tidyverse/dplyr v1.1.0 on GitHub

New features

.by/by is an
experimental alternative to group_by() that supports per-operation grouping
for mutate(), summarise(), filter(), and the slice() family (#6528).
Rather than:
```
starwars %>%
  group_by(species, homeworld) %>%
  summarise(mean_height = mean(height))
```
You can now write:
```
starwars %>%
  summarise(
    mean_height = mean(height),
    .by = c(species, homeworld)
  )
```
The most useful reason to do this is because .by only affects a single
operation. In the example above, an ungrouped data frame went into the
summarise() call, so an ungrouped data frame will come out; with .by, you
never need to remember to ungroup() afterwards and you never need to use
the .groups argument.
Additionally, using summarise() with .by will never sort the results by
the group key, unlike with group_by(). Instead, the results are returned
using the existing ordering of the groups from the original data. We feel this
is more predictable, better maintains any ordering you might have already
applied with a previous call to arrange(), and provides a way to maintain
the current ordering without having to resort to factors.
This feature was inspired by
data.table, where the
equivalent syntax looks like:
```
starwars[, .(mean_height = mean(height)), by = .(species, homeworld)]
```
with_groups() is superseded in favor of .by (#6582).
reframe() is a new experimental verb that creates a new data frame by
applying functions to columns of an existing data frame. It is very similar to
summarise(), with two big differences:
- reframe() can return an arbitrary number of rows per group, while
  summarise() reduces each group down to a single row.
- reframe() always returns an ungrouped data frame, while summarise()
  might return a grouped or rowwise data frame, depending on the scenario.
reframe() has been added in response to valid concern from the community
that allowing summarise() to return any number of rows per group increases
the chance for accidental bugs. We still feel that this is a powerful
technique, and is a principled replacement for do(), so we have moved these
features to reframe() (#6382).
group_by() now uses a new algorithm for computing groups. It is often faster
than the previous approach (especially when there are many groups), and in
most cases there should be no changes. The one exception is with character
vectors, see the C locale news bullet below for more details (#4406, #6297).
arrange() now uses a faster algorithm for sorting character vectors, which
is heavily inspired by data.table's forder(). See the C locale news bullet
below for more details (#4962).
Joins have been completely overhauled to enable more flexible join operations
and provide more tools for quality control. Many of these changes are inspired
by data.table's join syntax (#5914, #5661, #5413, #2240).
- A join specification can now be created through join_by(). This allows
  you to specify both the left and right hand side of a join using unquoted
  column names, such as join_by(sale_date == commercial_date). Join
  specifications can be supplied to any *_join() function as the by
  argument.
- Join specifications allow for new types of joins:
  - Equality joins: The most common join, specified by ==. For example,
    join_by(sale_date == commercial_date).
  - Inequality joins: For joining on inequalities, i.e.>=, >, <, and
    <=. For example, use join_by(sale_date >= commercial_date) to find
    every commercial that aired before a particular sale.
  - Rolling joins: For "rolling" the closest match forward or backwards when
    there isn't an exact match, specified by using the rolling helper,
    closest(). For example,
    join_by(closest(sale_date >= commercial_date)) to find only the most
    recent commercial that aired before a particular sale.
  - Overlap joins: For detecting overlaps between sets of columns, specified
    by using one of the overlap helpers: between(), within(), or
    overlaps(). For example, use
    join_by(between(commercial_date, sale_date_lower, sale_date)) to
    find commercials that aired before a particular sale, as long as they
    occurred after some lower bound, such as 40 days before the sale was made.
  Note that you cannot use arbitrary expressions in the join conditions, like
  join_by(sale_date - 40 >= commercial_date). Instead, use mutate() to
  create a new column containing the result of sale_date - 40 and refer
  to that by name in join_by().
- multiple is a new argument for controlling what happens when a row
  in x matches multiple rows in y. For equality joins and rolling joins,
  where this is usually surprising, this defaults to signalling a "warning",
  but still returns all of the matches. For inequality joins, where multiple
  matches are usually expected, this defaults to returning "all" of the
  matches. You can also return only the "first" or "last" match, "any"
  of the matches, or you can "error".
- keep now defaults to NULL rather than FALSE. NULL implies
  keep = FALSE for equality conditions, but keep = TRUE for inequality
  conditions, since you generally want to preserve both sides of an
  inequality join.
- unmatched is a new argument for controlling what happens when a row
  would be dropped because it doesn't have a match. For backwards
  compatibility, the default is "drop", but you can also choose to
  "error" if dropped rows would be surprising.
across() gains an experimental .unpack argument to optionally unpack
(as in, tidyr::unpack()) data frames returned by functions in .fns
(#6360).
consecutive_id() for creating groups based on contiguous runs of the
same values, like data.table::rleid() (#1534).
case_match() is a "vectorised switch" variant of case_when() that matches
on values rather than logical expressions. It is like a SQL "simple"
CASE WHEN statement, whereas case_when() is like a SQL "searched"
CASE WHEN statement (#6328).
cross_join() is a more explicit and slightly more correct replacement for
using by = character() during a join (#6604).
pick() makes it easy to access a subset of columns from the current group.
pick() is intended as a replacement for across(.fns = NULL), cur_data(),
and cur_data_all(). We feel that pick() is a much more evocative name when
you are just trying to select a subset of columns from your data (#6204).
symdiff() computes the symmetric difference (#4811).

