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> On 16 Jun 2017, at 15:59 , Robert McGehee <rmcgehee at walleyetrading.net> wrote: > > For instance, what would you expect to get from unlist() if each element of the list had different levels, or were both ordered, but in a different way, or if some elements of the list were factors and others were ordered factors? >> unlist(list(ordered(c("a","b")),

Dear all, ? I don't know if you consider this a bug or feature, but it breaks reasonable code: 'unlist' and 'sapply' convert 'ordered' to 'factor' even if all levels are equal. Here is a simple example: o <- ordered(letters) o[[1]] lapply(o, min)[[1]]??????????# ordered factor unlist(lapply(o, min))[[1]]? # no longer ordered sapply(o, min)[[1]]??????????# no

...ptimum? I struggle to recognize the project I loved in 2000. ? Gesendet:?Freitag, 16. Juni 2017 um 18:31 Uhr Von:?"peter dalgaard" <pdalgd at gmail.com> An:?"Robert McGehee" <rmcgehee at walleyetrading.net> Cc:?"Jens Oehlschl?gel" <jens.oehlschlaegel at truecluster.com>, "r-devel at r-project.org" <r-devel at r-project.org> Betreff:?Re: [Rd] 'ordered' destroyed to 'factor' > On 16 Jun 2017, at 15:59 , Robert McGehee <rmcgehee at walleyetrading.net> wrote: > > For instance, what would you expect to get from unl...

...R itself has limited memory access, ff can handle larger objects faster because it benefits from *all* RAM via filesystem-caching. Jens --------------------- Von: "Hunsicker, Lawrence" <lawrence-hunsicker at uiowa.edu> Gesendet: May 13, 2010 3:32:25 PM An: Jens.Oehlschlaegel at truecluster.com Betreff: ff for 64-bit windows and 64-bit R <!-- Converted from text/rtf format --> Jens: I am running R on a 64 bit PC, 64 bit Windows 7, and 64 bit R. I have to handle rather large data sets, and I need the 64 bit environment to run some of my analyses. Use of ff has been recommende...

...est advice, is to just be careful when combining or aggregating factors. --Robert -----Original Message----- From: R-devel [mailto:r-devel-bounces at r-project.org] On Behalf Of "Jens Oehlschl?gel" Sent: Friday, June 16, 2017 9:04 AM To: r-devel at r-project.org Cc: jens.oehlschlaegel at truecluster.com Subject: [Rd] 'ordered' destroyed to 'factor' Dear all, ? I don't know if you consider this a bug or feature, but it breaks reasonable code: 'unlist' and 'sapply' convert 'ordered' to 'factor' even if all levels are equal. Here is a simple ex...

...the official package presentation at UseR!2008 in Dortmund scheduled for August 13th. The ff authors Daniel Adler <dadler at uni-goettingen.de> Christian Gl?ser <christian_glaeser at gmx.de> Oleg Nenadic <onenadi at uni-goettingen.de> Jens Oehlschl?gel <Jens.Oehlschlaegel at truecluster.com> Walter Zucchini <wzucchi at uni-goettingen.de> _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages

...the official package presentation at UseR!2008 in Dortmund scheduled for August 13th. The ff authors Daniel Adler <dadler at uni-goettingen.de> Christian Gl?ser <christian_glaeser at gmx.de> Oleg Nenadic <onenadi at uni-goettingen.de> Jens Oehlschl?gel <Jens.Oehlschlaegel at truecluster.com> Walter Zucchini <wzucchi at uni-goettingen.de> _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages

Dear core group, Which operation in R guarantees to get a true copy of an atomic vector, not just a second symbol pointing to the same shared memory? y <- x[] #? y <- x y[1] <- y[1] #? Is there any function that returns its argument as a non-shared atomic but only copies if the argument was shared? Given an atomic vector x, what is the best official way to find out whether other

# A couple of years ago # I helped making R's character NA handling more consistent # Today I report an issue with R's factor NA handling # The core problem is that # levels(g) <- levels(g) # can change the levels of g # more details below # Kind regards # Jens Oehlschl?gel # Say I have an NA element in a vector or list x <- c("a","b",NA) # then using split()

Hello Everyone,   Learning about R and its wonderful array of functions. If it's not obvious, I usually try to find out what a function stands for. I think this helps me remember better.   One function that has me stumped is "sink." Can anyone tell me if this stands for something?   Thanks,   Paul         __________________________________________________ [[alternative HTML

Dear R community, Package 'bit' Version 1.0 is available on CRAN. It provides bitmapped vectors of booleans (no NAs), coercion from and to logicals, integers and integer subscripts; fast boolean operators and fast summary statistics. With bit vectors you can store true binary booleans {FALSE,TRUE} at the expense of 1 bit only, on a 32 bit architecture this means factor 32 less RAM

Dear R community, ff Version 2.1.1 is available on CRAN. It now supports large data.frames, csv import/export, packed atomic datatypes and bit filtering from package 'bit' on which it depends from now. Some performance results in seconds from test data with 78 mio rows and 7 columns on a 3 GB notebook: sequential reading 1 mio rows: csv = 32.7 ffdf = 1.3 sequential writing 1 mio

Dear R community, Package bit version 1.1-3 and ff version 2.1.2 is available on CRAN and should be useful to handle large datasets. It adds convenient utilities for managing ff objects and files (see ?ffsave) and removes some performance bottlenecks. In case you experience unexpected performance problems with ff, here is a couple of recommendations based on FAQs: 1) Compare the size of data

Dear R community, Package 'bit' Version 1.0 is available on CRAN. It provides bitmapped vectors of booleans (no NAs), coercion from and to logicals, integers and integer subscripts; fast boolean operators and fast summary statistics. With bit vectors you can store true binary booleans {FALSE,TRUE} at the expense of 1 bit only, on a 32 bit architecture this means factor 32 less RAM

Dear R community, ff Version 2.1.1 is available on CRAN. It now supports large data.frames, csv import/export, packed atomic datatypes and bit filtering from package 'bit' on which it depends from now. Some performance results in seconds from test data with 78 mio rows and 7 columns on a 3 GB notebook: sequential reading 1 mio rows: csv = 32.7 ffdf = 1.3 sequential writing 1 mio

Dear R community, Package bit version 1.1-3 and ff version 2.1.2 is available on CRAN and should be useful to handle large datasets. It adds convenient utilities for managing ff objects and files (see ?ffsave) and removes some performance bottlenecks. In case you experience unexpected performance problems with ff, here is a couple of recommendations based on FAQs: 1) Compare the size of data

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',

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,