A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:
mutate()adds new variables that are functions of existing variables
select()picks variables based on their names.
filter()picks cases based on their values.
summarise()reduces multiple values down to a single summary.
arrange()changes the ordering of the rows.
These all combine naturally with
group_by() which allows you to perform any operation "by group". You can learn more about them in
vignette("dplyr"). As well as these single-table verbs, dplyr also provides a variety of two-table verbs, which you can learn about in
dplyr is designed to abstract over how the data is stored. That means as well as working with local data frames, you can also work with remote database tables, using exactly the same R code. Install the dbplyr package then read
vignette("databases", package = "dbplyr").
If you are new to dplyr, the best place to start is the data import chapter in R for data science.
install.packages("tidyverse")# Alternatively, install just dplyr:install.packages("dplyr")# Or the development version from GitHub:# install.packages("devtools")devtools::install_github("tidyverse/dplyr")
library(dplyr)starwars %>%filter(species == "Droid")#> # A tibble: 5 x 13#> name height mass hair… skin… eye_… birt… gend… home… spec… films vehi…#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <lis> <lis>#> 1 C-3PO 167 75.0 <NA> gold yell… 112 <NA> Tato… Droid <chr… <chr…#> 2 R2-D2 96 32.0 <NA> "whi… red 33.0 <NA> Naboo Droid <chr… <chr…#> 3 R5-D4 97 32.0 <NA> "whi… red NA <NA> Tato… Droid <chr… <chr…#> 4 IG-88 200 140 none metal red 15.0 none <NA> Droid <chr… <chr…#> 5 BB8 NA NA none none black NA none <NA> Droid <chr… <chr…#> # ... with 1 more variable: starships <list>starwars %>%select(name, ends_with("color"))#> # A tibble: 87 x 4#> name hair_color skin_color eye_color#> <chr> <chr> <chr> <chr>#> 1 "Luke Skywalker" blond fair blue#> 2 C-3PO <NA> gold yellow#> 3 R2-D2 <NA> "white, blue" red#> 4 "Darth Vader" none white yellow#> 5 "Leia Organa" brown light brown#> # ... with 82 more rowsstarwars %>%mutate(name, bmi = mass / ((height / 100) ^ 2)) %>%select(name:mass, bmi)#> # A tibble: 87 x 4#> name height mass bmi#> <chr> <int> <dbl> <dbl>#> 1 "Luke Skywalker" 172 77.0 26.0#> 2 C-3PO 167 75.0 26.9#> 3 R2-D2 96 32.0 34.7#> 4 "Darth Vader" 202 136 33.3#> 5 "Leia Organa" 150 49.0 21.8#> # ... with 82 more rowsstarwars %>%arrange(desc(mass))#> # A tibble: 87 x 13#> name heig… mass hair… skin… eye_… birt… gend… home… spec… films vehi…#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <lis> <lis>#> 1 "Jabb… 175 1358 <NA> "gre… oran… 600 herm… "Nal… Hutt <chr… <chr…#> 2 Griev… 216 159 none "bro… "gre… NA male Kalee Kale… <chr… <chr…#> 3 IG-88 200 140 none metal red 15.0 none <NA> Droid <chr… <chr…#> 4 "Dart… 202 136 none white yell… 41.9 male Tato… Human <chr… <chr…#> 5 Tarff… 234 136 brown brown blue NA male Kash… Wook… <chr… <chr…#> # ... with 82 more rows, and 1 more variable: starships <list>starwars %>%group_by(species) %>%summarise(n = n(),mass = mean(mass, na.rm = TRUE)) %>%filter(n > 1)#> # A tibble: 9 x 3#> species n mass#> <chr> <int> <dbl>#> 1 Droid 5 69.8#> 2 Gungan 3 74.0#> 3 Human 35 82.8#> 4 Kaminoan 2 88.0#> 5 Mirialan 2 53.1#> # ... with 4 more rows
Fix recent Fedora and ASAN check errors (#3098).
Avoid dependency on Rcpp 0.12.10 (#3106).
Fixed protection error that occurred when creating a character column using grouped
Fixed a rare problem with accessing variable values in
summarise() when all groups have size one (#3050).
