Hi,
I have below lines of code to understand how R manages memory.
> library(pryr)
*Warning message:*
*package ‘pryr’ was built under R version 3.4.3 *
> mem_change(x <- 1:1e6)
4.01 MB
> mem_change(y <- x)
976 B
> mem_change(x[100] < NA)
976 B
> mem_change(rm(x))
864 B
> mem_change(rm(
> On Mar 22, 2016, at 10:00 AM, Martin Maechler
> wrote:
>
>> Roy Mendelssohn <- NOAA Federal >
>>on Tue, 22 Mar 2016 07:42:10 -0700 writes:
>
>> Hi All:
>> I am running prcomp on a very large array, roughly [50, 3650]. The
>> array itself is 16GB. I am running on a Unix mac
> Roy Mendelssohn <- NOAA Federal >
> on Tue, 22 Mar 2016 07:42:10 -0700 writes:
> Hi All:
> I am running prcomp on a very large array, roughly [50, 3650]. The
array itself is 16GB. I am running on a Unix machine and am running “top” at
the same time and am quite surpri
Hi All:
I am running prcomp on a very large array, roughly [50, 3650]. The array
itself is 16GB. I am running on a Unix machine and am running “top” at the
same time and am quite surprised to see that the application memory usage is
76GB. I have the “tol” set very high (.8) so that it s
Hi,
I am trying to do nonlinear minimization using nlm() function, but for
large amount of data it is going out of memory.
Code which i am using:
f<-function(p,n11,E){
sum(-log((p[5] * dnbinom(n11, size=p[1], prob=p[2]/(p[2]+E)) +
(1-p[5]) * dnbinom(n11, size=p[3], prob=p[4]/(p[4]+E)))
Is there a way to shrink the size of RandomForest-class (an S4
object), so that it requires less memory during run-time and less disk
space for serialization?
On my system the data slot is about 2GB, which is causing problems,
and I'd like to see whether predict() works without it.
# example with
MiB Xvnc
13.7 MiB + 515.5 KiB = 14.2 MiB yum-updatesd
16.3 MiB + 1.6 MiB = 17.9 MiB nautilus
20.8 MiB + 1.4 MiB = 22.2 MiB puplet
1.5 GiB + 438.0 KiB = 1.5 GiB java
-
1.7 GiB
==
: R mailing list
Date: 08/30/2013 07:14 PM
Subject: Re: [R] Memory usage bar plot
Here is how to parse the data and put it into groups. Not sure what
the 'timing' of each group is since not time information was given.
Also not sure is there is an 'MiB' qualifier o
## Here is a plot. The input was parsed with Jim Holtman's code.
## The panel.dumbell is something I devised to show differences.
## Rich
input <- readLines(textConnection("
Private + Shared = RAM used Program
96.0 KiB + 11.5 KiB = 107.5 KiB uuidd
108.0 KiB + 12.5 KiB = 12
Hi
From: mohan.radhakrish...@polarisft.com
[mailto:mohan.radhakrish...@polarisft.com]
Sent: Friday, August 30, 2013 3:16 PM
To: PIKAL Petr
Cc: r-help@r-project.org
Subject: RE: [R] Memory usage bar plot
Hello,
This memory usage should be graphed with time. Are there
examples of
be better.
Petr
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of mohan.radhakrish...@polarisft.com
> Sent: Friday, August 30, 2013 1:25 PM
> To: r-help@r-project.org
> Subject: [R] Memory usage bar plot
>
.0 KiB gpm
#14 162.5 KiB pam_timestamp_check
A.K.
- Original Message -
From: jim holtman
To: mohan.radhakrish...@polarisft.com
Cc: R mailing list
Sent: Friday, August 30, 2013 9:44 AM
Subject: Re: [R] Memory usage bar plot
Here is how to parse the data and put it into groups.
Here is how to parse the data and put it into groups. Not sure what
the 'timing' of each group is since not time information was given.
Also not sure is there is an 'MiB' qualifier on the data, but you have
the matrix of data which is easy to do with as you want.
> input <- readLines(textConnect
ect.org"
Date: 08/30/2013 05:33 PM
Subject: RE: [R] Memory usage bar plot
Hi
For reading data into R you shall look to read.table and similar.
