0, is now defunct.

• tbl_cube() and nasa have been pulled out into a separate cubelyr package
  (#4429).

• rbind_all() and rbind_list() have been removed (@bjungbogati, #4430).

• dr_dplyr() has been removed as it is no longer needed (#4433, @smwindecker).

Deprecated

• Use of pkgconfig for setting na_matches argument to join functions is now
  deprecated (#4914). This was rarely used, and I'm now confident that the
  default is correct for R.

• In add_count(), the drop argument has been deprecated because it didn't
  actually affect the output.

• add_rownames(): please use tibble::rownames_to_column() instead.

• as.tbl() and tbl_df(): please use as_tibble() instead.

• bench_tbls(), compare_tbls(), compare_tbls2(), eval_tbls() and
  eval_tbls2() are now deprecated. That were only used in a handful of
  packages, and we now believe that you're better off performing comparisons
  more directly (#4675).

• combine(): please use vctrs::vec_c() instead.

• funs(): please use list() instead.

• group_by(add = ): please use .add
  instead.

• group_by(.dots = )/group_by_prepare(.dots = ): please use !!!
  instead (#4734).

• The use of zero-arg group_indices() to retrieve the group id for the
  "current" group is deprecated; instead use cur_group_id().

• Passing arguments to group_keys() or group_indices() to change the
  grouping has been deprecated, instead do grouping first yourself.

• location() and changes(): please use lobstr::ref() instead.

• progress_estimated() is soft deprecated; it's not the responsibility of
  dplyr to provide progress bars (#4935).

• src_local() has been deprecated; it was part of an approach to testing
  dplyr backends that didn't pan out.

• src_mysql(), src_postgres(), and src_sqlite() has been deprecated.
  We've recommended against them for some time. Instead please use the approach
  described at http://dbplyr.tidyverse.org/.

• select_vars(), rename_vars(), select_var(), current_vars() are now
  deprecated (@perezp44, #4432)

Superseded

• The scoped helpers (all functions ending in _if, _at, or _all) have
  been superseded by across(). This dramatically reduces the API surface for
  dplyr, while at the same providing providing a more flexible and less
  error-prone interface (#4769).

  rename_*() and select_*() have been superseded by rename_with().

• do() is superseded in favour of summarise().

• sample_n() and sample_frac() have been superseded by slice_sample().
  See ?sample_n for details about why, and for examples converting from
  old to new usage.

• top_n() has been superseded byslice_min()/slice_max(). See ?top_n
  for details about why, and how to convert old to new usage (#4494).

Questioning

• all_equal() is questioning; it solves a problem that no longer seems
  important.

Stable

• rowwise() is no longer questioning.

Documentation improvements

• New vignette("base") which describes how dplyr verbs relate to the
  base R equivalents (@sastoudt, #4755)

• New vignette("grouping") gives more details about how dplyr verbs change
  when applied to grouped data frames (#4779, @MikeKSmith).

• vignette("programming") has been completely rewritten to reflect our
  latest vocabulary, the most recent rlang features, and our current
  recommendations. It should now be substantially easier to program with
  dplyr.

Minor improvements and bug fixes

• dplyr now has a rudimentary, experimental, and stop-gap, extension mechanism
  documented in ?dplyr_extending

• dplyr no longer provides a all.equal.tbl_df() method. It never should have
  done so in the first place because it owns neither the generic nor the class.
  It also provided a problematic implementation because, by default, it
  ignored the order of the rows and the columns which is usually important.
  This is likely to cause new test failures in downstream packages; but on
  the whole we believe those failures to either reflect unexpected behaviour
  or tests that need to be strengthened (#2751).

• coalesce() now uses vctrs recycling and common type coercion rules (#5186).

• count() and add_count() do a better job of preserving input class
  and attributes (#4086).

• distinct() errors if you request it use variables that don't exist
  (this was previously a warning) (#4656).

• filter(), mutate() and summarise() get better error messages.

• filter() handles data frame results when all columns are logical vectors
  by reducing them with & (#4678). In particular this means across() can
  be used in filter().

• left_join(), right_join(), and full_join() gain a keep argument so
  that you can optionally choose to keep both sets of join keys (#4589). This is
  useful when you want to figure out which rows were missing from either side.

• Join functions can now perform a cross-join by specifying by = character()
  (#4206.)

• groups() now returns list() for ungrouped data; previously it returned
  NULL which was type-unstable (when there are groups it returns a list
  of symbols).

• The first argument of group_map(), group_modify() and group_walk()
  has been changed to .data for consistency with other generics.

• group_keys.rowwise_df() gives a 0 column data frame with n() rows.

• group_map() is now a generic (#4576).

• group_by(..., .add = TRUE) replaces group_by(..., add = TRUE),
  with a deprecation message. The old argument name was a mistake because
  it prevents you from creating a new grouping var called add and
  it violates our naming conventions (#4137).

• intersect(), union(), setdiff() and setequal() generics are now
  imported from the generics package. This reduces a conflict with lubridate.

• order_by() gives an informative hint if you accidentally call it instead
  of arrange() #3357.

• tally() and count() now message if the default output name (n), already
  exists in the data frame. To quiet the message, you'll need to supply an
  explicit name (#4284). You can override the default weighting to using a
  constant by setting wt = 1.

• starwars dataset now does a better job of separating biological sex from
  gender identity. The previous gender column has been renamed to sex,
  since it actually describes the individual's biological sex. A new gender
  column encodes the actual gender identity using other information about
  the Star Wars universe (@MeganBeckett, #4456).

• src_tbls() accepts ... arguments (#4485, @ianmcook). This could be a
  breaking change for some dplyr backend packages that implement src_tbls().

• Better performance for extracting slices of factors and ordered factors (#4501).

• rename_at() and rename_all() call the function with a simple character
  vector, not a dplyr_sel_vars (#4459).

• ntile() is now more consistent with database implementations if the buckets have irregular size (#4495).