Fixed rare out-of-bounds memory write in
slice() when negative indices beyond the number of rows were involved (#3073).
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).
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
Grouping by character vectors is now faster (#2204).
Fixed a crash that occurred when an unexpected input was supplied to
call argument of
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
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
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).
Five new datasets provide some interesting built-in datasets to demonstrate dplyr verbs (#2094):
starwarsdataset about starwars characters; has list columns
stormshas the trajectories of ~200 tropical storms
band_instruments2has some simple data to demonstrate joins.
add_tally() for adding an
n column within groups
arrange() for grouped data frames gains a
.by_group argument so you
can choose to sort by groups if you want to (defaults to
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
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 prefered to
dplyr no longer messages that you need dtplyr to work with data.table (#2489).
summarise_each_q() functions have been removed.
failwith(). I'm not even sure why it was here.
summarise_each(), these functions
print a message which will be changed to a warning in the next release.
.env argument to
sample_frac() is defunct,
passing a value to this argument print a message which will be changed to a
warning in the next release.
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
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_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
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.
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).
group_by() for data frames that have UTF-8 encoded names (#2284, #2382).
group_vars() generic that returns the grouping as character vector, to
avoid the potentially lossy conversion to language symbols. The list returned
group_by_prepare() now has a new
group_names component (#1950, #2384).
transmute() now have scoped variants (verbs suffixed with
these variants apply an operation to a selection of variables.
The scoped verbs taking predicates (
etc) now support S3 objects and lazy tables. S3 objects should
implement methods for
tbl_vars(). For lazy
tables, the first 100 rows are collected and the predicate is
applied on this subset of the data. This is robust for the common
case of checking the type of a column (#2129).
Summarise and mutate colwise functions pass
... on the the manipulation
The performance of colwise verbs like
mutate_all() is now back to
where it was in
funs() has better handling of namespaced functions (#2089).
Fix issue with
summarise_if() when a predicate
function returns a vector of
FALSE (#1989, #2009, #2011).
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).
sample_frac() now use
tidyeval to capture their arguments by expression. This makes it
possible to use unquoting idioms (see
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
.env environments can be used inside
all verbs that operate on data:
.data$column_name accesses the column
.env$var accesses the external variable
Columns or external variables named
.env are shadowed, use
.env$... to access them. (
.data implements strict
matching also for the
$ operator (#2591).)
global() functions have been removed. They were never
documented officially. Use the new
.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
<- inside verbs. To prevent leaking of broken bindings,
the temporary environment is cleared after the evaluation (#2435).
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).
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
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,
select(mtcars, "cyl") are now allowed.
coalesce() now support splicing of
arguments with rlang's
count() now preserves the grouping of its input (#2021).
distinct() no longer duplicates variables (#2001).
distinct() with a grouped data frame works the same way as
distinct() on an ungrouped data frame, namely it uses all
copy_to() now returns it's output invisibly (since you're often just
calling for the side-effect).
lag() throw informative error if used with ts objects (#2219)
mutate() recycles list columns of length 1 (#2171).
mutate() gives better error message when attempting to add a non-vector
column (#2319), or attempting to remove a column with
NULL (#2187, #2439).
summarise() now correctly evaluates newly created factors (#2217), and
can create ordered factors (#2200).
summarise() uses summary variables correctly (#2404, #2453).
summarise() no longer converts character
NA to empty strings (#1839).
all_equal() now reports multiple problems as a character vector (#1819, #2442).
all_equal() checks that factor levels are equal (#2440, #2442).
bind_cols() give an error for database tables (#2373).
bind_rows() works correctly with
NULL arguments and an
(#2056), and also for zero-column data frames (#2175).
combine() are more strict when coercing.
