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Changelog

Source: NEWS.md

dplyr 1.1.4

  • join_by() now allows its helper functions to be namespaced with dplyr::, like join_by(dplyr::between(x, lower, upper)) (#6838).

  • left_join() and friends now return a specialized error message if they detect that your join would return more rows than dplyr can handle (#6912).

  • slice_*() now throw the correct error if you forget to name n while also prefixing the call with dplyr:: (#6946).

  • dplyr_reconstruct()’s default method has been rewritten to avoid materializing duckplyr queries too early (#6947).

  • Updated the storms data to include 2022 data (#6937, @steveharoz).

  • Updated the starwars data to use a new API, because the old one is defunct. There are very minor changes to the data itself (#6938, @steveharoz).

dplyr 1.1.3

CRAN release: 2023-09-03

dplyr 1.1.2

CRAN release: 2023-04-20

  • count() better documents that it has a .drop argument (#6820).

  • Fixed tests to maintain compatibility with the next version of waldo (#6823).

  • Joins better handle key columns will all NAs (#6804).

dplyr 1.1.1

CRAN release: 2023-03-22

  • Mutating joins now warn about multiple matches much less often. At a high level, a warning was previously being thrown when a one-to-many or many-to-many relationship was detected between the keys of x and y, but is now only thrown for a many-to-many relationship, which is much rarer and much more dangerous than one-to-many because it can result in a Cartesian explosion in the number of rows returned from the join (#6731, #6717).

    We’ve accomplished this in two steps:

    • multiple now defaults to "all", and the options of "error" and "warning" are now deprecated in favor of using relationship (see below). We are using an accelerated deprecation process for these two options because they’ve only been available for a few weeks, and relationship is a clearly superior alternative.

    • The mutating joins gain a new relationship argument, allowing you to optionally enforce one of the following relationship constraints between the keys of x and y: "one-to-one", "one-to-many", "many-to-one", or "many-to-many".

      For example, "many-to-one" enforces that each row in x can match at most 1 row in y. If a row in x matches >1 rows in y, an error is thrown. This option serves as the replacement for multiple = "error".

      The default behavior of relationship doesn’t assume that there is any relationship between x and y. However, for equality joins it will check for the presence of a many-to-many relationship, and will warn if it detects one.

    This change unfortunately does mean that if you have set multiple = "all" to avoid a warning and you happened to be doing a many-to-many style join, then you will need to replace multiple = "all" with relationship = "many-to-many" to silence the new warning, but we believe this should be rare since many-to-many relationships are fairly uncommon.

  • Fixed a major performance regression in case_when(). It is still a little slower than in dplyr 1.0.10, but we plan to improve this further in the future (#6674).

  • Fixed a performance regression related to nth(), first(), and last() (#6682).

  • Fixed an issue where expressions involving infix operators had an abnormally large amount of overhead (#6681).

  • group_data() on ungrouped data frames is faster (#6736).

  • n() is a little faster when there are many groups (#6727).

  • pick() now returns a 1 row, 0 column tibble when ... evaluates to an empty selection. This makes it more compatible with tidyverse recycling rules in some edge cases (#6685).

  • if_else() and case_when() again accept logical conditions that have attributes (#6678).

  • arrange() can once again sort the numeric_version type from base R (#6680).

  • slice_sample() now works when the input has a column named replace. slice_min() and slice_max() now work when the input has columns named na_rm or with_ties (#6725).

  • nth() now errors informatively if n is NA (#6682).

  • Joins now throw a more informative error when y doesn’t have the same source as x (#6798).

  • All major dplyr verbs now throw an informative error message if the input data frame contains a column named NA or "" (#6758).

  • Deprecation warnings thrown by filter() now mention the correct package where the problem originated from (#6679).

  • Fixed an issue where using <- within a grouped mutate() or summarise() could cross contaminate other groups (#6666).

  • The compatibility vignette has been replaced with a more general vignette on using dplyr in packages, vignette("in-packages") (#6702).

  • The developer documentation in ?dplyr_extending has been refreshed and brought up to date with all changes made in 1.1.0 (#6695).

