I thought that you wanted a separate file for each of the breaks
"GG!KK!KK!". If you want to read in some large number of lines and
then break them so that they have that many lines, you can do the same
thing, except scanning from the back for a break. So if your input
file has 14M breaks in it, then the code I sent would create that many
files. If you want a minimum number of lines per file, including the
breaks, then it can be done. You just have to be clearer on exactly
what the requirement are. From your sample data, it looks like there
were 7 text lines per record, so if your input was 14M lines, I would
expect that you would have something in the neighborhood of 1.8M files
with 7 lines each. If you had 14M lines in the file and you were
generating 14M files, then there is something wrong with your code is
that it is not recognizing the breaks. How many lines did each file
have in it?
On Tue, Oct 18, 2011 at 9:36 AM, johannes rara <johannesr...@gmail.com> wrote:
> Thanks Jim for your help. I tried this code using readLines and it
> works but not in way I wanted. It seems that this code is trying to
> separate all records from a text file so that I'm getting over 14 000
> 000 text files. My intention is to get only 15 text files all expect
> one containing 1 000 000 rows so that the record which is on the
> breakpoint (near at 1 000 000 line) does not cut from the "middle"...
>
> -J
>
> 2011/10/18 jim holtman <jholt...@gmail.com>:
>> Use 'readLines' instead of 'read.table'. We want to read in the text
>> file and convert it into separate text files, each of which can then
>> be read in using 'read.table'. My solution assumes that you have used
>> readLines. Trying to do this with data frames gets messy. Keep it
>> simple and do it in two phases; makes it easier to debug and to see
>> what is going on.
>>
>>
>>
>> On Tue, Oct 18, 2011 at 8:57 AM, johannes rara <johannesr...@gmail.com>
>> wrote:
>>> Thanks Jim,
>>>
>>> I tried to convert this solution into my situation (.txt file as an input);
>>>
>>> zz <- file("myfile.txt", "r")
>>>
>>> fileNo <- 1 # used for file name
>>> buffer <- NULL
>>> repeat{
>>> input <- read.csv(zz, as.is=T, nrows=1000000, sep='!',
>>> row.names=NULL, na.strings="")
>>> if (length(input) == 0) break # done
>>> buffer <- c(buffer, input)
>>> # find separator
>>> repeat{
>>> indx <- which(grepl("^GG!KK!KK!", buffer))[1]
>>> if (is.na(indx)) break # not found yet; read more
>>> writeLines(buffer[1:(indx - 1L)]
>>> , sprintf("newFile%04d.txt", fileNo)
>>> )
>>> buffer <- buffer[-c(1:indx)] # remove data
>>> fileNo <- fileNo + 1
>>> }
>>> }
>>>
>>> but it gives me an error
>>>
>>> Error in read.table(file = file, header = header, sep = sep, quote = quote,
>>> :
>>> no lines available in input
>>>>
>>>
>>> Do you know a reason for this?
>>>
>>> -J
>>>
>>> 2011/10/18 jim holtman <jholt...@gmail.com>:
>>>> Let's do it in two parts: first create all the separate files (which
>>>> if this what you are after, we can stop here). You can change the
>>>> value on readLines to read in as many lines as you want; I set it to 2
>>>> just for testing.
>>>>
>>>> x <- textConnection("APE!KKU!684!
>>>> APE!VAL!!
>>>> APE!UASU!!
>>>> APE!PLA!1!
>>>> APE!E!10!
>>>> APE!TPVA!17122009!
>>>> APE!STAP!1!
>>>> GG!KK!KK!
>>>> APE!KKU!684!
>>>> APE!VAL!!
>>>> APE!UASU!!
>>>> APE!PLA!1!
>>>> APE!E!10!
>>>> APE!TPVA!17122009!
>>>> APE!STAP!1!
>>>> GG!KK!KK!
>>>> APE!KKU!684!
>>>> APE!VAL!!
>>>> APE!UASU!!