Logical values are no longer coerced to integer and numeric. Date, POSIXct
and other integer or double-based classes are no longer coerced to integer or
double as there is chance of attributes or information being lost
bind_cols() now calls
tibble::repair_names() to ensure that all
names are unique (#2248).
bind_cols() handles empty argument list (#2048).
bind_cols() better handles
NULL inputs (#2303, #2443).
bind_rows() explicitly rejects columns containing data frames
bind_cols() now accept vectors. They are treated
as rows by the former and columns by the latter. Rows require inner
c(col1 = 1, col2 = 2), while columns require outer
col1 = c(1, 2). Lists are still treated as data frames but
can be spliced explicitly with
bind_rows(!!! x) (#1676).
rbind_all() now call
.Deprecated(), they will be removed
in the next CRAN release. Please use
NA values (#2203, @zeehio)
bind_rows() with character and factor types now always warn
about the coercion to character (#2317, @zeehio)
mutate coerces results from grouped dataframes accepting combinable data
types (such as
numeric). (#1892, @zeehio)
%in% gets new hybrid handler (#126).
between() returns NA if
NA (fixes #2562).
NA values (#2000, @tjmahr).
nth() have better default values for factor,
Dates, POSIXct, and data frame inputs (#2029).
Fixed segmentation faults in hybrid evaluation of
lag(). These functions now always fall back to the R
implementation if called with arguments that the hybrid evaluator cannot
handle (#948, #1980).
n_distinct() gets larger hash tables given slightly better performance (#977).
ntile() are more careful about proper data types of their return values (#2306).
NA when computing group membership (#2564).
lag() enforces integer
n (#2162, @kevinushey).
max() now always return a
numeric and work correctly
in edge cases (empty input, all
NA, ...) (#2305, #2436).
min_rank("string") no longer segfaults in hybrid evaluation (#2279, #2444).
recode() can now recode a factor to other types (#2268)
.dots argument to support passing replacements as list
Many error messages are more helpful by referring to a column name or a position in the argument list (#2448).
is_grouped_df() alias to
tbl_vars() now has a
group_vars argument set to
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
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() 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).
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
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).
Rcpp::interfaces() to register C callable interfaces, and registering all native exported functions via
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).
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
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
The select helper functions
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.
dplyr::select(mtcars, starts_with("m")) used to work, but
now you'll need
The long deprecated
%.% have been removed.
id() has been deprecated. Please use
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.
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
na_if() makes it easy to replace a certain value with an
In SQL it is translated to
near(x, y) is a helper for
abs(x - y) < tol (#1607).
recode() is vectorised equivalent to
union_all() method. Maps to
UNION ALL for SQL sources,
for data frames/tbl_dfs, and
combine() for vectors (#1045).
A new family of functions replace
mutate_each() (which will thus be deprecated in a future release).
mutate_all() apply a function to all columns
mutate_at() operate on a subset of
columns. These columuns are selected with either a character vector
of columns names, a numeric vector of column positions, or a column
select() semantics generated by the new
columns() helper. In addition,
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
(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-)
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.
Functions related to the creation and coercion of
tbl_dfs, now live in their own package: tibble. See
vignette("tibble") for more details.
[[ 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
as_data_frame() have been aligned,
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
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
(#1325). It now (invisibly) returns its first argument (#1570).
lst_() which create lists in the same way that
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
(#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)
setOldClass(c("tbl_df", "tbl", "data.frame")) to help
with S4 dispatch (#969).
tbl_df automatically generates column names (#1606).
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
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
met_name arguments in
matrix) for consistency with
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).
as_data_frame() on SQL sources now returns all rows (#1752, #1821,
compute() gets new parameters
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
You can now use
full_join() with remote tables (#1172).
src_memdb() is a session-local in-memory SQLite database.
memdb_frame() works like
data_frame(), but creates a new table in
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
filter.tbl_sql() now puts parens around each argument (#934).
- 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
is.na() gets a missing space (#1695).
is.null() get extra parens to make precendence
more clear (#1695).
pmax() are translated to
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)
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:
When applied to a
tbl_lazy, each dplyr verb captures its inputs
and stores in a
op (short for operation) object.
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.
sql_render() iterates through the queries and generates the SQL,
using generics (like
sql_select()) that can vary based on the
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
distinct(). It loses the
offset argument which was
never used (and hence never tested).
src_translate_env() has been replaced by
should have methods for the connection object.
There were two other tweaks to the exported API, but these are less likely to affect anyone.
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
Also note that the sql generation generics now have a default method, instead methods for DBIConnection and NULL.