  • rename_with() now includes an example of using paste0(recycle0 = TRUE) to correctly handle empty selections (#6688).

  • R >=3.5.0 is now explicitly required. This is in line with the tidyverse policy of supporting the 5 most recent versions of R.

dplyr 1.1.0

CRAN release: 2023-01-29

New features

  • .by/by is an experimental alternative to group_by() that supports per-operation grouping for mutate(), summarise(), filter(), and the slice() family (#6528).

    Rather than:

    starwars %>%
  group_by(species, homeworld) %>%
  summarise(mean_height = mean(height))

    You can now write:

    starwars %>%
  summarise(
    mean_height = mean(height),
    .by = c(species, homeworld)
  )

    The most useful reason to do this is because .by only affects a single operation. In the example above, an ungrouped data frame went into the summarise() call, so an ungrouped data frame will come out; with .by, you never need to remember to ungroup() afterwards and you never need to use the .groups argument.

    Additionally, using summarise() with .by will never sort the results by the group key, unlike with group_by(). Instead, the results are returned using the existing ordering of the groups from the original data. We feel this is more predictable, better maintains any ordering you might have already applied with a previous call to arrange(), and provides a way to maintain the current ordering without having to resort to factors.

    This feature was inspired by data.table, where the equivalent syntax looks like:

    starwars[, .(mean_height = mean(height)), by = .(species, homeworld)]

    with_groups() is superseded in favor of .by (#6582).

  • reframe() is a new experimental verb that creates a new data frame by applying functions to columns of an existing data frame. It is very similar to summarise(), with two big differences:

    • reframe() can return an arbitrary number of rows per group, while summarise() reduces each group down to a single row.

    • reframe() always returns an ungrouped data frame, while summarise() might return a grouped or rowwise data frame, depending on the scenario.

    reframe() has been added in response to valid concern from the community that allowing summarise() to return any number of rows per group increases the chance for accidental bugs. We still feel that this is a powerful technique, and is a principled replacement for do(), so we have moved these features to reframe() (#6382).

  • group_by() now uses a new algorithm for computing groups. It is often faster than the previous approach (especially when there are many groups), and in most cases there should be no changes. The one exception is with character vectors, see the C locale news bullet below for more details (#4406, #6297).

  • arrange() now uses a faster algorithm for sorting character vectors, which is heavily inspired by data.table’s forder(). See the C locale news bullet below for more details (#4962).

  • Joins have been completely overhauled to enable more flexible join operations and provide more tools for quality control. Many of these changes are inspired by data.table’s join syntax (#5914, #5661, #5413, #2240).

    • A join specification can now be created through join_by(). This allows you to specify both the left and right hand side of a join using unquoted column names, such as join_by(sale_date == commercial_date). Join specifications can be supplied to any *_join() function as the by argument.

    • Join specifications allow for new types of joins:

      • Equality joins: The most common join, specified by ==. For example, join_by(sale_date == commercial_date).

      • Inequality joins: For joining on inequalities, i.e.>=, >, <, and <=. For example, use join_by(sale_date >= commercial_date) to find every commercial that aired before a particular sale.

      • Rolling joins: For “rolling” the closest match forward or backwards when there isn’t an exact match, specified by using the rolling helper, closest(). For example, join_by(closest(sale_date >= commercial_date)) to find only the most recent commercial that aired before a particular sale.

      • Overlap joins: For detecting overlaps between sets of columns, specified by using one of the overlap helpers: between(), within(), or overlaps(). For example, use join_by(between(commercial_date, sale_date_lower, sale_date)) to find commercials that aired before a particular sale, as long as they occurred after some lower bound, such as 40 days before the sale was made.

      Note that you cannot use arbitrary expressions in the join conditions, like join_by(sale_date - 40 >= commercial_date). Instead, use mutate() to create a new column containing the result of sale_date - 40 and refer to that by name in join_by().