Lifecycle changes

Breaking changes

arrange() and group_by() now use the C locale, not the system locale,
when ordering or grouping character vectors. This brings substantial
performance improvements, increases reproducibility across R sessions, makes
dplyr more consistent with data.table, and we believe it should affect little
existing code. If it does affect your code, you can use
options(dplyr.legacy_locale = TRUE) to quickly revert to the previous
behavior. However, in general, we instead recommend that you use the new
.locale argument to precisely specify the desired locale. For a full
explanation please read the associated
grouping
and ordering
tidyups.
bench_tbls(), compare_tbls(), compare_tbls2(), eval_tbls(),
eval_tbls2(), location() and changes(), deprecated in 1.0.0, are now
defunct (#6387).
frame_data(), data_frame_(), lst_() and tbl_sum() are no longer
re-exported from tibble (#6276, #6277, #6278, #6284).
select_vars(), rename_vars(), select_var() and current_vars(),
deprecated in 0.8.4, are now defunct (#6387).

Newly deprecated

across(), c_across(), if_any(), and if_all() now require the
.cols and .fns arguments. In general, we now recommend that you use
pick() instead of an empty across() call or across() with no .fns
(e.g. across(c(x, y)). (#6523).
- Relying on the previous default of .cols = everything() is deprecated.
  We have skipped the soft-deprecation stage in this case, because indirect
  usage of across() and friends in this way is rare.
- Relying on the previous default of .fns = NULL is not yet formally
  soft-deprecated, because there was no good alternative until now, but it is
  discouraged and will be soft-deprecated in the next minor release.
Passing ... to across() is soft-deprecated because it's ambiguous when
those arguments are evaluated. Now, instead of (e.g.)
across(a:b, mean, na.rm = TRUE) you should write
across(a:b, ~ mean(.x, na.rm = TRUE)) (#6073).
all_equal() is deprecated. We've advised against it for some time, and
we explicitly recommend you use all.equal(), manually reordering the rows
and columns as needed (#6324).
cur_data() and cur_data_all() are soft-deprecated in favour of
pick() (#6204).
Using by = character() to perform a cross join is now soft-deprecated in
favor of cross_join() (#6604).
filter()ing with a 1-column matrix is deprecated (#6091).
progress_estimate() is deprecated for all uses (#6387).
Using summarise() to produce a 0 or >1 row "summary" is deprecated in favor
of the new reframe(). See the NEWS bullet about reframe() for more details
(#6382).
All functions deprecated in 1.0.0 (released April 2020) and earlier now warn
every time you use them (#6387). This includes combine(), src_local(),
src_mysql(), src_postgres(), src_sqlite(), rename_vars_(),
select_vars_(), summarise_each_(), mutate_each_(), as.tbl(),
tbl_df(), and a handful of older arguments. They are likely to be made
defunct in the next major version (but not before mid 2024).
slice()ing with a 1-column matrix is deprecated.

Newly superseded

recode() is superseded in favour of case_match() (#6433).
recode_factor() is superseded. We don't have a direct replacement for it
yet, but we plan to add one to forcats. In the meantime you can often use
case_match(.ptype = factor(levels = )) instead (#6433).
transmute() is superseded in favour of mutate(.keep = "none") (#6414).

