[see below]
From: Frederik Lang [mailto:frederikl...@gmail.com]
Sent: Thursday, April 14, 2011 12:56 PM
To: William Dunlap
Cc: r-help@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
<wdun...@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-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of
Freds
> Sent: Wednesday, April 13, 2011 10:58 AM
> To: r-help@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
>
>
> --
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