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compact() drops NULL elements from a list,
keeping other falsy values (NA, 0,
"") intact. The natural companion to
map()/foldr() when some calls legitimately
return nothing (e.g. an empty API response) and the NULLs
need dropping before folding or row-binding the rest.count(sort = TRUE) now breaks ties (groups with equal
counts) by ascending group key instead of leaving them in the grouping
engine’s internal enumeration order. That order was never part of the
documented contract and could silently change between
basetable versions (it did, between 1.3.1 and 1.4.0, as
part of the grouping-key codec work), which made
count(sort = TRUE) output non-reproducible across versions
for any workflow that samples from, or otherwise depends on, row order
among tied counts.benchmarking vignette no longer hard-requires
memory profiling: it now checks capabilities("profmem") and
falls back to timing-only bench::mark() output (dropping
the memory column/plot) when Rprofmem() is unavailable,
which was failing R CMD check on some r-devel build
machines.as_basetable() coerces a data frame, a list of
equal-length columns, or a matrix to the basetable class,
and is_basetable() tests for it. Both are now exported, so
other packages can build directly on the basetable class
instead of relying on a verb to stamp it or reaching into
internals.transliterate() help page no longer embeds literal
Greek and Cyrillic characters, which broke the PDF reference manual on
CRAN’s check machines. The example now builds its sample strings with
intToUtf8().stringi is no longer imported.
removeaccents() and transliterate() were its
only users; they now fold text with tables extracted verbatim from ICU’s
Latin-ASCII and Any-Latin; Latin-ASCII
transliterators (bench/make-translit-maps.R), so Latin
output is unchanged and Greek and Cyrillic match ICU except that an
upper-case letter expanding to a digraph stays upper case.
basetable now imports only base and recommended
packages.merge() and the inner/left cases behind it)
now probe the build side in parallel and gather the atomic output
columns on worker threads. On the 1e6-row reference join this moves
merge() from roughly 1.7x of data.table to
about parity.merge(), semimerge(),
antimerge() and updatemerge() on such
keys.merge() and subset() on character data.orderrows() then counts and buckets
the levels directly rather than ranking every string.pick() and drop() select columns by
shallow reference rather than routing through the row materialiser, so
narrowing a wide table no longer copies the retained column data.build_frame, behind every sort,
filter and the join fallback) now gathers each character column’s source
pointers on worker threads alongside the numeric columns, leaving only
the SET_STRING_ELT walk on the R thread. On the 1e6-row
orderrows(by = c(<chr>, <num>)) this roughly
halves the materialisation step.subset() filter compacts its match list
branch-free (write the index, advance the cursor by the 0/1 predicate)
instead of a mispredicting if/push_back,
bringing single-threaded subset(d, x > c) level with
data.table.casewhen(conditions, default = NA): vectorised
multi-branch selection. conditions is a named list of
equal-length logical vectors, the same shape as
collapsevalues() – the name of the first element that is
TRUE at a position is the value taken there; positions
matching nothing get default. Composes inside
transform().subset() gains a by argument: the
predicate is then evaluated within each group, so aggregate references
in it (mean(x), max(x), …) are per group. Kept
rows are returned in their original order. This is basetable’s explicit
answer to a grouped filter – there is still no stateful
group_by().samplerows() and samplefrac() gain a
by argument: sample n rows (capped at the
group size) or a fraction of each group. Sampled rows are returned in
their original order.basetable is now a self-contained data-manipulation package with a base-R interface and a bundled C++ execution engine. It has no external computation dependency. This is the first CRAN release.
data.table is no longer a dependency.
Every verb runs on basetable’s own compiled engine.
data.table and dplyr remain in
Suggests only, as a comparison target in the benchmark
vignette.basetable rather than a
data.table. A basetable is an ordinary data
frame with one extra class so it prints compactly and [
keeps the class and defaults to drop = FALSE.
as.data.frame() returns a plain frame. The
as = argument of btread() and the file readers
now takes "basetable" in place of
"data.table".bt_
prefix. aggregate(), count(),
uniquerows() and freq() now accept a single
file path as their first argument and route to the one-pass scan, so
there is one name per operation whether the input is a data frame or a
delimited file. bt_aggregate(), bt_count(),
bt_distinct() and bt_freq() are removed.contains() / matches()
(tidyselect selection helpers) are now containstext() /
matchestext(); glimpse() (tibble) is
preview(); cummean() (dplyr) is
cumavg(). distinct() is dropped –
uniquerows() is the data-frame form and also takes a file
path. The only names shared with dplyr are now
count() and pick(), both read as base-style
verbs..Call
kernels: projection, row subsetting, ordering, distinct and duplicate
detection, grouping, grouped reducers
(sum/mean/min/max/var/sd/n),
all join kinds (equi, semi, anti, update, cross, non-equi, range,
rolling), row-bind, and subset() predicate evaluation.
