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R/bit64-package.R
In bit64: A S3 Class for Vectors of 64bit Integers
#' A S3 class for vectors of 64bit integers
#'
#' @description
#' Package 'bit64' provides fast serializable S3 atomic 64bit (signed) integers
#' that can be used in vectors, matrices, arrays and data.frames. Methods are
#' available for coercion from and to logicals, integers, doubles, characters
#' and factors as well as many elementwise and summary functions.
#'
#' ### Version 0.8
#' With 'integer64' vectors you can store very large integers at the expense
#' of 64 bits, which is by factor 7 better than 'int64' from package 'int64'.
#' Due to the smaller memory footprint, the atomic vector architecture and
#' using only S3 instead of S4 classes, most operations are one to three orders
#' of magnitude faster: Example speedups are 4x for serialization, 250x for
#' adding, 900x for coercion and 2000x for object creation. Also 'integer64'
#' avoids an ongoing (potentially infinite) penalty for garbage collection
#' observed during existence of 'int64' objects (see code in example section).
#'
#' ### Version 0.9
#' Package 'bit64' - which extends R with fast 64-bit integers - now has fast
#' (single-threaded) implementations the most important univariate algorithmic
#' operations (those based on hashing and sorting). We now have methods for
#' 'match', '%in%', 'duplicated', 'unique', 'table', 'sort', 'order', 'rank',
#' 'quantile', 'median' and 'summary'. Regarding data management we also have
#' novel generics 'unipos' (positions of the unique values), 'tiepos' (
#' positions of ties), 'keypos' (positions of foreign keys in a sorted
#' dimension table) and derived methods 'as.factor' and 'as.ordered'. This 64-
#' bit functionality is implemented carefully to be not slower than the
#' respective 32-bit operations in Base R and also to avoid outlying waiting
#' times observed with 'order', 'rank' and 'table' (speedup factors 20/16/200
#' respective). This increases the dataset size with wich we can work truly
#' interactive. The speed is achieved by simple heuristic optimizers in high-
#' level functions choosing the best from multiple low-level algorithms and
#' further taking advantage of a novel caching if activated. In an example R
#' session using a couple of these operations the 64-bit integers performed 22x
#' faster than base 32-bit integers, hash-caching improved this to 24x,
#' sortorder-caching was most efficient with 38x (caching hashing and sorting
#' is not worth it with 32x at duplicated RAM consumption).
#'
#' # Design considerations
#'
#' 64 bit integers are related to big data: we need them to overcome address space
#' limitations. Therefore performance of the 64 bit integer type is critical. In the
#' S language -- designed in 1975 -- atomic objects were defined to be vectors for a
#' couple of good reasons: simplicity, option for implicit parallelization, good
#' cache locality. In recent years many analytical databases have learnt that lesson:
#' column based data bases provide superior performance for many applications, the
#' result are products such as MonetDB, Sybase IQ, Vertica, Exasol, Ingres Vectorwise.
#' If we introduce 64 bit integers not natively in Base R but as an external package,
#' we should at least strive to make them as 'basic' as possible. Therefore the design
#' choice of bit64 not only differs from package int64, it is obvious: Like the other
#' atomic types in Base R, we model data type 'integer64' as a contiguous [`atomic`]
#' vector in memory, and we use the more basic [S3] class system, not [S4]. Like
#' package int64 we want our 'integer64' to be [`serialize`]able, therefore we also
#' use an existing data type as the basis. Again the choice is obvious: R has only one
#' 64 bit data type: doubles. By using [`double`]s, `integer64` [`inherits`] some
#' functionality such as [is.atomic()], [length()], [`length<-`], [names()],
#' [`names<-`], [dim()], [`dim<-`], [dimnames()], [`dimnames<-`].
#'
#' Our R level functions strictly follow the functional programming paradigm:
#' no modification of arguments or other side-effects. Before version 0.93 we
#' internally deviated from the strict paradigm in order to boost performance. Our C
#' functions do not create new return values, instead we pass-in the memory to be
#' returned as an argument. This gives us the freedom to apply the C-function to new
#' or old vectors, which helps to avoid unnecessary memory allocation, unnecessary
#' copying and unnecessary garbage collection. Prior to 0.93 _within_ our R functions
#' we also deviated from conventional R programming by not using [`attr<-`] and
#' [`attributes<-`] because they always did new memory allocation and copying in older
#' R versions. If we wanted to set attributes of return values that we have freshly
#' created, we instead used functions [bit::setattr()] and [bit::setattributes()].
#' From version 0.93 `bit::setattr()` is only used for manipulating [`cache`] objects,
#' in [ramsort.integer64()], [sort.integer64()], and [as.data.frame.integer64()].
#'
#' # Arithmetic precision and coercion
#'
#' The fact that we introduce 64 bit long long integers -- without introducing 128-bit
#' long doubles -- creates some subtle challenges: Unlike 32 bit [`integer`]s, the
#' `integer64` are no longer a proper subset of [`double`]. If a binary arithmetic
#' operation does involve a `double` and a `integer`, it is a no-brainer to return
#' `double` without loss of information. If an `integer64` meets a `double`, it is not
#' trivial what type to return. Switching to `integer64` limits our ability to
#' represent very large numbers, switching to `double` limits our ability to
#' distinguish `x` from `x+1`. Since the latter is the purpose of introducing 64 bit
#' integers, we usually return `integer64` from functions involving `integer64`, for
#' example in [`c()`][c.integer64], [`cbind()`][cbind.integer64], and
#' [`rbind()`][rbind.integer64]
#'
#' Our operators [`+`][ops64], [`-`][ops64], [`%/%`][ops64], and [`%%`][ops64] coerce
#' their arguments to `integer64` and return `integer64` if they are `integer`,
#' `double` or `logical`. Otherwise the `integer64` argument is coerced to double and
#' the R base method for the other class is called.
#'
#' Our operators [`*`][ops64] and [`^`][ops64] coerce their first argument to
#' `integer64` and possibly the second to `integer64` if it is not `double` and
#' return `integer64` if they are `integer`, `double` or `logical`. Otherwise the
#' `integer64` argument is coerced to double and the R base method for the other
#' class is called.
#'
#' The division [`/`][ops64] operator also coerces its first argument to `integer64`
#' and possibly the second to `integer64` if it is not `double` and returns
#' `integer64` if they are `integer`, `double` or `logical`. Otherwise the `integer64`
#' argument is coerced to double and the R base method for the other class is called.
#'
#' [`sqrt()`][sqrt.integer64], [`log()`][log.integer64],
#' [`log2()`][log2.integer64], and [`log10()`][log10.integer64] coerce their first
#' argument to `integer64` and coerce internally their second argument to
#' 'long double', they return as `double`
#'
#' | **argument1** | **op** | **argument2** | **->** | **coerced1** | **op** | **coerced2** | **->** | **result** |
#' |:-------------:|:------:|:-------------:|:------:|:------------:|:------:|:------------:|:------:|:----------:|
#' | integer64 | + | double | -> | integer64 | + | integer64 | -> | integer64 |
#' | double | + | integer64 | -> | integer64 | + | integer64 | -> | integer64 |
#' | integer64 | + | complex | -> | double | + | complex | -> | complex |
#' | complex | + | integer64 | -> | complex | + | double | -> | complex |
#' | integer64 | - | double | -> | integer64 | - | integer64 | -> | integer64 |
#' | double | - | integer64 | -> | integer64 | - | integer64 | -> | integer64 |
#' | integer64 | - | complex | -> | double | - | complex | -> | complex |
#' | complex | - | integer64 | -> | complex | - | double | -> | complex |
#' | integer64 | %/% | double | -> | integer64 | %/% | integer64 | -> | integer64 |
#' | double | %/% | integer64 | -> | integer64 | %/% | integer64 | -> | integer64 |
#' | integer64 | %/% | complex | -> | double | %/% | complex | -> | complex |
#' | complex | %/% | integer64 | -> | complex | %/% | double | -> | complex |
#' | integer64 | %% | double | -> | integer64 | %% | integer64 | -> | integer64 |
#' | double | %% | integer64 | -> | integer64 | %% | integer64 | -> | integer64 |
#' | integer64 | %% | complex | -> | double | %% | complex | -> | complex |
#' | complex | %% | integer64 | -> | complex | %% | double | -> | complex |
#' | integer64 | * | double | -> | integer64 | * | long double | -> | integer64 |
#' | double | * | integer64 | -> | integer64 | * | integer64 | -> | integer64 |
#' | integer64 | * | complex | -> | double | * | complex | -> | complex |
#' | complex | * | integer64 | -> | complex | * | double | -> | complex |
#' | integer64 | / | double | -> | integer64 | / | long double | -> | double |
#' | double | / | integer64 | -> | integer64 | / | long double | -> | double |
#' | integer64 | / | complex | -> | double | / | complex | -> | complex |
#' | complex | / | integer64 | -> | complex | / | double | -> | complex |
#' | integer64 | ^ | double | -> | integer64 | ^ | long double | -> | double |
#' | double | ^ | integer64 | -> | integer64 | ^ | long double | -> | double |
#' | integer64 | ^ | complex | -> | double | ^ | complex | -> | complex |
#' | complex | ^ | integer64 | -> | complex | ^ | double | -> | complex |
#' | integer64 | %*% | double | -> | integer64 | %*% | integer64 | -> | integer64 |
#' | double | %*% | integer64 | -> | integer64 | %*% | integer64 | -> | integer64 |
#' | integer64 | %*% | complex | -> | double | %*% | complex | -> | complex |
#' | complex | %*% | integer64 | -> | complex | %*% | double | -> | complex |
#'
#' # Creating and testing S3 class 'integer64'
#'
#' Our creator function `integer64` takes an argument `length`, creates an atomic
#' double vector of this length, attaches an S3 class attribute 'integer64' to it,
#' and that's it. We simply rely on S3 method dispatch and interpret those 64-bit
#' elements as 'long long int'.