For plotting ggplot could handle it. However I wonder if 100 times 50 bars
is the way how to present your data. You shall think over
Hi,
I haven't tried the code yet. Is there a way to parse this data
using R and create bar plots so that each program's 'RAM used' figures are
grouped together.
So 'uuidd' bars will be together. The data will have about 50 sets. So if
there are 100 processes each will have about 50 bar
Merci beaucoup Milan, thank you very much Martin and Kjetil for your
responses.
I appreciate the caveat about virtual memory. I gather that besides
resident memory and swap space, it may also include memory mapped files,
which don't "cost" anything. Maybe by pure chance, in my case virtual
memor
On Thursday 18. April 2013 12.18.03 Milan Bouchet-Valat wrote:
> First, completely stop looking at virtual memory: it does not mean much, if
> anything. What you care about is resident memory. See e.g.:
> http://serverfault.com/questions/138427/top-what-does-virtual-memory-size-m
> ean-linux-ubuntu
On 04/18/2013 03:18 AM, Milan Bouchet-Valat wrote:
Le mercredi 17 avril 2013 à 23:17 -0400, Christian Brechbühler a écrit :
In help(gc) I read, "...the primary purpose of calling 'gc' is for the
report on memory usage".
What memory usage does gc() report? And more importantly, which memory
uses
Le mercredi 17 avril 2013 à 23:17 -0400, Christian Brechbühler a écrit :
> In help(gc) I read, "...the primary purpose of calling 'gc' is for the
> report on memory usage".
> What memory usage does gc() report? And more importantly, which memory
> uses does it NOT report? Because I see one answer
In help(gc) I read, "...the primary purpose of calling 'gc' is for the
report on memory usage".
What memory usage does gc() report? And more importantly, which memory
uses does it NOT report? Because I see one answer from gc():
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 148759
-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1
On 25/09/12 01:29, mcelis wrote:
> I am working with some large text files (up to 16 GBytes). I am interested
> in extracting the
> words and counting each time each word appears in the text. I have written a
> very simple R
> program by following
.txt,sep="\t")
cat("Word\tFREQ",words.txt,file="frequencies",sep="\n")
})
#Read 4 items
# user system elapsed
# 0.148 0.000 0.150
There is improvement in the speed. Output also looked similar. This code may
be still improved.
A.K.
Le lundi 24 septembre 2012 à 16:29 -0700, mcelis a écrit :
> I am working with some large text files (up to 16 GBytes). I am interested
> in extracting the words and counting each time each word appears in the
> text. I have written a very simple R program by following some suggestions
> and examp
elapsed
> # 0.036 0.008 0.043
> A.K.
Well, dear A.K., your definition of "word" is really different,
and in my view clearly much too simplistic, compared to what the
OP (= original-poster) asked from.
E.g., from the above paragraph, your method will get words such as
&qu
ower(txt1),"\\s"))),decreasing=TRUE)
words.txt<-paste(names(words.txt),words.txt,sep="\t")
cat("Word\tFREQ",words.txt,file="frequencies",sep="\n")
})
#Read 4 items
#user system elapsed
# 0.036 0.008 0.043
A.K.
- Original M
;)),decreasing=TRUE)
words.txt<-paste(names(words.txt),words.txt,sep="\t")
cat("Word\tFREQ",words.txt,file="frequencies",sep="\n")
})
# user system elapsed
# 0.016 0.000 0.014
A.K.
- Original Message -
From: mcelis
To: r-hel
I am working with some large text files (up to 16 GBytes). I am interested
in extracting the words and counting each time each word appears in the
text. I have written a very simple R program by following some suggestions
and examples I found online.
If my input file is 1 GByte, I see that R us
This is an "I was just wondering" question.
When the package "dataframe" was announced, the author claimed to
reduce the number of times a data frame was copied, I started to
wonder if I should care about this in my projects. Has anybody
written a general guide for how to write R code that doesn'
Dear Community,
my program below runs quite slow and I'm not sure whether the http-requests are
to blame for this. Also, when running it gradually increases the memory usage
enormously. After the program finishes, the memory is not freed. Can someone
point out a problem in the code? Sorry my bas
Hi Jim & Gabor -
Apparently, it was most likely a hardware issue (shortly after
sending my last e-mail, the computer promptly died). After buying a
new system and restoring, the script runs fine. Thanks for your help!
