On 20/06/2014 15:37, Ista Zahn wrote:
Hello,
I've noticed that dget() is much slower in the current and devel R
versions than in previous versions. In 2.15 reading a 10000-row
data.frame takes less than half a second:
(which.r <- R.Version()$version.string)
[1] "R version 2.15.2 (2012-10-26)"
x <- data.frame(matrix(sample(letters, 100000, replace = TRUE), ncol = 10))
dput(x, which.r)
system.time(y <- dget(which.r))
user system elapsed
0.546 0.033 0.586
While in 3.1.0 and r-devel it takes around 7 seconds.
(which.r <- R.Version()$version.string)
[1] "R version 3.1.0 (2014-04-10)"
x <- data.frame(matrix(sample(letters, 100000, replace = TRUE), ncol = 10))
dput(x, which.r)
system.time(y <- dget(which.r))
user system elapsed
6.920 0.060 7.074
(which.r <- R.Version()$version.string)
[1] "R Under development (unstable) (2014-06-19 r65979)"
x <- data.frame(matrix(sample(letters, 100000, replace = TRUE), ncol = 10))
dput(x, which.r)
system.time(y <- dget(which.r))
user system elapsed
6.886 0.047 6.943
I know dput/dget is probably not the right tool for this job:
nevertheless the slowdown in quite dramatic so I thought it was worth
calling attention to.
This is completely the wrong way to do this. See ?dump.
dget() basically calls eval(parse()). parse() is much slower in R >=
3.0 mainly because it keeps more information. Using keep.source=FALSE
here speeds things up a lot.
> system.time(y <- dget(which.r))
user system elapsed
3.233 0.012 3.248
> options(keep.source=FALSE)
> system.time(y <- dget(which.r))
user system elapsed
0.090 0.001 0.092
--
Brian D. Ripley, rip...@stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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