#'
#' [is.double()] currently returns TRUE for `integer64` and might return `FALSE` in
#' a later release. Consider `is.double()` to have undefined behavior and do query
#' [is.integer64()] _before_ querying `is.double()`.
#'
# As a second line of defense against misinterpretation we make `is.double()` return
# FALSE by making it S3 generic and adding a method `as.double.integer64()`.
#
#' The methods [is.integer64()] and [is.vector()] both return `TRUE` for `integer64`.
#' Note that we did not patch [storage.mode()] and [typeof()], which both continue
#' returning 'double'. Like for 32 bit [`integer`], [mode()] returns 'numeric' and
#' [as.double()] tries coercing to [`double`]. It is possible that 'integer64' becomes
#' a `vmode` in package ff.
#'
#' Further methods for creating `integer64` are [`range()`][range.integer64] which
#' returns the range of the data type if calles without arguments,
#' [`rep()`][rep.integer64], [`seq()`][seq.integer64].
#'
#' For all available methods on `integer64` vectors see the index below and the examples.
#'
#' # Index of implemented methods
#'
#' | **creating, testing, printing** | **see also** | **description** |
#' |--------------------------------:|----------------:|:--------------------------|
#' | `NA_integer64_` | [`NA_integer_`] | NA constant |
#' | `integer64` | [`integer`] | create zero atomic vector |
#' | [runif64()] | [runif()] | create random vector |
#' | [rep.integer64()] | [rep()] | |
#' | [seq.integer64()] | [seq()] | |
#' | [is.integer64()] | [is()] | |
#' | | [is.integer()] | inherited from Base R |
# | [is.double.integer64()] | [is.double()] | |
#' | [is.vector.integer64()] | [is.vector()] | |
#' | [identical.integer64()] | [identical()] | |
#' | [`length<-.integer64`] | [`length<-`] | |
#' | | [length()] | inherited from Base R |
#' | [`names<-`] | inherited from Base R |
#' | | [names()] | inherited from Base R |
#' | | [`dim<-`] | inherited from Base R |
#' | | [dim()] | inherited from Base R |
#' | | [`dimnames<-`] | inherited from Base R |
#' | | [dimnames()] | inherited from Base R |
#' | | [str()] | inherited from Base R, does not print values correctly |
#' | [print.integer64()] | [print()] | |
#' | [str.integer64()] | [str()] | |
#'
#' | **coercing to integer64** | **see also** | **description** |
#' |---------------------------:|-----------------:|:----------------|
#' | [as.integer64()] | | generic |
#' | [as.integer64.bitstring()] | [as.bitstring()] | |
#' | [as.integer64.character()] | [character()] | |
#' | [as.integer64.double()] | [double()] | |
#' | [as.integer64.integer()] | [integer()] | |
#' | [as.integer64.integer64()] | `integer64` | |
#' | [as.integer64.logical()] | [logical()] | |
#' | [as.integer64.NULL()] | [NULL()] | |
#'
#' | **coercing from integer64** | **see also** | **description** |
#' |----------------------------:|-----------------:|:----------------|
#' | [as.list.integer64()] | [as.list()] | generic |
#' | [as.bitstring()] | [as.bitstring()] | generic |
#' | [as.bitstring.integer64()] | | |
#' | [as.character.integer64()] | [as.character()] | |
#' | [as.double.integer64()] | [as.double()] | |
#' | [as.integer.integer64()] | [as.integer()] | |
#' | [as.logical.integer64()] | [as.logical()] | |
# removed as requested by the CRAN maintainer
# | [as.vector.integer64()] | [as.vector()] | |
#'
#' | **data structures** | **see also** | **description** |
#' |----------------------------:|------------------:|:-----------------------------------|
#' | [c.integer64()] | [c()] | vector concatenate |
#' | [cbind.integer64()] | [cbind()] | column bind |
#' | [rbind.integer64()] | [rbind()] | row bind |
#' | [as.data.frame.integer64()] | [as.data.frame()] | coerce atomic object to data.frame |
#' | | [data.frame()] | inherited from Base R since we have coercion |
#'
#' | **subscripting** | **see also** | **description** |
#' |----------------------------------------------:|------------------------:|:-------------------------|
#' | [`[.integer64`][extract.replace.integer64] | [`[`][base::Extract] | vector and array extract |
#' | [`[<-.integer64`][extract.replace.integer64] | [`[<-`][base::Extract] | vector and array assign |
#' | [`[[.integer64`][extract.replace.integer64] | [`[[`][base::Extract] | scalar extract |
#' | [`[[<-.integer64`][extract.replace.integer64] | [`[[<-`][base::Extract] | scalar assign |
#'
#' | **binary operators** | **see also** | **description** |
#' |---------------------:|-------------:|:------------------|
#' | [`+.integer64`] | [`+`] | returns integer64 |
#' | [`-.integer64`] | [`-`] | returns integer64 |
#' | [`*.integer64`] | [`*`] | returns integer64 |
#' | [`^.integer64`] | [`^`] | returns double |
#' | [`/.integer64`] | [`/`] | returns double |
#' | \code{\link[=ops64]{\%/\%}} | \code{\link[=Arithmetic]{\%/\%}} | returns integer64 |
#' | \code{\link[=ops64]{\%\%}} | \code{\link[=Arithmetic]{\%\%}} | returns integer64 |
#'
#' | **comparison operators** | **see also** | **description** |
#' |-------------------------:|-------------:|:----------------|
#' | [`==.integer64`] | [`==`] | |
#' | [`!=.integer64`] | [`!=`] | |
#' | [`<.integer64`] | [`<`] | |
#' | [`<=.integer64`] | [`<=`] | |
#' | [`>.integer64`] | [`>`] | |
#' | [`>=.integer64`] | [`>=`] | |
#'
#' \tabular{rrl}{
#' \strong{logical operators} \tab \strong{see also} \tab \strong{description} \cr
#' \code{\link{!.integer64}} \tab \code{\link{!}} \tab \cr
#' \code{\link{&.integer64}} \tab \code{\link{&}} \tab \cr
#' \code{\link{|.integer64}} \tab \code{\link[base:Logic]{|}} \tab \cr
#' }
# TODO(r-lib/roxygen2#1668): Restore the markdown representation of the table.
# | **logical operators** | **see also** | **description** |
# |----------------------:|-------------:|:----------------|
# | [`!.integer64`] | [`!`] | |
# | [`&.integer64`] | [`&`] | |
# | [`\|.integer64`] | [`\|`][base::Logic] | |
#'
#' | **math functions** | **see also** | **description** |
#' |----------------------:|-------------:|:-----------------------------|
#' | [is.na.integer64()] | [is.na()] | returns logical |
#' | [format.integer64()] | [format()] | returns character |
#' | [abs.integer64()] | [abs()] | returns integer64 |
#' | [sign.integer64()] | [sign()] | returns integer64 |
#' | [log.integer64()] | [log()] | returns double |
#' | [log10.integer64()] | [log10()] | returns double |
#' | [log2.integer64()] | [log2()] | returns double |
#' | [sqrt.integer64()] | [sqrt()] | returns double |
#' | [ceiling.integer64()] | [ceiling()] | dummy returning its argument |
#' | [floor.integer64()] | [floor()] | dummy returning its argument |
#' | [trunc.integer64()] | [trunc()] | dummy returning its argument |
#' | [round.integer64()] | [round()] | dummy returning its argument |
#' | [signif.integer64()] | [signif()] | dummy returning its argument |
#'
#' | **cumulative functions** | **see also** | **description** |
#' |-------------------------:|-------------:|:----------------|
#' | [cummin.integer64()] | [cummin()] | |
#' | [cummax.integer64()] | [cummax()] | |
#' | [cumsum.integer64()] | [cumsum()] | |
#' | [cumprod.integer64()] | [cumprod()] | |
#' | [diff.integer64()] | [diff()] | |
#'
#' | **summary functions** | **see also** | **description** |
#' |----------------------:|-------------:|:----------------|
#' | [range.integer64()] | [range()] | |
#' | [min.integer64()] | [min()] | |
#' | [max.integer64()] | [max()] | |
#' | [sum.integer64()] | [sum()] | |
#' | [mean.integer64()] | [mean()] | |
#' | [prod.integer64()] | [prod()] | |
#' | [all.integer64()] | [all()] | |
#' | [any.integer64()] | [any()] | |
#'
#' | **algorithmically complex functions** | **see also** | **description (caching)** |
#' |--------------------------------------:|-------------:|:---------------------------|
#' | [match.integer64()] | [match()] | position of x in table (h//o/so) |
#' | \code{\link[=match.integer64]{\%in\%.integer64}} | \code{\link[=match]{\%in\%}} | is x in table? (h//o/so) |
#' | [duplicated.integer64()] | [duplicated()] | is current element duplicate of previous one? (h//o/so) |
#' | [unique.integer64()] | [unique()] | (shorter) vector of unique values only (h/s/o/so) |
#' | [unipos.integer64()] | [unipos()] | positions corresponding to unique values (h/s/o/so) |
#' | [tiepos.integer64()] | [tiepos()] | positions of values that are tied (//o/so) |
#' | [keypos.integer64()] | [keypos()] | position of current value in sorted list of unique values (//o/so) |
#' | [table.integer64()] | [table()] | unique values and their frequencies (h/s/o/so) |
#' | [sort.integer64()] | [sort()] | sorted vector (/s/o/so) |
#' | [order.integer64()] | [order()] | positions of elements that would create sorted vector (//o/so) |
#' | [rank.integer64()] | [rank()] | (average) ranks of non-NAs, NAs kept in place (/s/o/so) |
#' | [quantile.integer64()] | [quantile()] | (existing) values at specified percentiles (/s/o/so) |
#' | [median.integer64()] | [median()] | (existing) value at percentile 0.5 (/s/o/so) |
#' | [summary.integer64()] | [summary()] | (/s/o/so) |
#' | [all.equal.integer64()] | [all.equal()] | test if two objects are (nearly) equal (/s/o/so) |
#'
#' | **helper functions** | **see also** | **description** |
#' |-----------------:|---------------:|:---------------------------|
#' | [minusclass()] | [minusclass()] | removing class attritbute |
#' | [plusclass()] | [plusclass()] | inserting class attribute |
#' | [binattr()] | [binattr()] | define binary op behaviour |
#'
#' | **tested I/O functions** | **see also** | **description** |
#' |-------------------------:|----------------:|:----------------------|
#' | | [read.table()] | inherited from Base R |
#' | | [write.table()] | inherited from Base R |
#' | | [serialize()] | inherited from Base R |
#' | | [unserialize()] | inherited from Base R |
#' | | [save()] | inherited from Base R |
#' | | [load()] | inherited from Base R |
#' | | [dput()] | inherited from Base R |
#' | | [dget()] | inherited from Base R |
#'
#' # Limitations inherited from implementing 64 bit integers via an external package
#'
#' - **vector size** of atomic vectors is still limited to
#' [`.Machine$integer.max`][.Machine]. However, external memory extending packages
#' such as ff or bigmemory can extend their address space now with `integer64`.