    • multiple is a new argument for controlling what happens when a row in x matches multiple rows in y. For equality joins and rolling joins, where this is usually surprising, this defaults to signalling a "warning", but still returns all of the matches. For inequality joins, where multiple matches are usually expected, this defaults to returning "all" of the matches. You can also return only the "first" or "last" match, "any" of the matches, or you can "error".

    • keep now defaults to NULL rather than FALSE. NULL implies keep = FALSE for equality conditions, but keep = TRUE for inequality conditions, since you generally want to preserve both sides of an inequality join.

    • unmatched is a new argument for controlling what happens when a row would be dropped because it doesn’t have a match. For backwards compatibility, the default is "drop", but you can also choose to "error" if dropped rows would be surprising.

  • across() gains an experimental .unpack argument to optionally unpack (as in, tidyr::unpack()) data frames returned by functions in .fns (#6360).

  • consecutive_id() for creating groups based on contiguous runs of the same values, like data.table::rleid() (#1534).

  • case_match() is a “vectorised switch” variant of case_when() that matches on values rather than logical expressions. It is like a SQL “simple” CASE WHEN statement, whereas case_when() is like a SQL “searched” CASE WHEN statement (#6328).

  • cross_join() is a more explicit and slightly more correct replacement for using by = character() during a join (#6604).

  • pick() makes it easy to access a subset of columns from the current group. pick() is intended as a replacement for across(.fns = NULL), cur_data(), and cur_data_all(). We feel that pick() is a much more evocative name when you are just trying to select a subset of columns from your data (#6204).

  • symdiff() computes the symmetric difference (#4811).

Lifecycle changes

Breaking changes

Newly deprecated

  • across(), c_across(), if_any(), and if_all() now require the .cols and .fns arguments. In general, we now recommend that you use pick() instead of an empty across() call or across() with no .fns (e.g. across(c(x, y)). (#6523).

    • Relying on the previous default of .cols = everything() is deprecated. We have skipped the soft-deprecation stage in this case, because indirect usage of across() and friends in this way is rare.

    • Relying on the previous default of .fns = NULL is not yet formally soft-deprecated, because there was no good alternative until now, but it is discouraged and will be soft-deprecated in the next minor release.

  • Passing ... to across() is soft-deprecated because it’s ambiguous when those arguments are evaluated. Now, instead of (e.g.) across(a:b, mean, na.rm = TRUE) you should write across(a:b, ~ mean(.x, na.rm = TRUE)) (#6073).

  • all_equal() is deprecated. We’ve advised against it for some time, and we explicitly recommend you use all.equal(), manually reordering the rows and columns as needed (#6324).

  • cur_data() and cur_data_all() are soft-deprecated in favour of pick() (#6204).

  • Using by = character() to perform a cross join is now soft-deprecated in favor of cross_join() (#6604).

  • filter()ing with a 1-column matrix is deprecated (#6091).

  • progress_estimate() is deprecated for all uses (#6387).

  • Using summarise() to produce a 0 or >1 row “summary” is deprecated in favor of the new reframe(). See the NEWS bullet about reframe() for more details (#6382).

  • All functions deprecated in 1.0.0 (released April 2020) and earlier now warn every time you use them (#6387). This includes combine(), src_local(), src_mysql(), src_postgres(), src_sqlite(), rename_vars_(), select_vars_(), summarise_each_(), mutate_each_(), as.tbl(), tbl_df(), and a handful of older arguments. They are likely to be made defunct in the next major version (but not before mid 2024).

  • slice()ing with a 1-column matrix is deprecated.

Newly superseded

  • recode() is superseded in favour of case_match() (#6433).

  • recode_factor() is superseded. We don’t have a direct replacement for it yet, but we plan to add one to forcats. In the meantime you can often use case_match(.ptype = factor(levels = )) instead (#6433).

  • transmute() is superseded in favour of mutate(.keep = "none") (#6414).

Newly stable

  • The .keep, .before, and .after arguments to mutate() have moved from experimental to stable.

  • The rows_*() family of functions have moved from experimental to stable.

vctrs

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

  • between() can now work with all vector types, not just numeric and date-time. Additionally, left and right can now also be vectors (with the same length as x), and x, left, and right are cast to the common type before the comparison is made (#6183, #6260, #6478).

