https://github.com/hadley/dplyr
Tip revision: 98b8a0f5de25e238ac97514da24ec228610c8701 authored by Lionel Henry on 19 January 2021, 09:23:23 UTC
Merge pull request #5686 from lionel-/fix-warning-overhead
Merge pull request #5686 from lionel-/fix-warning-overhead
Tip revision: 98b8a0f
NEWS.md
# dplyr (development version)
# dplyr 1.0.3
* 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()`.
* `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 https://github.com/hadley/cubelyr/pull/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 extra class at the end causes some vctrs operations to fail with a message 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](https://lifecycle.r-lib.org),
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 <https://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 by`slice_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).
# dplyr 0.8.5 (2020-03-07)
* Maintenance release for compatibility with R-devel.
# dplyr 0.8.4 (2020-01-30)
* Adapt tests to changes in dependent packages.
# dplyr 0.8.3 (2019-07-04)
* Fixed performance regression introduced in version 0.8.2 (#4458).
# dplyr 0.8.2 (2019-06-28)
## 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).
# dplyr 0.8.1 (2019-05-14)
## Breaking changes
* `group_modify()` is the new name of the function previously known as `group_map()`
## New functions
* `group_map()` now only calls the function on each group and return a list.
* `group_by_drop_default()`, previously known as `dplyr:::group_drops()` is exported (#4245).
## Minor changes
* Lists of formulas passed to colwise verbs are now automatically named.
* `group_by()` does a shallow copy even in the no groups case (#4221).
* Fixed `mutate()` on rowwise data frames with 0 rows (#4224).
* Fixed handling of bare formulas in colwise verbs (#4183).
* Fixed performance of `n_distinct()` (#4202).
* `group_indices()` now ignores empty groups by default for `data.frame`, which is
consistent with the default of `group_by()` (@yutannihilation, #4208).
* Fixed integer overflow in hybrid `ntile()` (#4186).
* colwise functions `summarise_at()` ... can rename vars in the case of multiple functions (#4180).
* `select_if()` and `rename_if()` handle logical vector predicate (#4213).
* hybrid `min()` and `max()` cast to integer when possible (#4258).
* `bind_rows()` correctly handles the cases where there are multiple consecutive `NULL` (#4296).
* Support for R 3.1.* has been dropped. The minimal R version supported is now 3.2.0.
https://www.tidyverse.org/articles/2019/04/r-version-support/
* `rename_at()` handles empty selection (#4324).
# dplyr 0.8.0.1 (2019-02-15)
* Fixed integer C/C++ division, forced released by CRAN (#4185).
# dplyr 0.8.0 (2019-02-14)
## Breaking changes
* The error `could not find function "n"` or the warning
```Calling `n()` without importing or prefixing it is deprecated, use `dplyr::n()` ```
indicates when functions like `n()`, `row_number()`, ... are not imported or prefixed.
The easiest fix is to import dplyr with `import(dplyr)` in your `NAMESPACE` or
`#' @import dplyr` in a roxygen comment, alternatively such functions can be
imported selectively as any other function with `importFrom(dplyr, n)` in the
`NAMESPACE` or `#' @importFrom dplyr n` in a roxygen comment. The third option is
to prefix them, i.e. use `dplyr::n()`
* If you see `checking S3 generic/method consistency` in R CMD check for your
package, note that :
- `sample_n()` and `sample_frac()` have gained `...`
- `filter()` and `slice()` have gained `.preserve`
- `group_by()` has gained `.drop`
* ```Error: `.data` is a corrupt grouped_df, ...``` signals code that makes
wrong assumptions about the internals of a grouped data frame.
## New functions
* New selection helpers `group_cols()`. It can be called in selection contexts
such as `select()` and matches the grouping variables of grouped tibbles.
* `last_col()` is re-exported from tidyselect (#3584).
* `group_trim()` drops unused levels of factors that are used as grouping variables.
* `nest_join()` creates a list column of the matching rows. `nest_join()` + `tidyr::unnest()`
is equivalent to `inner_join` (#3570).
```r
band_members %>%
nest_join(band_instruments)
```
* `group_nest()` is similar to `tidyr::nest()` but focusing on the variables to nest by
instead of the nested columns.
```r
starwars %>%
group_by(species, homeworld) %>%
group_nest()
starwars %>%
group_nest(species, homeworld)
```
* `group_split()` is similar to `base::split()` but operating on existing groups when
applied to a grouped data frame, or subject to the data mask on ungrouped data frames
```r
starwars %>%
group_by(species, homeworld) %>%
group_split()
starwars %>%
group_split(species, homeworld)
```
* `group_map()` and `group_walk()` are purrr-like functions to iterate on groups
of a grouped data frame, jointly identified by the data subset (exposed as `.x`) and the
data key (a one row tibble, exposed as `.y`). `group_map()` returns a grouped data frame that
combines the results of the function, `group_walk()` is only used for side effects and returns
its input invisibly.
```r
mtcars %>%
group_by(cyl) %>%
group_map(~ head(.x, 2L))
```
* `distinct_prepare()`, previously known as `distinct_vars()` is exported. This is mostly useful for
alternative backends (e.g. `dbplyr`).
## Major changes
* `group_by()` gains the `.drop` argument. When set to `FALSE` the groups are generated
based on factor levels, hence some groups may be empty (#341).
```r
# 3 groups
tibble(
x = 1:2,
f = factor(c("a", "b"), levels = c("a", "b", "c"))
) %>%
group_by(f, .drop = FALSE)
# the order of the grouping variables matter
df <- tibble(
x = c(1,2,1,2),
f = factor(c("a", "b", "a", "b"), levels = c("a", "b", "c"))
)
df %>% group_by(f, x, .drop = FALSE)
df %>% group_by(x, f, .drop = FALSE)
```
The default behaviour drops the empty groups as in the previous versions.
```r
tibble(
x = 1:2,
f = factor(c("a", "b"), levels = c("a", "b", "c"))
) %>%
group_by(f)
```
* `filter()` and `slice()` gain a `.preserve` argument to control which groups it should keep. The default
`filter(.preserve = FALSE)` recalculates the grouping structure based on the resulting data,
otherwise it is kept as is.
```r
df <- tibble(
x = c(1,2,1,2),
f = factor(c("a", "b", "a", "b"), levels = c("a", "b", "c"))
) %>%
group_by(x, f, .drop = FALSE)
df %>% filter(x == 1)
df %>% filter(x == 1, .preserve = TRUE)
```
* The notion of lazily grouped data frames have disappeared. All dplyr verbs now recalculate
immediately the grouping structure, and respect the levels of factors.
* Subsets of columns now properly dispatch to the `[` or `[[` method when the column
is an object (a vector with a class) instead of making assumptions on how the
column should be handled. The `[` method must handle integer indices, including
`NA_integer_`, i.e. `x[NA_integer_]` should produce a vector of the same class
as `x` with whatever represents a missing value.
## Minor changes
* `tally()` works correctly on non-data frame table sources such as `tbl_sql` (#3075).
* `sample_n()` and `sample_frac()` can use `n()` (#3527)
* `distinct()` respects the order of the variables provided (#3195, @foo-bar-baz-qux)
and handles the 0 rows and 0 columns special case (#2954).
* `combine()` uses tidy dots (#3407).
* `group_indices()` can be used without argument in expressions in verbs (#1185).
* Using `mutate_all()`, `transmute_all()`, `mutate_if()` and `transmute_if()`
with grouped tibbles now informs you that the grouping variables are
ignored. In the case of the `_all()` verbs, the message invites you to use
`mutate_at(df, vars(-group_cols()))` (or the equivalent `transmute_at()` call)
instead if you'd like to make it explicit in your code that the operation is
not applied on the grouping variables.
* Scoped variants of `arrange()` respect the `.by_group` argument (#3504).
* `first()` and `last()` hybrid functions fall back to R evaluation when given no arguments (#3589).
* `mutate()` removes a column when the expression evaluates to `NULL` for all groups (#2945).
* grouped data frames support `[, drop = TRUE]` (#3714).
* New low-level constructor `new_grouped_df()` and validator `validate_grouped_df` (#3837).
* `glimpse()` prints group information on grouped tibbles (#3384).