#' Having 64 bit integers also help with those not so obvious address issues that
#' arise once we exchange data with SQL databases and datawarehouses, which use big
#' integers as surrogate keys, e.g. on indexed primary key columns. This puts R into
#' a relatively strong position compared to certain commercial statistical softwares,
#' which sell database connectivity but neither have the range of 64 bit integers,
#' nor have integers at all, nor have a single numeric data type in their
#' macro-glue-language.
#' - **literals** such as `123LL` would require changes to Base R, up to then we need
#' to write (and call) `as.integer64(123L)` or `as.integer64(123)` or
#' `as.integer64('123')`. Only the latter allows to specify numbers beyond Base R's
#' numeric data types and therefore is the recommended way to use -- using only one
#' way may facilitate migrating code to literals at a later stage.
#'
#' # Limitations inherited from Base R, Core team, can you change this?
#'
#' - **[identical()]** with default parameters does not distinguish all bit-patterns of
#' doubles. For testing purposes we provide a wrapper [identical.integer64()] that
#' will distinguish all bit-patterns. It would be desireable to have a single call
#' of `identical()` handle both, [`double`] and `integer64`.
#'
#' - the **colon** operator [:] officially does not dispatch S3 methods, however, we
#' have made it generic:
#'
#' ```r
#' from <- lim.integer64()[1]
#' to <- from+99
#' from:to
#' ```
#'
#' As a limitation remains: it will only dispatch at its first argument `from` but
#' not at its second `to`.
#'
#' - **[is.double()]** does not dispatch S3 methods, However, we have made it generic
#' and it will return `FALSE` on `integer64`.
#'
#' - **[c()]** only dispatches [c.integer64()] if the first argument is `integer64`
#' and it does not recursively dispatch the proper method when called with argument
#' `recursive=TRUE`. Therefore `c(list(integer64, integer64))` does not work and
#' for now you can only call `c.integer64(list(x, x))`.
#'
#' - **[unlist()]** is not generic and if it were, we would face similar problems as
#' with [c()]
#' - **[vector()]** with argument `mode='integer64'` cannot work without adjustment
#' of Base R
#' - **[as.vector()]** with argument `mode='integer64'` cannot work without adjustment
#' of Base R
#' - **[is.vector()]** does not dispatch its method [is.vector.integer64()]
#' - **[mode<-()]** drops the class 'integer64' which is returned from
#' `as.integer64()`. Also it does not remove an existing class 'integer64' when
#' assigning mode 'integer'.
#' - **[storage.mode<-()]** does not support external data types such as `integer64`
#' - **[matrix()]** does drop the 'integer64' class attribute.
#' - **[array()]** does drop the 'integer64' class attribute.
#' + In current R versions (1.15.1) this can be circumvented by activating the
#' function `as.vector.integer64()`. However, the CRAN maintainer has requested
#' to remove `as.vector.integer64()`, even at the price of breaking previously
#' working functionality of the package.
#'
#' - **[str()]** does not print the values of `integer64` correctly
#'
#' # Further limitations
#'
#' - **subscripting** non-existing elements and subscripting with `NA`s is currently
#' not supported. Such subscripting currently returns `9218868437227407266` instead
#' of `NA` (the `NA` value of the underlying double code). Following the full R
#' behaviour here would either destroy performance or require extensive C-coding.
#'
#' @note `integer64` are useful for handling database keys and exact counting in +-2^63.
#' Do not use them as replacement for 32bit integers, integer64 are not supported for
#' subscripting by R-core and they have different semantics when combined with double.
#' Do understand that `integer64` can only be useful over `double` if we do not coerce
#' it to `double`.
#'
#' While
#'
#' integer + double -> double + double -> double
#'
#' or
#'
#' 1L + 0.5 -> 1.5
#'
#' for additive operations we coerce to `integer64`
#'
#' integer64 + double -> integer64 + integer64 -> integer64
#'
#' hence
#'
#' as.integer64(1) + 0.5 -> 1LL + 0LL -> 1LL
#'
#' see section "Arithmetic precision and coercion" above
#'
#' @seealso [integer()] in base R
#' @examples
#' message("Using integer64 in vector")
#' x <- integer64(8) # create 64 bit vector
#' x
#' is.atomic(x) # TRUE
#' is.integer64(x) # TRUE
#' is.numeric(x) # TRUE
#' is.integer(x) # FALSE - debatable
#' is.double(x) # FALSE - might change
#' x[] <- 1:2 # assigned value is recycled as usual
#' x[1:6] # subscripting as usual
#' length(x) <- 13 # changing length as usual
#' x
#' rep(x, 2) # replicate as usual
#' seq(as.integer64(1), 10) # seq.integer64 is dispatched on first given argument
#' seq(to=as.integer64(10), 1) # seq.integer64 is dispatched on first given argument
#' seq.integer64(along.with=x) # or call seq.integer64 directly
#' # c.integer64 is dispatched only if *first* argument is integer64 ...
#' x <- c(x,runif(length(x), max=100))
#' # ... and coerces everything to integer64 - including double
#' x
#' names(x) <- letters # use names as usual
#' x
#'
#' message("Using integer64 in array - note that 'matrix' currently does not work")
#' message("as.vector.integer64 removed as requested by the CRAN maintainer")
#' message("as consequence 'array' also does not work anymore")
# y <- array(as.integer64(NA), dim=c(3,4), dimnames=list(letters[1:3], LETTERS[1:4]))
#' message("we still can create a matrix or array by assigning 'dim'")
#' y <- rep(as.integer64(NA), 12)
#' dim(y) <- c(3,4)
#' dimnames(y) <- list(letters[1:3], LETTERS[1:4])
#' y["a",] <- 1:2 # assigning as usual
#' y
#' y[1:2,-4] # subscripting as usual
#' # cbind.integer64 dispatched on any argument and coerces everything to integer64
#' cbind(E=1:3, F=runif(3, 0, 100), G=c("-1","0","1"), y)
#'
#' message("Using integer64 in data.frame")
#' str(as.data.frame(x))
#' str(as.data.frame(y))
#' str(data.frame(y))
#' str(data.frame(I(y)))
#' d <- data.frame(x=x, y=runif(length(x), 0, 100))
#' d
#' d$x
#'
#' message("Using integer64 with csv files")
#' fi64 <- tempfile()
#' write.csv(d, file=fi64, row.names=FALSE)
#' e <- read.csv(fi64, colClasses=c("integer64", NA))
#' unlink(fi64)
#' str(e)
#' identical.integer64(d$x,e$x)
#'
#' message("Serializing and unserializing integer64")
#' dput(d, fi64)
#' e <- dget(fi64)
#' identical.integer64(d$x,e$x)
#' e <- d[,]
#' save(e, file=fi64)
#' rm(e)
#' load(file=fi64)
#' identical.integer64(d,e)
#'
#' \dontrun{
#' message("== Differences between integer64 and int64 ==")
#' require(bit64)
#' require(int64)
#'
#' message("-- integer64 is atomic --")
#' is.atomic(integer64())
#' #is.atomic(int64())
#' str(integer64(3))
#' #str(int64(3))
#'
#' message("-- The following performance numbers are measured under RWin64 --")
#' message("-- under RWin32 the advantage of integer64 over int64 is smaller --")
#'
#' message("-- integer64 needs 7x/5x less RAM than int64 under 64/32 bit OS
#' (and twice the RAM of integer as it should be) --")
#' #as.vector(object.size(int64(1e6))/object.size(integer64(1e6)))
#' as.vector(object.size(integer64(1e6))/object.size(integer(1e6)))
#'
#' message("-- integer64 creates 2000x/1300x faster than int64 under 64/32 bit OS
#' (and 3x the time of integer) --")
#' t32 <- system.time(integer(1e8))
#' t64 <- system.time(integer64(1e8))
#' #T64 <- system.time(int64(1e7))*10 # using 1e8 as above stalls our R on an i7 8 GB RAM Thinkpad
#' #T64/t64
#' t64/t32
#'
#' i32 <- sample(1e6)
#' d64 <- as.double(i32)
#'
#' message("-- the following timings are rather conservative since timings
#' of integer64 include garbage collection -- due to looped calls")