Custom aggregation functions still run as ordinary R closures per
group.orderrows() sorts with a stable multi-column radix over
order-preserving integer codes (character columns ranked in
strcmp order) instead of a comparison sort; ordering is
unchanged (C-locale byte order).subset() compiles a supported predicate (column and
scalar references; + - * / ^ %%; comparisons;
& | !; unary -; ifelse()) to
a small stack machine, with an eval() fallback for anything
outside that grammar.setthreads(): grouped aggregation, the sort pipeline, the
filter mask compaction and column gather, and the join membership
probe.btread() /
btwrite(), memory-mapped, RFC 4180, threaded parsing,
optional ALTREP lazy columns), and aggregate() /
count() / uniquerows() / freq()
on a file path fuse the parse with the grouping so unused columns are
never materialised.Measured against data.table at 1e6 rows on the reference
machine (Linux, 8 threads); see the Benchmarks vignette for
the full report.
data.table: uniquerows(),
sd / var by group at every cardinality,
count and mean by group at high cardinality,
semimerge() (parity at low cardinality down to ~0.25x at
high), pick() / column projection, and
subset() on the common col <op> scalar
shapes (single or &-joined comparisons over a numeric
column), now evaluated and materialised in one threaded pass with no
intermediate logical vector.merge(), mean by group
at low cardinality, rbindfill().data.table: string and integer
orderrows() (~2x; the radix byte passes are
memory-bandwidth bound). data.table’s parallel radix sort
is the one operation basetable does not yet match.aggregate() or
count() allocates near zero where the other engines
allocate tens of megabytes.uniquerows() also takes a single file path: a fused
one-pass scan that returns the distinct combinations of
cols without materialising the file.rbindfill() bulk-copies columns that already share a
type across every input and now keeps a shared column class such as
Date.stack() is exported and consistently returns a
basetable, masking utils::stack() like the
rest of the base-flavored API.inst/benchmarks/benchmark-scale.R: a size x cardinality
harness against base R, data.table and
dplyr.btread(lazy = TRUE) now returns
character columns as ALTREP too (not only
integer/double), so a lazy read parses nothing until a column is
touched. Combined with the parallel row indexer this makes “open a file”
and “read a few columns of a wide file” faster than both
data.table::fread and vroom
(vroom still wins nothing here; fread keeps
the lead only when every value is parsed with all threads). See
bench/RESULTS.md.btread() and btwrite(): a from-scratch
delimited-file reader and writer implemented in C++ (src/).
The reader memory-maps the file, scans it once for row boundaries with
RFC 4180 quoting, guesses column types from a bounded sample, and
materialises typed vectors with numeric columns filled by a
std::thread pool. btread(lazy = TRUE) returns
integer and double columns as ALTREP vectors that parse their column
only on first access, which pairs well with col_select for
wide files. btwrite() formats disjoint row ranges into
private buffers in parallel. Supports delimiter sniffing,
col_types, col_select, na,
comment, skip, n_max,
trim_ws, and .gz input. See
bench/benchmark-io.R.subset(data, select = -colname), negative bare-symbol
column selection, a base R subset() idiom, errored with
“object ‘colname’ not found” instead of dropping the column.
