similar to: Speed difference between df$a[1] and df[1,"a"]

Tested in: R version 2.8.1 (2008-12-22) / Windows Recursive default argument references normally give nice clear errors. In the first set of examples, you get the error: Error in ... : promise already under evaluation: recursive default argument reference or earlier problems? (function(a = a) a ) () (function(a = a) c(a) ) () (function(a = a) a[1] ) () (function(a = a)

In R 2.8.0 on Windows (tested both under ESS and under R Console in case there was an I/O issue) There is a bug in printing val <- matrix(list(NA_complex_,NA_complex_),1). > dput(val) structure(list(NA_complex_, NA_complex_), .Dim = 1:2) > print(val) [,1] [1,] [,2] [1,] Note that a large number of spaces are printed instead of NA. Compare the unproblematic real case:

I have two vectors, a and b. b is a text file. I want to find in b those elements of a which occur at the beginning of the line in b. I have the following code, but it only returns a value for the first value in a, but I want both. Any ideas please. a = c(2,3) b = NULL b[1] = "aaa 2 aaa" b[2] = "2 aaa" b[3] = "3 aaa" b[4] = "aaa 3 aaa"

R factors are the natural way to represent factors -- and should be efficient since they use small integers. But in fact, for many (but not all) operations, R factors are considerably slower than integers, or even character strings. This appears to be because whenever a factor vector is subsetted, the entire levels vector is copied. For example: > i1 <- sample(1e4,1e6,replace=T) > c1

I recently traced a bug of mine to the fact that cumsum(s)[length(s)] is not always exactly equal to sum(s). For example, x<-1/(12:14) sum(x) - cumsum(x)[3] => 2.8e-17 Floating-point addition is of course not exact, and in particular is not associative, so there are various possible reasons for this. Perhaps sum uses clever summing tricks to get more accurate results? In some

To test two environments for object equality (Lisp EQ), I can use 'identity': > e1 <- environment(local(function()x)) > e2 <- environment(local(function()x)) > identical(e1,e2) # compares object identity [1] FALSE > identical(as.list(e1),as.list(e2)) # compares values as name->value mapping [1] TRUE # (is there a

When I use grid.rect to print a multi-coloured grid, it is incredibly slow compared to a single colour grid, or even a two colour grid. I've set out some simplified examples below. This is something I run literally thousands of times a day, so I would greatly appreciate any hints on how I might improve the speed??? library(grid) pushViewport(viewport( width = unit(100,

What is the recommended class for time of day (independent of calendar date)? And what is the recommended way to get the time of day from a POSIXct object? (Not a string representation, but a computable representation.) I have looked in the man page for DateTimeClasses, in the Time Series Analysis Task View and in Spector's Data Manipulation book but haven't found these. Clearly I can

On Tue, Feb 10, 2009 at 10:11 AM, Duncan Murdoch <murdoch at stats.uwo.ca> wrote: > Stavros Macrakis wrote: >> On Tue, Feb 10, 2009 at 8:31 AM, Duncan Murdoch <murdoch at stats.uwo.ca>wrote: >>> The evaluator recognizes the context of usage and will get the >>> function for a function call.... >> Can you point me to chapter and verse in the language

What is the 'idiomatic' way of writing a vectorized switch statement? That is, I would like to write, e.g., vswitch( c('a','x','b','a'), a= 1:4, b=11:14, 100 ) => c(1, 100, 13, 4 ) equivalent to ifelse( c('a','x','b','a') ==

Hi, I fit a Cox PH model to estimate the cause-specific hazards (in a competing risks setting). Then , I compute the survival estimates for all the individuals in my data set using the `survfit' function. I am currently playing with a data set that has about 6000 observations and 12 covariates. I am finding that the survfit function is very slow. Here is a simple simulation example

Judging from the traffic on this mailing list, a lot of R beginners are trying to write things like assign( paste( "myvar", i), ...) where they really should probably be writing myvar[i] <- ... Do we have any idea where this bizarre habit comes from? -s

I was wondering what the criteria were for including books on the Books Related to R page <http://www.r-project.org/doc/bib/R-books.html>. (There is no maintainer listed on this page.) In particular, I was wondering why the following two books are not listed: * Andrew Gelman, Jennifer Hill, *Data Analysis Using Regression and Multilevel/Hierarchical Models*. (CRAN package 'arm') *

I've read in many places that R semantics are based on Scheme semantics. As a long-time Lisp user and implementor, I've tried to make this more precise, and this is what I've found so far. I've excluded trivial things that aren't basic semantic issues: support for arbitrary-precision integers; subscripting; general style; etc. I would appreciate corrections or additions from

NOTA BENE: This email is about `=`, the assignment operator (e.g. {a=1} which is equivalent to { `=`(a,1) } ), not `=` the named-argument syntax (e.g. f(a=1), which is equivalent to eval(structure(quote(f(1)),names=c('','a'))). As far as I can tell from the documentation, assignment with = is precisely equivalent to assignment with <-. Yet they call different primitives: >

NOTA BENE: This email is about `=`, the assignment operator (e.g. {a=1} which is equivalent to { `=`(a,1) } ), not `=` the named-argument syntax (e.g. f(a=1), which is equivalent to eval(structure(quote(f(1)),names=c('','a'))). As far as I can tell from the documentation, assignment with = is precisely equivalent to assignment with <-. Yet they call different primitives: >

Is there a generic binary search routine in a standard library which a) works for character vectors b) runs in O(log(N)) time? I'm aware of findInterval(x,vec), but it is restricted to numeric vectors. I'm also aware of various hashing solutions (e.g. new.env(hash=TRUE) and fastmatch), but I need the greatest-lower-bound match in my application. findInterval is also slow for

A table without names displays like a vector: > unname(table(2:3)) [1] 1 1 1 and preserves the table class (as with unname in general): > dput(unname(table(2:3))) structure(c(1L, 1L), .Dim = 2L, class = "table") Does that make sense? R is not consistent in its treatment of such unname'd tables: In plot, they are considered erroneous input: >

At one point I believe I heard of an R package that would automatically find the most empty space in a plot, and then that answer could then be used to intelligently place a legend. I would like to try to apply that R package to the contrived example shown below, so thank you for any hints or tips that can be provided. x = seq(0, 1000, by = 1) y1_vals<-rnorm(1000, mean = 0, sd = 50)

The R FAQ is very helpful about installing R on various Linuxes, but doesn't seem to discuss the advantages of one distribution over another. I am new to Linux (though not to Unix!), and would appreciate some guidance from those with experience. I plan to set up a headless Linux x86 server for the sole purpose of running 64-bit R. Are there reasons to prefer some Linux distributions over