#' message("-- integer64 coerces 900x/100x faster than int64
#' under 64/32 bit OS (and 2x the time of coercing to integer) --")
#' t32 <- system.time(for(i in 1:1000)as.integer(d64))
#' t64 <- system.time(for(i in 1:1000)as.integer64(d64))
#' #T64 <- system.time(as.int64(d64))*1000
#' #T64/t64
#' t64/t32
#' td64 <- system.time(for(i in 1:1000)as.double(i32))
#' t64 <- system.time(for(i in 1:1000)as.integer64(i32))
#' #T64 <- system.time(for(i in 1:10)as.int64(i32))*100
#' #T64/t64
#' t64/td64
#'
#' message("-- integer64 serializes 4x/0.8x faster than int64
#' under 64/32 bit OS (and less than 2x/6x the time of integer or double) --")
#' t32 <- system.time(for(i in 1:10)serialize(i32, NULL))
#' td64 <- system.time(for(i in 1:10)serialize(d64, NULL))
#' i64 <- as.integer64(i32);
#' t64 <- system.time(for(i in 1:10)serialize(i64, NULL))
#' rm(i64); gc()
#' #I64 <- as.int64(i32);
#' #T64 <- system.time(for(i in 1:10)serialize(I64, NULL))
#' #rm(I64); gc()
#' #T64/t64
#' t64/t32
#' t64/td64
#'
#'
#' message("-- integer64 adds 250x/60x faster than int64
#' under 64/32 bit OS (and less than 6x the time of integer or double) --")
#' td64 <- system.time(for(i in 1:100)d64+d64)
#' t32 <- system.time(for(i in 1:100)i32+i32)
#' i64 <- as.integer64(i32);
#' t64 <- system.time(for(i in 1:100)i64+i64)
#' rm(i64); gc()
#' #I64 <- as.int64(i32);
#' #T64 <- system.time(for(i in 1:10)I64+I64)*10
#' #rm(I64); gc()
#' #T64/t64
#' t64/t32
#' t64/td64
#'
#' message("-- integer64 sums 3x/0.2x faster than int64
#' (and at about 5x/60X the time of integer and double) --")
#' td64 <- system.time(for(i in 1:100)sum(d64))
#' t32 <- system.time(for(i in 1:100)sum(i32))
#' i64 <- as.integer64(i32);
#' t64 <- system.time(for(i in 1:100)sum(i64))
#' rm(i64); gc()
#' #I64 <- as.int64(i32);
#' #T64 <- system.time(for(i in 1:100)sum(I64))
#' #rm(I64); gc()
#' #T64/t64
#' t64/t32
#' t64/td64
#'
#' message("-- integer64 diffs 5x/0.85x faster than integer and double
#' (int64 version 1.0 does not support diff) --")
#' td64 <- system.time(for(i in 1:10)diff(d64, lag=2L, differences=2L))
#' t32 <- system.time(for(i in 1:10)diff(i32, lag=2L, differences=2L))
#' i64 <- as.integer64(i32);
#' t64 <- system.time(for(i in 1:10)diff(i64, lag=2L, differences=2L))
#' rm(i64); gc()
#' t64/t32
#' t64/td64
#'
#'
#' message("-- integer64 subscripts 1000x/340x faster than int64
#' (and at the same speed / 10x slower as integer) --")
#' ts32 <- system.time(for(i in 1:1000)sample(1e6, 1e3))
#' t32<- system.time(for(i in 1:1000)i32[sample(1e6, 1e3)])
#' i64 <- as.integer64(i32);
#' t64 <- system.time(for(i in 1:1000)i64[sample(1e6, 1e3)])
#' rm(i64); gc()
#' #I64 <- as.int64(i32);
#' #T64 <- system.time(for(i in 1:100)I64[sample(1e6, 1e3)])*10
#' #rm(I64); gc()
#' #(T64-ts32)/(t64-ts32)
#' (t64-ts32)/(t32-ts32)
#'
#' message("-- integer64 assigns 200x/90x faster than int64
#' (and 50x/160x slower than integer) --")
#' ts32 <- system.time(for(i in 1:100)sample(1e6, 1e3))
#' t32 <- system.time(for(i in 1:100)i32[sample(1e6, 1e3)] <- 1:1e3)
#' i64 <- as.integer64(i32);
#' i64 <- system.time(for(i in 1:100)i64[sample(1e6, 1e3)] <- 1:1e3)
#' rm(i64); gc()
#' #I64 <- as.int64(i32);
#' #I64 <- system.time(for(i in 1:10)I64[sample(1e6, 1e3)] <- 1:1e3)*10
#' #rm(I64); gc()
#' #(T64-ts32)/(t64-ts32)
#' (t64-ts32)/(t32-ts32)
#'
#'
#' tdfi32 <- system.time(dfi32 <- data.frame(a=i32, b=i32, c=i32))
#' tdfsi32 <- system.time(dfi32[1e6:1,])
#' fi32 <- tempfile()
#' tdfwi32 <- system.time(write.csv(dfi32, file=fi32, row.names=FALSE))
#' tdfri32 <- system.time(read.csv(fi32, colClasses=rep("integer", 3)))
#' unlink(fi32)
#' rm(dfi32); gc()
#'
#' i64 <- as.integer64(i32);
#' tdfi64 <- system.time(dfi64 <- data.frame(a=i64, b=i64, c=i64))
#' tdfsi64 <- system.time(dfi64[1e6:1,])
#' fi64 <- tempfile()
#' tdfwi64 <- system.time(write.csv(dfi64, file=fi64, row.names=FALSE))
#' tdfri64 <- system.time(read.csv(fi64, colClasses=rep("integer64", 3)))
#' unlink(fi64)
#' rm(i64, dfi64); gc()
#'
#' #I64 <- as.int64(i32);
#' #tdfI64 <- system.time(dfI64<-data.frame(a=I64, b=I64, c=I64))
#' #tdfsI64 <- system.time(dfI64[1e6:1,])
#' #fI64 <- tempfile()
#' #tdfwI64 <- system.time(write.csv(dfI64, file=fI64, row.names=FALSE))
#' #tdfrI64 <- system.time(read.csv(fI64, colClasses=rep("int64", 3)))
#' #unlink(fI64)
#' #rm(I64, dfI64); gc()
#'
#' message("-- integer64 coerces 40x/6x faster to data.frame than int64
#' (and factor 1/9 slower than integer) --")
#' #tdfI64/tdfi64
#' tdfi64/tdfi32
#' message("-- integer64 subscripts from data.frame 20x/2.5x faster than int64
#' (and 3x/13x slower than integer) --")
#' #tdfsI64/tdfsi64
#' tdfsi64/tdfsi32
#' message("-- integer64 csv writes about 2x/0.5x faster than int64
#' (and about 1.5x/5x slower than integer) --")
#' #tdfwI64/tdfwi64
#' tdfwi64/tdfwi32
#' message("-- integer64 csv reads about 3x/1.5 faster than int64
#' (and about 2x slower than integer) --")
#' #tdfrI64/tdfri64
#' tdfri64/tdfri32
#'
#' rm(i32, d64); gc()
#'
#'
#' message("-- investigating the impact on garbage collection: --")
#' message("-- the fragmented structure of int64 messes up R's RAM --")
#' message("-- and slows down R's gargbage collection just by existing --")
#'
#' td32 <- double(21)
#' td32[1] <- system.time(d64 <- double(1e7))[3]
#' for (i in 2:11)td32[i] <- system.time(gc(), gcFirst=FALSE)[3]
#' rm(d64)
#' for (i in 12:21)td32[i] <- system.time(gc(), gcFirst=FALSE)[3]
#'
#' t64 <- double(21)
#' t64[1] <- system.time(i64 <- integer64(1e7))[3]
#' for (i in 2:11)t64[i] <- system.time(gc(), gcFirst=FALSE)[3]
#' rm(i64)
#' for (i in 12:21)t64[i] <- system.time(gc(), gcFirst=FALSE)[3]
#'
#' #T64 <- double(21)
#' #T64[1] <- system.time(I64 <- int64(1e7))[3]
#' #for (i in 2:11)T64[i] <- system.time(gc(), gcFirst=FALSE)[3]
#' #rm(I64)
#' #for (i in 12:21)T64[i] <- system.time(gc(), gcFirst=FALSE)[3]
#'
#' #matplot(1:21, cbind(td32, t64, T64), pch=c("d","i","I"), log="y")
#' matplot(1:21, cbind(td32, t64), pch=c("d","i"), log="y")
#' }
#' @aliases bit64 is.integer.integer64 is.vector.integer64
#' @keywords internal package classes manip
#' @useDynLib bit64, .registration = TRUE, .fixes = "C_"
"_PACKAGE"
## usethis namespace: start
#' @importFrom bit clone is.sorted keyorder keysort keysortorder mergeorder
#' mergesort mergesortorder na.count nties nunique nvalid quickorder
#' quicksort quicksortorder radixorder radixsort radixsortorder ramorder
#' ramsort ramsortorder repeat.time setattr shellorder shellsort
#' shellsortorder still.identical
#' @importFrom graphics barplot par title
#' @importFrom methods as is
#' @importFrom stats cor median quantile
#' @importFrom utils head packageDescription strOptions tail getS3method
#' @export : %in% is.double match order print.cache rank
# TODO(>=4.9.0): un-export these
#' @export [<-.integer64 abs.integer64 all.integer64 any.integer64
#' @export as.character.integer64 as.data.frame.integer64 as.double.integer64
#' @export as.integer.integer64 as.integer64.character as.integer64.double
#' @export as.integer64.integer as.integer64.logical c.integer64
#' @export format.integer64 is.na.integer64 log.integer64 max.integer64
#' @export min.integer64 print.integer64 rank.integer64 rep.integer64
#' @export seq.integer64 str.integer64 sum.integer64 unique.integer64
## usethis namespace: end
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#' A S3 class for vectors of 64bit integers
#'
#' @description
#' Package 'bit64' provides fast serializable S3 atomic 64bit (signed) integers
#' that can be used in vectors, matrices, arrays and data.frames. Methods are
#' available for coercion from and to logicals, integers, doubles, characters
#' and factors as well as many elementwise and summary functions.