  • case_when() (#5106):

    • Has a new .default argument that is intended to replace usage of TRUE ~ default_value as a more explicit and readable way to specify a default value. In the future, we will deprecate the unsafe recycling of the LHS inputs that allows TRUE ~ to work, so we encourage you to switch to using .default.

    • No longer requires exact matching of the types of RHS values. For example, the following no longer requires you to use NA_character_.

      x <- c("little", "unknown", "small", "missing", "large")

case_when(
  x %in% c("little", "small") ~ "one",
  x %in% c("big", "large") ~ "two",
  x %in% c("missing", "unknown") ~ NA
)

    • Supports a larger variety of RHS value types. For example, you can use a data frame to create multiple columns at once.

    • Has new .ptype and .size arguments which allow you to enforce a particular output type and size.

    • Has a better error when types or lengths were incompatible (#6261, #6206).

  • coalesce() (#6265):

    • Discards NULL inputs up front.

    • No longer iterates over the columns of data frame input. Instead, a row is now only coalesced if it is entirely missing, which is consistent with vctrs::vec_detect_missing() and greatly simplifies the implementation.

    • Has new .ptype and .size arguments which allow you to enforce a particular output type and size.

  • first(), last(), and nth() (#6331):

    • When used on a data frame, these functions now return a single row rather than a single column. This is more consistent with the vctrs principle that a data frame is generally treated as a vector of rows.

    • The default is no longer “guessed”, and will always automatically be set to a missing value appropriate for the type of x.

    • Error if n is not an integer. nth(x, n = 2) is fine, but nth(x, n = 2.5) is now an error.

    • No longer support indexing into scalar objects, like <lm> or scalar S4 objects (#6670).

    Additionally, they have all gained an na_rm argument since they are summary functions (#6242, with contributions from @tnederlof).

  • if_else() gains most of the same benefits as case_when(). In particular, if_else() now takes the common type of true, false, and missing to determine the output type, meaning that you can now reliably use NA, rather than NA_character_ and friends (#6243).

    if_else() also no longer allows you to supply NULL for either true or false, which was an undocumented usage that we consider to be off-label, because true and false are intended to be (and documented to be) vector inputs (#6730).

  • na_if() (#6329) now casts y to the type of x before comparison, which makes it clearer that this function is type and size stable on x. In particular, this means that you can no longer do na_if(<tibble>, 0), which previously accidentally allowed you to replace any instance of 0 across every column of the tibble with NA. na_if() was never intended to work this way, and this is considered off-label usage.

    You can also now replace NaN values in x with na_if(x, NaN).

  • lag() and lead() now cast default to the type of x, rather than taking the common type. This ensures that these functions are type stable on x (#6330).

  • row_number(), min_rank(), dense_rank(), ntile(), cume_dist(), and percent_rank() are faster and work for more types. You can now rank by multiple columns by supplying a data frame (#6428).

  • with_order() now checks that the size of order_by is the same size as x, and now works correctly when order_by is a data frame (#6334).

Minor improvements and bug fixes

  • Fixed an issue with latest rlang that caused internal tools (such as mask$eval_all_summarise()) to be mentioned in error messages (#6308).

  • Warnings are enriched with contextualised information in summarise() and filter() just like they have been in mutate() and arrange().

  • Joins now reference the correct column in y when a type error is thrown while joining on two columns with different names (#6465).

  • Joins on very wide tables are no longer bottlenecked by the application of suffix (#6642).

  • *_join() now error if you supply them with additional arguments that aren’t used (#6228).

  • across() used without functions inside a rowwise-data frame no longer generates an invalid data frame (#6264).

  • Anonymous functions supplied with function() and \() are now inlined by across() if possible, which slightly improves performance and makes possible further optimisations in the future.

  • Functions supplied to across() are no longer masked by columns (#6545). For instance, across(1:2, mean) will now work as expected even if there is a column called mean.

  • across() will now error when supplied ... without a .fns argument (#6638).