Newly stable

The .keep, .before, and .after arguments to mutate() have moved
from experimental to stable.
The rows_*() family of functions have moved from experimental to stable.

vctrs

Many of dplyr's vector functions have been rewritten to make use of the vctrs
package, bringing greater consistency and improved performance.

between() can now work with all vector types, not just numeric and
date-time. Additionally, left and right can now also be vectors (with the
same length as x), and x, left, and right are cast to the common type
before the comparison is made (#6183, #6260, #6478).
case_when() (#5106):
- Has a new .default argument that is intended to replace usage of
  TRUE ~ default_value as a more explicit and readable way to specify
  a default value. In the future, we will deprecate the unsafe recycling of
  the LHS inputs that allows TRUE ~ to work, so we encourage you to switch
  to using .default.
- No longer requires exact matching of the types of RHS values. For example,
  the following no longer requires you to use NA_character_.
```
x <- c("little", "unknown", "small", "missing", "large")

case_when(
  x %in% c("little", "small") ~ "one",
  x %in% c("big", "large") ~ "two",
  x %in% c("missing", "unknown") ~ NA
)
```
- Supports a larger variety of RHS value types. For example, you can use a
  data frame to create multiple columns at once.
- Has new .ptype and .size arguments which allow you to enforce
  a particular output type and size.
- Has a better error when types or lengths were incompatible (#6261, #6206).
coalesce() (#6265):
- Discards NULL inputs up front.
- No longer iterates over the columns of data frame input. Instead, a row is
  now only coalesced if it is entirely missing, which is consistent with
  vctrs::vec_detect_missing() and greatly simplifies the implementation.
- Has new .ptype and .size arguments which allow you to enforce
  a particular output type and size.
first(), last(), and nth() (#6331):
- When used on a data frame, these functions now return a single row rather
  than a single column. This is more consistent with the vctrs principle that
  a data frame is generally treated as a vector of rows.
- The default is no longer "guessed", and will always automatically be set
  to a missing value appropriate for the type of x.
- Error if n is not an integer. nth(x, n = 2) is fine, but
  nth(x, n = 2.5) is now an error.
Additionally, they have all gained an na_rm argument since they
are summary functions (#6242, with contributions from @tnederlof).
if_else() gains most of the same benefits as case_when(). In particular,
if_else() now takes the common type of true, false, and missing to
determine the output type, meaning that you can now reliably use NA,
rather than NA_character_ and friends (#6243).
na_if() (#6329) now casts y to the type of x before comparison, which
makes it clearer that this function is type and size stable on x. In
particular, this means that you can no longer do na_if(<tibble>, 0), which
previously accidentally allowed you to replace any instance of 0 across
every column of the tibble with NA. na_if() was never intended to work
this way, and this is considered off-label usage.
You can also now replace NaN values in x with na_if(x, NaN).
lag() and lead() now cast default to the type of x, rather than taking
the common type. This ensures that these functions are type stable on x
(#6330).
row_number(), min_rank(), dense_rank(), ntile(), cume_dist(), and
percent_rank() are faster and work for more types. You can now rank by
multiple columns by supplying a data frame (#6428).
with_order() now checks that the size of order_by is the same size as x,
and now works correctly when order_by is a data frame (#6334).

