bit64-package: A S3 class for vectors of 64bit integers

In bit64: 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', ' '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).

Usage

 integer64(length)
 ## S3 method for class 'integer64'
is(x)
 ## S3 replacement method for class 'integer64'
length(x) <- value
 ## S3 method for class 'integer64'
print(x, quote=FALSE, ...)
 ## S3 method for class 'integer64'
str(object, vec.len  = strO$vec.len, give.head = TRUE, give.length = give.head, ...)

Arguments

length	length of vector using integer
x	an integer64 vector
object	an integer64 vector
value	an integer64 vector of values to be assigned
quote	logical, indicating whether or not strings should be printed with surrounding quotes.
vec.len	see str
give.head	see str
give.length	see str
...	further arguments to the NextMethod

Details

Package:	bit64
Type:	Package
Version:	0.5.0
Date:	2011-12-12
License:	GPL-2
LazyLoad:	yes
Encoding:	latin1

Value

integer64 returns a vector of 'integer64', i.e. a vector of double decorated with class 'integer64'.

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 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 serializeable, 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 doubles, 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 paragdim: no modification of arguments or other sideffects. 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 unnessary 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 setattr and setattributes from package bit. From version 0.93 setattr is only used for manipulating cache objects, in ramsort.integer64 and sort.integer64 and in 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 integers, 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, cbind and rbind.
Different from Base R, our operators +, -, %/% and %% coerce their arguments to integer64 and always return integer64.
The multiplication operator * coerces its first argument to integer64 but allows its second argument to be also double: the second argument is internaly coerced to 'long double' and the result of the multiplication is returned as integer64.
The division / and power ^ operators also coerce their first argument to integer64 and coerce internally their second argument to 'long double', they return as double, like sqrt, log, log2 and log10 do.

argument1	op	argument2	->	coerced1	op	coerced2	->	result
integer64	+	double	->	integer64	+	integer64	->	integer64
double	+	integer64	->	integer64	+	integer64	->	integer64
integer64	-	double	->	integer64	-	integer64	->	integer64
double	-	integer64	->	integer64	-	integer64	->	integer64
integer64	%/%	double	->	integer64	%/%	integer64	->	integer64
double	%/%	integer64	->	integer64	%/%	integer64	->	integer64
integer64	%%	double	->	integer64	%%	integer64	->	integer64
double	%%	integer64	->	integer64	%%	integer64	->	integer64
integer64	*	double	->	integer64	*	long double	->	integer64
double	*	integer64	->	integer64	*	integer64	->	integer64
integer64	/	double	->	integer64	/	long double	->	double
double	/	integer64	->	integer64	/	long double	->	double
integer64	^	double	->	integer64	/	long double	->	double
double	^	integer64	->	integer64	/	long double	->	double

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 64bit 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 behaviour and do query is.integer64 before querying is.double. 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 likely that 'integer64' becomes a vmode in package ff.
Further methods for creating integer64 are range which returns the range of the data type if calles without arguments, rep, seq.
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.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.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
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	[	vector and array extract
[<-.integer64	[<-	vector and array assign
[[.integer64	[[	scalar extract
[[<-.integer64	[[<-	scalar assign
binary operators	see also	description
+.integer64	+	returns integer64
-.integer64	-	returns integer64
*.integer64	*	returns integer64
^.integer64	^	returns double
/.integer64	/	returns double
%/%.integer64	%/%	returns integer64
%%.integer64	%%	returns integer64
comparison operators	see also	description
==.integer64	==
!=.integer64	!=
<.integer64	<
<=.integer64	<=
>.integer64	>
>=.integer64	>=
logical operators	see also	description
!.integer64	!
&.integer64	&
|.integer64	|
xor.integer64	xor
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)
%in%.integer64	%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)
as.factor.integer64	as.factor	convert to (unordered) factor with sorted levels of previous values (//o/so)
as.ordered.integer64	as.ordered	convert to ordered factor with sorted levels of previous 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)
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. 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 dispatches S3 methods, however, we have made it generic

       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 dispatches 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))
       

• generic binary operators fail to dispatch *any* user-defined S3 method if the two arguments have two different S3 classes. For example we have two classes bit and bitwhich sparsely representing boolean vectors and we have methods &.bit and &.bitwhich. For an expression involving both as in bit & bitwhich, none of the two methods is dispatched. Instead a standard method is dispatched, which neither handles bit nor bitwhich. Although it lacks symmetry, the better choice would be to dispatch simply the method of the class of the first argument in case of class conflict. This choice would allow authors of extension packages providing coherent behaviour at least within their contributed classes. But as long as none of the package authors methods is dispatched, he cannot handle the conflicting classes at all.

• 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 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 further down this file. 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 NAs 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

Author(s)

Jens Oehlschlägel <[e-mail]> Maintainer: Jens Oehlschlägel <[e-mail]>

See Also

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")

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)

### A couple of unit tests follow hidden in a dontshow{} directive ###
  

  ## Not run: 
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")
  
## End(Not run)

bit64 documentation built on May 2, 2019, 4:49 p.m.