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 <gley...@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 <pda...@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 <marklee...@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 <gley...@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 <marklee...@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 <gley...@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@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@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....@cbs.dk Priv: pda...@gmail.com
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>
>> ______________________________________________
>> R-help@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....@cbs.dk Priv: pda...@gmail.com
______________________________________________
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