>>>> APE!PLA!1!
>>>> APE!E!10!
>>>> APE!TPVA!17122009!
>>>> APE!STAP!1!
>>>> GG!KK!KK!")
>>>>
>>>> fileNo <- 1 # used for file name
>>>> buffer <- NULL
>>>> repeat{
>>>> input <- readLines(x, n = 100)
>>>> if (length(input) == 0) break # done
>>>> buffer <- c(buffer, input)
>>>> # find separator
>>>> repeat{
>>>> indx <- which(grepl("^GG!KK!KK!", buffer))[1]
>>>> if (is.na(indx)) break # not found yet; read more
>>>> writeLines(buffer[1:(indx - 1L)]
>>>> , sprintf("newFile%04d", fileNo)
>>>> )
>>>> buffer <- buffer[-c(1:indx)] # remove data
>>>> fileNo <- fileNo + 1
>>>> }
>>>> }
>>>>
>>>>
>>>> On Tue, Oct 18, 2011 at 8:12 AM, johannes rara <johannesr...@gmail.com>
>>>> wrote:
>>>>> I have a data set like this in one .txt file (cols separated by !):
>>>>>
>>>>> APE!KKU!684!
>>>>> APE!VAL!!
>>>>> APE!UASU!!
>>>>> APE!PLA!1!
>>>>> APE!E!10!
>>>>> APE!TPVA!17122009!
>>>>> APE!STAP!1!
>>>>> GG!KK!KK!
>>>>> APE!KKU!684!
>>>>> APE!VAL!!
>>>>> APE!UASU!!
>>>>> APE!PLA!1!
>>>>> APE!E!10!
>>>>> APE!TPVA!17122009!
>>>>> APE!STAP!1!
>>>>> GG!KK!KK!
>>>>> APE!KKU!684!
>>>>> APE!VAL!!
>>>>> APE!UASU!!
>>>>> APE!PLA!1!
>>>>> APE!E!10!
>>>>> APE!TPVA!17122009!
>>>>> APE!STAP!1!
>>>>> GG!KK!KK!
>>>>>
>>>>> it contains over 14 000 000 records. Now because I'm out of memory
>>>>> when trying to handle this data in R, I'm trying to read it
>>>>> sequentially and write it out in several .csv files (or .RData files)
>>>>> and then read these into R one-by-one. One record in this data is
>>>>> between lines GG!KK!KK! and GG!KK!KK!. I tried to implement Jim
>>>>> Holtman's approach
>>>>> (http://tolstoy.newcastle.edu.au/R/e6/help/09/03/8416.html) but the
>>>>> problem is how to avoid cutting one record from the middle? I mean
>>>>> that if I put nrows = 1000000, I don't know if one record (between
>>>>> marks GG!KK!KK! and GG!KK!KK! is ending up in two files). How to avoid
>>>>> that? My code so far:
>>>>>
>>>>> zz <- file("myfile.txt", "r")
>>>>> fileNo <- 1
>>>>> repeat{
>>>>>
>>>>> gotError <- 1 # set to 2 if there is an error # catch the
>>>>> error if not more data
>>>>> tryCatch(input <- read.csv(zz, as.is=T, nrows=1000000, sep='!',
>>>>> row.names=NULL, na.strings="", header=FALSE),
>>>>> error=function(x) gotError <<- 2)
>>>>>
>>>>> if (gotError == 2) break
>>>>> # save the intermediate data
>>>>> save(input, file=sprintf("file%03d.RData", fileNo))
>>>>> fileNo <- fileNo + 1
>>>>> }
>>>>> close(zz)
>>>>>
>>>>> ______________________________________________
>>>>> 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.
>>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> Jim Holtman
>>>> Data Munger Guru
>>>>
>>>> What is the problem that you are trying to solve?
>>>>
>>>
>>
>>
>>
>> --
>> Jim Holtman
>> Data Munger Guru
>>
>> What is the problem that you are trying to solve?
>>
>
--
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
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