#'
#' ### Version 0.8
#' With 'integer64' vectors you can store very large integers at the expense
#' of 64 bits, which is by factor 7 better than 'int64' from package 'int64'.
#' Due to the smaller memory footprint, the atomic vector architecture and
#' using only S3 instead of S4 classes, most operations are one to three orders
#' of magnitude faster: Example speedups are 4x for serialization, 250x for
#' adding, 900x for coercion and 2000x for object creation. Also 'integer64'
#' avoids an ongoing (potentially infinite) penalty for garbage collection
#' observed during existence of 'int64' objects (see code in example section).
#'
#' ### Version 0.9
#' Package 'bit64' - which extends R with fast 64-bit integers - now has fast
#' (single-threaded) implementations the most important univariate algorithmic
#' operations (those based on hashing and sorting). We now have methods for
#' 'match', '%in%', 'duplicated', 'unique', 'table', 'sort', 'order', 'rank',
#' 'quantile', 'median' and 'summary'. Regarding data management we also have
#' novel generics 'unipos' (positions of the unique values), 'tiepos' (
#' positions of ties), 'keypos' (positions of foreign keys in a sorted
#' dimension table) and derived methods 'as.factor' and 'as.ordered'. This 64-
#' bit functionality is implemented carefully to be not slower than the
#' respective 32-bit operations in Base R and also to avoid outlying waiting
#' times observed with 'order', 'rank' and 'table' (speedup factors 20/16/200
#' respective). This increases the dataset size with wich we can work truly
#' interactive. The speed is achieved by simple heuristic optimizers in high-
#' level functions choosing the best from multiple low-level algorithms and
#' further taking advantage of a novel caching if activated. In an example R
#' session using a couple of these operations the 64-bit integers performed 22x
#' faster than base 32-bit integers, hash-caching improved this to 24x,
#' sortorder-caching was most efficient with 38x (caching hashing and sorting
#' is not worth it with 32x at duplicated RAM consumption).
#'
#' # Design considerations
#'
#' 64 bit integers are related to big data: we need them to overcome address space
#' limitations. Therefore performance of the 64 bit integer type is critical. In the
#' S language -- designed in 1975 -- atomic objects were defined to be vectors for a
#' couple of good reasons: simplicity, option for implicit parallelization, good
#' cache locality. In recent years many analytical databases have learnt that lesson:
#' column based data bases provide superior performance for many applications, the
#' result are products such as MonetDB, Sybase IQ, Vertica, Exasol, Ingres Vectorwise.
#' If we introduce 64 bit integers not natively in Base R but as an external package,
#' we should at least strive to make them as 'basic' as possible. Therefore the design
#' choice of bit64 not only differs from package int64, it is obvious: Like the other
#' atomic types in Base R, we model data type 'integer64' as a contiguous [`atomic`]
#' vector in memory, and we use the more basic [S3] class system, not [S4]. Like
#' package int64 we want our 'integer64' to be [`serialize`]able, therefore we also
#' use an existing data type as the basis. Again the choice is obvious: R has only one
#' 64 bit data type: doubles. By using [`double`]s, `integer64` [`inherits`] some
#' functionality such as [is.atomic()], [length()], [`length<-`], [names()],
#' [`names<-`], [dim()], [`dim<-`], [dimnames()], [`dimnames<-`].
#'
#' Our R level functions strictly follow the functional programming paradigm:
#' no modification of arguments or other side-effects. Before version 0.93 we
#' internally deviated from the strict paradigm in order to boost performance. Our C
#' functions do not create new return values, instead we pass-in the memory to be
#' returned as an argument. This gives us the freedom to apply the C-function to new
#' or old vectors, which helps to avoid unnecessary memory allocation, unnecessary
#' copying and unnecessary garbage collection. Prior to 0.93 _within_ our R functions
#' we also deviated from conventional R programming by not using [`attr<-`] and
#' [`attributes<-`] because they always did new memory allocation and copying in older
#' R versions. If we wanted to set attributes of return values that we have freshly
#' created, we instead used functions [bit::setattr()] and [bit::setattributes()].
#' From version 0.93 `bit::setattr()` is only used for manipulating [`cache`] objects,
#' in [ramsort.integer64()], [sort.integer64()], and [as.data.frame.integer64()].
#'
#' # Arithmetic precision and coercion
#'
#' The fact that we introduce 64 bit long long integers -- without introducing 128-bit
#' long doubles -- creates some subtle challenges: Unlike 32 bit [`integer`]s, the
#' `integer64` are no longer a proper subset of [`double`]. If a binary arithmetic
#' operation does involve a `double` and a `integer`, it is a no-brainer to return
#' `double` without loss of information. If an `integer64` meets a `double`, it is not
#' trivial what type to return. Switching to `integer64` limits our ability to
#' represent very large numbers, switching to `double` limits our ability to
#' distinguish `x` from `x+1`. Since the latter is the purpose of introducing 64 bit
#' integers, we usually return `integer64` from functions involving `integer64`, for
#' example in [`c()`][c.integer64], [`cbind()`][cbind.integer64], and
#' [`rbind()`][rbind.integer64]
#'
#' Our operators [`+`][ops64], [`-`][ops64], [`%/%`][ops64], and [`%%`][ops64] coerce
#' their arguments to `integer64` and return `integer64` if they are `integer`,
#' `double` or `logical`. Otherwise the `integer64` argument is coerced to double and
#' the R base method for the other class is called.
#'
#' Our operators [`*`][ops64] and [`^`][ops64] coerce their first argument to
#' `integer64` and possibly the second to `integer64` if it is not `double` and
#' return `integer64` if they are `integer`, `double` or `logical`. Otherwise the
#' `integer64` argument is coerced to double and the R base method for the other
#' class is called.
#'
#' The division [`/`][ops64] operator also coerces its first argument to `integer64`
#' and possibly the second to `integer64` if it is not `double` and returns
#' `integer64` if they are `integer`, `double` or `logical`. Otherwise the `integer64`
#' argument is coerced to double and the R base method for the other class is called.
#'
#' [`sqrt()`][sqrt.integer64], [`log()`][log.integer64],
#' [`log2()`][log2.integer64], and [`log10()`][log10.integer64] coerce their first
#' argument to `integer64` and coerce internally their second argument to
#' 'long double', they return as `double`
#'
#' | **argument1** | **op** | **argument2** | **->** | **coerced1** | **op** | **coerced2** | **->** | **result** |
#' |:-------------:|:------:|:-------------:|:------:|:------------:|:------:|:------------:|:------:|:----------:|
#' | integer64 | + | double | -> | integer64 | + | integer64 | -> | integer64 |
#' | double | + | integer64 | -> | integer64 | + | integer64 | -> | integer64 |
#' | integer64 | + | complex | -> | double | + | complex | -> | complex |
#' | complex | + | integer64 | -> | complex | + | double | -> | complex |
#' | integer64 | - | double | -> | integer64 | - | integer64 | -> | integer64 |
#' | double | - | integer64 | -> | integer64 | - | integer64 | -> | integer64 |
#' | integer64 | - | complex | -> | double | - | complex | -> | complex |
#' | complex | - | integer64 | -> | complex | - | double | -> | complex |
#' | integer64 | %/% | double | -> | integer64 | %/% | integer64 | -> | integer64 |
#' | double | %/% | integer64 | -> | integer64 | %/% | integer64 | -> | integer64 |
#' | integer64 | %/% | complex | -> | double | %/% | complex | -> | complex |
#' | complex | %/% | integer64 | -> | complex | %/% | double | -> | complex |
#' | integer64 | %% | double | -> | integer64 | %% | integer64 | -> | integer64 |
#' | double | %% | integer64 | -> | integer64 | %% | integer64 | -> | integer64 |
#' | integer64 | %% | complex | -> | double | %% | complex | -> | complex |
#' | complex | %% | integer64 | -> | complex | %% | double | -> | complex |
#' | integer64 | * | double | -> | integer64 | * | long double | -> | integer64 |
#' | double | * | integer64 | -> | integer64 | * | integer64 | -> | integer64 |
#' | integer64 | * | complex | -> | double | * | complex | -> | complex |
#' | complex | * | integer64 | -> | complex | * | double | -> | complex |
#' | integer64 | / | double | -> | integer64 | / | long double | -> | double |
#' | double | / | integer64 | -> | integer64 | / | long double | -> | double |
#' | integer64 | / | complex | -> | double | / | complex | -> | complex |
#' | complex | / | integer64 | -> | complex | / | double | -> | complex |
#' | integer64 | ^ | double | -> | integer64 | ^ | long double | -> | double |
#' | double | ^ | integer64 | -> | integer64 | ^ | long double | -> | double |
#' | integer64 | ^ | complex | -> | double | ^ | complex | -> | complex |
#' | complex | ^ | integer64 | -> | complex | ^ | double | -> | complex |
#' | integer64 | %*% | double | -> | integer64 | %*% | integer64 | -> | integer64 |
#' | double | %*% | integer64 | -> | integer64 | %*% | integer64 | -> | integer64 |
#' | integer64 | %*% | complex | -> | double | %*% | complex | -> | complex |
#' | complex | %*% | integer64 | -> | complex | %*% | double | -> | complex |
#'
#' # Creating and testing S3 class 'integer64'
#'
#' Our creator function `integer64` takes an argument `length`, creates an atomic
#' double vector of this length, attaches an S3 class attribute 'integer64' to it,
#' and that's it. We simply rely on S3 method dispatch and interpret those 64-bit
#' elements as 'long long int'.
