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Fix for failing R CMD check.
dtplyr no longer directly depends on
crayon.
lazy_dt() must now
explicitly be called by the user (#312).across() output can now be used as a data frame
(#341).
.by/by has been implemented for
mutate(), summarise(), filter(),
and the slice() family (#399).
New translations for add_count(),
pick() (#341), and unite().
min_rank(), dense_rank(),
percent_rank(), & cume_dist() are now
mapped to their data.table equivalents (#396).
arrange() now utilizes setorder() when
possible for improved performance (#364).
select() now drops columns by reference when
possible for improved performance (#367).
slice() uses an intermediate variable to reduce
computation time of row selection (#377).
dtplyr no longer directly depends on
ellipsis.
Chained operations properly prevent modify-by-reference (#210).
across(), if_any(), and
if_all() evaluate the .cols argument in the
environment from which the function was called.
count() properly handles grouping variables
(#356).
desc() now supports use of .data
pronoun inside in arrange() (#346).
full_join() now produces output with correctly named
columns when a non-default value for suffix is supplied.
Previously the suffix argument was ignored (#382).
if_any() and if_all() now work without
specifying the .fns argument (@mgirlich, #325) and for a list of
functions specified in the (@mgirlich, #335).
pivot_wider()’s names_glue now works
even when names_from contains NAs
(#394).
In semi_join() the y table is again
coerced to a lazy table if copy = TRUE (@mgirlich, #322).
mutate() can now use .keep.
mutate()/summarize() correctly
translates anonymous functions (#362).
mutate()/transmute() now supports
glue::glue() and stringr::str_glue() without
specifying .envir.
where() now clearly errors because dtplyr doesn’t
support selection by predicate (#271).
@markfairbanks, @mgirlich, and @eutwt are now dtplyr authors in recognition of their significant and sustained contributions. Along with @eutwt, they supplied the bulk of the improvements in this release!
dtplyr gains translations for many more tidyr verbs:
drop_na() (@markfairbanks, #194)complete() (@markfairbanks, #225)expand() (@markfairbanks, #225)fill() (@markfairbanks, #197)pivot_longer() (@markfairbanks, #204)replace_na() (@markfairbanks, #202)nest() (@mgirlich, #251)separate() (@markfairbanks, #269)tally() gains a translation (@mgirlich, #201).
ifelse() is mapped to fifelse() (@markfairbanks,
#220).
slice() helpers (slice_head(),
slice_tail(), slice_min(),
slice_max() and slice_sample()) now accept
negative values for n and prop.
across() defaults to everything() when
.cols isn’t provided (@markfairbanks, #231), and handles
named selections (@eutwt #293). It ˜ow handles .fns
arguments in more forms (@eutwt #288):
function(x) x + 1~ 1arrange(dt, desc(col)) is translated to
dt[order(-col)] in order to take advantage of data.table’s
fast order (@markfairbanks, #227).
count() applied to data.tables no longer breaks when
dtplyr is loaded (@mgirlich, #201).
case_when() supports use of T to
specify the default (#272).
filter() errors for named input,
e.g. filter(dt, x = 1) (@mgirlich, #267) and works for negated
logical columns (@mgirlich, @211).
group_by() ungroups when no grouping variables are
specified (@mgirlich, #248), and supports inline
mutation like group_by(dt, y = x) (@mgirlich, #246).
if_else() named arguments are translated to the
correct arguments in data.table::fifelse() (@markfairbanks,
#234). if_else() supports .data and
.env pronouns (@markfairbanks, #220).
if_any() and if_all() default to
everything() when .cols isn’t provided (@eutwt, #294).
intersect()/union()/union_all()/setdiff()
convert data.table inputs to lazy_dt() (#278).
lag()/lead() are translated to
shift().
lazy_dt() keeps groups (@mgirlich, #206).
left_join() produces the same column order as dplyr
(@markfairbanks, #139).
left_join(), right_join(),
full_join(), and inner_join() perform a cross
join for by = character() (@mgirlich, #242).
left_join(), right_join(), and
inner_join() are always translated to the
[.data.table equivalent. For simple merges the translation
gets a bit longer but thanks to the simpler code base it helps to better
handle names in by and duplicated variables names produced
in the data.table join (@mgirlich, #222).
mutate() and transmute() work when
called without variables (@mgirlich, #248).
mutate() gains new experimental arguments
.before and .after that allow you to control
where the new columns are placed (to match dplyr 1.0.0) (@eutwt #291).
mutate() can modify grouping columns (instead of
creating another column with the same name) (@mgirlich, #246).
n_distinct() is translated to
uniqueN().
tally() and count() follow the dplyr
convention of creating a unique name if the default output
name (n) already exists (@eutwt, #295).
pivot_wider() names the columns correctly when
names_from is a numeric column (@mgirlich, #214).
pull() supports the name argument
(@mgirlich,
#263).
slice() no longer returns excess rows
(#10).
slice_*() functions after group_by()
are faster (@mgirlich, #216).
slice_max() works when ordering by a character
column (@mgirlich,
#218).
summarise() supports the .groups
argument (@mgirlich,
#245).
summarise(), tally(), and
count() can change the value of a grouping variables (@eutwt, #295).
transmute() doesn’t produce duplicate columns when
assigning to the same variable (@mgirlich, #249). It correctly flags
grouping variables so they selected (@mgirlich, #246).
ungroup() removes variables in ... from
grouping (@mgirlich,
#253).
