[R] read.table performance
Petr PIKAL
petr.pikal at precheza.cz
Thu Dec 8 09:32:25 CET 2011
Hi
> system.time(dat<-read.table("test2.txt"))
user system elapsed
32.38 0.00 32.40
> system.time(dat <- read.table('test2.txt', nrows=-1, sep='\t',
header=TRUE))
user system elapsed
32.30 0.03 32.36
Couldn't.it be a Windows issue?
_
platform i386-pc-mingw32
arch i386
os mingw32
system i386, mingw32
status Under development (unstable)
major 2
minor 14.0
year 2011
month 04
day 27
svn rev 55657
language R
version.string R version 2.14.0 Under development (unstable) (2011-04-27
r55657)
>
> dim(dat)
[1] 7 3765
>
But from the dat file it seems to me that its structure is somehow weird.
> head(names(dat))
[1] "X..Hydrogen" "Helium" "Lithium" "Beryllium" "Boron"
[6] "Carbon"
> tail(names(dat))
[1] "Sulfur.32" "Chlorine.32" "Argon.32" "Potassium.32"
"Calcium.32"
[6] "Scandium.32"
>
There is row of names which has repeating values. Maybe the most time is
spent by checking the names validity.
Regards
Petr
r-help-bounces at r-project.org napsal dne 07.12.2011 23:11:10:
> peter dalgaard <pdalgd at gmail.com>
> Odeslal: r-help-bounces at r-project.org
>
> 07.12.2011 23:11
>
> Komu
>
> "R. Michael Weylandt" <michael.weylandt at gmail.com>
>
> Kopie
>
> r-help at r-project.org, Gene Leynes <gleynes at gmail.com>
>
> Předmět
>
> Re: [R] read.table performance
>
>
> On Dec 7, 2011, at 22:37 , R. Michael Weylandt wrote:
>
> > R 2.13.2 on Mac OS X 10.5.8 takes about 1.8s to read the file
> > verbatim: system.time(read.table("test2.txt"))
>
> About 2.3s with 2.14 on a 1.86 GHz MacBook Air 10.6.8.
>
> Gene, are you by any chance storing the file in a heavily virus-scanned
> system directory?
>
> -pd
>
> > Michael
> >
> > 2011/12/7 Gene Leynes <gleynes at gmail.com>:
> >> Peter,
> >>
> >> You're quite right; it's nearly impossible to make progress without a
> >> working example.
> >>
> >> I created an ** extremely simplified ** example for distribution. The
real
> >> data has numeric, character, and boolean classes.
> >>
> >> The file still takes 25.08 seconds to read, despite it's small size.
> >>
> >> I neglected to mention that I'm using R 2.13.0 and I"m on a windows 7
> >> machine (not that it should particularly matter with this type of
data /
> >> functions).
> >>
> >> ## The code:
> >> options(stringsAsFactors=FALSE)
> >> system.time(dat <- read.table('test2.txt', nrows=-1, sep='\t',
header=TRUE))
> >> str(dat, 0)
> >>
> >>
> >> Thanks again!
> >>
> >>
> >>
> >> On Wed, Dec 7, 2011 at 1:21 AM, peter dalgaard <pdalgd at gmail.com>
wrote:
> >>
> >>>
> >>> On Dec 6, 2011, at 22:33 , Gene Leynes wrote:
> >>>
> >>>> Mark,
> >>>>
> >>>> Thanks for your suggestions.
> >>>>
> >>>> That's a good idea about the NULL columns; I didn't think of that.
> >>>> Surprisingly, it didn't have any effect on the time.
> >>>
> >>> Hmm, I think you want "character" and "NULL" there (i.e., quoted).
Did you
> >>> fix both?
> >>>
> >>>>> read.table(whatever, as.is=TRUE, colClasses = c(rep(character,4),
> >>>>> rep(NULL,3696)).
> >>>
> >>> As a general matter, if you want people to dig into this, they need
some
> >>> paraphrase of the file to play with. Would it be possible to set up
a small
> >>> R program that generates a data file which displays the issue?
Everything I
> >>> try seems to take about a second to read in.
> >>>
> >>> -pd
> >>>
> >>>>
> >>>> This problem was just a curiosity, I already did the import using
Excel
> >>> and
> >>>> VBA. I was just going to illustrate the power and simplicity of R,
but
> >>> it
> >>>> ironically it's been much slower and harder in R...
> >>>> The VBA was painful and messy, and took me over an hour to write;
but at
> >>>> least it worked quickly and reliably.