#'
#' [is.double()] currently returns TRUE for `integer64` and might return `FALSE` in
#' a later release. Consider `is.double()` to have undefined behavior and do query
#' [is.integer64()] _before_ querying `is.double()`.
#'
# As a second line of defense against misinterpretation we make `is.double()` return
# FALSE by making it S3 generic and adding a method `as.double.integer64()`.
#
#' The methods [is.integer64()] and [is.vector()] both return `TRUE` for `integer64`.
#' Note that we did not patch [storage.mode()] and [typeof()], which both continue
#' returning 'double'. Like for 32 bit [`integer`], [mode()] returns 'numeric' and
#' [as.double()] tries coercing to [`double`]. It is possible that 'integer64' becomes
#' a `vmode` in package ff.
#'
#' Further methods for creating `integer64` are [`range()`][range.integer64] which
#' returns the range of the data type if calles without arguments,
#' [`rep()`][rep.integer64], [`seq()`][seq.integer64].
#'
#' For all available methods on `integer64` vectors see the index below and the examples.
#'
#' # Index of implemented methods
#'
#' | **creating, testing, printing** | **see also** | **description** |
#' |--------------------------------:|----------------:|:--------------------------|
#' | `NA_integer64_` | [`NA_integer_`] | NA constant |
#' | `integer64` | [`integer`] | create zero atomic vector |
#' | [runif64()] | [runif()] | create random vector |
#' | [rep.integer64()] | [rep()] | |
#' | [seq.integer64()] | [seq()] | |
#' | [is.integer64()] | [is()] | |
#' | | [is.integer()] | inherited from Base R |
# | [is.double.integer64()] | [is.double()] | |
#' | [is.vector.integer64()] | [is.vector()] | |
#' | [identical.integer64()] | [identical()] | |
#' | [`length<-.integer64`] | [`length<-`] | |
#' | | [length()] | inherited from Base R |
#' | [`names<-`] | inherited from Base R |
#' | | [names()] | inherited from Base R |
#' | | [`dim<-`] | inherited from Base R |
#' | | [dim()] | inherited from Base R |
#' | | [`dimnames<-`] | inherited from Base R |
#' | | [dimnames()] | inherited from Base R |
#' | | [str()] | inherited from Base R, does not print values correctly |
#' | [print.integer64()] | [print()] | |
#' | [str.integer64()] | [str()] | |
#'
#' | **coercing to integer64** | **see also** | **description** |
#' |---------------------------:|-----------------:|:----------------|
#' | [as.integer64()] | | generic |
#' | [as.integer64.bitstring()] | [as.bitstring()] | |
#' | [as.integer64.character()] | [character()] | |
#' | [as.integer64.double()] | [double()] | |
#' | [as.integer64.integer()] | [integer()] | |
#' | [as.integer64.integer64()] | `integer64` | |
#' | [as.integer64.logical()] | [logical()] | |
#' | [as.integer64.NULL()] | [NULL()] | |
#'
#' | **coercing from integer64** | **see also** | **description** |
#' |----------------------------:|-----------------:|:----------------|
#' | [as.list.integer64()] | [as.list()] | generic |
#' | [as.bitstring()] | [as.bitstring()] | generic |
#' | [as.bitstring.integer64()] | | |
#' | [as.character.integer64()] | [as.character()] | |
#' | [as.double.integer64()] | [as.double()] | |
#' | [as.integer.integer64()] | [as.integer()] | |
#' | [as.logical.integer64()] | [as.logical()] | |
# removed as requested by the CRAN maintainer
# | [as.vector.integer64()] | [as.vector()] | |
#'
#' | **data structures** | **see also** | **description** |
#' |----------------------------:|------------------:|:-----------------------------------|
#' | [c.integer64()] | [c()] | vector concatenate |
#' | [cbind.integer64()] | [cbind()] | column bind |
#' | [rbind.integer64()] | [rbind()] | row bind |
#' | [as.data.frame.integer64()] | [as.data.frame()] | coerce atomic object to data.frame |
#' | | [data.frame()] | inherited from Base R since we have coercion |
#'
#' | **subscripting** | **see also** | **description** |
#' |----------------------------------------------:|------------------------:|:-------------------------|
#' | [`[.integer64`][extract.replace.integer64] | [`[`][base::Extract] | vector and array extract |
#' | [`[<-.integer64`][extract.replace.integer64] | [`[<-`][base::Extract] | vector and array assign |
#' | [`[[.integer64`][extract.replace.integer64] | [`[[`][base::Extract] | scalar extract |
#' | [`[[<-.integer64`][extract.replace.integer64] | [`[[<-`][base::Extract] | scalar assign |
#'
#' | **binary operators** | **see also** | **description** |
#' |---------------------:|-------------:|:------------------|
#' | [`+.integer64`] | [`+`] | returns integer64 |
#' | [`-.integer64`] | [`-`] | returns integer64 |
#' | [`*.integer64`] | [`*`] | returns integer64 |
#' | [`^.integer64`] | [`^`] | returns double |
#' | [`/.integer64`] | [`/`] | returns double |
#' | \code{\link[=ops64]{\%/\%}} | \code{\link[=Arithmetic]{\%/\%}} | returns integer64 |
#' | \code{\link[=ops64]{\%\%}} | \code{\link[=Arithmetic]{\%\%}} | returns integer64 |
#'
#' | **comparison operators** | **see also** | **description** |
#' |-------------------------:|-------------:|:----------------|
#' | [`==.integer64`] | [`==`] | |
#' | [`!=.integer64`] | [`!=`] | |
#' | [`<.integer64`] | [`<`] | |
#' | [`<=.integer64`] | [`<=`] | |
#' | [`>.integer64`] | [`>`] | |
#' | [`>=.integer64`] | [`>=`] | |
#'
#' \tabular{rrl}{
#' \strong{logical operators} \tab \strong{see also} \tab \strong{description} \cr
#' \code{\link{!.integer64}} \tab \code{\link{!}} \tab \cr
#' \code{\link{&.integer64}} \tab \code{\link{&}} \tab \cr
#' \code{\link{|.integer64}} \tab \code{\link[base:Logic]{|}} \tab \cr
#' }
# TODO(r-lib/roxygen2#1668): Restore the markdown representation of the table.
# | **logical operators** | **see also** | **description** |
# |----------------------:|-------------:|:----------------|
# | [`!.integer64`] | [`!`] | |
# | [`&.integer64`] | [`&`] | |
# | [`\|.integer64`] | [`\|`][base::Logic] | |
#'
#' | **math functions** | **see also** | **description** |
#' |----------------------:|-------------:|:-----------------------------|
#' | [is.na.integer64()] | [is.na()] | returns logical |
#' | [format.integer64()] | [format()] | returns character |
#' | [abs.integer64()] | [abs()] | returns integer64 |
#' | [sign.integer64()] | [sign()] | returns integer64 |
#' | [log.integer64()] | [log()] | returns double |
#' | [log10.integer64()] | [log10()] | returns double |
#' | [log2.integer64()] | [log2()] | returns double |
#' | [sqrt.integer64()] | [sqrt()] | returns double |
#' | [ceiling.integer64()] | [ceiling()] | dummy returning its argument |
#' | [floor.integer64()] | [floor()] | dummy returning its argument |
#' | [trunc.integer64()] | [trunc()] | dummy returning its argument |
#' | [round.integer64()] | [round()] | dummy returning its argument |
#' | [signif.integer64()] | [signif()] | dummy returning its argument |
#'
#' | **cumulative functions** | **see also** | **description** |
#' |-------------------------:|-------------:|:----------------|
#' | [cummin.integer64()] | [cummin()] | |
#' | [cummax.integer64()] | [cummax()] | |
#' | [cumsum.integer64()] | [cumsum()] | |
#' | [cumprod.integer64()] | [cumprod()] | |
#' | [diff.integer64()] | [diff()] | |
#'
#' | **summary functions** | **see also** | **description** |
#' |----------------------:|-------------:|:----------------|
#' | [range.integer64()] | [range()] | |
#' | [min.integer64()] | [min()] | |
#' | [max.integer64()] | [max()] | |
#' | [sum.integer64()] | [sum()] | |
#' | [mean.integer64()] | [mean()] | |
#' | [prod.integer64()] | [prod()] | |
#' | [all.integer64()] | [all()] | |
#' | [any.integer64()] | [any()] | |
#'
#' | **algorithmically complex functions** | **see also** | **description (caching)** |
#' |--------------------------------------:|-------------:|:---------------------------|
#' | [match.integer64()] | [match()] | position of x in table (h//o/so) |
#' | \code{\link[=match.integer64]{\%in\%.integer64}} | \code{\link[=match]{\%in\%}} | is x in table? (h//o/so) |
#' | [duplicated.integer64()] | [duplicated()] | is current element duplicate of previous one? (h//o/so) |
#' | [unique.integer64()] | [unique()] | (shorter) vector of unique values only (h/s/o/so) |
#' | [unipos.integer64()] | [unipos()] | positions corresponding to unique values (h/s/o/so) |
#' | [tiepos.integer64()] | [tiepos()] | positions of values that are tied (//o/so) |
#' | [keypos.integer64()] | [keypos()] | position of current value in sorted list of unique values (//o/so) |
#' | [table.integer64()] | [table()] | unique values and their frequencies (h/s/o/so) |
#' | [sort.integer64()] | [sort()] | sorted vector (/s/o/so) |
#' | [order.integer64()] | [order()] | positions of elements that would create sorted vector (//o/so) |
#' | [rank.integer64()] | [rank()] | (average) ranks of non-NAs, NAs kept in place (/s/o/so) |
#' | [quantile.integer64()] | [quantile()] | (existing) values at specified percentiles (/s/o/so) |
#' | [median.integer64()] | [median()] | (existing) value at percentile 0.5 (/s/o/so) |
#' | [summary.integer64()] | [summary()] | (/s/o/so) |
#' | [all.equal.integer64()] | [all.equal()] | test if two objects are (nearly) equal (/s/o/so) |
#'
#' | **helper functions** | **see also** | **description** |
#' |-----------------:|---------------:|:---------------------------|
#' | [minusclass()] | [minusclass()] | removing class attritbute |
#' | [plusclass()] | [plusclass()] | inserting class attribute |
#' | [binattr()] | [binattr()] | define binary op behaviour |
#'
#' | **tested I/O functions** | **see also** | **description** |
#' |-------------------------:|----------------:|:----------------------|
#' | | [read.table()] | inherited from Base R |
#' | | [write.table()] | inherited from Base R |
#' | | [serialize()] | inherited from Base R |
#' | | [unserialize()] | inherited from Base R |
#' | | [save()] | inherited from Base R |
#' | | [load()] | inherited from Base R |
#' | | [dput()] | inherited from Base R |
#' | | [dget()] | inherited from Base R |
#'
#' # Limitations inherited from implementing 64 bit integers via an external package
#'
#' - **vector size** of atomic vectors is still limited to
#' [`.Machine$integer.max`][.Machine]. However, external memory extending packages
#' such as ff or bigmemory can extend their address space now with `integer64`.