On Tue, Jan 19, 2010 at 2:02 PM, jim holtman - jholt...@gmail.com
<+nabble+m
You could also try read.csv.sql in sqldf. See examples on sqldf home page:
http://code.google.com/p/sqldf/#Example_13._read.csv.sql_and_read.csv2.sql
On Tue, Jan 19, 2010 at 9:25 AM, wrote:
> I'm sure this has gotten some attention before, but I have two CSV
> files generated from vmstat and f
I read vmstat data in just fine without any problems. Here is an
example of how I do it:
VMstat <- read.table('vmstat.txt', header=TRUE, as.is=TRUE)
vmstat.txt looks like this:
date time r b w swap free re mf pi po fr de sr intr syscalls cs user sys id
07/27/05 00:13:06 0 0 0 27755440 13051648
I'm sure this has gotten some attention before, but I have two CSV
files generated from vmstat and free that are roughly 6-8 Mb (about
80,000 lines) each. When I try to use read.csv(), R allocates all
available memory (about 4.9 Gb) when loading the files, which is over
300 times the size of the ra
Duncan:
I took your suggestion and upgraded to R 2.9.2, but the problem persists.
I am not able to reproduce the problem in a simple case. In my
actual code the functions some.function.1() and some.function.2() are
quite complicated and call various other functions which also access
elements of
You need to give reproducible code for a question like this, not pseudocode.
And you should consider using a recent version of R, not the relatively
ancient 2.8.1 (which was released in late 2008.
Duncan Murdoch
On 03/10/2009 1:30 PM, Rajeev Ayyagari wrote:
Hello,
I can't think of an explan
Hello,
I can't think of an explanation for this memory allocation behaviour
and was hoping someone on the list could help out.
Setup:
--
R version 2.8.1, 32-bit Ubuntu 9.04 Linux, Core 2 Duo with 3GB ram
Description:
Inside a for loop, I am passing a list to a function. The f
Hello,
I do not know whether my package "colbycol" may help you. It can help
you read files that would not have fitted into memory otherwise.
Internally, as the name indicates, data is read into R in a column by
column fashion.
IO times increase but you need just a fraction of "intermediate memo
On Tue, 15 Sep 2009, Evan Klitzke wrote:
On Mon, Sep 14, 2009 at 10:01 PM, Henrik Bengtsson
wrote:
As already suggested, you're (much) better off if you specify colClasses, e.g.
tab <- read.table("~/20090708.tab", colClasses=c("factor", "double", "double"));
Otherwise, R has to load all the
On Mon, Sep 14, 2009 at 10:01 PM, Henrik Bengtsson
wrote:
> As already suggested, you're (much) better off if you specify colClasses, e.g.
>
> tab <- read.table("~/20090708.tab", colClasses=c("factor", "double",
> "double"));
>
> Otherwise, R has to load all the data, make a best guess of the co
As already suggested, you're (much) better off if you specify colClasses, e.g.
tab <- read.table("~/20090708.tab", colClasses=c("factor", "double", "double"));
Otherwise, R has to load all the data, make a best guess of the column
classes, and then coerce (which requires a copy).
/Henrik
On Mon
> its 32-bit representation. This seems like it might be too
> conservative for me, since it implies that R allocated exactly as much
> memory for the lists as there were numbers in the list (e.g. typically
> in an interpreter like this you'd be allocating on order-of-two
> boundaries, i.e. sizeof(
> I think this is just because you picked short strings. If the factor
> is mapping the string to a native integer type, the strings would have
> to be larger for you to notice:
>
>> object.size(sample(c("a pretty long string", "another pretty long string"),
>> 1000, replace=TRUE))
> 8184 bytes
>>
On Mon, Sep 14, 2009 at 8:58 PM, Eduardo Leoni wrote:
> And, by the way, factors take up _more_ memory than character vectors.
>
>> object.size(sample(c("a","b"), 1000, replace=TRUE))
> 4088 bytes
>> object.size(factor(sample(c("a","b"), 1000, replace=TRUE)))
> 4296 bytes
I think this is just bec
On Mon, Sep 14, 2009 at 8:35 PM, jim holtman wrote:
> When you read your file into R, show the structure of the object:
...
Here's the data I get:
> tab <- read.table("~/20090708.tab")
> str(tab)
'data.frame': 1797601 obs. of 3 variables:
$ V1: Factor w/ 6 levels "biz_details",..: 4 4 4 4 4
And, by the way, factors take up _more_ memory than character vectors.
