similar to: Generic 'diff'

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') ==

Vectorize is defined to return a function that acts as if 'mapply' was called. So we have: > mapply(dput,1:2) # mapply form 1L # calls dput on each element of 1:2 2L [1] 1 2 > Vectorize(dput)(1:2) # Vectorize form 1L # same behavior 2L [1] 1 2 Same thing with a named argument: >

I have some data measured with a coarsely-quantized clock. Let's say the real data are q<- sort(rexp(100,.5)) The quantized form is floor(q), so a simple quantile plot of one against the other can be calculated using: plot(q,type="l"); points(floor(q),col="red") which of course shows the characteristic stair-step. I would like to smooth the quantized

I'm new to R and very excited about its possibilities. But I'm struggling with some very simple things, probably because I haven't found the correct documentation. Here's a simple example which illustrates several of my problems. Suppose I want to have a regexp match against a string, and return all the matching substrings in a vector of strings. regexp <-

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"

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

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)

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

Inspired by the exchange between Rolf Turner and Wacek Kusnierczyk, I thought I'd clear up for myself the exact relationship among the various sequence concepts in R, including not only generic vectors (lists) and atomic vectors, but also pairlists, factor sequences, date/time sequences, and difftime sequences. I tabulated type of sequence vs. property to see if I could make sense of all

Inspired by the exchange between Rolf Turner and Wacek Kusnierczyk, I thought I'd clear up for myself the exact relationship among the various sequence concepts in R, including not only generic vectors (lists) and atomic vectors, but also pairlists, factor sequences, date/time sequences, and difftime sequences. I tabulated type of sequence vs. property to see if I could make sense of all

I was surprised to find that df$a[1] is an order of magnitude faster than df[1,"a"]: > df <- data.frame(a=1:10) > system.time(replicate(100000, df$a[3])) user system elapsed 0.36 0.00 0.36 > system.time(replicate(100000, df[3,"a"])) user system elapsed 4.09 0.00 4.09 A priori, I'd have thought that combining the row and column

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: >