* `sample_n()` and `sample_frac()` gain `...` (#2888).
* Scoped filter variants now support functions and purrr-like lambdas:
```r
mtcars %>% filter_at(vars(hp, vs), ~ . %% 2 == 0)
```
## Lifecycle
* `do()`, `rowwise()` and `combine()` are questioning (#3494).
* `funs()` is soft-deprecated and will start issuing warnings in a future version.
## Changes to column wise functions
* Scoped variants for `distinct()`: `distinct_at()`, `distinct_if()`, `distinct_all()` (#2948).
* `summarise_at()` excludes the grouping variables (#3613).
* `mutate_all()`, `mutate_at()`, `summarise_all()` and `summarise_at()` handle utf-8 names (#2967).
## Performance
* R expressions that cannot be handled with native code are now evaluated with
unwind-protection when available (on R 3.5 and later). This improves the
performance of dplyr on data frames with many groups (and hence many
expressions to evaluate). We benchmarked that computing a grouped average is
consistently twice as fast with unwind-protection enabled.
Unwind-protection also makes dplyr more robust in corner cases because it
ensures the C++ destructors are correctly called in all circumstances
(debugger exit, captured condition, restart invokation).
* `sample_n()` and `sample_frac()` gain `...` (#2888).
* Improved performance for wide tibbles (#3335).
* Faster hybrid `sum()`, `mean()`, `var()` and `sd()` for logical vectors (#3189).
* Hybrid version of `sum(na.rm = FALSE)` exits early when there are missing values.
This considerably improves performance when there are missing values early in the vector (#3288).
* `group_by()` does not trigger the additional `mutate()` on simple uses of the `.data` pronoun (#3533).
## Internal
* The grouping metadata of grouped data frame has been reorganized in a single tidy tibble, that can be accessed
with the new `group_data()` function. The grouping tibble consists of one column per grouping variable,
followed by a list column of the (1-based) indices of the groups. The new `group_rows()` function retrieves
that list of indices (#3489).
```r
# the grouping metadata, as a tibble
group_by(starwars, homeworld) %>%
group_data()
# the indices
group_by(starwars, homeworld) %>%
group_data() %>%
pull(.rows)
group_by(starwars, homeworld) %>%
group_rows()
```
* Hybrid evaluation has been completely redesigned for better performance and stability.
## Documentation
* Add documentation example for moving variable to back in `?select` (#3051).
* column wise functions are better documented, in particular explaining when
grouping variables are included as part of the selection.
### Deprecated and defunct functions
* `mutate_each()` and `summarise_each()` are deprecated.
# dplyr 0.7.6
* `exprs()` is no longer exported to avoid conflicts with `Biobase::exprs()`
(#3638).
* The MASS package is explicitly suggested to fix CRAN warnings on R-devel
(#3657).
* Set operations like `intersect()` and `setdiff()` reconstruct groups metadata (#3587) and keep the order of the rows (#3839).
* Using namespaced calls to `base::sort()` and `base::unique()` from C++ code
to avoid ambiguities when these functions are overridden (#3644).
* Fix rchk errors (#3693).
# dplyr 0.7.5 (2018-04-14)
## Breaking changes for package developers
* The major change in this version is that dplyr now depends on the selecting
backend of the tidyselect package. If you have been linking to
`dplyr::select_helpers` documentation topic, you should update the link to
point to `tidyselect::select_helpers`.
* Another change that causes warnings in packages is that dplyr now exports the
`exprs()` function. This causes a collision with `Biobase::exprs()`. Either
import functions from dplyr selectively rather than in bulk, or do not import
`Biobase::exprs()` and refer to it with a namespace qualifier.
## Bug fixes
* `distinct(data, "string")` now returns a one-row data frame again. (The
previous behavior was to return the data unchanged.)
* `do()` operations with more than one named argument can access `.` (#2998).
* Reindexing grouped data frames (e.g. after `filter()` or `..._join()`)
never updates the `"class"` attribute. This also avoids unintended updates
to the original object (#3438).
* Fixed rare column name clash in `..._join()` with non-join
columns of the same name in both tables (#3266).
* Fix `ntile()` and `row_number()` ordering to use the locale-dependent
ordering functions in R when dealing with character vectors, rather than
always using the C-locale ordering function in C (#2792, @foo-bar-baz-qux).
* Summaries of summaries (such as `summarise(b = sum(a), c = sum(b))`) are
now computed using standard evaluation for simplicity and correctness, but
slightly slower (#3233).
* Fixed `summarise()` for empty data frames with zero columns (#3071).
## Major changes
* `enexpr()`, `expr()`, `exprs()`, `sym()` and `syms()` are now
exported. `sym()` and `syms()` construct symbols from strings or character
vectors. The `expr()` variants are equivalent to `quo()`, `quos()` and
`enquo()` but return simple expressions rather than quosures. They support
quasiquotation.
* dplyr now depends on the new tidyselect package to power `select()`,
`rename()`, `pull()` and their variants (#2896). Consequently
`select_vars()`, `select_var()` and `rename_vars()` are
soft-deprecated and will start issuing warnings in a future version.
Following the switch to tidyselect, `select()` and `rename()` fully support
character vectors. You can now unquote variables like this:
```
vars <- c("disp", "cyl")
select(mtcars, !! vars)
select(mtcars, -(!! vars))
```
Note that this only works in selecting functions because in other contexts
strings and character vectors are ambiguous. For instance strings are a valid
input in mutating operations and `mutate(df, "foo")` creates a new column by
recycling "foo" to the number of rows.
## Minor changes
* Support for raw vector columns in `arrange()`, `group_by()`, `mutate()`,
`summarise()` and `..._join()` (minimal `raw` x `raw` support initially) (#1803).
* `bind_cols()` handles unnamed list (#3402).
* `bind_rows()` works around corrupt columns that have the object bit set
while having no class attribute (#3349).
* `combine()` returns `logical()` when all inputs are `NULL` (or when there
are no inputs) (#3365, @zeehio).
* `distinct()` now supports renaming columns (#3234).
* Hybrid evaluation simplifies `dplyr::foo()` to `foo()` (#3309). Hybrid
functions can now be masked by regular R functions to turn off hybrid
evaluation (#3255). The hybrid evaluator finds functions from dplyr even if
dplyr is not attached (#3456).
* In `mutate()` it is now illegal to use `data.frame` in the rhs (#3298).
* Support `!!!` in `recode_factor()` (#3390).
* `row_number()` works on empty subsets (#3454).
* `select()` and `vars()` now treat `NULL` as empty inputs (#3023).
* Scoped select and rename functions (`select_all()`, `rename_if()` etc.)
now work with grouped data frames, adapting the grouping as necessary
(#2947, #3410). `group_by_at()` can group by an existing grouping variable
(#3351). `arrange_at()` can use grouping variables (#3332).
* `slice()` no longer enforce tibble classes when input is a simple
`data.frame`, and ignores 0 (#3297, #3313).
* `transmute()` no longer prints a message when including a group variable.
## Documentation
* Improved documentation for `funs()` (#3094) and set operations (e.g. `union()`) (#3238, @edublancas).
## Error messages
* Better error message if dbplyr is not installed when accessing database
backends (#3225).
* `arrange()` fails gracefully on `data.frame` columns (#3153).
* Corrected error message when calling `cbind()` with an object of wrong
length (#3085).
* Add warning with explanation to `distinct()` if any of the selected columns
are of type `list` (#3088, @foo-bar-baz-qux), or when used on unknown columns
(#2867, @foo-bar-baz-qux).
* Show clear error message for bad arguments to `funs()` (#3368).
* Better error message in `..._join()` when joining data frames with duplicate
or `NA` column names. Joining such data frames with a semi- or anti-join
now gives a warning, which may be converted to an error in future versions
(#3243, #3417).
* Dedicated error message when trying to use columns of the `Interval`
or `Period` classes (#2568).
* Added an `.onDetach()` hook that allows for plyr to be loaded and attached
without the warning message that says functions in dplyr will be masked,
since dplyr is no longer attached (#3359, @jwnorman).
## Performance
* `sample_n()` and `sample_frac()` on grouped data frame are now faster
especially for those with large number of groups (#3193, @saurfang).
## Internal
* Compute variable names for joins in R (#3430).