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
count() can now count a variable
n (#1633). Weighted
The progress bar in
do() is now updated at most 20 times per second,
avoiding uneccessary 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
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.
returns all columns, instead of no columns (#1176)
select() helpers are now exported and have their own
one_of() gives a useful error message if
variables names are not found in data frame (#1407).
The naming behaviour of
mutate_each() has been
tweaked so that you can force inclusion of both the function and the
summarise_each(mtcars, funs(mean = mean), everything())
mutate() handles factors that are all
NA (#1645), or have different
levels in different groups (#1414). It disambiguates
and silently promotes groups that only contain
NA (#1463). It deep copies
data in list columns (#1643), and correctly fails on incompatible columns
mutate() on a grouped data no longer droups grouping attributes
rowwise() mutate gives expected results (#1381).
one_of() tolerates unknown variables in
vars, but warns (#1848, @jennybc).
print.grouped_df() passes on
slice() correctly handles grouped attributes (#1405).
ungroup() generic gains
bind_cols() matches the behaviour of
bind_rows() and ignores
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).
POSIXlt columns (#1875, @krlmlr).
bind_rows() infer classes and grouping information
from the first data frame (#1692).
grouped_df() methods that make it harder to
create corrupt data frames (#1385). You should still prefer
Joins now use correct class when joining on
(#1582, @joel23888), and consider time zones (#819). Joins handle a
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
by contains unavailable columns (#1928, #1932).
Warnings about join column inconsistencies now contain the column names
full_join() gain a
suffix argument which allows you to control what suffix duplicated variable
names recieve (#1296).
Set operations (
union() etc) respect coercion rules
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,
combine() silently drops
NULL inputs (#1596).
cummean() is more stable against floating point errors (#1387).
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
(#1434). and handle default values for string columns.
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.
selects the 2nd value from the end of
top_n() can now also select bottom
n values by passing a negative value
n (#1008, #1352).
Hybrid evaluation leaves formulas untouched (#1447).
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
distinct() (#1179), and joins (#1315).
print.tbl_df() also recieved a fix for strings with invalid encodings (#851).
frame_data() provides a means for constructing
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).
mutate() work on empty data frames (#1142).
summarise() preserve data frame
meta attributes (#1064).
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() 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
all inputs is the same, it's used, otherwise its set to
data_frame() always produces a
tbl_df (#1151, @kevinushey)
filter(x, TRUE, TRUE) now just returns
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
with heterogenous representations (some
Dates are integers, while other
are numeric). This also improves
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).
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,
median(), which that sometimes returns an integer, and other times a
slice() silently drops columns corresponding to an NA (#1235).
ungroup.rowwise_df() gives a
More explicit duplicated column name error message (#996).
When "," is already being used as the decimal point (
use "." as the thousands separator when printing out formatted numbers
build_sql rather than
Improved handling of
n_distinct(x) is translated to
COUNT(DISTINCT(x)) (@skparkes, #873).
print(n = Inf) now works for remote sources (#1310).
Hybrid evaluation does not take place for objects with a class (#1237).
$ handling (#1134).
Simplified code for
lag() and make sure they work properly on
factors (#955). Both repsect 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).
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.
lead() for grouped data were confused about indices and therefore
produced wrong results (#925, #937).
lag() once again overrides
instead of just the default method
lag.default(). This is necesary due to
changes in R CMD check. To use the lag function provided by another package,
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
all.equal() correctly handles character missing values (#1095).
bind_cols() always produces a
bind_rows() gains a test for a form of data frame corruption (#1074).
summarise() now handles complex columns (#933).
Workaround for using the constructor of
DataFrame on an unprotected object
Improved performance when working with large number of columns (#879).
add_rownames() turns row names into an explicit variable (#639).
as_data_frame() efficiently coerces a list into a data frame (#749).
bind_cols() efficiently bind a list of data frames by
row or column.
combine() applies the same coercion rules to vectors
(it works like
unlist() but is consistent with the
right_join() (include all rows in
y, and matching rows in
full_join() (include all rows in
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
vignette("data_frames") describes dplyr functions that make it easier
and faster to create and coerce data frames. It subsumes the old
vignette("two-table") describes how two-table verbs work in dplyr.
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
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).
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.)
now works in all verbs (#579).
