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_instruments2 has some simple data to demonstrate joins.
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 (as a string) or a position (either from the left or the right). Thanks to @paulponcet for the idea (#2054).
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).
regroup() has been removed.
failwith(). I’m not even sure why it was here.
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_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.
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 by
group_by_prepare() now has a new
group_names component (#1950, #2384).
transmute() now have scoped variants (verbs suffixed with
summarise_if(), etc, these variants apply an operation to a selection of variables.
The scoped verbs taking predicates (
summarise_if(), 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 functions.
funs() has better handling of namespaced functions (#2089).
dplyr has a new approach to non-standard evaluation (NSE) called tidyeval. Tidyeval 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
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
.env environments can be used inside all verbs that operate on data:
.data$column_name accesses the column
.env$var accesses the external variable
var. 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 assignments using
<- inside verbs. To prevent leaking of broken bindings, the temporary environment is cleared after the evaluation (#2435).
xxx_join.tbl_df() by default 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 can also 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).
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
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).
count() now preserves the grouping of its input (#2021).
distinct() no longer duplicates variables (#2001).
copy_to() now returns it’s output invisibly (since you’re often just calling for the side-effect).
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_rows() works correctly with
NULL arguments and an
.id argument (#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 (#2209, @zeehio).
bind_cols() now calls
tibble::repair_names() to ensure that all names are unique (#2248).
bind_cols() handles empty argument list (#2048).
bind_cols() better handles
NULL inputs (#2303, #2443).
bind_rows() explicitly rejects columns containing data frames (#2015, #2446).
bind_cols() now accept vectors. They are treated as rows by the former and columns by the latter. Rows require inner names like
c(col1 = 1, col2 = 2), while columns require outer names:
col1 = c(1, 2). Lists are still treated as data frames but can be spliced explicitly with
bind_rows(!!! x) (#1676).
NA values (#2203, @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).
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).
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 (#2110, @jlegewie).
Many error messages are more helpful by referring to a column name or a position in the argument list (#2448).
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 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 is registered officially as an S3 class. This makes it easier to use with S4 (#2276, , @joranE).
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).
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
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
The long deprecated
%.% have been removed. Please use
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
NA (#1707). In SQL it is translated to
near(x, y) is a helper for
abs(x - y) < tol (#1607).
recode() is vectorised equivalent to
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 while
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 specification with
select() semantics generated by the new
columns() helper. In addition,
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-)
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.
[[ 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)
The internals of
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).
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)
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 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
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, @krlmlr).
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
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).
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 (#857).
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 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.
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).
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).
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 naming behaviour of
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
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 droups 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
slice() correctly handles grouped attributes (#1405).
ungroup() generic gains
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).
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), and anti and semi joins give correct result when by variable is a factor (#1571). A clear error message is given for joins where an explicit
by contains unavailable columns (#1928, #1932, @krlmlr).
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).
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
summarise() (#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).
top_n() can now also select bottom
n values by passing a negative value to
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
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).
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
data_frame() always produces a
tbl_df (#1151, @kevinushey)
filter(x, TRUE, TRUE) now just returns
x (#1210), it doesn’t internally modify the first argument (#971), and it now works with rowwise data (#1099). It once again works with data tables (#906).
glimpse() also prints out the number of variables in addition to the number of observations (@ilarischeinin, #988).
Joins handles matrix columns better (#1230), and can join
Date objects with heterogenous representations (some
Dates are integers, while other are numeric). This also improves
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, 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
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).
build_sql rather than
paste0 (#926, @NikNakk)
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).
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
lag() 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, use
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
TRUE (#1065) and
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).
Workaround for using the constructor of
DataFrame on an unprotected object (#998)
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
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).
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).
*_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
.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)
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).
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:
%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.
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)
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.
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.
anti_join()) 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)
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
funs_q has been replaced with
%.% has been deprecated: please use
chain() is defunct. (#518)
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. Instead use
src_monetdb() is now implemented in MonetDB.R, not dplyr.
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
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).
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
matches() (#481, @leondutoit). Fixed buglet in
select() so that you can now create variables called
Switched from RC to R6.
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).
mutate() creates a new variable that uses a window function, automatically wrap the result in a subquery (#484).
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
nth() now correctly preserve the class when using dates, times and factors (#509).
[.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
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)
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 :(
joining two data.tables now correctly dispatches to data table methods, and result is a data table (#470)
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.
If you use an unnamed argument, the result should be a data frame. This allows you to apply arbitrary functions to each group.
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.
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
src_monetdb() (#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 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
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
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)
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
Collecter allows 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
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).
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).
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).
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).
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
mutate.data.frame() correctly mutates the same variable repeatedly (#243).
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
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.
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
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
FALSE 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.