* Bumped Rcpp dependency to 0.12.15 to avoid imperfect detection of `NA`
values in hybrid evaluation fixed in RcppCore/Rcpp#790 (#2919).
* Avoid cleaning the data mask, a temporary environment used to evaluate
expressions. If the environment, in which e.g. a `mutate()` expression
is evaluated, is preserved until after the operation, accessing variables
from that environment now gives a warning but still returns `NULL` (#3318).
# dplyr 0.7.4
* Fix recent Fedora and ASAN check errors (#3098).
* Avoid dependency on Rcpp 0.12.10 (#3106).
# dplyr 0.7.3
* Fixed protection error that occurred when creating a character column using grouped `mutate()` (#2971).
* Fixed a rare problem with accessing variable values in `summarise()` when all groups have size one (#3050).
* `distinct()` now throws an error when used on unknown columns
(#2867, @foo-bar-baz-qux).
* Fixed rare out-of-bounds memory write in `slice()` when negative indices beyond the number of rows were involved (#3073).
* `select()`, `rename()` and `summarise()` no longer change the grouped vars of the original data (#3038).
* `nth(default = var)`, `first(default = var)` and `last(default = var)` fall back to standard evaluation in a grouped operation instead of triggering an error (#3045).
* `case_when()` now works if all LHS are atomic (#2909), or when LHS or RHS values are zero-length vectors (#3048).
* `case_when()` accepts `NA` on the LHS (#2927).
* Semi- and anti-joins now preserve the order of left-hand-side data frame (#3089).
* Improved error message for invalid list arguments to `bind_rows()` (#3068).
* Grouping by character vectors is now faster (#2204).
* Fixed a crash that occurred when an unexpected input was supplied to
the `call` argument of `order_by()` (#3065).
# dplyr 0.7.2
* Move build-time vs. run-time checks out of `.onLoad()` and into `dr_dplyr()`.
# dplyr 0.7.1
* Use new versions of bindrcpp and glue to avoid protection problems.
Avoid wrapping arguments to internal error functions (#2877). Fix
two protection mistakes found by rchk (#2868).
* Fix C++ error that caused compilation to fail on mac cran (#2862)
* Fix undefined behaviour in `between()`, where `NA_REAL` were
assigned instead of `NA_LOGICAL`. (#2855, @zeehio)
* `top_n()` now executes operations lazily for compatibility with
database backends (#2848).
* Reuse of new variables created in ungrouped `mutate()` possible
again, regression introduced in dplyr 0.7.0 (#2869).
* Quosured symbols do not prevent hybrid handling anymore. This should
fix many performance issues introduced with tidyeval (#2822).
# dplyr 0.7.0
## New data, functions, and features
* Five new datasets provide some interesting built-in datasets to demonstrate
dplyr verbs (#2094):
* `starwars` dataset about starwars characters; has list columns
* `storms` has the trajectories of ~200 tropical storms
* `band_members`, `band_instruments` and `band_instruments2`
has some simple data to demonstrate joins.
* New `add_count()` and `add_tally()` for adding an `n` column within groups
(#2078, @dgrtwo).
* `arrange()` for grouped data frames gains a `.by_group` argument so you
can choose to sort by groups if you want to (defaults to `FALSE`) (#2318)
* New `pull()` generic for extracting a single column either by name or position
(either from the left or the right). Thanks to @paulponcet for the idea (#2054).
This verb is powered with the new `select_var()` internal helper,
which is exported as well. It is like `select_vars()` but returns a
single variable.
* `as_tibble()` is re-exported from tibble. This is the recommend way to create
tibbles from existing data frames. `tbl_df()` has been softly deprecated.
`tribble()` is now imported from tibble (#2336, @chrMongeau); this
is now preferred to `frame_data()`.
## Deprecated and defunct
* dplyr no longer messages that you need dtplyr to work with data.table (#2489).
* Long deprecated `regroup()`, `mutate_each_q()` and
`summarise_each_q()` functions have been removed.
* Deprecated `failwith()`. I'm not even sure why it was here.
* Soft-deprecated `mutate_each()` and `summarise_each()`, these functions
print a message which will be changed to a warning in the next release.
* The `.env` argument to `sample_n()` and `sample_frac()` is defunct,
passing a value to this argument print a message which will be changed to a
warning in the next release.
## Databases
This version of dplyr includes some major changes to how database connections work. By and large, you should be able to continue using your existing dplyr database code without modification, but there are two big changes that you should be aware of:
* Almost all database related code has been moved out of dplyr and into a
new package, [dbplyr](https://github.com/tidyverse/dbplyr/). This makes dplyr
simpler, and will make it easier to release fixes for bugs that only affect
databases. `src_mysql()`, `src_postgres()`, and `src_sqlite()` will still
live dplyr so your existing code continues to work.
* It is no longer necessary to create a remote "src". Instead you can work
directly with the database connection returned by DBI. This reflects the
maturity of the DBI ecosystem. Thanks largely to the work of Kirill Muller
(funded by the R Consortium) DBI backends are now much more consistent,
comprehensive, and easier to use. That means that there's no longer a
need for a layer in between you and DBI.
You can continue to use `src_mysql()`, `src_postgres()`, and `src_sqlite()`, but I recommend a new style that makes the connection to DBI more clear:
```R
library(dplyr)
con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
DBI::dbWriteTable(con, "mtcars", mtcars)
mtcars2 <- tbl(con, "mtcars")
mtcars2
```
This is particularly useful if you want to perform non-SELECT queries as you can do whatever you want with `DBI::dbGetQuery()` and `DBI::dbExecute()`.
If you've implemented a database backend for dplyr, please read the [backend news](https://github.com/tidyverse/dbplyr/blob/master/NEWS.md#backends) to see what's changed from your perspective (not much). If you want to ensure your package works with both the current and previous version of dplyr, see `wrap_dbplyr_obj()` for helpers.
## UTF-8
* Internally, column names are always represented as character vectors,
and not as language symbols, to avoid encoding problems on Windows
(#1950, #2387, #2388).
* Error messages and explanations of data frame inequality are now encoded in
UTF-8, also on Windows (#2441).
* Joins now always reencode character columns to UTF-8 if necessary. This gives
a nice speedup, because now pointer comparison can be used instead of string
comparison, but relies on a proper encoding tag for all strings (#2514).
* Fixed problems when joining factor or character encodings with a mix of
native and UTF-8 encoded values (#1885, #2118, #2271, #2451).
* Fix `group_by()` for data frames that have UTF-8 encoded names (#2284, #2382).
* New `group_vars()` generic that returns the grouping as character vector, to
avoid the potentially lossy conversion to language symbols. The list returned
by `group_by_prepare()` now has a new `group_names` component (#1950, #2384).
## Colwise functions
* `rename()`, `select()`, `group_by()`, `filter()`, `arrange()` and
`transmute()` now have scoped variants (verbs suffixed with `_if()`,
`_at()` and `_all()`). Like `mutate_all()`, `summarise_if()`, etc,
these variants apply an operation to a selection of variables.
* The scoped verbs taking predicates (`mutate_if()`, `summarise_if()`,
etc) now support S3 objects and lazy tables. S3 objects should
implement methods for `length()`, `[[` and `tbl_vars()`. For lazy
tables, the first 100 rows are collected and the predicate is
applied on this subset of the data. This is robust for the common
case of checking the type of a column (#2129).
* Summarise and mutate colwise functions pass `...` on the the manipulation
functions.
* The performance of colwise verbs like `mutate_all()` is now back to
where it was in `mutate_each()`.
* `funs()` has better handling of namespaced functions (#2089).
* Fix issue with `mutate_if()` and `summarise_if()` when a predicate
function returns a vector of `FALSE` (#1989, #2009, #2011).
## Tidyeval
dplyr has a new approach to non-standard evaluation (NSE) called tidyeval.
It is described in detail in `vignette("programming")` but, in brief, gives you
the ability to interpolate values in contexts where dplyr usually works with expressions:
```{r}
my_var <- quo(homeworld)
starwars %>%
group_by(!!my_var) %>%
summarise_at(vars(height:mass), mean, na.rm = TRUE)
```
This means that the underscored version of each main verb is no longer needed,
and so these functions have been deprecated (but remain around for backward compatibility).
