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...attice: Multivariate Data Visualization with R at chapter 8. Unfortunately all the examples refers to log transformation of data, but actually what I'm looking for is exactly the reverse (exp). An example would better show what I'm looking for. library(latticeExtra) xyplot(Sepal.Length*10e3 ~ Sepal.Width, iris) If the figures are higher, the format of the labels also changes xyplot(Sepal.Length*10e12 ~ Sepal.Width, iris) The y axis of the above plot is what I'm currently get with my data I would like the y axis in the form 50^3 . I would better say I want the y axis labels in t...

...ory footprint small, I can equally well reassign the variable the value of a small object (e.g. a <- NULL), which is significantly faster than using rm(). SOME BENCHMARKS: A toy example imitating an iterative algorithm with "large" temporary objects. x <- matrix(rnorm(100e6), ncol=10e3) t1 <- system.time(for (k in 1:ncol(x)) { a <- x[,k] colSum <- sum(a) rm(a) # Not needed anymore b <- x[k,] rowSum <- sum(b) rm(b) # Not needed anymore }) t2 <- system.time(for (k in 1:ncol(x)) { a <- x[,k] colSum <- sum(a) rm(list="a") # Not ne...

Interestingly, as.list(substitute(...())) also works. On Sun, Aug 12, 2018 at 1:16 PM, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > On 12/08/2018 4:00 PM, Henrik Bengtsson wrote: >> >> Hi. For any number of *known* arguments, we can do: >> >> one <- function(a) list(a = substitute(a)) >> two <- function(a, b) list(a = substitute(a), b =

...LSE)[["..."]] > > stats <- microbenchmark::microbenchmark( > dots1(1+2, "a", rnorm(3), stop("bang!")), > dots2(1+2, "a", rnorm(3), stop("bang!")), > dots3(1+2, "a", rnorm(3), stop("bang!")), > times = 10e3 > ) > print(stats) > # Unit: microseconds > # expr min lq mean median > uq max neval > # dots1(1 + 2, "a", rnorm(3), stop("bang!")) 2.14 2.45 3.04 2.58 > 2.73 1110 10000 > # dots2(1 + 2, "a", rnorm(...

...) match.call(expand.dots = FALSE)[["..."]] stats <- microbenchmark::microbenchmark( dots1(1+2, "a", rnorm(3), stop("bang!")), dots2(1+2, "a", rnorm(3), stop("bang!")), dots3(1+2, "a", rnorm(3), stop("bang!")), times = 10e3 ) print(stats) # Unit: microseconds # expr min lq mean median uq max neval # dots1(1 + 2, "a", rnorm(3), stop("bang!")) 2.14 2.45 3.04 2.58 2.73 1110 10000 # dots2(1 + 2, "a", rnorm(3), stop("bang!")) 1.81 2.10 2...

...t("dim", envir=getNamespace("base"), inherits=FALSE, mode="function") + } + dim(x) + } + }) > stats <- microbenchmark( + dim(x), + base::dim(x), + dim_R(x), + dim_R_memoized(x), + dim_illegal(x), + sum(x), + unit="ns", + times=10e3 + ) Warning message: In microbenchmark(dim(x), base::dim(x), dim_R(x), dim_R_memoized(x), : Could not measure a positive execution time for 3859 evaluations. > print(stats) Unit: nanoseconds expr min lq mean median uq max neval cld dim(x) 0 0...

Hello there, I’m computing the total value of an order from the price of the order items using a “for” loop and the “ifelse” function. I do this on a large dataframe (close to 1m lines). The computation of this function is painfully slow: in 1min only about 90 rows are calculated. The computation time taken for a given number of rows increases with the size of the dataset, see the example with

I'm getting a compile error when trying to compile the flex output of ppl.l in tools/wpp. This is under Debian (Woody) with latest updates. Can anybody help me out? Enclosed is the output of the build and folling that, the installed packages. I have also tried the latest version (20030408) with identical results. TIA Luke ================================================================