CODEWITHSUNDEEP
r
Michael Ohlrogge
Michael Ohlrogge

Reputation: 10990

splitting a continuous variable into groups of equal number of elements - return numeric vector from bin values

I have a continuous variable that I want to split into bins, returning a numeric vector (of length equal to my original vector) whose values relate to the values of the bins. Each bin should have roughly the same number of elements.

This question: splitting a continuous variable into equal sized groups describes a number of techniques for related situations. For instance, if I start with

x = c(1,5,3,12,5,6,7)

I can use cut() to get:

cut(x, 3, labels = FALSE)
[1] 1 2 1 3 2 2 2

This is undesirable because the values of the factor are just sequential integers, they have no direct relation to the underlying original values in my vector.

Another possibility is cut2: for instance:

library(Hmisc)
cut2(x, g = 3, levels.mean = TRUE)
[1] 3.5 3.5 3.5 9.5 3.5 6.0 9.5

This better because now the return values relate to the values of the bins. It is still less than ideal though since:

I know that there are also options using regex on the factors returns from cut or cut2 to get the top or bottom points of the intervals. These too seem overly cumbersome.

Is this just a situation that requires some not-so-elegant hacking? Or, is there some easier functionality to accomplish this?

My current best effort is as follows:

MyDiscretize = function(x, N_Bins){
    f = cut2(x, g = N_Bins, levels.mean = TRUE)
    return(as.numeric(levels(f))[f])
}

My goal is to find something faster, more elegant, and easily adaptable to use either of the endpoints, rather than just the means.

Edit:

To clarify: my desired output would be:

Upvotes: 0

Views: 3446

Answers (2)

R. Schifini
R. Schifini

Reputation: 9313

Use ave like this:

Given:

x = c(1,5,3,12,5,6,7)

Mean:

ave(x,cut2(x,g = 3), FUN = mean)
[1] 3.5 3.5 3.5 9.5 3.5 6.0 9.5

Min:

ave(x,cut2(x,g = 3), FUN = min)
[1] 1 1 1 7 1 6 7

Max:

ave(x,cut2(x,g = 3), FUN = max)
[1]  5  5  5 12  5  6 12

Or standard deviation:

ave(x,cut2(x,g = 3), FUN = sd)
[1] 1.914854 1.914854 1.914854 3.535534 1.914854       NA 3.535534

Note the NA result for only one data point in interval.

Hope this is what you need.

NOTE:
Parameter g in cut2 is number of quantile groups. Groups might not have the same amount of data points, and the intervals might not have the same length.
On the other hand, cut splits the interval into several of equal length.

Upvotes: 1

nicola
nicola

Reputation: 24520

Maybe not much elegant, but should be efficient. Try this function:

myCut<-function(x,breaks,retValues=c("means","highs","lows")) {
    retValues<-match.arg(retValues)
    if (length(breaks)!=1) stop("breaks must be a single number")
    breaks<-as.integer(breaks)
    if (is.na(breaks)||breaks<2) stop("breaks must greater than or equal to 2") 
    intervals<-seq(min(x),max(x),length.out=breaks+1)
    bins<-findInterval(x,intervals,all.inside=TRUE)
    if (retValues=="means") return(rowMeans(cbind(intervals[-(breaks+1)],intervals[-1]))[bins])
    if (retValues=="highs") return(intervals[-1][bins]) 
    intervals[-(breaks+1)][bins]
}
x = c(1,5,3,12,5,6,7)
myCut(x,3)
#[1]  2.833333  6.500000  2.833333 10.166667  6.500000  6.500000  6.500000
myCut(x,3,"highs")
#[1]  4.666667  8.333333  4.666667 12.000000  8.333333  8.333333  8.333333
myCut(x,3,"lows")
#[1] 1.000000 4.666667 1.000000 8.333333 4.666667 4.666667 4.666667

Upvotes: 1

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