* `order_by()`, `top_n()`, `sample_n()` and `sample_frac()` now use
tidyeval to capture their arguments by expression. This makes it
possible to use unquoting idioms (see `vignette("programming")`) and
fixes scoping issues (#2297).
* Most verbs taking dots now ignore the last argument if empty. This
makes it easier to copy lines of code without having to worry about
deleting trailing commas (#1039).
* [API] The new `.data` and `.env` environments can be used inside
all verbs that operate on data: `.data$column_name` accesses the column
`column_name`, whereas `.env$var` accesses the external variable `var`.
Columns or external variables named `.data` or `.env` are shadowed, use
`.data$...` and/or `.env$...` to access them. (`.data` implements strict
matching also for the `$` operator (#2591).)
The `column()` and `global()` functions have been removed. They were never
documented officially. Use the new `.data` and `.env` environments instead.
* Expressions in verbs are now interpreted correctly in many cases that
failed before (e.g., use of `$`, `case_when()`, nonstandard evaluation, ...).
These expressions are now evaluated in a specially constructed temporary
environment that retrieves column data on demand with the help of the
`bindrcpp` package (#2190). This temporary environment poses restrictions on
assignments using `<-` inside verbs. To prevent leaking of broken bindings,
the temporary environment is cleared after the evaluation (#2435).
## Verbs
### Joins
* [API] `xxx_join.tbl_df(na_matches = "never")` treats all `NA` values as
different from each other (and from any other value), so that they never
match. This corresponds to the behavior of joins for database sources,
and of database joins in general. To match `NA` values, pass
`na_matches = "na"` to the join verbs; this is only supported for data frames.
The default is `na_matches = "na"`, kept for the sake of compatibility
to v0.5.0. It can be tweaked by calling
`pkgconfig::set_config("dplyr::na_matches", "na")` (#2033).
* `common_by()` gets a better error message for unexpected inputs (#2091)
* Fix groups when joining grouped data frames with duplicate columns
(#2330, #2334, @davidkretch).
* One of the two join suffixes can now be an empty string, dplyr no longer
hangs (#2228, #2445).
* Anti- and semi-joins warn if factor levels are inconsistent (#2741).
* Warnings about join column inconsistencies now contain the column names
(#2728).
### Select
* For selecting variables, the first selector decides if it's an inclusive
selection (i.e., the initial column list is empty), or an exclusive selection
(i.e., the initial column list contains all columns). This means that
`select(mtcars, contains("am"), contains("FOO"), contains("vs"))` now returns
again both `am` and `vs` columns like in dplyr 0.4.3 (#2275, #2289, @r2evans).
* Select helpers now throw an error if called when no variables have been
set (#2452)
* Helper functions in `select()` (and related verbs) are now evaluated
in a context where column names do not exist (#2184).
* `select()` (and the internal function `select_vars()`) now support
column names in addition to column positions. As a result,
expressions like `select(mtcars, "cyl")` are now allowed.
### Other
* `recode()`, `case_when()` and `coalesce()` now support splicing of
arguments with rlang's `!!!` operator.
* `count()` now preserves the grouping of its input (#2021).
* `distinct()` no longer duplicates variables (#2001).
* Empty `distinct()` with a grouped data frame works the same way as
an empty `distinct()` on an ungrouped data frame, namely it uses all
variables (#2476).
* `copy_to()` now returns it's output invisibly (since you're often just
calling for the side-effect).
* `filter()` and `lag()` throw informative error if used with ts objects (#2219)
* `mutate()` recycles list columns of length 1 (#2171).
* `mutate()` gives better error message when attempting to add a non-vector
column (#2319), or attempting to remove a column with `NULL` (#2187, #2439).
* `summarise()` now correctly evaluates newly created factors (#2217), and
can create ordered factors (#2200).
* Ungrouped `summarise()` uses summary variables correctly (#2404, #2453).
* Grouped `summarise()` no longer converts character `NA` to empty strings (#1839).
## Combining and comparing
* `all_equal()` now reports multiple problems as a character vector (#1819, #2442).
* `all_equal()` checks that factor levels are equal (#2440, #2442).
* `bind_rows()` and `bind_cols()` give an error for database tables (#2373).
* `bind_rows()` works correctly with `NULL` arguments and an `.id` argument
(#2056), and also for zero-column data frames (#2175).
* Breaking change: `bind_rows()` and `combine()` are more strict when coercing.
Logical values are no longer coerced to integer and numeric. Date, POSIXct
and other integer or double-based classes are no longer coerced to integer or
double as there is chance of attributes or information being lost
(#2209, @zeehio).
* `bind_cols()` now calls `tibble::repair_names()` to ensure that all
names are unique (#2248).
* `bind_cols()` handles empty argument list (#2048).
* `bind_cols()` better handles `NULL` inputs (#2303, #2443).
* `bind_rows()` explicitly rejects columns containing data frames
(#2015, #2446).
* `bind_rows()` and `bind_cols()` now accept vectors. They are treated
as rows by the former and columns by the latter. Rows require inner
names like `c(col1 = 1, col2 = 2)`, while columns require outer
names: `col1 = c(1, 2)`. Lists are still treated as data frames but
can be spliced explicitly with `!!!`, e.g. `bind_rows(!!! x)` (#1676).
* `rbind_list()` and `rbind_all()` now call `.Deprecated()`, they will be removed
in the next CRAN release. Please use `bind_rows()` instead.
* `combine()` accepts `NA` values (#2203, @zeehio)
* `combine()` and `bind_rows()` with character and factor types now always warn
about the coercion to character (#2317, @zeehio)
* `combine()` and `bind_rows()` accept `difftime` objects.
* `mutate` coerces results from grouped dataframes accepting combinable data
types (such as `integer` and `numeric`). (#1892, @zeehio)
## Vector functions
* `%in%` gets new hybrid handler (#126).
* `between()` returns NA if `left` or `right` is `NA` (fixes #2562).
* `case_when()` supports `NA` values (#2000, @tjmahr).
* `first()`, `last()`, and `nth()` have better default values for factor,
Dates, POSIXct, and data frame inputs (#2029).
* Fixed segmentation faults in hybrid evaluation of `first()`, `last()`,
`nth()`, `lead()`, and `lag()`. These functions now always fall back to the R
implementation if called with arguments that the hybrid evaluator cannot
handle (#948, #1980).
* `n_distinct()` gets larger hash tables given slightly better performance (#977).
* `nth()` and `ntile()` are more careful about proper data types of their return values (#2306).
* `ntile()` ignores `NA` when computing group membership (#2564).
* `lag()` enforces integer `n` (#2162, @kevinushey).
* hybrid `min()` and `max()` now always return a `numeric` and work correctly
in edge cases (empty input, all `NA`, ...) (#2305, #2436).
* `min_rank("string")` no longer segfaults in hybrid evaluation (#2279, #2444).
* `recode()` can now recode a factor to other types (#2268)
* `recode()` gains `.dots` argument to support passing replacements as list
(#2110, @jlegewie).
## Other minor changes and bug fixes
* Many error messages are more helpful by referring to a column name or a
position in the argument list (#2448).
* New `is_grouped_df()` alias to `is.grouped_df()`.
* `tbl_vars()` now has a `group_vars` argument set to `TRUE` by
default. If `FALSE`, group variables are not returned.
* Fixed segmentation fault after calling `rename()` on an invalid grouped
data frame (#2031).
* `rename_vars()` gains a `strict` argument to control if an
error is thrown when you try and rename a variable that doesn't
exist.
* Fixed undefined behavior for `slice()` on a zero-column data frame (#2490).
* Fixed very rare case of false match during join (#2515).
* Restricted workaround for `match()` to R 3.3.0. (#1858).
* dplyr now warns on load when the version of R or Rcpp during installation is
different to the currently installed version (#2514).
* Fixed improper reuse of attributes when creating a list column in `summarise()`
and perhaps `mutate()` (#2231).
* `mutate()` and `summarise()` always strip the `names` attribute from new
or updated columns, even for ungrouped operations (#1689).
* Fixed rare error that could lead to a segmentation fault in
`all_equal(ignore_col_order = FALSE)` (#2502).
* The "dim" and "dimnames" attributes are always stripped when copying a
vector (#1918, #2049).