#' Having 64 bit integers also help with those not so obvious address issues that
#' arise once we exchange data with SQL databases and datawarehouses, which use big
#' integers as surrogate keys, e.g. on indexed primary key columns. This puts R into
#' a relatively strong position compared to certain commercial statistical softwares,
#' which sell database connectivity but neither have the range of 64 bit integers,
#' nor have integers at all, nor have a single numeric data type in their
#' macro-glue-language.
#' - **literals** such as `123LL` would require changes to Base R, up to then we need
#' to write (and call) `as.integer64(123L)` or `as.integer64(123)` or
#' `as.integer64('123')`. Only the latter allows to specify numbers beyond Base R's
#' numeric data types and therefore is the recommended way to use -- using only one
#' way may facilitate migrating code to literals at a later stage.
#'
#' # Limitations inherited from Base R, Core team, can you change this?
#'
#' - **[identical()]** with default parameters does not distinguish all bit-patterns of
#' doubles. For testing purposes we provide a wrapper [identical.integer64()] that
#' will distinguish all bit-patterns. It would be desireable to have a single call
#' of `identical()` handle both, [`double`] and `integer64`.
#'
#' - the **colon** operator [:] officially does not dispatch S3 methods, however, we
#' have made it generic:
#'
#' ```r
#' from <- lim.integer64()[1]
#' to <- from+99
#' from:to
#' ```
#'
#' As a limitation remains: it will only dispatch at its first argument `from` but
#' not at its second `to`.
#'
#' - **[is.double()]** does not dispatch S3 methods, However, we have made it generic
#' and it will return `FALSE` on `integer64`.
#'
#' - **[c()]** only dispatches [c.integer64()] if the first argument is `integer64`
#' and it does not recursively dispatch the proper method when called with argument
#' `recursive=TRUE`. Therefore `c(list(integer64, integer64))` does not work and
#' for now you can only call `c.integer64(list(x, x))`.
#'
#' - **[unlist()]** is not generic and if it were, we would face similar problems as
#' with [c()]
#' - **[vector()]** with argument `mode='integer64'` cannot work without adjustment
#' of Base R
#' - **[as.vector()]** with argument `mode='integer64'` cannot work without adjustment
#' of Base R
#' - **[is.vector()]** does not dispatch its method [is.vector.integer64()]
#' - **[mode<-()]** drops the class 'integer64' which is returned from
#' `as.integer64()`. Also it does not remove an existing class 'integer64' when
#' assigning mode 'integer'.
#' - **[storage.mode<-()]** does not support external data types such as `integer64`
#' - **[matrix()]** does drop the 'integer64' class attribute.
#' - **[array()]** does drop the 'integer64' class attribute.
#' + In current R versions (1.15.1) this can be circumvented by activating the
#' function `as.vector.integer64()`. However, the CRAN maintainer has requested
#' to remove `as.vector.integer64()`, even at the price of breaking previously
#' working functionality of the package.
#'
#' - **[str()]** does not print the values of `integer64` correctly
#'
#' # Further limitations
#'
#' - **subscripting** non-existing elements and subscripting with `NA`s is currently
#' not supported. Such subscripting currently returns `9218868437227407266` instead
#' of `NA` (the `NA` value of the underlying double code). Following the full R
#' behaviour here would either destroy performance or require extensive C-coding.
#'
#' @note `integer64` are useful for handling database keys and exact counting in +-2^63.
#' Do not use them as replacement for 32bit integers, integer64 are not supported for
#' subscripting by R-core and they have different semantics when combined with double.
#' Do understand that `integer64` can only be useful over `double` if we do not coerce
#' it to `double`.
#'
#' While
#'
#' integer + double -> double + double -> double
#'
#' or
#'
#' 1L + 0.5 -> 1.5
#'
#' for additive operations we coerce to `integer64`
#'
#' integer64 + double -> integer64 + integer64 -> integer64
#'
#' hence
#'
#' as.integer64(1) + 0.5 -> 1LL + 0LL -> 1LL
#'
#' see section "Arithmetic precision and coercion" above
#'
#' @seealso [integer()] in base R
#' @examples
#' message("Using integer64 in vector")
#' x <- integer64(8) # create 64 bit vector
#' x
#' is.atomic(x) # TRUE
#' is.integer64(x) # TRUE
#' is.numeric(x) # TRUE
#' is.integer(x) # FALSE - debatable
#' is.double(x) # FALSE - might change
#' x[] <- 1:2 # assigned value is recycled as usual
#' x[1:6] # subscripting as usual
#' length(x) <- 13 # changing length as usual
#' x
#' rep(x, 2) # replicate as usual
#' seq(as.integer64(1), 10) # seq.integer64 is dispatched on first given argument
#' seq(to=as.integer64(10), 1) # seq.integer64 is dispatched on first given argument
#' seq.integer64(along.with=x) # or call seq.integer64 directly
#' # c.integer64 is dispatched only if *first* argument is integer64 ...
#' x <- c(x,runif(length(x), max=100))
#' # ... and coerces everything to integer64 - including double
#' x
#' names(x) <- letters # use names as usual
#' x
#'
#' message("Using integer64 in array - note that 'matrix' currently does not work")
#' message("as.vector.integer64 removed as requested by the CRAN maintainer")
#' message("as consequence 'array' also does not work anymore")
# y <- array(as.integer64(NA), dim=c(3,4), dimnames=list(letters[1:3], LETTERS[1:4]))
#' message("we still can create a matrix or array by assigning 'dim'")
#' y <- rep(as.integer64(NA), 12)
#' dim(y) <- c(3,4)
#' dimnames(y) <- list(letters[1:3], LETTERS[1:4])
#' y["a",] <- 1:2 # assigning as usual
#' y
#' y[1:2,-4] # subscripting as usual
#' # cbind.integer64 dispatched on any argument and coerces everything to integer64
#' cbind(E=1:3, F=runif(3, 0, 100), G=c("-1","0","1"), y)
#'
#' message("Using integer64 in data.frame")
#' str(as.data.frame(x))
#' str(as.data.frame(y))
#' str(data.frame(y))
#' str(data.frame(I(y)))
#' d <- data.frame(x=x, y=runif(length(x), 0, 100))
#' d
#' d$x
#'
#' message("Using integer64 with csv files")
#' fi64 <- tempfile()
#' write.csv(d, file=fi64, row.names=FALSE)
#' e <- read.csv(fi64, colClasses=c("integer64", NA))
#' unlink(fi64)
#' str(e)
#' identical.integer64(d$x,e$x)
#'
#' message("Serializing and unserializing integer64")
#' dput(d, fi64)
#' e <- dget(fi64)
#' identical.integer64(d$x,e$x)
#' e <- d[,]
#' save(e, file=fi64)
#' rm(e)
#' load(file=fi64)
#' identical.integer64(d,e)
#'
#' \dontrun{
#' message("== Differences between integer64 and int64 ==")
#' require(bit64)
#' require(int64)
#'
#' message("-- integer64 is atomic --")
#' is.atomic(integer64())
#' #is.atomic(int64())
#' str(integer64(3))
#' #str(int64(3))
#'
#' message("-- The following performance numbers are measured under RWin64 --")
#' message("-- under RWin32 the advantage of integer64 over int64 is smaller --")
#'
#' message("-- integer64 needs 7x/5x less RAM than int64 under 64/32 bit OS
#' (and twice the RAM of integer as it should be) --")
#' #as.vector(object.size(int64(1e6))/object.size(integer64(1e6)))
#' as.vector(object.size(integer64(1e6))/object.size(integer(1e6)))
#'
#' message("-- integer64 creates 2000x/1300x faster than int64 under 64/32 bit OS
#' (and 3x the time of integer) --")
#' t32 <- system.time(integer(1e8))
#' t64 <- system.time(integer64(1e8))
#' #T64 <- system.time(int64(1e7))*10 # using 1e8 as above stalls our R on an i7 8 GB RAM Thinkpad
#' #T64/t64
#' t64/t32
#'
#' i32 <- sample(1e6)