All verbs now have (very basic) documentation pointing back to the dplyr generic, and providing a (very rough) description of the translation accompanied with a few examples.
Passing a data.table to a dplyr generic now converts it to a
lazy_dt(), making it a little easier to move between
data.table and dplyr syntax.
dtplyr has been bought up to compatibility with dplyr 1.0.0. This includes new translations for:
across(), if_any(),
if_all() (#154).
count() (#159).
relocate() (@smingerson, #162).
rename_with() (#160)
slice_min(), slice_max(),
slice_head(), slice_tail(), and
slice_sample() (#174).
And rename() and select() now support dplyr
1.0.0 tidyselect syntax (apart from predicate functions which can’t
easily work on lazily evaluated data tables).
We have begun the process of adding translations for tidyr verbs
beginning with pivot_wider() (@markfairbanks, #189).
compute() now creates an intermediate assignment
within the translation. This will generally have little impact on
performance but it allows you to use intermediate variables to simplify
complex translations.
case_when() is now translated to
fcase() (#190).
cur_data() (.SD),
cur_group() (.BY), cur_group_id()
(.GRP), and cur_group_rows() (.I`) are now
tranlsated to their data.table equivalents (#166).
filter() on grouped data nows use a much faster
translation using on .I rather than .SD (and
requiring an intermediate assignment) (#176). Thanks to suggestion from
@myoung3 and @ColeMiller1.
Translation of individual expressions:
x[[1]] is now translated correctly.
Anonymous functions are now preserved (@smingerson, #155)
Environment variables used in the i argument of
[.data.table are now correctly inlined when not in the
global environment (#164).
T and F are correctly translated to
TRUE and FALSE (#140).
Grouped filter, mutate, and slice no longer affect ordering of output (#178).
as_tibble() gains a .name_repair
argument (@markfairbanks).
as.data.table() always calls [] so that
the result will print (#146).
print.lazy_dt() shows total rows, and grouping, if
present.
group_map() and group_walk() are now
translated (#108).
Better handling for .data and .env
pronouns (#138).
dplyr verbs now work with NULL inputs
(#129).
joins do better job at determining output variables in the
presence of duplicated outputs (#128). When joining based on different
variables in x and y, joins consistently
preserve column from x, not y (#137).
lazy_dt() objects now have a useful
glimpse() method (#132).
group_by() now has an arrange parameter
which, if set to FALSE, sets the data.table translation to
use by rather than keyby (#85).
rename() now works without data.table
attached, as intended (@michaelchirico, #123).
dtplyr has been re-licensed as MIT (#165).
Converted from eager approach to lazy approach. You now must use
lazy_dt() to begin a translation pipeline, and must use
collect(), as.data.table(),
as.data.frame(), or as_tibble() to finish the
translation and actually perform the computation (#38).
This represents a complete overhaul of the package replacing the eager evaluation used in the previous releases. This unfortunately breaks all existing code that used dtplyr, but frankly the previous version was extremely inefficient so offered little of data.table’s impressive speed, and was used by very few people.
dtplyr provides methods for data.tables that warning you that
they use the data frame implementation and you should use
lazy_dt() (#77)
Joins now pass ... on to data.table’s merge method
(#41).
ungroup() now copies its input (@christophsax,
#54).
mutate() preserves grouping (@christophsax, #17).
if_else() and coalesce() are mapped to
data.table’s fifelse() and fcoalesce()
respectively (@michaelchirico, #112).
Maintenance release for CRAN checks.
inner_join(), left_join(),
right_join(), and full_join(): new
suffix argument which allows you to control what suffix
duplicated variable names receive, as introduced in dplyr 0.5 (#40,
@christophsax).
Joins use extended merge.data.table() and the
on argument, introduced in data.table 1.9.6. Avoids copy
and allows joins by different keys (#20, #21, @christophsax).
distinct() gains .keep_all argument
(#30, #31).
Slightly improve test coverage (#6).
Install devtools from GitHub on Travis
(#32).
Joins return data.table. Right and full join are now
implemented (#16, #19).
Remove warnings from tests (#4).
Extracted from dplyr at revision
e5f2952923028803.
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