dplyr 1.0.5

• Fixed edge case of slice_sample() when weight_by= is used and there
  0 rows (#5729).

• across() can again use columns in functions defined inline (#5734).

• Using testthat 3rd edition.

• Fixed bugs introduced in across() in previous version (#5765).

• group_by() keeps attributes unrelated to the grouping (#5760).

• The .cols= argument of if_any() and if_all() defaults to everything().

dplyr 1.0.4

• Improved performance for across(). This makes summarise(across()) and
  mutate(across()) perform as well as the superseded colwise equivalents (#5697).

• New functions if_any() and if_all() (#4770, #5713).

• summarise() silently ignores NULL results (#5708).

• Fixed a performance regression in mutate() when warnings occur once per
  group (#5675). We no longer instrument warnings with debugging information
  when mutate() is called within suppressWarnings().

dplyr 1.0.3

• summarise() no longer informs when the result is ungrouped (#5633).

• group_by(.drop = FALSE) preserves ordered factors (@brianrice2, #5545).

• count() and tally() are now generic.

• Removed default fallbacks to lazyeval methods; this will yield better error messages when
  you call a dplyr function with the wrong input, and is part of our long term
  plan to remove the deprecated lazyeval interface.

• inner_join() gains a keep parameter for consistency with the other
  mutating joins (@patrickbarks, #5581).

• Improved performance with many columns, with a dynamic data mask using active
  bindings and lazy chops (#5017).

• mutate() and friends preserves row names in data frames once more (#5418).

• group_by() uses the ungrouped data for the implicit mutate step (#5598).
  You might have to define an ungroup() method for custom classes.
  For example, see hadley/cubelyr#3.

• relocate() can rename columns it relocates (#5569).

• distinct() and group_by() have better error messages when the mutate step fails (#5060).

• Clarify that between() is not vectorised (#5493).

• Fixed across() issue where data frame columns would could not be referred to
  with all_of() in the nested case (mutate() within mutate()) (#5498).

• across() handles data frames with 0 columns (#5523).

• mutate() always keeps grouping variables, unconditional to .keep= (#5582).

• dplyr now depends on R 3.3.0

dplyr 1.0.2

• Fixed across() issue where data frame columns would mask objects referred to
  from all_of() (#5460).

• bind_cols() gains a .name_repair argument, passed to vctrs::vec_cbind() (#5451)

• summarise(.groups = "rowwise") makes a rowwise data frame even if the input data
  is not grouped (#5422).

dplyr 1.0.1

• New function cur_data_all() similar to cur_data() but includes the grouping variables (#5342).

• count() and tally() no longer automatically weights by column n if
  present (#5298). dplyr 1.0.0 introduced this behaviour because of Hadley's
  faulty memory. Historically tally() automatically weighted and count()
  did not, but this behaviour was accidentally changed in 0.8.2 (#4408) so that
  neither automatically weighted by n. Since 0.8.2 is almost a year old,
  and the automatically weighting behaviour was a little confusing anyway,
  we've removed it from both count() and tally().

  Use of wt = n() is now deprecated; now just omit the wt argument.

• coalesce() now supports data frames correctly (#5326).

• cummean() no longer has off-by-one indexing problem (@Cropgen, #5287).

• The call stack is preserved on error. This makes it possible to recover()
  into problematic code called from dplyr verbs (#5308).

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 !!!
  inst...

v0.8.5

Maintenance release for compatibility with R-devel.

dplyr 0.8.4

• Adapt tests to changes in dependent packages.

dplyr 0.8.3

• Fixed performance regression introduced in version 0.8.2 (#4458).

dplyr 0.8.2

New functions

• top_frac(data, proportion) is a shorthand for top_n(data, proportion * n()) (#4017).

colwise changes

• Using quosures in colwise verbs is deprecated (#4330).

• Updated distinct_if(), distinct_at() and distinct_all() to include .keep_all argument (@beansrowning, #4343).

• rename_at() handles empty selection (#4324).

• *_if() functions correctly handle columns with special names (#4380).

• colwise functions support constants in formulas (#4374).

Hybrid evaluation changes

• hybrid rank functions correctly handle NA (#4427).

• first(), last() and nth() hybrid version handles factors (#4295).

Minor changes

• top_n() quotes its n argument, n no longer needs to be constant for all groups (#4017).

• tbl_vars() keeps information on grouping columns by returning a dplyr_sel_vars object (#4106).

• group_split() always sets the ptype attribute, which make it more robust in the case where there
  are 0 groups.

• group_map() and group_modify() work in the 0 group edge case (#4421)

• select.list() method added so that select() does not dispatch on lists (#4279).

• view() is reexported from tibble (#4423).

• group_by() puts NA groups last in character vectors (#4227).

• arrange() handles integer64 objects (#4366).

• summarise() correctly resolves summarised list columns (#4349).