> >>>> The R code was clean and only took me about 5 minutes to write, but
the
> >>> run
> >>>> time was prohibitively slow!
> >>>>
> >>>> I profiled the code, but that offers little insight to me.
> >>>>
> >>>> Profile results with 10 line file:
> >>>>
> >>>>> summaryRprof("C:/Users/gene.leynes/Desktop/test.out")
> >>>> $by.self
> >>>> self.time self.pct total.time total.pct
> >>>> scan 12.24 53.50 12.24 53.50
> >>>> read.table 10.58 46.24 22.88 100.00
> >>>> type.convert 0.04 0.17 0.04 0.17
> >>>> make.names 0.02 0.09 0.02 0.09
> >>>>
> >>>> $by.total
> >>>> total.time total.pct self.time self.pct
> >>>> read.table 22.88 100.00 10.58 46.24
> >>>> scan 12.24 53.50 12.24 53.50
> >>>> type.convert 0.04 0.17 0.04 0.17
> >>>> make.names 0.02 0.09 0.02 0.09
> >>>>
> >>>> $sample.interval
> >>>> [1] 0.02
> >>>>
> >>>> $sampling.time
> >>>> [1] 22.88
> >>>>
> >>>>
> >>>> Profile results with 250 line file:
> >>>>
> >>>>> summaryRprof("C:/Users/gene.leynes/Desktop/test.out")
> >>>> $by.self
> >>>> self.time self.pct total.time total.pct
> >>>> scan 23.88 68.15 23.88 68.15
> >>>> read.table 10.78 30.76 35.04 100.00
> >>>> type.convert 0.30 0.86 0.32 0.91
> >>>> character 0.02 0.06 0.02 0.06
> >>>> file 0.02 0.06 0.02 0.06
> >>>> lapply 0.02 0.06 0.02 0.06
> >>>> unlist 0.02 0.06 0.02 0.06
> >>>>
> >>>> $by.total
> >>>> total.time total.pct self.time self.pct
> >>>> read.table 35.04 100.00 10.78 30.76
> >>>> scan 23.88 68.15 23.88 68.15
> >>>> type.convert 0.32 0.91 0.30 0.86
> >>>> sapply 0.04 0.11 0.00 0.00
> >>>> character 0.02 0.06 0.02 0.06
> >>>> file 0.02 0.06 0.02 0.06
> >>>> lapply 0.02 0.06 0.02 0.06
> >>>> unlist 0.02 0.06 0.02 0.06
> >>>> simplify2array 0.02 0.06 0.00 0.00
> >>>>
> >>>> $sample.interval
> >>>> [1] 0.02
> >>>>
> >>>> $sampling.time
> >>>> [1] 35.04
> >>>>
> >>>>
> >>>>
> >>>>
> >>>> On Tue, Dec 6, 2011 at 2:34 PM, Mark Leeds <markleeds2 at gmail.com>
wrote:
> >>>>
> >>>>> hi gene: maybe someone else will reply with some subtleties that
I'm
> >>> not
> >>>>> aware of. one other thing
> >>>>> that might help: if you know which columns you want , you can set
the
> >>>>> others to NULL through
> >>>>> colClasses and this should speed things up also. For example, say
you
> >>> knew
> >>>>> you only wanted the
> >>>>> first four columns and they were character. then you could do,
> >>>>>
> >>>>> read.table(whatever, as.is=TRUE, colClasses = c(rep(character,4),
> >>>>> rep(NULL,3696)).
> >>>>>
> >>>>> hopefully someone else will say something that does the trick. it
seems
> >>>>> odd to me as far as the
> >>>>> difference in timings ? good luck.
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>> On Tue, Dec 6, 2011 at 1:55 PM, Gene Leynes <gleynes at gmail.com>
wrote:
> >>>>>
> >>>>>> Mark,
> >>>>>>
> >>>>>> Thank you for the reply
> >>>>>>
> >>>>>> I neglected to mention that I had already set
> >>>>>> options(stringsAsFactors=FALSE)
> >>>>>>
> >>>>>> I agree, skipping the factor determination can help performance.
> >>>>>>
> >>>>>> The main reason that I wanted to use read.table is because it
will
> >>>>>> correctly determine the column classes for me. I don't really
want to
> >>>>>> specify 3700 column classes! (I'm not sure what they are
anyway).
