I think a more productive conversation could be: what additions to R
would allow for user-defined types / classes that behave just like the
built-in vector types? As a motivating example, one cannot currently
use the 64bit integer objects from bit64 to subset data frames:
> library(bit64);
> 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):
>
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
> 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
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
Hi Juan,
Comments inline.
On Wed, May 29, 2019 at 12:48 PM Juan Telleria Ruiz de Aguirre <
jtelleria.rproj...@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
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,
