Is there a better way to create quantile "dummies" / factors in R?

i´d like to assign factors representing quantiles. Thus I need them to be numeric. That´s why I wrote the following function, which is basically the answer to my problem:

qdum <- function(v,q){

qd = quantile(v,1:(q)/q)
v = as.data.frame(v)
v$b = 0
names(v) <- c("a","b")
i=1
for (i in 1:q){

    if(i == 1)
        v$b[ v$a < qd[1]] = 1
开发者_开发问答    else
        v$b[v$a > qd[i-1] & v$a <= qd[i]] = i
}

all = list(qd,v)
return(all)

    }

you may laugh now :) . The returned list contains a variable that can be used to assign every observation to its corresponding quantile. My question is now: is there a better way (more "native" or "core") to do it? I know about quantcut (from the gtools package), but at least with the parameters I got, I ended up with only with those unhandy(? - at least to me) thresholds.

Any feedback thats helps to get better is appreciated!

With base R, use quantiles to figure out the splits and then cut to convert the numeric variable to discrete:

qcut <- function(x, n) {
  cut(x, quantile(x, seq(0, 1, length = n + 1)), labels = seq_len(n),
    include.lowest = TRUE)
}

or if you just want the number:

qcut2 <- function(x, n) {
  findInterval(x, quantile(x, seq(0, 1, length = n + 1)), all.inside = T)
}

I'm not sure what quantcut is but I would do the following

qdum <- function(v, q) {
 library(Hmisc)
 quantilenum <- cut2(v, g=q)
 levels(quantilenum) <- 1:q
 cbind(v, quantilenum)
}