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  • How to use R's ellipsis feature when writing your own function?

    - by Ryan Thompson
    The R language has a nifty feature for defining functions that can take a variable number of arguments. For example, the function data.frame takes any number of arguments, and each argument becomes the data for a column in the resulting data table. Example usage: > data.frame(letters=c("a", "b", "c"), numbers=c(1,2,3), notes=c("do", "re", "mi")) letters numbers notes 1 a 1 do 2 b 2 re 3 c 3 mi The function's signature includes an ellipsis, like this: function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, stringsAsFactors = default.stringsAsFactors()) { [FUNCTION DEFINITION HERE] } I would like to write a function that does something similar, taking multiple values and consolidating them into a single return value (as well as doing some other processing). In order to do this, I need to figure out how to "unpack" the ... from the function's arguments within the function. I don't know how to do this. The relevant line in the function definition of data.frame is object <- as.list(substitute(list(...)))[-1L], which I can't make any sense of. So how can I convert the ellipsis from the function's signature into, for example, a list? To be more specific, how can I write get_list_from_ellipsis in the code below? my_ellipsis_function(...) { input_list <- get.list.from.ellipsis(...) output_list <- lapply(X=input_list, FUN=do_something_interesting) return(output_list) } my_ellipsis_function(a=1:10,b=11:20,c=21:30)

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  • Optimizing a "set in a string list" to a "set as a matrix" operation

    - by Eric Fournier
    I have a set of strings which contain space-separated elements. I want to build a matrix which will tell me which elements were part of which strings. For example: "" "A B C" "D" "B D" Should give something like: A B C D 1 2 1 1 1 3 1 4 1 1 Now I've got a solution, but it runs slow as molasse, and I've run out of ideas on how to make it faster: reverseIn <- function(vector, value) { return(value %in% vector) } buildCategoryMatrix <- function(valueVector) { allClasses <- c() for(classVec in unique(valueVector)) { allClasses <- unique(c(allClasses, strsplit(classVec, " ", fixed=TRUE)[[1]])) } resMatrix <- matrix(ncol=0, nrow=length(valueVector)) splitValues <- strsplit(valueVector, " ", fixed=TRUE) for(cat in allClasses) { if(cat=="") { catIsPart <- (valueVector == "") } else { catIsPart <- sapply(splitValues, reverseIn, cat) } resMatrix <- cbind(resMatrix, catIsPart) } colnames(resMatrix) <- allClasses return(resMatrix) } Profiling the function gives me this: $by.self self.time self.pct total.time total.pct "match" 31.20 34.74 31.24 34.79 "FUN" 30.26 33.70 74.30 82.74 "lapply" 13.56 15.10 87.86 97.84 "%in%" 12.92 14.39 44.10 49.11 So my actual questions would be: - Where are the 33% spent in "FUN" coming from? - Would there be any way to speed up the %in% call? I tried turning the strings into factors prior to going into the loop so that I'd be matching numbers instead of strings, but that actually makes R crash. I've also tried going for partial matrix assignment (IE, resMatrix[i,x] <- 1) where i is the number of the string and x is the vector of factors. No dice there either, as it seems to keep on running infinitely.

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