#' d64 <- as.double(i32)
#'
#' message("-- the following timings are rather conservative since timings
#' of integer64 include garbage collection -- due to looped calls")
#' message("-- integer64 coerces 900x/100x faster than int64
#' under 64/32 bit OS (and 2x the time of coercing to integer) --")
#' t32 <- system.time(for(i in 1:1000)as.integer(d64))
#' t64 <- system.time(for(i in 1:1000)as.integer64(d64))
#' #T64 <- system.time(as.int64(d64))*1000
#' #T64/t64
#' t64/t32
#' td64 <- system.time(for(i in 1:1000)as.double(i32))
#' t64 <- system.time(for(i in 1:1000)as.integer64(i32))
#' #T64 <- system.time(for(i in 1:10)as.int64(i32))*100
#' #T64/t64
#' t64/td64
#'
#' message("-- integer64 serializes 4x/0.8x faster than int64
#' under 64/32 bit OS (and less than 2x/6x the time of integer or double) --")
#' t32 <- system.time(for(i in 1:10)serialize(i32, NULL))
#' td64 <- system.time(for(i in 1:10)serialize(d64, NULL))
#' i64 <- as.integer64(i32);
#' t64 <- system.time(for(i in 1:10)serialize(i64, NULL))
#' rm(i64); gc()
#' #I64 <- as.int64(i32);
#' #T64 <- system.time(for(i in 1:10)serialize(I64, NULL))
#' #rm(I64); gc()
#' #T64/t64
#' t64/t32
#' t64/td64
#'
#'
#' message("-- integer64 adds 250x/60x faster than int64
#' under 64/32 bit OS (and less than 6x the time of integer or double) --")
#' td64 <- system.time(for(i in 1:100)d64+d64)
#' t32 <- system.time(for(i in 1:100)i32+i32)
#' i64 <- as.integer64(i32);
#' t64 <- system.time(for(i in 1:100)i64+i64)
#' rm(i64); gc()
#' #I64 <- as.int64(i32);
#' #T64 <- system.time(for(i in 1:10)I64+I64)*10
#' #rm(I64); gc()
#' #T64/t64
#' t64/t32
#' t64/td64
#'
#' message("-- integer64 sums 3x/0.2x faster than int64
#' (and at about 5x/60X the time of integer and double) --")
#' td64 <- system.time(for(i in 1:100)sum(d64))
#' t32 <- system.time(for(i in 1:100)sum(i32))
#' i64 <- as.integer64(i32);
#' t64 <- system.time(for(i in 1:100)sum(i64))
#' rm(i64); gc()
#' #I64 <- as.int64(i32);
#' #T64 <- system.time(for(i in 1:100)sum(I64))
#' #rm(I64); gc()
#' #T64/t64
#' t64/t32
#' t64/td64
#'
#' message("-- integer64 diffs 5x/0.85x faster than integer and double
#' (int64 version 1.0 does not support diff) --")
#' td64 <- system.time(for(i in 1:10)diff(d64, lag=2L, differences=2L))
#' t32 <- system.time(for(i in 1:10)diff(i32, lag=2L, differences=2L))
#' i64 <- as.integer64(i32);
#' t64 <- system.time(for(i in 1:10)diff(i64, lag=2L, differences=2L))
#' rm(i64); gc()
#' t64/t32
#' t64/td64
#'
#'
#' message("-- integer64 subscripts 1000x/340x faster than int64
#' (and at the same speed / 10x slower as integer) --")
#' ts32 <- system.time(for(i in 1:1000)sample(1e6, 1e3))
#' t32<- system.time(for(i in 1:1000)i32[sample(1e6, 1e3)])
#' i64 <- as.integer64(i32);
#' t64 <- system.time(for(i in 1:1000)i64[sample(1e6, 1e3)])
#' rm(i64); gc()
#' #I64 <- as.int64(i32);
#' #T64 <- system.time(for(i in 1:100)I64[sample(1e6, 1e3)])*10
#' #rm(I64); gc()
#' #(T64-ts32)/(t64-ts32)
#' (t64-ts32)/(t32-ts32)
#'
#' message("-- integer64 assigns 200x/90x faster than int64
#' (and 50x/160x slower than integer) --")
#' ts32 <- system.time(for(i in 1:100)sample(1e6, 1e3))
#' t32 <- system.time(for(i in 1:100)i32[sample(1e6, 1e3)] <- 1:1e3)
#' i64 <- as.integer64(i32);
#' i64 <- system.time(for(i in 1:100)i64[sample(1e6, 1e3)] <- 1:1e3)
#' rm(i64); gc()
#' #I64 <- as.int64(i32);
#' #I64 <- system.time(for(i in 1:10)I64[sample(1e6, 1e3)] <- 1:1e3)*10
#' #rm(I64); gc()
#' #(T64-ts32)/(t64-ts32)
#' (t64-ts32)/(t32-ts32)
#'
#'
#' tdfi32 <- system.time(dfi32 <- data.frame(a=i32, b=i32, c=i32))
#' tdfsi32 <- system.time(dfi32[1e6:1,])
#' fi32 <- tempfile()
#' tdfwi32 <- system.time(write.csv(dfi32, file=fi32, row.names=FALSE))
#' tdfri32 <- system.time(read.csv(fi32, colClasses=rep("integer", 3)))
#' unlink(fi32)
#' rm(dfi32); gc()
#'
#' i64 <- as.integer64(i32);
#' tdfi64 <- system.time(dfi64 <- data.frame(a=i64, b=i64, c=i64))
#' tdfsi64 <- system.time(dfi64[1e6:1,])
#' fi64 <- tempfile()
#' tdfwi64 <- system.time(write.csv(dfi64, file=fi64, row.names=FALSE))
#' tdfri64 <- system.time(read.csv(fi64, colClasses=rep("integer64", 3)))
#' unlink(fi64)
#' rm(i64, dfi64); gc()
#'
#' #I64 <- as.int64(i32);
#' #tdfI64 <- system.time(dfI64<-data.frame(a=I64, b=I64, c=I64))
#' #tdfsI64 <- system.time(dfI64[1e6:1,])
#' #fI64 <- tempfile()
#' #tdfwI64 <- system.time(write.csv(dfI64, file=fI64, row.names=FALSE))
#' #tdfrI64 <- system.time(read.csv(fI64, colClasses=rep("int64", 3)))
#' #unlink(fI64)
#' #rm(I64, dfI64); gc()
#'
#' message("-- integer64 coerces 40x/6x faster to data.frame than int64
#' (and factor 1/9 slower than integer) --")
#' #tdfI64/tdfi64
#' tdfi64/tdfi32
#' message("-- integer64 subscripts from data.frame 20x/2.5x faster than int64
#' (and 3x/13x slower than integer) --")
#' #tdfsI64/tdfsi64
#' tdfsi64/tdfsi32
#' message("-- integer64 csv writes about 2x/0.5x faster than int64
#' (and about 1.5x/5x slower than integer) --")
#' #tdfwI64/tdfwi64
#' tdfwi64/tdfwi32
#' message("-- integer64 csv reads about 3x/1.5 faster than int64
#' (and about 2x slower than integer) --")
#' #tdfrI64/tdfri64
#' tdfri64/tdfri32
#'
#' rm(i32, d64); gc()
#'
#'
#' message("-- investigating the impact on garbage collection: --")
#' message("-- the fragmented structure of int64 messes up R's RAM --")
#' message("-- and slows down R's gargbage collection just by existing --")
#'
#' td32 <- double(21)
#' td32[1] <- system.time(d64 <- double(1e7))[3]
#' for (i in 2:11)td32[i] <- system.time(gc(), gcFirst=FALSE)[3]
#' rm(d64)
#' for (i in 12:21)td32[i] <- system.time(gc(), gcFirst=FALSE)[3]
#'
#' t64 <- double(21)
#' t64[1] <- system.time(i64 <- integer64(1e7))[3]
#' for (i in 2:11)t64[i] <- system.time(gc(), gcFirst=FALSE)[3]
#' rm(i64)
#' for (i in 12:21)t64[i] <- system.time(gc(), gcFirst=FALSE)[3]
#'
#' #T64 <- double(21)
#' #T64[1] <- system.time(I64 <- int64(1e7))[3]
#' #for (i in 2:11)T64[i] <- system.time(gc(), gcFirst=FALSE)[3]
#' #rm(I64)
#' #for (i in 12:21)T64[i] <- system.time(gc(), gcFirst=FALSE)[3]
#'
#' #matplot(1:21, cbind(td32, t64, T64), pch=c("d","i","I"), log="y")
#' matplot(1:21, cbind(td32, t64), pch=c("d","i"), log="y")
#' }
#' @aliases bit64 is.integer.integer64 is.vector.integer64
#' @keywords internal package classes manip
#' @useDynLib bit64, .registration = TRUE, .fixes = "C_"
"_PACKAGE"
## usethis namespace: start
#' @importFrom bit clone is.sorted keyorder keysort keysortorder mergeorder
#' mergesort mergesortorder na.count nties nunique nvalid quickorder
#' quicksort quicksortorder radixorder radixsort radixsortorder ramorder
#' ramsort ramsortorder repeat.time setattr shellorder shellsort
#' shellsortorder still.identical
#' @importFrom graphics barplot par title
#' @importFrom methods as is
#' @importFrom stats cor median quantile
#' @importFrom utils head packageDescription strOptions tail getS3method
#' @export : %in% is.double match order print.cache rank
# TODO(>=4.9.0): un-export these
#' @export [<-.integer64 abs.integer64 all.integer64 any.integer64
#' @export as.character.integer64 as.data.frame.integer64 as.double.integer64
#' @export as.integer.integer64 as.integer64.character as.integer64.double
#' @export as.integer64.integer as.integer64.logical c.integer64
#' @export format.integer64 is.na.integer64 log.integer64 max.integer64
#' @export min.integer64 print.integer64 rank.integer64 rep.integer64
#' @export seq.integer64 str.integer64 sum.integer64 unique.integer64
## usethis namespace: end
NULL
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