similar to: Native 64 Integers

Dear R-Core team, Dear Rcpp team and other package teams, Dear R users, The new package 'bit64' is available on CRAN for beta-testing and code-reviewing. 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,

Dear R-Core team, Dear Rcpp team and other package teams, Dear R users, The new package 'bit64' is available on CRAN for beta-testing and code-reviewing. 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,

Dear package maintainers, Dear users of packages `bit`, `bit64`, `ff`, Everyone interested in sustainable sorting algorithms, I submitted updated versions for the upcoming R 4.5.0. The are only minor changes (see the NEWS files) but there is one important change in bit64: ??? o setting options(integer64_semantics="new") ????? gives the better semantics suggested by Ofek Shilon.

Dear package maintainers, Dear users of packages `bit`, `bit64`, `ff`, Everyone interested in sustainable sorting algorithms, I submitted updated versions for the upcoming R 4.5.0. The are only minor changes (see the NEWS files) but there is one important change in bit64: ??? o setting options(integer64_semantics="new") ????? gives the better semantics suggested by Ofek Shilon.

One good intro to this is https://github.com/networkupstools/nut/blob/master/docs/snmp-subdrivers.txt although it focuses on adding new subdrivers - but more or less the same workflow applies to extending existing ones. Sometimes it helps to generate a new one for a currently-partially-supported device, and then compare with existing subdriver (using meld or similar GUI helps a lot) to port the

Well thanks, The solution for my problem was kinda easy (still need some testing though): found out my ups status is a string, so then I changed the way snmp-ups.c gets the status. Had to change *nut_snmp_get_int *to *nut_snmp_get_str *at around line 3470, then grab fourth char out, use *strtol *function to then send it to *su_status_set *function. I created my copy of *apc-mib.c *with my OID at

Thank you Martin for giving to know and developing 'Rmpfr' library for unlimited size integers (GNU C GMP) and arbitrary precision floats (GNU C MPFR): https://cran.r-project.org/package=Rmpfr My question is: In the long term (For R3.7.0 or R3.8.0): Does it have sense that CMP substitutes INTSXP, and MPFR substitutes REALSXP code? With this we would achieve that an integer is always an

Hi, I do have < INT_MAX. This looks attractive but since they are unique identifiers, storing them as factor will be likely to be counter-productive. (a string version + an int32 for each) I was looking to https://cran.r-project.org/web/packages/csvread/index.html This looks like a good feet for my needs. Any chances such an external package for int64 would be integrated in core ? Le 20

I?m in the process of preparing a package for CRAN. The package is called ?exportR? and since it really just consists of one function, I found it natural to call it ?exportR? too. The function returns a function that does the actual job, but it is set up to work in different ways, depending on the arguments given to its creator. In short: library( exportR ) exporter <- exportR( the, arguments

Dear R community, The new version of package 'bit64' - which extends R with fast 64-bit integers - now has fast (single-threaded) implementations of the most important univariate algorithmic operations (those based on hashing and sorting). Package 'bit64' now has methods for 'match', '%in%', 'duplicated', 'unique', 'table',

Dear R community, The new version of package 'bit64' - which extends R with fast 64-bit integers - now has fast (single-threaded) implementations of the most important univariate algorithmic operations (those based on hashing and sorting). Package 'bit64' now has methods for 'match', '%in%', 'duplicated', 'unique', 'table',

Hi Juan, Comments inline. On Wed, May 29, 2019 at 12:48 PM Juan Telleria Ruiz de Aguirre < jtelleria.rproject at gmail.com> wrote: > Dear R Developers, > > There is an interesting issue related to "reticulate" R package which > discusses how to convert Python's non-32 bit integers to R, which has had > quite an exhaustive discussion: > >

>>>>> Juan Telleria Ruiz de Aguirre >>>>> on Mon, 3 Jun 2019 06:50:17 +0200 writes: > Thank you Martin for giving to know and developing 'Rmpfr' library for > unlimited size integers (GNU C GMP) and arbitrary precision floats (GNU C > MPFR): > https://cran.r-project.org/package=Rmpfr > My question is: In the long term

>>>>> Juan Telleria Ruiz de Aguirre >>>>> on Thu, 30 May 2019 18:46:29 +0200 writes: >Thank you Gabriel for valuable insights on the 64-bit integers topic. >In addition, my statement was wrong, as Python3 seems to have unlimited >(and variable) size integers. .... If you are interested in using unlimited size integers, you could use the

Some of you may recall that I have been working on building a box to convert H323 to SIP. After a significant amount of outside help and slicing and dicing of the ohh323 code to get it to compile on AMD64 we finally got it working. Now we are working on improving the performance. This box takes H323 from one device and converts to SIP and spits it back out to another device. The codec is g729 but

Dear R Developers, There is an interesting issue related to "reticulate" R package which discusses how to convert Python's non-32 bit integers to R, which has had quite an exhaustive discussion: https://github.com/rstudio/reticulate/issues/323 Python seems to handle integers differently from R, and is dependant on the system arquitecture: On 32 bit systems uses 32-bit integers,

Thank you Gabriel for valuable insights on the 64-bit integers topic. In addition, my statement was wrong, as Python3 seems to have unlimited (and variable) size integers. Here is related CPython Code: https://github.com/python/cpython/blob/master/Objects/longobject.c Division between Int-32 and Int-64 seems to only happen in Python2. Best, Juan El mi?rcoles, 29 de mayo de 2019, Gabriel

How many unique idenfiiers do you have? If they are large (in terms of bytes) but you don't have that many of them (eg the total possible number you'll ever have is < INT_MAX), you could store them as factors. You get the speed of integers but the labeling of full "precision" strings. Factors are fast for joins. ~G On Fri, Jan 20, 2017 at 9:47 AM, Nicolas Paris

To summarise this thread, there are basically three ways of handling int64 in R: * coerce to character * coerce to double * store in double There is no ideal solution, and each have pros and cons that I've attempted to summarise below. ## Coerce to character This is the easiest approach if the data is used as identifiers. It will have some performance drawbacks when loading and will

Well I definitely cannot use them as numeric because join is the main reason of those identifiers. About int64 and bit64 packages, it's not a solution, because I am releasing a dataset for external users. I cannot ask them to install a package in order to exploit them. I have to be very carefull when releasing the data. If a user just use read.csv functions, they by default cast the