*_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
suffixes to distinguish the source (#655).
mutate() handles complex vectors (#436) and forbids
(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
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
summarise()) are more careful about
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
rbind()ing a column full of
NAs is allowed and just
collects the appropriate missing value for the column type being collected
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
A number of functions gained new or improved hybrid handlers:
%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.
min_rank() correctly handles
NaN values (#726). Hybrid
nth() falls back to R evaluation when
n is not
a length one integer or numeric, e.g. when it's an expression (#734).
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
Date objects (#819), and only
only warn once about each incompatibility (#798).
[.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).
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).
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).
NAs introduced by coercion to integer range on 32-bit Windows (#2708).
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
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.
now allow you to join on different variables in
y tables by
supplying a named vector to
by. For example,
by = c("a" = "b") joins
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
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.
vignette("nse") for full details.
regroup() is deprecated. Please use the more flexible
mutate_each_q() are deprecated. Please use
funs_q has been replaced with
%.% has been deprecated: please use
filter.numeric() removed. Need to figure out how to reimplement with
new lazy eval system.
Progress refclass is no longer exported to avoid conflicts with shiny.
src_monetdb() is now implemented in MonetDB.R, not dplyr.
explain_sql() and matching global options
dplyr.explain_sql have been removed. Instead use
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).
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
which should avoid clobbering lag methods added by other packages.
mutate(data, a = NULL) removes the variable
a from the returned
trunc_mat() and hence
print.tbl_df() and friends gets a
to control the deafult output width. Set
options(dplyr.width = Inf) to
always show all columns (#589).
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
(#481, @leondutoit). Fixed buglet in
select() so that you can now create
Switched from RC to R6.
top_n() work consistently: neither accidentally
evaluates the the
wt param. (#426, @mnel)
rename handles grouped data (#640).
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
mutate() creates a new variable that uses a window function,
automatically wrap the result in a subquery (#484).
Correct SQL generation for
order_by() now works in conjunction with window functions in databases
that support them.
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
cumall() properly handle
nth() now correctly preserve the class when using dates, times and
no longer substitutes within
order_by() needs to do
its own NSE (#169).
[.tbl_df always returns a tbl_df (i.e.
drop = FALSE is the default)
[.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
filter() asserts all variables are white listed (#566).
mutate() makes a
rowwise_df when given a
tbl_df objects instead of raw
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_frac() methods for data.frames exported.
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
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).
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).
Fix major omission in
grouped_dt() methods - I was
accidentally doing a deep copy on every result :(
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)
summarise.tbl_cube()works with single grouping variable (#480).
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 %>% 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
library(magrittr). For more details, see
%.% 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
do() has been completely overhauled. There are now two ways to use it, either with multiple named arguments or a single unnamed arguments.
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.
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).
mutate_each() make it easy to apply one or more
functions to multiple columns in a tbl (#178).
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)
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
(#8, thanks to @hannesmuehleisen).
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
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
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
"comment" attribute is allowed (white listed) as well as names (#346).
hybrid versions of
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)
Code adapted to Rcpp > 0.11.1
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_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
collection of integer and logical vectors. The integer
Collecter also collects
logical values (#321).
sum correctly handles integer (under/over)flow (#308).
summarise() checks consistency of outputs (#300) and drops
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).
n rows instead of
n - 1 (@leondutoit, #367).
SQL translation always evaluates subsetting operators (
select() now renames variables in remote sql tbls (#317) and
implicitly adds grouping variables (#170).
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).
select() actually renames columns in a data table (#284).
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).
select() is substantially more powerful. You can use named arguments to
rename existing variables, and new functions
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)
filter() now fails when given anything other than a logical vector, and
correctly handles missing values (#249).
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
correctly mutates the same variable repeatedly (#243).
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
if(n() > 5) 1 else 2 the subexpression
n() was not
substituted correctly. It also correctly processes
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.
changes() functions which provide more information
about how data frames are stored in memory so that you can see what
sort argument to sort output so highest counts
come first (#173).
as.data.frame.tbl_df() now only
make shallow copies of their inputs (#191).
benchmark-baseball vignette now contains fairer (including grouping
times) comparisons with
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
F as aliases to
if there are no
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.