* `grouped_df` and `rowwise` are registered officially as S3 classes.
This makes them easier to use with S4 (#2276, @joranE, #2789).
* All operations that return tibbles now include the `"tbl"` class.
This is important for correct printing with tibble 1.3.1 (#2789).
* Makeflags uses PKG_CPPFLAGS for defining preprocessor macros.
* astyle formatting for C++ code, tested but not changed as part of the tests
(#2086, #2103).
* Update RStudio project settings to install tests (#1952).
* Using `Rcpp::interfaces()` to register C callable interfaces, and registering all native exported functions via `R_registerRoutines()` and `useDynLib(.registration = TRUE)` (#2146).
* Formatting of grouped data frames now works by overriding the `tbl_sum()` generic instead of `print()`. This means that the output is more consistent with tibble, and that `format()` is now supported also for SQL sources (#2781).
# dplyr 0.5.0
## Breaking changes
### Existing functions
* `arrange()` once again ignores grouping (#1206).
* `distinct()` now only keeps the distinct variables. If you want to return
all variables (using the first row for non-distinct values) use
`.keep_all = TRUE` (#1110). For SQL sources, `.keep_all = FALSE` is
implemented using `GROUP BY`, and `.keep_all = TRUE` raises an error
(#1937, #1942, @krlmlr). (The default behaviour of using all variables
when none are specified remains - this note only applies if you select
some variables).
* The select helper functions `starts_with()`, `ends_with()` etc are now
real exported functions. This means that you'll need to import those
functions if you're using from a package where dplyr is not attached.
i.e. `dplyr::select(mtcars, starts_with("m"))` used to work, but
now you'll need `dplyr::select(mtcars, dplyr::starts_with("m"))`.
### Deprecated and defunct functions
* The long deprecated `chain()`, `chain_q()` and `%.%` have been removed.
Please use `%>%` instead.
* `id()` has been deprecated. Please use `group_indices()` instead
(#808).
* `rbind_all()` and `rbind_list()` are formally deprecated. Please use
`bind_rows()` instead (#803).
* Outdated benchmarking demos have been removed (#1487).
* Code related to starting and signalling clusters has been moved out to
[multidplyr](https://github.com/tidyverse/multidplyr).
## New functions
* `coalesce()` finds the first non-missing value from a set of vectors.
(#1666, thanks to @krlmlr for initial implementation).
* `case_when()` is a general vectorised if + else if (#631).
* `if_else()` is a vectorised if statement: it's a stricter (type-safe),
faster, and more predictable version of `ifelse()`. In SQL it is
translated to a `CASE` statement.
* `na_if()` makes it easy to replace a certain value with an `NA` (#1707).
In SQL it is translated to `NULL_IF`.
* `near(x, y)` is a helper for `abs(x - y) < tol` (#1607).
* `recode()` is vectorised equivalent to `switch()` (#1710).
* `union_all()` method. Maps to `UNION ALL` for SQL sources, `bind_rows()`
for data frames/tbl\_dfs, and `combine()` for vectors (#1045).
* A new family of functions replace `summarise_each()` and
`mutate_each()` (which will thus be deprecated in a future release).
`summarise_all()` and `mutate_all()` apply a function to all columns
while `summarise_at()` and `mutate_at()` operate on a subset of
columns. These columns are selected with either a character vector
of columns names, a numeric vector of column positions, or a column
specification with `select()` semantics generated by the new
`columns()` helper. In addition, `summarise_if()` and `mutate_if()`
take a predicate function or a logical vector (these verbs currently
require local sources). All these functions can now take ordinary
functions instead of a list of functions generated by `funs()`
(though this is only useful for local sources). (#1845, @lionel-)
* `select_if()` lets you select columns with a predicate function.
Only compatible with local sources. (#497, #1569, @lionel-)
## Local backends
### dtplyr
All data table related code has been separated out in to a new dtplyr package. This decouples the development of the data.table interface from the development of the dplyr package. If both data.table and dplyr are loaded, you'll get a message reminding you to load dtplyr.
### Tibble
Functions related to the creation and coercion of `tbl_df`s, now live in their own package: [tibble](https://blog.rstudio.com/2016/03/24/tibble-1-0-0/). See `vignette("tibble")` for more details.
* `$` and `[[` methods that never do partial matching (#1504), and throw
an error if the variable does not exist.
* `all_equal()` allows to compare data frames ignoring row and column order,
and optionally ignoring minor differences in type (e.g. int vs. double)
(#821). The test handles the case where the df has 0 columns (#1506).
The test fails fails when convert is `FALSE` and types don't match (#1484).
* `all_equal()` shows better error message when comparing raw values
or when types are incompatible and `convert = TRUE` (#1820, @krlmlr).
* `add_row()` makes it easy to add a new row to data frame (#1021)
* `as_data_frame()` is now an S3 generic with methods for lists (the old
`as_data_frame()`), data frames (trivial), and matrices (with efficient
C++ implementation) (#876). It no longer strips subclasses.
* The internals of `data_frame()` and `as_data_frame()` have been aligned,
so `as_data_frame()` will now automatically recycle length-1 vectors.
Both functions give more informative error messages if you attempting to
create an invalid data frame. You can no longer create a data frame with
duplicated names (#820). Both check for `POSIXlt` columns, and tell you to
use `POSIXct` instead (#813).
* `frame_data()` properly constructs rectangular tables (#1377, @kevinushey),
and supports list-cols.
* `glimpse()` is now a generic. The default method dispatches to `str()`
(#1325). It now (invisibly) returns its first argument (#1570).
* `lst()` and `lst_()` which create lists in the same way that
`data_frame()` and `data_frame_()` create data frames (#1290).
* `print.tbl_df()` is considerably faster if you have very wide data frames.
It will now also only list the first 100 additional variables not already
on screen - control this with the new `n_extra` parameter to `print()`
(#1161). When printing a grouped data frame the number of groups is now
printed with thousands separators (#1398). The type of list columns
is correctly printed (#1379)
* Package includes `setOldClass(c("tbl_df", "tbl", "data.frame"))` to help
with S4 dispatch (#969).
* `tbl_df` automatically generates column names (#1606).
### tbl_cube
* new `as_data_frame.tbl_cube()` (#1563, @krlmlr).
* `tbl_cube`s are now constructed correctly from data frames, duplicate
dimension values are detected, missing dimension values are filled
with `NA`. The construction from data frames now guesses the measure
variables by default, and allows specification of dimension and/or
measure variables (#1568, @krlmlr).
* Swap order of `dim_names` and `met_name` arguments in `as.tbl_cube`
(for `array`, `table` and `matrix`) for consistency with `tbl_cube` and
`as.tbl_cube.data.frame`. Also, the `met_name` argument to
`as.tbl_cube.table` now defaults to `"Freq"` for consistency with
`as.data.frame.table` (@krlmlr, #1374).
## Remote backends
* `as_data_frame()` on SQL sources now returns all rows (#1752, #1821,
@krlmlr).
* `compute()` gets new parameters `indexes` and `unique_indexes` that make
it easier to add indexes (#1499, @krlmlr).
* `db_explain()` gains a default method for DBIConnections (#1177).
* The backend testing system has been improved. This lead to the removal of
`temp_srcs()`. In the unlikely event that you were using this function,
you can instead use `test_register_src()`, `test_load()`, and `test_frame()`.
* You can now use `right_join()` and `full_join()` with remote tables (#1172).
### SQLite
* `src_memdb()` is a session-local in-memory SQLite database.
`memdb_frame()` works like `data_frame()`, but creates a new table in
that database.
* `src_sqlite()` now uses a stricter quoting character, `` ` ``, instead of
`"`. SQLite "helpfully" will convert `"x"` into a string if there is
no identifier called x in the current scope (#1426).
* `src_sqlite()` throws errors if you try and use it with window functions
(#907).
### SQL translation
* `filter.tbl_sql()` now puts parens around each argument (#934).
* Unary `-` is better translated (#1002).
* `escape.POSIXt()` method makes it easier to use date times. The date is
rendered in ISO 8601 format in UTC, which should work in most databases
(#857).
* `is.na()` gets a missing space (#1695).
* `if`, `is.na()`, and `is.null()` get extra parens to make precedence
more clear (#1695).
