NEWS.md

dplyr 0.4.1.9000

• lag and lead for grouped data were confused about indices and therefore produced wrong results (#925, #937)

• fixed a number of memory issues identified by valgrind

• bind_rows gains a test for a form of data frame corruption (#1074).

• bind_rows handles complex columns (#933).

• Fixed issue about complex variables used in summarise (#933).

• 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).

• bind_cols always produces a tbl_df (#779).

• all.equal correctly treats character missing values (#1095).

• Improved performance when working with large number of columns (#879).

dplyr 0.4.1

• Don't assume that RPostgreSQL is available.

dplyr 0.4.0

New features

• add_rownames() turns row names into an explicit variable (#639).

• as_data_frame() efficiently coerces a list into a data frame (#749).

• bind_rows() and bind_cols() efficiently bind a list of data frames by row or column. combine() applies the same coercion rules to vectors (it works like c() or unlist() but is consistent with the bind_rows() rules).

• right_join() (include all rows in y, and matching rows in x) and full_join() (include all rows in x and y) complete the family of mutating joins (#96).

• group_indices() computes a unique integer id for each group (#771). It can be called on a grouped_df without any arguments or on a data frame with same arguments as group_by().

New vignettes

• vignette("data_frames") describes dplyr functions that make it easier and faster to create and coerce data frames. It subsumes the old memory vignette.

• vignette("two-table") describes how two-table verbs work in dplyr.

Minor improvements

• data_frame() (and as_data_frame() & tbl_df()) now explicitly forbid columns that are data frames or matrices (#775). All columns must be either a 1d atomic vector or a 1d list.

• do() uses lazyeval to correctly evaluate its arguments in the correct environment (#744), and new do_() is the SE equivalent of do() (#718). You can modify grouped data in place: this is probably a bad idea but it's sometimes convenient (#737). do() on grouped data tables now passes in all columns (not all columns except grouping vars) (#735, thanks to @kismsu). do() with database tables no longer potentially includes grouping variables twice (#673). Finally, do() gives more consistent outputs when there are no rows or no groups (#625).

• first() and last() preserve factors, dates and times (#509).

• Overhaul of single table verbs for data.table backend. They now all use a consistent (and simpler) code base. This ensures that (e.g.) n() now works in all verbs (#579).

• In *_join(), you can now name only those variables that are different between the two tables, e.g. inner_join(x, y, c("a", "b", "c" = "d")) (#682). If non-join colums 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 recieved a number of small tweaks. All print() method 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 meaninful 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)

dplyr 0.3.0.1

• Fixed problem with test script on Windows.

dplyr 0.3

New functions

• between() vector function efficiently determines if numeric values fall in a range, and is translated to special form for SQL (#503).

• count() makes it even easier to do (weighted) counts (#358).

• data_frame() by @kevinushey is a nicer way of creating data frames. It never coerces column types (no more stringsAsFactors = FALSE!), never munges column names, and never adds row names. You can use previously defined columns to compute new columns (#376).

• distinct() returns distinct (unique) rows of a tbl (#97). Supply additional variables to return the first row for each unique combination of variables.

• Set operations, intersect(), union() and setdiff() now have methods for data frames, data tables and SQL database tables (#93). They pass their arguments down to the base functions, which will ensure they raise errors if you pass in two many arguments.

• Joins (e.g. left_join(), inner_join(), semi_join(), anti_join()) now allow you to join on different variables in x and y tables by supplying a named vector to by. For example, by = c("a" = "b") joins x.a to y.b.

• n_groups() function tells you how many groups in a tbl. It returns 1 for ungrouped data. (#477)

• transmute() works like mutate() but drops all variables that you didn't explicitly refer to (#302).

• rename() makes it easy to rename variables - it works similarly to select() but it preserves columns that you didn't otherwise touch.

• slice() allows you to selecting rows by position (#226). It includes positive integers, drops negative integers and you can use expression like n().