> object.size(sample(c("a","b"), 1000, replace=TRUE))
4088 bytes
> object.size(factor(sample(c("a","b"), 1000, replace=TRUE)))
4296 bytes
On Mon, Sep 14, 2009 at 11:35 PM, jim holtman wrote:
> When you read your file into R,
When you read your file into R, show the structure of the object:
str(tab)
also the size of the object:
object.size(tab)
This will tell you what your data looks like and the size taken in R.
Also in read.table, use colClasses to define what the format of the
data is; may make it faster. You mi
Hello all,
To start with, these measurements are on Linux with R 2.9.2 (64-bit
build) and Python 2.6 (also 64-bit).
I've been investigating R for some log file analysis that I've been
doing. I'm coming at this from the angle of a programmer whose
primarily worked in Python. As I've been playing a
lliam Dunlap [mailto:wdun...@tibco.com]
Sent: Friday, May 15, 2009 10:09 AM
To: Ping-Hsun Hsieh
Subject: RE: [R] memory usage grows too fast
rowMeans(dataMatrix=="02") must
(a) make a logical matrix the dimensions of dataMatrix in which to put
the result of dataMatrix=="02"
lto:palsp...@hortresearch.co.nz]
Sent: Thursday, May 14, 2009 4:47 PM
To: Ping-Hsun Hsieh
Subject: RE: [R] memory usage grows too fast
Tena koe Mike
If I understand you correctly, you should be able to use something like:
apply(yourMatrix, 1, function(x)
length(x[x==yourPattern]))/ncol(yourMatrix
On Thu, May 14, 2009 at 6:21 PM, Ping-Hsun Hsieh wrote:
> Hi All,
>
> I have a 1000x100 matrix.
> The calculation I would like to do is actually very simple: for each row,
> calculate the frequency of a given pattern. For example, a toy dataset is as
> follows.
>
> Col1 Col2 Col3 Co
Hi All,
I have a 1000x100 matrix.
The calculation I would like to do is actually very simple: for each row,
calculate the frequency of a given pattern. For example, a toy dataset is as
follows.
Col1Col2Col3Col4
01 02 02 00 => Freq of “02” is 0.5
02
Please read ?"Memory-limits" and the R-admin manual for basic
information.
On Thu, 5 Feb 2009, Tom Quarendon wrote:
I have a general question about R's usage or memory and what limits exist on
the size of datasets it can deal with.
My understanding was that all object in a session are held in
I have a general question about R's usage or memory and what limits
exist on the size of datasets it can deal with.
My understanding was that all object in a session are held in memory.
This implies that you're limited in the size of datasets that you can
process by the amount of memory you've g
;>> gnome-system-monitor and free both show the full 4GB as being available.
>>>
>>> In R I was doing some processing and I got the following message (when
>>> collecting 100 307200*8 dataframes into a single data-frame (for
>>> plotting):
>>>
>>
allocate vector of size 2.3 Mb
So I checked the R memory usage:
$ ps -C R -o size
SZ
3102548
I tried removing some objects and running gc() R then shows much less
memory being used:
$ ps -C R -o size
SZ
2732124
Which should give me an extra 300MB in R.
I still get the same error about R be
Doen't work.
\misiek
Prof Brian Ripley wrote:
> See ?"Memory-size"
> On Wed, 15 Oct 2008, B. Bogart wrote:
[...]
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do
B as being available.
In R I was doing some processing and I got the following message (when
collecting 100 307200*8 dataframes into a single data-frame (for plotting):
Error: cannot allocate vector of size 2.3 Mb
So I checked the R memory usage:
$ ps -C R -o size
SZ
3102548
I tried remov
following message (when
collecting 100 307200*8 dataframes into a single data-frame (for plotting):
Error: cannot allocate vector of size 2.3 Mb
So I checked the R memory usage:
$ ps -C R -o size
SZ
3102548
I tried removing some objects and running gc() R then shows much less
memory being used:
Hello,
I have aggregate a data.frame from 16MB (Object size). After some
minutes I get the error message "cannot allocate vector of size 64.5MB".
My computer has a physical memory of 4GB under Windows Vista.
I have test the same command on another computer with the same OS and
2GB RAM. In nearly
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