  • arrange() now correctly ignores NULL inputs (#6193).

  • arrange() now works correctly when across() calls are used as the 2nd (or more) ordering expression (#6495).

  • arrange(df, mydesc::desc(x)) works correctly when mydesc re-exports dplyr::desc() (#6231).

  • c_across() now evaluates all_of() correctly and no longer allows you to accidentally select grouping variables (#6522).

  • c_across() now throws a more informative error if you try to rename during column selection (#6522).

  • dplyr no longer provides count() and tally() methods for tbl_sql. These methods have been accidentally overriding the tbl_lazy methods that dbplyr provides, which has resulted in issues with the grouping structure of the output (#6338, tidyverse/dbplyr#940).

  • cur_group() now works correctly with zero row grouped data frames (#6304).

  • desc() gives a useful error message if you give it a non-vector (#6028).

  • distinct() now retains attributes of bare data frames (#6318).

  • distinct() returns columns ordered the way you request, not the same as the input data (#6156).

  • Error messages in group_by(), distinct(), tally(), and count() are now more relevant (#6139).

  • group_by_prepare() loses the caller_env argument. It was rarely used and it is no longer needed (#6444).

  • group_walk() gains an explicit .keep argument (#6530).

  • Warnings emitted inside mutate() and variants are now collected and stashed away. Run the new last_dplyr_warnings() function to see the warnings emitted within dplyr verbs during the last top-level command.

    This fixes performance issues when thousands of warnings are emitted with rowwise and grouped data frames (#6005, #6236).

  • mutate() behaves a little better with 0-row rowwise inputs (#6303).

  • A rowwise mutate() now automatically unlists list-columns containing length 1 vectors (#6302).

  • nest_join() has gained the na_matches argument that all other joins have.

  • nest_join() now preserves the type of y (#6295).

  • n_distinct() now errors if you don’t give it any input (#6535).

  • nth(), first(), last(), and with_order() now sort character order_by vectors in the C locale. Using character vectors for order_by is rare, so we expect this to have little practical impact (#6451).

  • ntile() now requires n to be a single positive integer.

  • relocate() now works correctly with empty data frames and when .before or .after result in empty selections (#6167).

  • relocate() no longer drops attributes of bare data frames (#6341).

  • relocate() now retains the last name change when a single column is renamed multiple times while it is being moved. This better matches the behavior of rename() (#6209, with help from @eutwt).

  • rename() now contains examples of using all_of() and any_of() to rename using a named character vector (#6644).

  • rename_with() now disallows renaming in the .cols tidy-selection (#6561).

  • rename_with() now checks that the result of .fn is the right type and size (#6561).

  • rows_insert() now checks that y contains the by columns (#6652).

  • setequal() ignores differences between freely coercible types (e.g. integer and double) (#6114) and ignores duplicated rows (#6057).

  • slice() helpers again produce output equivalent to slice(.data, 0) when the n or prop argument is 0, fixing a bug introduced in the previous version (@eutwt, #6184).

  • slice() with no inputs now returns 0 rows. This is mostly for theoretical consistency (#6573).

  • slice() now errors if any expressions in ... are named. This helps avoid accidentally misspelling an optional argument, such as .by (#6554).

  • slice_*() now requires n to be an integer.

  • slice_*() generics now perform argument validation. This should make methods more consistent and simpler to implement (#6361).

  • slice_min() and slice_max() can order_by multiple variables if you supply them as a data.frame or tibble (#6176).

  • slice_min() and slice_max() now consistently include missing values in the result if necessary (i.e. there aren’t enough non-missing values to reach the n or prop you have selected). If you don’t want missing values to be included at all, set na_rm = TRUE (#6177).

  • slice_sample() now accepts negative n and prop values (#6402).

  • slice_sample() returns a data frame or group with the same number of rows as the input when replace = FALSE and n is larger than the number of rows or prop is larger than 1. This reverts a change made in 1.0.8, returning to the behavior of 1.0.7 (#6185)

  • slice_sample() now gives a more informative error when replace = FALSE and the number of rows requested in the sample exceeds the number of rows in the data (#6271).