Programming with dplyr (non-standard evaluation)

• You can now program with dplyr - every function that does non-standard evaluation (NSE) has a standard evaluation (SE) version ending in _. This is powered by the new lazyeval package which provides all the tools needed to implement NSE consistently and correctly.

• See vignette("nse") for full details.

• regroup() is deprecated. Please use the more flexible group_by_() instead.

• summarise_each_q() and mutate_each_q() are deprecated. Please use summarise_each_() and mutate_each_() instead.

• funs_q has been replaced with funs_.

Removed and deprecated features

• %.% has been deprecated: please use %>% instead. chain() is defunct. (#518)

• filter.numeric() removed. Need to figure out how to reimplement with new lazy eval system.

• The Progress refclass is no longer exported to avoid conflicts with shiny. Instead use progress_estimated() (#535).

• src_monetdb() is now implemented in MonetDB.R, not dplyr.

• show_sql() and explain_sql() and matching global options dplyr.show_sql and dplyr.explain_sql have been removed. Instead use show_query() and explain().

Minor improvements and bug fixes

• Main verbs now have individual documentation pages (#519).

• %>% is simply re-exported from magrittr, instead of creating a local copy (#496, thanks to @jimhester)

• Examples now use nycflights13 instead of hflights because it the variables have better names and there are a few interlinked tables (#562). Lahman and nycflights13 are (once again) suggested packages. This means many examples will not work unless you explicitly install them with install.packages(c("Lahman", "nycflights13")) (#508). dplyr now depends on Lahman 3.0.1. A number of examples have been updated to reflect modified field names (#586).

• do() now displays the progress bar only when used in interactive prompts and not when knitting (#428, @jimhester).

• glimpse() now prints a trailing new line (#590).

• group_by() has more consistent behaviour when grouping by constants: it creates a new column with that value (#410). It renames grouping variables (#410). The first argument is now .data so you can create new groups with name x (#534).

• Now instead of overriding lag(), dplyr overrides lag.default(), which should avoid clobbering lag methods added by other packages. (#277).

• mutate(data, a = NULL) removes the variable a from the returned dataset (#462).

• trunc_mat() and hence print.tbl_df() and friends gets a width argument to control the deafult output width. Set options(dplyr.width = Inf) to always show all columns (#589).

• select() gains one_of() selector: this allows you to select variables provided by a character vector (#396). It fails immediately if you give an empty pattern to starts_with(), ends_with(), contains() or matches() (#481, @leondutoit). Fixed buglet in select() so that you can now create variables called val (#564).

• Switched from RC to R6.

• tally() and top_n() work consistently: neither accidentally evaluates the the wt param. (#426, @mnel)

• rename handles grouped data (#640).

Minor improvements and bug fixes by backend

Databases

• 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 it's inputs: it requires one symbol which must be from the data frame (#567).

• rbind_*() handle data frames with 0 rows (#597). They fill character vector columns with NA instead of blanks (#595). They work with list columns (#463).

• Improved handling of encoding for column names (#636).

• Improved handling of hybrid evaluation re $ and @ (#645).

Data tables

• Fix major omission in tbl_dt() and grouped_dt() methods - I was accidentally doing a deep copy on every result :(

• summarise() and group_by() now retain over-allocation when working with data.tables (#475, @arunsrinivasan).

• joining two data.tables now correctly dispatches to data table methods, and result is a data table (#470)

Cubes

• summarise.tbl_cube() works with single grouping variable (#480).

dplyr 0.2

Piping

dplyr now imports %>% from magrittr (#330). I recommend that you use this instead of %.% because it is easier to type (since you can hold down the shift key) and is more flexible. With you %>%, you can control which argument on the RHS recieves the LHS by using the pronoun .. This makes %>% more useful with base R functions because they don't always take the data frame as the first argument. For example you could pipe mtcars to xtabs() with:

mtcars %>% xtabs( ~ cyl + vs, data = .)