Minor improvements and bug fixes

Fixed an issue with latest rlang that caused internal tools (such as
mask$eval_all_summarise()) to be mentioned in error messages (#6308).
Warnings are enriched with contextualised information in summarise() and
filter() just like they have been in mutate() and arrange().
Joins now reference the correct column in y when a type error is thrown
while joining on two columns with different names (#6465).
Joins on very wide tables are no longer bottlenecked by the application of
suffix (#6642).
*_join() now error if you supply them with additional arguments that
aren't used (#6228).
across() used without functions inside a rowwise-data frame no longer
generates an invalid data frame (#6264).
Anonymous functions supplied with function() and \() are now inlined by
across() if possible, which slightly improves performance and makes possible
further optimisations in the future.
Functions supplied to across() are no longer masked by columns (#6545). For
instance, across(1:2, mean) will now work as expected even if there is a
column called mean.
across() will now error when supplied ... without a .fns argument
(#6638).
arrange() now correctly ignores NULL inputs (#6193).
arrange() now works correctly when across() calls are used as the 2nd
(or more) ordering expression (#6495).
arrange(df, mydesc::desc(x)) works correctly when mydesc re-exports
dplyr::desc() (#6231).
c_across() now evaluates all_of() correctly and no longer allows you to
accidentally select grouping variables (#6522).
c_across() now throws a more informative error if you try to rename during
column selection (#6522).
dplyr no longer provides count() and tally() methods for tbl_sql.
These methods have been accidentally overriding the tbl_lazy methods that
dbplyr provides, which has resulted in issues with the grouping structure of
the output (#6338, tidyverse/dbplyr#940).
cur_group() now works correctly with zero row grouped data frames (#6304).
desc() gives a useful error message if you give it a non-vector (#6028).
distinct() now retains attributes of bare data frames (#6318).
distinct() returns columns ordered the way you request, not the same
as the input data (#6156).
Error messages in group_by(), distinct(), tally(), and count() are now
more relevant (#6139).
group_by_prepare() loses the caller_env argument. It was rarely used
and it is no longer needed (#6444).
group_walk() gains an explict .keep argument (#6530).
Warnings emitted inside mutate() and variants are now collected and stashed
away. Run the new last_dplyr_warnings() function to see the warnings emitted
within dplyr verbs during the last top-level command.
This fixes performance issues when thousands of warnings are emitted with
rowwise and grouped data frames (#6005, #6236).
mutate() behaves a little better with 0-row rowwise inputs (#6303).
A rowwise mutate() now automatically unlists list-columns containing
length 1 vectors (#6302).
nest_join() has gained the na_matches argument that all other joins have.
nest_join() now preserves the type of y (#6295).
n_distinct() now errors if you don't give it any input (#6535).
nth(), first(), last(), and with_order() now sort character order_by
vectors in the C locale. Using character vectors for order_by is rare, so we
expect this to have little practical impact (#6451).
ntile() now requires n to be a single positive integer.
relocate() now works correctly with empty data frames and when .before or
.after result in empty selections (#6167).
relocate() no longer drops attributes of bare data frames (#6341).
relocate() now retains the last name change when a single column is renamed
multiple times while it is being moved. This better matches the behavior of
rename() (#6209, with help from @eutwt).
rename() now contains examples of using all_of() and any_of() to rename
using a named character vector (#6644).
rename_with() now disallows renaming in the .cols tidy-selection (#6561).
rename_with() now checks that the result of .fn is the right type and size
(#6561).
rows_insert() now checks that y contains the by columns (#6652).
setequal() ignores differences between freely coercible types (e.g. integer
and double) (#6114) and ignores duplicated rows (#6057).
slice() helpers again produce output equivalent to slice(.data, 0) when
the n or prop argument is 0, fixing a bug introduced in the previous
version (@eutwt, #6184).
slice() with no inputs now returns 0 rows. This is mostly for theoretical
consistency (#6573).
slice() now errors if any expressions in ... are named. This helps avoid
accidentally misspelling an optional argument, such as .by (#6554).
slice_*() now requires n to be an integer.
slice_*() generics now perform argument validation. This should make
methods more consistent and simpler to implement (#6361).
slice_min() and slice_max() can order_by multiple variables if you
supply them as a data.frame or tibble (#6176).
slice_min() and slice_max() now consistently include missing values in
the result if necessary (i.e. there aren't enough non-missing values to
reach the n or prop you have selected). If you don't want missing values
to be included at all, set na_rm = TRUE (#6177).
slice_sample() now accepts negative n and prop values (#6402).
slice_sample() returns a data frame or group with the same number of rows as
the input when replace = FALSE and n is larger than the number of rows or
prop is larger than 1. This reverts a change made in 1.0.8, returning to the
behavior of 1.0.7 (#6185)
slice_sample() now gives a more informative error when replace = FALSE and
the number of rows requested in the sample exceeds the number of rows in the
data (#6271).
storms has been updated to include 2021 data and some missing storms that
were omitted due to an error (@steveharoz, #6320).
summarise() now correctly recycles named 0-column data frames (#6509).
union_all(), like union(), now requires that data frames be compatible:
i.e. they have the same columns, and the columns have compatible types.
where() is re-exported from tidyselect (#6597).

tidyverse/dplyr v1.1.0 dplyr 1.1.0 on GitHub

New features

Lifecycle changes

Breaking changes

Newly deprecated

Newly superseded

Newly stable

vctrs

Minor improvements and bug fixes

tidyverse/dplyr v1.1.0
dplyr 1.1.0

on GitHub