  • storms has been updated to include 2021 data and some missing storms that were omitted due to an error (@steveharoz, #6320).

  • summarise() now correctly recycles named 0-column data frames (#6509).

  • union_all(), like union(), now requires that data frames be compatible: i.e. they have the same columns, and the columns have compatible types.

  • where() is re-exported from tidyselect (#6597).

dplyr 1.0.10

CRAN release: 2022-09-01

Hot patch release to resolve R CMD check failures.

dplyr 1.0.9

CRAN release: 2022-04-28

  • New rows_append() which works like rows_insert() but ignores keys and allows you to insert arbitrary rows with a guarantee that the type of x won’t change (#6249, thanks to @krlmlr for the implementation and @mgirlich for the idea).

  • The rows_*() functions no longer require that the key values in x uniquely identify each row. Additionally, rows_insert() and rows_delete() no longer require that the key values in y uniquely identify each row. Relaxing this restriction should make these functions more practically useful for data frames, and alternative backends can enforce this in other ways as needed (i.e. through primary keys) (#5553).

  • rows_insert() gained a new conflict argument allowing you greater control over rows in y with keys that conflict with keys in x. A conflict arises if a key in y already exists in x. By default, a conflict results in an error, but you can now also "ignore" these y rows. This is very similar to the ON CONFLICT DO NOTHING command from SQL (#5588, with helpful additions from @mgirlich and @krlmlr).

  • rows_update(), rows_patch(), and rows_delete() gained a new unmatched argument allowing you greater control over rows in y with keys that are unmatched by the keys in x. By default, an unmatched key results in an error, but you can now also "ignore" these y rows (#5984, #5699).

  • rows_delete() no longer requires that the columns of y be a strict subset of x. Only the columns specified through by will be utilized from y, all others will be dropped with a message.

  • The rows_*() functions now always retain the column types of x. This behavior was documented, but previously wasn’t being applied correctly (#6240).

  • The rows_*() functions now fail elegantly if y is a zero column data frame and by isn’t specified (#6179).

dplyr 1.0.8

CRAN release: 2022-02-08

dplyr 1.0.7

CRAN release: 2021-06-18

dplyr 1.0.6

CRAN release: 2021-05-05

  • add_count() is now generic (#5837).

  • if_any() and if_all() abort when a predicate is mistakingly used as .cols= (#5732).

  • Multiple calls to if_any() and/or if_all() in the same expression are now properly disambiguated (#5782).

  • filter() now inlines if_any() and if_all() expressions. This greatly improves performance with grouped data frames.

  • Fixed behaviour of ... in top-level across() calls (#5813, #5832).

  • across() now inlines lambda-formulas. This is slightly more performant and will allow more optimisations in the future.

  • Fixed issue in bind_rows() causing lists to be incorrectly transformed as data frames (#5417, #5749).

  • select() no longer creates duplicate variables when renaming a variable to the same name as a grouping variable (#5841).

  • dplyr_col_select() keeps attributes for bare data frames (#5294, #5831).

  • Fixed quosure handling in dplyr::group_by() that caused issues with extra arguments (tidyverse/lubridate#959).

  • Removed the name argument from the compute() generic (@ianmcook, #5783).

  • row-wise data frames of 0 rows and list columns are supported again (#5804).

dplyr 1.0.5

CRAN release: 2021-03-05

dplyr 1.0.4

CRAN release: 2021-02-02

dplyr 1.0.3

CRAN release: 2021-01-15

  • 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

CRAN release: 2020-08-18

dplyr 1.0.1

CRAN release: 2020-07-31

  • 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

CRAN release: 2020-05-29

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

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()

rowwise()

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

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

vctrs

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

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

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

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

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

Grouping

  • group_by() uses hashing from the vctrs package.