* `pmin()` and `pmax()` are translated to `MIN()` and `MAX()` (#1711).
* Window functions:
* Work on ungrouped data (#1061).
* Warning if order is not set on cumulative window functions.
* Multiple partitions or ordering variables in windowed functions no
longer generate extra parentheses, so should work for more databases
(#1060)
### Internals
This version includes an almost total rewrite of how dplyr verbs are translated into SQL. Previously, I used a rather ad-hoc approach, which tried to guess when a new subquery was needed. Unfortunately this approach was fraught with bugs, so in this version I've implemented a much richer internal data model. Now there is a three step process:
1. When applied to a `tbl_lazy`, each dplyr verb captures its inputs
and stores in a `op` (short for operation) object.
2. `sql_build()` iterates through the operations building to build up an
object that represents a SQL query. These objects are convenient for
testing as they are lists, and are backend agnostics.
3. `sql_render()` iterates through the queries and generates the SQL,
using generics (like `sql_select()`) that can vary based on the
backend.
In the short-term, this increased abstraction is likely to lead to some minor performance decreases, but the chance of dplyr generating correct SQL is much much higher. In the long-term, these abstractions will make it possible to write a query optimiser/compiler in dplyr, which would make it possible to generate much more succinct queries.
If you have written a dplyr backend, you'll need to make some minor changes to your package:
* `sql_join()` has been considerably simplified - it is now only responsible
for generating the join query, not for generating the intermediate selects
that rename the variable. Similarly for `sql_semi_join()`. If you've
provided new methods in your backend, you'll need to rewrite.
* `select_query()` gains a distinct argument which is used for generating
queries for `distinct()`. It loses the `offset` argument which was
never used (and hence never tested).
* `src_translate_env()` has been replaced by `sql_translate_env()` which
should have methods for the connection object.
There were two other tweaks to the exported API, but these are less likely to affect anyone.
* `translate_sql()` and `partial_eval()` got a new API: now use connection +
variable names, rather than a `tbl`. This makes testing considerably easier.
`translate_sql_q()` has been renamed to `translate_sql_()`.
* Also note that the sql generation generics now have a default method, instead
methods for DBIConnection and NULL.
## Minor improvements and bug fixes
### Single table verbs
* Avoiding segfaults in presence of `raw` columns (#1803, #1817, @krlmlr).
* `arrange()` fails gracefully on list columns (#1489) and matrices
(#1870, #1945, @krlmlr).
* `count()` now adds additional grouping variables, rather than overriding
existing (#1703). `tally()` and `count()` can now count a variable
called `n` (#1633). Weighted `count()`/`tally()` ignore `NA`s (#1145).
* The progress bar in `do()` is now updated at most 20 times per second,
avoiding unneccessary redraws (#1734, @mkuhn)
* `distinct()` doesn't crash when given a 0-column data frame (#1437).
* `filter()` throws an error if you supply an named arguments. This is usually
a type: `filter(df, x = 1)` instead of `filter(df, x == 1)` (#1529).
* `summarise()` correctly coerces factors with different levels (#1678),
handles min/max of already summarised variable (#1622), and
supports data frames as columns (#1425).
* `select()` now informs you that it adds missing grouping variables
(#1511). It works even if the grouping variable has a non-syntactic name
(#1138). Negating a failed match (e.g. `select(mtcars, -contains("x"))`)
returns all columns, instead of no columns (#1176)
The `select()` helpers are now exported and have their own
documentation (#1410). `one_of()` gives a useful error message if
variables names are not found in data frame (#1407).
* The naming behaviour of `summarise_each()` and `mutate_each()` has been
tweaked so that you can force inclusion of both the function and the
variable name: `summarise_each(mtcars, funs(mean = mean), everything())`
(#442).
* `mutate()` handles factors that are all `NA` (#1645), or have different
levels in different groups (#1414). It disambiguates `NA` and `NaN` (#1448),
and silently promotes groups that only contain `NA` (#1463). It deep copies
data in list columns (#1643), and correctly fails on incompatible columns
(#1641). `mutate()` on a grouped data no longer groups grouping attributes
(#1120). `rowwise()` mutate gives expected results (#1381).
* `one_of()` tolerates unknown variables in `vars`, but warns (#1848, @jennybc).
* `print.grouped_df()` passes on `...` to `print()` (#1893).
* `slice()` correctly handles grouped attributes (#1405).
* `ungroup()` generic gains `...` (#922).
### Dual table verbs
* `bind_cols()` matches the behaviour of `bind_rows()` and ignores `NULL`
inputs (#1148). It also handles `POSIXct`s with integer base type (#1402).
* `bind_rows()` handles 0-length named lists (#1515), promotes factors to
characters (#1538), and warns when binding factor and character (#1485).
bind_rows()` is more flexible in the way it can accept data frames,
lists, list of data frames, and list of lists (#1389).
* `bind_rows()` rejects `POSIXlt` columns (#1875, @krlmlr).
* Both `bind_cols()` and `bind_rows()` infer classes and grouping information
from the first data frame (#1692).
* `rbind()` and `cbind()` get `grouped_df()` methods that make it harder to
create corrupt data frames (#1385). You should still prefer `bind_rows()`
and `bind_cols()`.
* Joins now use correct class when joining on `POSIXct` columns
(#1582, @joel23888), and consider time zones (#819). Joins handle a `by`
that is empty (#1496), or has duplicates (#1192). Suffixes grow progressively
to avoid creating repeated column names (#1460). Joins on string columns
should be substantially faster (#1386). Extra attributes are ok if they are
identical (#1636). Joins work correct when factor levels not equal
(#1712, #1559). Anti- and semi-joins give correct result when by variable
is a factor (#1571), but warn if factor levels are inconsistent (#2741).
A clear error message is given for joins where an
explicit `by` contains unavailable columns (#1928, #1932).
Warnings about join column inconsistencies now contain the column names
(#2728).
* `inner_join()`, `left_join()`, `right_join()`, and `full_join()` gain a
`suffix` argument which allows you to control what suffix duplicated variable
names receive (#1296).
* Set operations (`intersect()`, `union()` etc) respect coercion rules
(#799). `setdiff()` handles factors with `NA` levels (#1526).
* There were a number of fixes to enable joining of data frames that don't
have the same encoding of column names (#1513), including working around
bug 16885 regarding `match()` in R 3.3.0 (#1806, #1810,
@krlmlr).
### Vector functions
* `combine()` silently drops `NULL` inputs (#1596).
* Hybrid `cummean()` is more stable against floating point errors (#1387).
* Hybrid `lead()` and `lag()` received a considerable overhaul. They are more
careful about more complicated expressions (#1588), and falls back more
readily to pure R evaluation (#1411). They behave correctly in `summarise()`
(#1434). and handle default values for string columns.
* Hybrid `min()` and `max()` handle empty sets (#1481).
* `n_distinct()` uses multiple arguments for data frames (#1084), falls back to R
evaluation when needed (#1657), reverting decision made in (#567).
Passing no arguments gives an error (#1957, #1959, @krlmlr).
* `nth()` now supports negative indices to select from end, e.g. `nth(x, -2)`
selects the 2nd value from the end of `x` (#1584).
* `top_n()` can now also select bottom `n` values by passing a negative value
to `n` (#1008, #1352).
* Hybrid evaluation leaves formulas untouched (#1447).
# dplyr 0.4.3
## Improved encoding support
Until now, dplyr's support for non-UTF8 encodings has been rather shaky. This release brings a number of improvement to fix these problems: it's probably not perfect, but should be a lot better than the previously version. This includes fixes to `arrange()` (#1280), `bind_rows()` (#1265), `distinct()` (#1179), and joins (#1315). `print.tbl_df()` also received a fix for strings with invalid encodings (#851).
## Other minor improvements and bug fixes
* `frame_data()` provides a means for constructing `data_frame`s using
a simple row-wise language. (#1358, @kevinushey)
* `all.equal()` no longer runs all outputs together (#1130).
* `as_data_frame()` gives better error message with NA column names (#1101).
* `[.tbl_df` is more careful about subsetting column names (#1245).
* `arrange()` and `mutate()` work on empty data frames (#1142).