Thanks to @smbache for the excellent magrittr package. dplyr only provides %>% from magrittr, but it contains many other useful functions. To use them, load magrittr explicitly: library(magrittr). For more details, see vignette("magrittr").

%.% will be deprecated in a future version of dplyr, but it won't happen for a while. I've also deprecated chain() to encourage a single style of dplyr usage: please use %>% instead.

Do

do() has been completely overhauled. There are now two ways to use it, either with multiple named arguments or a single unnamed arguments. group_by() + do() is equivalent to plyr::dlply, except it always returns a data frame.

If you use named arguments, each argument becomes a list-variable in the output. A list-variable can contain any arbitrary R object so it's particularly well suited for storing models.

library(dplyr)
models <- mtcars %>% group_by(cyl) %>% do(lm = lm(mpg ~ wt, data = .))
models %>% summarise(rsq = summary(lm)$r.squared)

If you use an unnamed argument, the result should be a data frame. This allows you to apply arbitrary functions to each group.

mtcars %>% group_by(cyl) %>% do(head(., 1))

Note the use of the . pronoun to refer to the data in the current group.

do() also has an automatic progress bar. It appears if the computation takes longer than 5 seconds and lets you know (approximately) how much longer the job will take to complete.

New verbs

dplyr 0.2 adds three new verbs:

• glimpse() makes it possible to see all the columns in a tbl, displaying as much data for each variable as can be fit on a single line.

• sample_n() randomly samples a fixed number of rows from a tbl; sample_frac() randomly samples a fixed fraction of rows. Only works for local data frames and data tables (#202).

• summarise_each() and mutate_each() make it easy to apply one or more functions to multiple columns in a tbl (#178).

Minor improvements

• If you load plyr after dplyr, you'll get a message suggesting that you load plyr first (#347).

• as.tbl_cube() gains a method for matrices (#359, @paulstaab)

• compute() gains temporary argument so you can control whether the results are temporary or permanent (#382, @cpsievert)

• group_by() now defaults to add = FALSE so that it sets the grouping variables rather than adding to the existing list. I think this is how most people expected group_by to work anyway, so it's unlikely to cause problems (#385).

• Support for MonetDB tables with src_monetdb() (#8, thanks to @hannesmuehleisen).

• New vignettes:

  • memory vignette which discusses how dplyr minimises memory usage for local data frames (#198).

  • new-sql-backend vignette which discusses how to add a new SQL backend/source to dplyr.

• changes() output more clearly distinguishes which columns were added or deleted.

• explain() is now generic.

• dplyr is more careful when setting the keys of data tables, so it never accidentally modifies an object that it doesn't own. It also avoids unnecessary key setting which negatively affected performance. (#193, #255).

• print() methods for tbl_df, tbl_dt and tbl_sql gain n argument to control the number of rows printed (#362). They also works better when you have columns containing lists of complex objects.

• row_number() can be called without arguments, in which case it returns the same as 1:n() (#303).

• "comment" attribute is allowed (white listed) as well as names (#346).

• hybrid versions of min, max, mean, var, sd and sum handle the na.rm argument (#168). This should yield substantial performance improvements for those functions.

• Special case for call to arrange() on a grouped data frame with no arguments. (#369)

Bug fixes

• Code adapted to Rcpp > 0.11.1

• internal DataDots class protects against missing variables in verbs (#314), including the case where ... is missing. (#338)

• all.equal.data.frame from base is no longer bypassed. we now have all.equal.tbl_df and all.equal.tbl_dt methods (#332).

• arrange() correctly handles NA in numeric vectors (#331) and 0 row data frames (#289).

• copy_to.src_mysql() now works on windows (#323)

• *_join() doesn't reorder column names (#324).

• rbind_all() is stricter and only accepts list of data frames (#288)

• rbind_* propagates time zone information for POSIXct columns (#298).

• rbind_* is less strict about type promotion. The numeric Collecter allows collection of integer and logical vectors. The integer Collecter also collects logical values (#321).

• internal sum correctly handles integer (under/over)flow (#308).