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

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

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

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

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

Lifecycle changes

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

Removed

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

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

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

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

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

Deprecated

Superseded

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

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

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

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

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

Questioning

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

Stable

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)

CRAN release: 2020-03-07

  • Maintenance release for compatibility with R-devel.

dplyr 0.8.4 (2020-01-30)

CRAN release: 2020-01-31

  • Adapt tests to changes in dependent packages.

dplyr 0.8.3 (2019-07-04)

CRAN release: 2019-07-04

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

dplyr 0.8.2 (2019-06-28)

CRAN release: 2019-06-29

New functions

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

colwise changes

Hybrid evaluation changes

Minor changes

dplyr 0.8.1 (2019-05-14)

CRAN release: 2019-05-14

Breaking changes

New functions

Minor changes

dplyr 0.8.0.1 (2019-02-15)

CRAN release: 2019-02-15

  • Fixed integer C/C++ division, forced released by CRAN (#4185).

dplyr 0.8.0 (2019-02-14)

CRAN release: 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 :

  • Error: `.data` is a corrupt grouped_df, ... signals code that makes wrong assumptions about the internals of a grouped data frame.

New functions

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).

    
# 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.

    
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.

    
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

Lifecycle

Changes to column wise functions

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 invocation).

  • 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).

    
# 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

dplyr 0.7.6

CRAN release: 2018-06-29

  • 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)

CRAN release: 2018-05-19

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

Documentation

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

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

CRAN release: 2017-09-28

  • Fix recent Fedora and ASAN check errors (#3098).

  • Avoid dependency on Rcpp 0.12.10 (#3106).

dplyr 0.7.3

CRAN release: 2017-09-09

  • 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

CRAN release: 2017-07-20

  • Move build-time vs. run-time checks out of .onLoad() and into dr_dplyr().

dplyr 0.7.1

CRAN release: 2017-06-22

  • 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

CRAN release: 2017-06-09

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. 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:


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 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

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:

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

Combining and comparing

Vector functions

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

CRAN release: 2016-06-24

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.

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_dfs, now live in their own package: tibble. 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_cubes 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 NAs (#1145).

  • The progress bar in do() is now updated at most 20 times per second, avoiding unnecessary 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 POSIXcts 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

CRAN release: 2015-09-01

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_frames 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 POSIXcts (#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 heterogeneous representations (some Dates 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

CRAN release: 2015-06-16

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

CRAN release: 2015-01-14

  • Don’t assume that RPostgreSQL is available.

dplyr 0.4.0

CRAN release: 2015-01-08

New features

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 NAs is allowed and just collects the appropriate missing value for the column type being collected (#493).

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

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

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

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

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

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

Bug fixes

  • [.tbl_df correctly computes row names for 0-column data frames, avoiding problems with xtable (#656). [.grouped_df will silently drop grouping if you don’t include the grouping columns (#733).

  • data_frame() now acts correctly if the first argument is a vector to be recycled. (#680 thanks @jimhester)

  • filter.data.table() works if the table has a variable called “V1” (#615).

  • *_join() keeps columns in original order (#684). Joining a factor to a character vector doesn’t segfault (#688). *_join functions can now deal with multiple encodings (#769), and correctly name results (#855).

  • *_join.data.table() works when data.table isn’t attached (#786).

  • group_by() on a data table preserves original order of the rows (#623). group_by() supports variables with more than 39 characters thanks to a fix in lazyeval (#705). It gives 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 Dates built on numeric vectors (#755).

  • rename_() generic gets missing .dots argument (#708).

  • row_number(), min_rank(), percent_rank(), dense_rank(), ntile() and cume_dist() handle data frames with 0 rows (#762). They all preserve missing values (#774). row_number() doesn’t segfault when giving an external variable with the wrong number of variables (#781).

  • group_indices handles the edge case when there are no variables (#867).

  • Removed bogus NAs introduced by coercion to integer range on 32-bit Windows (#2708).

dplyr 0.3.0.1

CRAN release: 2014-10-08

  • Fixed problem with test script on Windows.

dplyr 0.3

CRAN release: 2014-10-04

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 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.frames.

  • 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 its 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

CRAN release: 2014-05-21

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 receives 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 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

CRAN release: 2014-03-15

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

CRAN release: 2014-02-24

New features

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

CRAN release: 2014-01-29

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.