select was being force-evaluated as an ordinary argument
(via is.null(select)/is.character(select))
before its NSE form ever got a chance to run, and the column-index mask
used to evaluate NSE selections was unnamed. Rewritten to defer
evaluation and build a proper named index mask, matching
select/subset and
select = -c(a, b) style multi-column negative selection
too.reorder(). It was an exact duplicate of
orderrows() (added in 0.6.0 to replace the dplyr-named
arrange()), and it masked stats::reorder() the
same way the removed dplyr verbs masked dplyr’s, which silently broke
ggplot2::aes(reorder(...)) for anyone with
basetable attached. Use orderrows()
instead.split()’s keep.by now defaults to
TRUE (was FALSE), so the grouping column is
kept in each piece by default, matching base R’s split()
behavior on data frames. Pass keep.by = FALSE for the old
default.aggregate() no longer forces na.rm onto
every fun. A custom summary function with no
na.rm argument (and no ...) previously errored
with “unused argument”; na.rm is now only forwarded when
fun actually accepts it.transform() gained a by argument. When
supplied, each expression in ... is evaluated within every
group instead of across the whole table
(e.g. transform(data, dev = x - mean(x), by = "g") computes
the deviation from each group’s own mean), so the result still has one
row per input row. Use summaries() when you want one row
per group instead.rbindfill()’s typeconflict argument now
does something: "error" (the default) checks every shared
column across inputs up front and stops with a message naming the column
and the conflicting types before any coercion happens;
"coerce" skips the check and falls back to
data.table::rbindlist()’s usual coercion.R/verbs.R: filter(), select(),
rename(), arrange(), mutate(),
transmute(),
summarise()/summarize(),
distinct(), slice(), relocate(),
bind_rows(), and bind_cols(). These duplicated
dplyr’s verb names exactly, which meant attaching basetable
and dplyr in the same session could silently mask either
package’s verb grammar depending on load order. The underlying
functionality already existed under base-flavored names and continues to
be exported: use subset(), pick(),
reorder(), transform() (with
.keep = FALSE in place of transmute()),
summaries(), move(), and
rbindfill() instead. bind_cols() had no
package-specific behavior beyond base R’s cbind(); use
cbind() directly. Two functions had no existing
base-flavored equivalent and are now available under new names:
renamecols() (was rename()) and
uniquerows() (was distinct()).removeaccents() and transliterate()
deterministic across Linux, Windows, and macOS by using ICU
transliteration instead of the platform-dependent
iconv(..., "ASCII//TRANSLIT") behavior.R CMD build’s temporary
library on every supported platform.transform(), mutate(), and
transmute() now resolve ordinary values from their true
calling function, including when used inside a helper.value; the evaluated expression result is now assigned
through a standard-evaluation data.table setter.filter(),
rename(), distinct(), slice(),
relocate(), and bind_rows().filter() to one logical mask in the common
one-expression case and avoided allocating an NA mask when no missing
values are present.orderrows() and every helper that uses the shared base
ordering path.orderrows() through the package’s
data.table-backed ordering implementation, removing an avoidable
data.frame conversion while preserving input immutability.Author:/Maintainer: fields from
DESCRIPTION (they had drifted out of sync with
Authors@R, tripping an R CMD check --as-cran
NOTE); both are now derived from Authors@R as usual.R-CMD-check.yaml workflow
(macOS/Windows/Ubuntu, release/devel/oldrel-1) and matching README
badges.R CMD check --as-cran is clean apart from the two
expected first-submission notes (new submission, unable to verify
current time).Initial development cycle covering performance, correctness, documentation, and CRAN readiness.
rollingmerge() reimplemented a rolling/nearest
join with a hand-rolled per-group R loop that measured at roughly 1500x
the cost of data.table’s native roll= join; it now runs at
parity.count(), duplicated_keys(),
freq(), filldown(), fillup(),
split(), and
summarise()/summarize()/summaries()
were rewritten to route through data.table’s native grouping instead of
base-R
interaction()/table()/split()
idioms.inst/benchmarks/benchmark-all.R as a standing
benchmark harness tracking basetable against raw data.table.floordate()/
ceilingdate()/rounddate() were no-ops;
denserank() ranked by first-appearance order instead of
sorted value; padleft()/padright() ignored
their pad argument; datediff() ignored
units; dateseq() errored whenever
length.out was supplied; parsedatetime()
dropped its requested time zone; expandlevels() prepended
instead of appended new levels; emptycols() crashed on more
than one row; towide()’s reshape formula was backwards;
nonequimerge()/rangemerge() silently performed
a plain equi-merge despite their names;
removeduplicates(keep = "none") deleted every row instead
of keeping non-duplicates; equalrows(by =) compared only
the key columns; changedrows() had no actual
change-detection; changedcols() accepted a by
argument it never used; missingness() used a narrower
“missing” definition than its sibling functions;
antimerge()/semimerge()/nonequimerge()
did not validate an empty by, unlike the rest of the merge
family.distinct(),
denserank(), completegrid().vignettes/functions-reference.qmd, a
Quarto-authored PDF reference vignette walking through every exported
function with a real, runnable example, organized into 14 progressive
sections.R CMD check --as-cran passes with 0 errors/warnings
(only the expected “New submission” NOTE).cran-comments.md, CONTRIBUTING.md,
URL/BugReports fields.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.