From willem at wligtenberg.nl  Fri Jan 20 20:28:32 2017
From: willem at wligtenberg.nl (Willem Ligtenberg)
Date: Fri, 20 Jan 2017 20:28:32 +0100
Subject: [Rd] How to handle INT8 data
In-Reply-To: <20170120174755.GE32106@aphp.fr>
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Message-ID: <8426e22e-6060-c0d5-b0c0-9403192f8938@wligtenberg.nl>

You might want to use a data.table then.
It will automatically detect that it is a 64 bit int.
Although also in that case the user will have to install the data.table
package.
(which is a good idea anyway in my opinion :) )

It will then obviously allow you to join tables.

Willem

On 20-01-17 18:47, Nicolas Paris wrote:
> 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 identifiers as numeric.
>
> $ more res.csv
> "col1";"col2"
> "-1311071933951566764";"toto"
> "-1311071933951566764";"tata"
>
>
>> read.table("res.csv",sep=";",header=T)
>            col1 col2
> 1 -1.311072e+18 toto
> 2 -1.311072e+18 tata
>
>> sapply(read.table("res.csv",sep=";",header=T),class)
>      col1      col2
> "numeric"  "factor"
>
>> read.table("res.csv",sep=";",header=T,colClasses="character")
> col1 col2
> 1 -1311071933951566764 toto
> 2 -1311071933951566764 tata
>
> Am I comdemned to provide a R script with the data in order to exploit the dataset ?
>
> Le 20 janv. 2017 ? 18h29, Murray Stokely ?crivait :
>> 2^53 == 2^53+1
>> TRUE
>>
>> Which makes joining or grouping data sets with 64 bit identifiers problematic.
>>
>> Murray (mobile)
>>
>> On Jan 20, 2017 9:15 AM, "Nicolas Paris" <nicolas.paris at aphp.fr> wrote:
>>
>>     Le 20 janv. 2017 ? 18h09, Murray Stokely ?crivait :
>>     > The lack of 64 bit integer support causes lots of problems when dealing
>>     with
>>     > certain types of data where the loss of precision from coercing to 53
>>     bits with
>>     > double is unacceptable.
>>
>>     Hello Murray,
>>     Do you mean, by eg. -1311071933951566764 loses in precision during
>>     as.numeric(-1311071933951566764) process ?
>>     Thanks,
>>     >
>>     > Two packages were developed to deal with this:  int64 and bit64.
>>     >
>>     > You may need to find archival versions of these packages if they've
>>     fallen off
>>     > cran.
>>     >
>>     > Murray (mobile phone)
>>     >
>>     > On Jan 20, 2017 7:20 AM, "Gabriel Becker" <gmbecker at ucdavis.edu> wrote:
>>     >
>>     >     I am not on R-core, so cannot speak to future plans to internally
>>     support
>>     >     int8 (though my impression is that there aren't any, at least none
>>     that are
>>     >     close to fruition).
>>     >
>>     >     The standard way of dealing with whole numbers too big to fit in an
>>     integer
>>     >     is to put them in a numeric (double down in C land). this can
>>     represent
>>     >     integers up to 2^53 without loss of precision see (
>>     >     http://stackoverflow.com/questions/1848700/biggest-
>>     >     integer-that-can-be-stored-in-a-double).
>>     >     This is how long vector indices are (currently) implemented in R. If
>>     it's
>>     >     good enough for indices it's probably good enough for whatever you
>>     need
>>     >     them for.
>>     >
>>     >     Hope that helps.
>>     >
>>     >     ~G
>>     >
>>     >
>>     >     On Fri, Jan 20, 2017 at 6:33 AM, Nicolas Paris <nicolas.paris at aphp.fr
>>     >
>>     >     wrote:
>>     >
>>     >     > Hello r users,
>>     >     >
>>     >     > I have to deal with int8 data with R. AFAIK  R does only handle
>>     int4
>>     >     > with `as.integer` function [1]. I wonder:
>>     >     > 1. what is the better approach to handle int8 ? `as.character` ?
>>     >     > `as.numeric` ?
>>     >     > 2. is there any plan to handle int8 in the future ? As you might
>>     know,
>>     >     > int4 is to small to deal with earth population right now.
>>     >     >
>>     >     > Thanks for you ideas,
>>     >     >
>>     >     > int8 eg:
>>     >     >
>>     >     >      human_id
>>     >     > ----------------------
>>     >     >  -1311071933951566764
>>     >     >  -4708675461424073238
>>     >     >  -6865005668390999818
>>     >     >   5578000650960353108
>>     >     >  -3219674686933841021
>>     >     >  -6469229889308771589
>>     >     >   -606871692563545028
>>     >     >  -8199987422425699249
>>     >     >   -463287495999648233
>>     >     >   7675955260644241951
>>     >     >
>>     >     > reference:
>>     >     > 1. https://www.r-bloggers.com/r-in-a-64-bit-world/
>>     >     >
>>     >     > --
>>     >     > Nicolas PARIS
>>     >     >
>>     >     > ______________________________________________
>>     >     > R-devel at r-project.org mailing list
>>     >     > https://stat.ethz.ch/mailman/listinfo/r-devel
>>     >     >
>>     >
>>     >
>>     >
>>     >     --
>>     >     Gabriel Becker, PhD
>>     >     Associate Scientist (Bioinformatics)
>>     >     Genentech Research
>>     >
>>     >             [[alternative HTML version deleted]]
>>     >
>>     >     ______________________________________________
>>     >     R-devel at r-project.org mailing list
>>     >     https://stat.ethz.ch/mailman/listinfo/r-devel
>>     >
>>     >
>>
>>     --
>>     Nicolas PARIS
>>
>>


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[Rd] How to handle INT8 data
Willem Ligtenberg