> >>>>>>
> >>>>>>
> >>>>>> On Tue, Dec 6, 2011 at 12:40 PM, Mark Leeds
<markleeds2 at gmail.com>
> >>> wrote:
> >>>>>>
> >>>>>>> Hi Gene: Sometimes using colClasses in read.table can speed
things up.
> >>>>>>> If you know what your variables are ahead of time and what you
want
> >>> them to
> >>>>>>> be, this allows you to be specific by specifying, character or
> >>> numeric,
> >>>>>>> etc and often it makes things faster. others will have more to
say.
> >>>>>>>
> >>>>>>> also, if most of your variables are characters, R will try to
turn
> >>>>>>> convert them into factors by default. If you use as.is = TRUE it
> >>> won't
> >>>>>>> do this and that might speed things up also.
> >>>>>>>
> >>>>>>>
> >>>>>>> Rejoinder: above tidbits are just from experience. I don't
know if
> >>>>>>> it's in stone or a hard and fast rule.
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>> On Tue, Dec 6, 2011 at 1:15 PM, Gene Leynes <gleynes at gmail.com>
> >>> wrote:
> >>>>>>>
> >>>>>>>> ** Disclaimer: I'm looking for general suggestions **
> >>>>>>>> I'm sorry, but can't send out the file I'm using, so there is
no
> >>>>>>>> reproducible example.
> >>>>>>>>
> >>>>>>>> I'm using read.table and it's taking over 30 seconds to read a
tiny
> >>>>>>>> file.
> >>>>>>>> The strange thing is that it takes roughly the same amount of
time if
> >>>>>>>> the
> >>>>>>>> file is 100 times larger.
> >>>>>>>>
> >>>>>>>> After re-reviewing the data Import / Export manual I think the
best
> >>>>>>>> approach would be to use Python, or perhaps the readLines
function,
> >>> but
> >>>>>>>> I
> >>>>>>>> was hoping to understand why the simple read.table approach
wasn't
> >>>>>>>> working
> >>>>>>>> as expected.
> >>>>>>>>
> >>>>>>>> Some relevant facts:
> >>>>>>>>
> >>>>>>>> 1. There are about 3700 columns. Maybe this is the problem?
Still
> >>>>>>>> the
> >>>>>>>>
> >>>>>>>> file size is not very large.
> >>>>>>>> 2. The file encoding is ANSI, but I'm not specifying that in
the
> >>>>>>>>
> >>>>>>>> function. Setting fileEncoding="ANSI" produces an
"unsupported
> >>>>>>>> conversion"
> >>>>>>>> error
> >>>>>>>> 3. readLines imports the lines quickly
> >>>>>>>> 4. scan imports the file quickly also
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> Obviously, scan and readLines would require more coding to
identify
> >>>>>>>> columns, etc.
> >>>>>>>>
> >>>>>>>> my code:
> >>>>>>>> system.time(dat <- read.table('C:/test.txt', nrows=-1,
sep='\t',
> >>>>>>>> header=TRUE))
> >>>>>>>>
> >>>>>>>> It's taking 33.4 seconds and the file size is only 315 kb!
> >>>>>>>>
> >>>>>>>> Thanks
> >>>>>>>>
> >>>>>>>> Gene
> >>>>>>>>
> >>>>>>>> [[alternative HTML version deleted]]
> >>>>>>>>
> >>>>>>>> ______________________________________________
> >>>>>>>> R-help at r-project.org mailing list
> >>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-help
> >>>>>>>> PLEASE do read the posting guide
> >>>>>>>> http://www.R-project.org/posting-guide.html
> >>>>>>>> and provide commented, minimal, self-contained, reproducible
code.
> >>>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>
> >>>>>
> >>>>
> >>>> [[alternative HTML version deleted]]
> >>>>
> >>>> ______________________________________________
> >>>> R-help at r-project.org mailing list
> >>>> https://stat.ethz.ch/mailman/listinfo/r-help
> >>>> PLEASE do read the posting guide
> >>> http://www.R-project.org/posting-guide.html
> >>>> and provide commented, minimal, self-contained, reproducible code.
> >>>
> >>> --
> >>> Peter Dalgaard, Professor,
> >>> Center for Statistics, Copenhagen Business School
> >>> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> >>> Phone: (+45)38153501
> >>> Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>
> >> ______________________________________________
> >> R-help at r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-help
> >> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> >> and provide commented, minimal, self-contained, reproducible code.
> >>
>
> --
> Peter Dalgaard, Professor,
> Center for Statistics, Copenhagen Business School
> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> Phone: (+45)38153501
> Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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