• summarise() checks consistency of outputs (#300) and drops names attribute of output columns (#357).

• join functions throw error instead of crashing when there are no common variables between the data frames, and also give a better error message when only one data frame has a by variable (#371).

• top_n() returns n rows instead of n - 1 (@leondutoit, #367).

• SQL translation always evaluates subsetting operators ($, [, [[) locally. (#318).

• select() now renames variables in remote sql tbls (#317) and
  implicitly adds grouping variables (#170).

• internal grouped_df_impl function errors if there are no variables to group by (#398).

• n_distinct did not treat NA correctly in the numeric case #384.

• Some compiler warnings triggered by -Wall or -pedantic have been eliminated.

• group_by only creates one group for NA (#401).

• Hybrid evaluator did not evaluate expression in correct environment (#403).

dplyr 0.1.3

Bug fixes

• select() actually renames columns in a data table (#284).

• rbind_all() and rbind_list() now handle missing values in factors (#279).

• SQL joins now work better if names duplicated in both x and y tables (#310).

• Builds against Rcpp 0.11.1

• select() correctly works with the vars attribute (#309).

• Internal code is stricter when deciding if a data frame is grouped (#308): this avoids a number of situations which previously causedd .

• More data frame joins work with missing values in keys (#306).

dplyr 0.1.2

New features

• select() is substantially more powerful. You can use named arguments to rename existing variables, and new functions starts_with(), ends_with(), contains(), matches() and num_range() to select variables based on their names. It now also makes a shallow copy, substantially reducing its memory impact (#158, #172, #192, #232).

• summarize() added as alias for summarise() for people from countries that don't don't spell things correctly ;) (#245)

Bug fixes

• filter() now fails when given anything other than a logical vector, and correctly handles missing values (#249). filter.numeric() proxies stats::filter() so you can continue to use filter() function with numeric inputs (#264).

• summarise() correctly uses newly created variables (#259).

• mutate() correctly propagates attributes (#265) and mutate.data.frame() correctly mutates the same variable repeatedly (#243).

• lead() and lag() preserve attributes, so they now work with dates, times and factors (#166).

• n() never accepts arguments (#223).

• row_number() gives correct results (#227).

• rbind_all() silently ignores data frames with 0 rows or 0 columns (#274).

• group_by() orders the result (#242). It also checks that columns are of supported types (#233, #276).

• The hybrid evaluator did not handle some expressions correctly, for example in if(n() > 5) 1 else 2 the subexpression n() was not substituted correctly. It also correctly processes $ (#278).

• arrange() checks that all columns are of supported types (#266). It also handles list columns (#282).

• Working towards Solaris compatibility.

• Benchmarking vignette temporarily disabled due to microbenchmark problems reported by BDR.

dplyr 0.1.1

Improvements

• new location() and changes() functions which provide more information about how data frames are stored in memory so that you can see what gets copied.

• renamed explain_tbl() to explain() (#182).

• tally() gains sort argument to sort output so highest counts come first (#173).

• ungroup.grouped_df(), tbl_df(), as.data.frame.tbl_df() now only make shallow copies of their inputs (#191).

• The benchmark-baseball vignette now contains fairer (including grouping times) comparisons with data.table. (#222)

Bug fixes

• filter() (#221) and summarise() (#194) correctly propagate attributes.

• summarise() throws an error when asked to summarise an unknown variable instead of crashing (#208).

• group_by() handles factors with missing values (#183).

• filter() handles scalar results (#217) and better handles scoping, e.g. filter(., variable) where variable is defined in the function that calls filter. It also handles T and F as aliases to TRUE and FALSE if there are no T or F variables in the data or in the scope.

• select.grouped_df fails when the grouping variables are not included in the selected variables (#170)

• all.equal.data.frame() handles a corner case where the data frame has NULL names (#217)

• mutate() gives informative error message on unsupported types (#179)

• dplyr source package no longer includes pandas benchmark, reducing download size from 2.8 MB to 0.5 MB.