* `arrange()`, `filter()`, `slice()`, and `summarise()` preserve data frame
meta attributes (#1064).
* `bind_rows()` and `bind_cols()` accept lists (#1104): during initial data
cleaning you no longer need to convert lists to data frames, but can
instead feed them to `bind_rows()` directly.
* `bind_rows()` gains a `.id` argument. When supplied, it creates a
new column that gives the name of each data frame (#1337, @lionel-).
* `bind_rows()` respects the `ordered` attribute of factors (#1112), and
does better at comparing `POSIXct`s (#1125). The `tz` attribute is ignored
when determining if two `POSIXct` vectors are comparable. If the `tz` of
all inputs is the same, it's used, otherwise its set to `UTC`.
* `data_frame()` always produces a `tbl_df` (#1151, @kevinushey)
* `filter(x, TRUE, TRUE)` now just returns `x` (#1210),
it doesn't internally modify the first argument (#971), and
it now works with rowwise data (#1099). It once again works with
data tables (#906).
* `glimpse()` also prints out the number of variables in addition to the number
of observations (@ilarischeinin, #988).
* Joins handles matrix columns better (#1230), and can join `Date` objects
with heterogenous representations (some `Date`s are integers, while other
are numeric). This also improves `all.equal()` (#1204).
* Fixed `percent_rank()` and `cume_dist()` so that missing values no longer
affect denominator (#1132).
* `print.tbl_df()` now displays the class for all variables, not just those
that don't fit on the screen (#1276). It also displays duplicated column
names correctly (#1159).
* `print.grouped_df()` now tells you how many groups there are.
* `mutate()` can set to `NULL` the first column (used to segfault, #1329) and
it better protects intermediary results (avoiding random segfaults, #1231).
* `mutate()` on grouped data handles the special case where for the first few
groups, the result consists of a `logical` vector with only `NA`. This can
happen when the condition of an `ifelse` is an all `NA` logical vector (#958).
* `mutate.rowwise_df()` handles factors (#886) and correctly handles
0-row inputs (#1300).
* `n_distinct()` gains an `na_rm` argument (#1052).
* The `Progress` bar used by `do()` now respects global option
`dplyr.show_progress` (default is TRUE) so you can turn it off globally
(@jimhester #1264, #1226).
* `summarise()` handles expressions that returning heterogenous outputs,
e.g. `median()`, which that sometimes returns an integer, and other times a
numeric (#893).
* `slice()` silently drops columns corresponding to an NA (#1235).
* `ungroup.rowwise_df()` gives a `tbl_df` (#936).
* More explicit duplicated column name error message (#996).
* When "," is already being used as the decimal point (`getOption("OutDec")`),
use "." as the thousands separator when printing out formatted numbers
(@ilarischeinin, #988).
## Databases
* `db_query_fields.SQLiteConnection` uses `build_sql` rather than `paste0`
(#926, @NikNakk)
* Improved handling of `log()` (#1330).
* `n_distinct(x)` is translated to `COUNT(DISTINCT(x))` (@skparkes, #873).
* `print(n = Inf)` now works for remote sources (#1310).
## Hybrid evaluation
* Hybrid evaluation does not take place for objects with a class (#1237).
* Improved `$` handling (#1134).
* Simplified code for `lead()` and `lag()` and make sure they work properly on
factors (#955). Both respect the `default` argument (#915).
* `mutate` can set to `NULL` the first column (used to segfault, #1329).
* `filter` on grouped data handles indices correctly (#880).
* `sum()` issues a warning about integer overflow (#1108).
# dplyr 0.4.2
This is a minor release containing fixes for a number of crashes and issues identified by R CMD CHECK. There is one new "feature": dplyr no longer complains about unrecognised attributes, and instead just copies them over to the output.
* `lag()` and `lead()` for grouped data were confused about indices and therefore
produced wrong results (#925, #937). `lag()` once again overrides `lag()`
instead of just the default method `lag.default()`. This is necessary due to
changes in R CMD check. To use the lag function provided by another package,
use `pkg::lag`.
* Fixed a number of memory issues identified by valgrind.
* Improved performance when working with large number of columns (#879).
* Lists-cols that contain data frames now print a slightly nicer summary
(#1147)
* Set operations give more useful error message on incompatible data frames
(#903).
* `all.equal()` gives the correct result when `ignore_row_order` is `TRUE`
(#1065) and `all.equal()` correctly handles character missing values (#1095).
* `bind_cols()` always produces a `tbl_df` (#779).
* `bind_rows()` gains a test for a form of data frame corruption (#1074).
* `bind_rows()` and `summarise()` now handles complex columns (#933).
* Workaround for using the constructor of `DataFrame` on an unprotected object
(#998)
* Improved performance when working with large number of columns (#879).
# dplyr 0.4.1
* Don't assume that RPostgreSQL is available.
# 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_frames")` 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 columns 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 received a number of small tweaks. All `print()` 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 `NA`s 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 meaningful 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 `Date`s 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).
* Removed bogus `NAs introduced by coercion to integer range` on 32-bit Windows (#2708).
# dplyr 0.3.0.1
* Fixed problem with test script on Windows.
# dplyr 0.3
## New functions
* `between()` vector function efficiently determines if numeric values fall
in a range, and is translated to special form for SQL (#503).
* `count()` makes it even easier to do (weighted) counts (#358).
* `data_frame()` by @kevinushey is a nicer way of creating data frames.
It never coerces column types (no more `stringsAsFactors = FALSE`!),
never munges column names, and never adds row names. You can use previously
defined columns to compute new columns (#376).
* `distinct()` returns distinct (unique) rows of a tbl (#97). Supply
additional variables to return the first row for each unique combination
of variables.
* Set operations, `intersect()`, `union()` and `setdiff()` now have methods
for data frames, data tables and SQL database tables (#93). They pass their
arguments down to the base functions, which will ensure they raise errors if
you pass in two many arguments.
* Joins (e.g. `left_join()`, `inner_join()`, `semi_join()`, `anti_join()`)
now allow you to join on different variables in `x` and `y` tables by
supplying a named vector to `by`. For example, `by = c("a" = "b")` joins
`x.a` to `y.b`.
* `n_groups()` function tells you how many groups in a tbl. It returns
1 for ungrouped data. (#477)
* `transmute()` works like `mutate()` but drops all variables that you didn't
explicitly refer to (#302).
* `rename()` makes it easy to rename variables - it works similarly to
`select()` but it preserves columns that you didn't otherwise touch.
* `slice()` allows you to selecting rows by position (#226). It includes
positive integers, drops negative integers and you can use expression like
`n()`.
## Programming with dplyr (non-standard evaluation)
* You can now program with dplyr - every function that does non-standard
evaluation (NSE) has a standard evaluation (SE) version ending in `_`.
This is powered by the new lazyeval package which provides all the tools
needed to implement NSE consistently and correctly.
* See `vignette("nse")` for full details.
* `regroup()` is deprecated. Please use the more flexible `group_by_()`
instead.
* `summarise_each_q()` and `mutate_each_q()` are deprecated. Please use
`summarise_each_()` and `mutate_each_()` instead.
* `funs_q` has been replaced with `funs_`.
## Removed and deprecated features
* `%.%` has been deprecated: please use `%>%` instead. `chain()` is
defunct. (#518)
* `filter.numeric()` removed. Need to figure out how to reimplement with
new lazy eval system.
* The `Progress` refclass is no longer exported to avoid conflicts with shiny.
Instead use `progress_estimated()` (#535).
* `src_monetdb()` is now implemented in MonetDB.R, not dplyr.
* `show_sql()` and `explain_sql()` and matching global options `dplyr.show_sql`
and `dplyr.explain_sql` have been removed. Instead use `show_query()` and
`explain()`.
## Minor improvements and bug fixes
* Main verbs now have individual documentation pages (#519).
* `%>%` is simply re-exported from magrittr, instead of creating a local copy
(#496, thanks to @jimhester)
* Examples now use `nycflights13` instead of `hflights` because it the variables
have better names and there are a few interlinked tables (#562). `Lahman` and
`nycflights13` are (once again) suggested packages. This means many examples
will not work unless you explicitly install them with
`install.packages(c("Lahman", "nycflights13"))` (#508). dplyr now depends on
Lahman 3.0.1. A number of examples have been updated to reflect modified
field names (#586).
