Re: [R] Incremental ReadLines

From: William Dunlap <wdunlap_at_tibco.com>
Date: Thu, 14 Apr 2011 13:58:33 -0700

[see below]

From: Frederik Lang [mailto:frederiklang_at_gmail.com] Sent: Thursday, April 14, 2011 12:56 PM
To: William Dunlap
Cc: r-help_at_r-project.org
Subject: Re: [R] Incremental ReadLines

        Hi Bill,         

        Thank you so much for your suggestions. I will try and alter my code.                  

        Regarding the even shorter solution outside the loop it looks good but my problem is that not all observations have the same variables so that three different observations might look like this:                  

	Id: 1
	Var1: false
	Var2: 6
	Var3: 8
	
	Id: 2
	missing
	
	Id: 3
	Var1: true
	3 4 5
	Var2: 7
	Var3: 3
	
	
	Doing it without looping through I thought my data had to quite
systematic, which it is not. I might be wrong though.

Doing the simple preallocation that I describe should speed it up a lot with very little effort. It is more work to manipulate the columns one at a time instead of using data.frame subscripting and it may not be worth it if you have lots of columns.

If you have a lot of this sort of file and feel that it will be worth the programming time to do something fancier, here is some code that reads lines of the form

> cat(lines, sep="\n")

Id: First

  Var1: false
  Var2: 6
  Var3: 8

Id: Second
Id: Last
  Var1: true
  Var3: 8

and produces a matrix with the Id's along the rows and the Var's along the columns:

> f(lines)

       Var1 Var2 Var3
First "false" "6" "8"
Second NA NA NA
Last "true" NA "8"

The function f is:

f <- function (lines)
{

    # keep only lines with colons
    lines <- grep(value = TRUE, "^.+:", lines)     lines <- gsub("^[[:space:]]+|[[:space:]]+$", "", lines)     isIdLine <- grepl("^Id:", lines)
    group <- cumsum(isIdLine)
    rownames <- sub("^Id:[[:space:]]*", "", lines[isIdLine])     lines <- lines[!isIdLine]
    group <- group[!isIdLine]
    varname <- sub("[[:space:]]*:.*$", "", lines)     value <- sub(".*:[[:space:]]*", "", lines)     colnames <- unique(varname)
    col <- match(varname, colnames)
    retval <- array(NA_character_, c(length(rownames), length(colnames)),

        dimnames = list(rownames, colnames))     retval[cbind(group, col)] <- value
    retval
}

The main trick is the matrix subscript given to retval on the penultimate line.

        Thanks again,                  

        Frederik                           

        On Thu, Apr 14, 2011 at 12:56 PM, William Dunlap <wdunlap_at_tibco.com> wrote:         

		I have two suggestions to speed up your code, if you
		must use a loop.
		
		First, don't grow your output dataset at each iteration.
		Instead of
		    cases <- 0
		    output <- numeric(cases)
		    while(length(line <- readLines(input, n=1))==1) {
		       cases <- cases + 1
		       output[cases] <- as.numeric(line)
		    }
		preallocate the output vector to be about the size of
		its eventual length (slightly bigger is better),
replacing
		    output <- numeric(0)
		with the likes of
		    output <- numeric(500000)
		and when you are done with the loop trim down the length
		if it is too big
		    if (cases < length(output)) length(output) <- cases
		Growing your dataset in a loop can cause quadratic or
worse
		growth in time with problem size and the above sort of
		code should make the time grow linearly with problem
size.                 

                Second, don't do data.frame subscripting inside your loop.

		Instead of
		    data <- data.frame(Id=numeric(cases))
		    while(...) {
		        data[cases, 1] <- newValue
		    }
		do
		    Id <- numeric(cases)
		    while(...) {
		        Id[cases] <- newValue
		    }
		    data <- data.frame(Id = Id)
		This is just the general principal that you don't want
to
		repeat the same operation over and over in a loop.
		dataFrame[i,j] first extracts column j then extracts
element
		i from that column.  Since the column is the same every
iteration
		you may as well extract the column outside of the loop.
		
		Avoiding the loop altogether is the fastest.  E.g., the
code
		you showed does the same thing as
		  idLines <- grep(value=TRUE, "Id:", readLines(file))
		  data.frame(Id = as.numeric(sub("^.*Id:[[:space:]]*",
"", idLines)))
		You can also use an external process (perl or grep) to
filter
		out the lines that are not of interest.
		
		
		Bill Dunlap
		Spotfire, TIBCO Software
		wdunlap tibco.com
		

		> -----Original Message-----
		> From: r-help-bounces_at_r-project.org
		> [mailto:r-help-bounces_at_r-project.org] On Behalf Of
Freds
		> Sent: Wednesday, April 13, 2011 10:58 AM
		> To: r-help_at_r-project.org
		> Subject: Re: [R] Incremental ReadLines
		>
		
		> Hi there,
		>
		> I am having a similar problem with reading in a large
text
		> file with around
		> 550.000 observations with each 10 to 100 lines of
		> description. I am trying
		> to parse it in R but I have troubles with the size of
the
		> file. It seems
		> like it is slowing down dramatically at some point. I
would
		> be happy for any
		> suggestions. Here is my code, which works fine when I
am
		> doing a subsample
		> of my dataset.
		>
		> #Defining datasource
		> file <- "filename.txt"
		>
		> #Creating placeholder for data and assigning column
names
		> data <- data.frame(Id=NA)
		>
		> #Starting by case = 0
		> case <- 0
		>
		> #Opening a connection to data
		> input <- file(file, "rt")
		>
		> #Going through cases
		> repeat {
		>   line <- readLines(input, n=1)
		>   if (length(line)==0) break
		>   if (length(grep("Id:",line)) != 0) {
		>     case <- case + 1 ; data[case,] <-NA
		>     split_line <- strsplit(line,"Id:")
		>     data[case,1] <- as.numeric(split_line[[1]][2])
		>     }
		> }
		>
		> #Closing connection
		> close(input)
		>
		> #Saving dataframe
		> write.csv(data,'data.csv')
		>
		>
		> Kind regards,
		>
		>
		> Frederik
		>
		>
		> --
		> View this message in context:
		>
http://r.789695.n4.nabble.com/Incremental-ReadLines-tp878581p3
		447859.html

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		>
		> ______________________________________________
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		> https://stat.ethz.ch/mailman/listinfo/r-help
		> PLEASE do read the posting guide
		> http://www.R-project.org/posting-guide.html
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		>
		

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