* `do()` now displays the progress bar only when used in interactive prompts
and not when knitting (#428, @jimhester).
* `glimpse()` now prints a trailing new line (#590).
* `group_by()` has more consistent behaviour when grouping by constants:
it creates a new column with that value (#410). It renames grouping
variables (#410). The first argument is now `.data` so you can create
new groups with name x (#534).
* Now instead of overriding `lag()`, dplyr overrides `lag.default()`,
which should avoid clobbering lag methods added by other packages.
(#277).
* `mutate(data, a = NULL)` removes the variable `a` from the returned
dataset (#462).
* `trunc_mat()` and hence `print.tbl_df()` and friends gets a `width` argument
to control the default output width. Set `options(dplyr.width = Inf)` to
always show all columns (#589).
* `select()` gains `one_of()` selector: this allows you to select variables
provided by a character vector (#396). It fails immediately if you give an
empty pattern to `starts_with()`, `ends_with()`, `contains()` or `matches()`
(#481, @leondutoit). Fixed buglet in `select()` so that you can now create
variables called `val` (#564).
* Switched from RC to R6.
* `tally()` and `top_n()` work consistently: neither accidentally
evaluates the the `wt` param. (#426, @mnel)
* `rename` handles grouped data (#640).
## Minor improvements and bug fixes by backend
### Databases
* Correct SQL generation for `paste()` when used with the collapse parameter
targeting a Postgres database. (@rbdixon, #1357)
* The db backend system has been completely overhauled in order to make
it possible to add backends in other packages, and to support a much
wider range of databases. See `vignette("new-sql-backend")` for instruction
on how to create your own (#568).
* `src_mysql()` gains a method for `explain()`.
* When `mutate()` creates a new variable that uses a window function,
automatically wrap the result in a subquery (#484).
* Correct SQL generation for `first()` and `last()` (#531).
* `order_by()` now works in conjunction with window functions in databases
that support them.
### Data frames/`tbl_df`
* All verbs now understand how to work with `difftime()` (#390) and
`AsIs` (#453) objects. They all check that colnames are unique (#483), and
are more robust when columns are not present (#348, #569, #600).
* Hybrid evaluation bugs fixed:
* Call substitution stopped too early when a sub expression contained a
`$` (#502).
* Handle `::` and `:::` (#412).
* `cumany()` and `cumall()` properly handle `NA` (#408).
* `nth()` now correctly preserve the class when using dates, times and
factors (#509).
* no longer substitutes within `order_by()` because `order_by()` needs to do
its own NSE (#169).
* `[.tbl_df` always returns a tbl_df (i.e. `drop = FALSE` is the default)
(#587, #610). `[.grouped_df` preserves important output attributes (#398).
* `arrange()` keeps the grouping structure of grouped data (#491, #605),
and preserves input classes (#563).
* `contains()` accidentally matched regular expressions, now it passes
`fixed = TRUE` to `grep()` (#608).
* `filter()` asserts all variables are white listed (#566).
* `mutate()` makes a `rowwise_df` when given a `rowwise_df` (#463).
* `rbind_all()` creates `tbl_df` objects instead of raw `data.frame`s.
* If `select()` doesn't match any variables, it returns a 0-column data frame,
instead of the original (#498). It no longer fails when if some columns
are not named (#492)
* `sample_n()` and `sample_frac()` methods for data.frames exported.
(#405, @alyst)
* A grouped data frame may have 0 groups (#486). Grouped df objects
gain some basic validity checking, which should prevent some crashes
related to corrupt `grouped_df` objects made by `rbind()` (#606).
* More coherence when joining columns of compatible but different types,
e.g. when joining a character vector and a factor (#455),
or a numeric and integer (#450)
* `mutate()` works for on zero-row grouped data frame, and
with list columns (#555).
* `LazySubset` was confused about input data size (#452).
* Internal `n_distinct()` is stricter about it's inputs: it requires one symbol
which must be from the data frame (#567).
* `rbind_*()` handle data frames with 0 rows (#597). They fill character
vector columns with `NA` instead of blanks (#595). They work with
list columns (#463).
* Improved handling of encoding for column names (#636).
* Improved handling of hybrid evaluation re $ and @ (#645).
### Data tables
* Fix major omission in `tbl_dt()` and `grouped_dt()` methods - I was
accidentally doing a deep copy on every result :(
* `summarise()` and `group_by()` now retain over-allocation when working with
data.tables (#475, @arunsrinivasan).
* joining two data.tables now correctly dispatches to data table methods,
and result is a data table (#470)
### Cubes
* `summarise.tbl_cube()` works with single grouping variable (#480).
# dplyr 0.2
## 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](http://www.monetdb.org) 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).
# dplyr 0.1.3
## Bug fixes
* `select()` actually renames columns in a data table (#284).
* `rbind_all()` and `rbind_list()` now handle missing values in factors (#279).
* SQL joins now work better if names duplicated in both x and y tables (#310).
* Builds against Rcpp 0.11.1
* `select()` correctly works with the vars attribute (#309).
* Internal code is stricter when deciding if a data frame is grouped (#308):
this avoids a number of situations which previously caused problems.
* More data frame joins work with missing values in keys (#306).
# dplyr 0.1.2
## New features
* `select()` is substantially more powerful. You can use named arguments to
rename existing variables, and new functions `starts_with()`, `ends_with()`,
`contains()`, `matches()` and `num_range()` to select variables based on
their names. It now also makes a shallow copy, substantially reducing its
memory impact (#158, #172, #192, #232).
* `summarize()` added as alias for `summarise()` for people from countries
that don't don't spell things correctly ;) (#245)
## Bug fixes
* `filter()` now fails when given anything other than a logical vector, and
correctly handles missing values (#249). `filter.numeric()` proxies
`stats::filter()` so you can continue to use `filter()` function with
numeric inputs (#264).
* `summarise()` correctly uses newly created variables (#259).
* `mutate()` correctly propagates attributes (#265) and `mutate.data.frame()`
correctly mutates the same variable repeatedly (#243).
* `lead()` and `lag()` preserve attributes, so they now work with
dates, times and factors (#166).
* `n()` never accepts arguments (#223).
* `row_number()` gives correct results (#227).
* `rbind_all()` silently ignores data frames with 0 rows or 0 columns (#274).
* `group_by()` orders the result (#242). It also checks that columns
are of supported types (#233, #276).
* The hybrid evaluator did not handle some expressions correctly, for
example in `if(n() > 5) 1 else 2` the subexpression `n()` was not
substituted correctly. It also correctly processes `$` (#278).
* `arrange()` checks that all columns are of supported types (#266). It also
handles list columns (#282).
* Working towards Solaris compatibility.
* Benchmarking vignette temporarily disabled due to microbenchmark
problems reported by BDR.
# dplyr 0.1.1
## Improvements
* new `location()` and `changes()` functions which provide more information
about how data frames are stored in memory so that you can see what
gets copied.
* renamed `explain_tbl()` to `explain()` (#182).
* `tally()` gains `sort` argument to sort output so highest counts
come first (#173).
* `ungroup.grouped_df()`, `tbl_df()`, `as.data.frame.tbl_df()` now only
make shallow copies of their inputs (#191).
* The `benchmark-baseball` vignette now contains fairer (including grouping
times) comparisons with `data.table`. (#222)
## Bug fixes
* `filter()` (#221) and `summarise()` (#194) correctly propagate attributes.
* `summarise()` throws an error when asked to summarise an unknown variable
instead of crashing (#208).
* `group_by()` handles factors with missing values (#183).
* `filter()` handles scalar results (#217) and better handles scoping, e.g.
`filter(., variable)` where `variable` is defined in the function that calls
`filter`. It also handles `T` and `F` as aliases to `TRUE` and `FALSE`
if there are no `T` or `F` variables in the data or in the scope.
* `select.grouped_df` fails when the grouping variables are not included
in the selected variables (#170)
* `all.equal.data.frame()` handles a corner case where the data frame has
`NULL` names (#217)
* `mutate()` gives informative error message on unsupported types (#179)
* dplyr source package no longer includes pandas benchmark, reducing
download size from 2.8 MB to 0.5 MB.
