title: Dealing with ties in quantcut()
body: <p>I'm trying to use the R function quantcut() to recode a numeric variable as a factor with levels corresponding to quantiles. For example:</p>
  
  <pre><code>&gt; X
  [1]  6  4  9  6  1  2  5  3  5  7 10  7  2  7  7  5  6  6  3  4  6  4  2  7  6  7
  [27]  4  3  5  3  7  6  8 12  4  4  0  1  7  6  7  4  7  1  1  1  2  3  3  1  1  6
  [53]  5  3  1  1  1  3  3  3  1  1  3  1  1  1  3  3  0  1  3  1  8  5  3  0  0  2
  [79]  1  3  8  0  1  4  1  1  1  1  1  1  3  2  1  4  1  5  5 12  7  2  6  6  2  6
  [105]  0  1  4  1  4  0  7  3  2  1  1  8  5  5  3  0  5  6  2  4  2  2  2  6  4  2
  [131]  2  2  2  6  8  5  1  2  8  3  2  7  4  6  6  6  7  5  1  5  5  6  1  4  4  5
  [157]  6  2  4  7  2  4 10  6  3  5  2  2  6  6  2  4  5  7  4  5 11  6  6  8  2  4
  [183]  4  6 12 16  9  7 14 13 11  5  5  2  2  7  7  6  4  3  4  3  5  4  5  7  9  4
  [209]  3 12  4  4  4  8  7  6  1  3  6  7  5  5  6  9  6  4  7  8  5  6  3  6  4  7
  [235]  3  3  4  7  5  7  5  9  5  8  3  4  3  2  5  2  4  3  8  4  2  2  1  5  3  5
  [261]  8  5  6  4  5  1  1  2  6  2  7  2  4  4  3  3  4 10  5  6 10  2  5  5  0  1
  [287]  6  2  5  4  6  6  9  5  5  6  3  8  1  5  1  8  5  2  5  2  4  2  4  4
  
  bins=10
  labels = 1:bins
  library(gtools)
  x2 = quantcut(X, q = seq(0, 1, by=1/bins), labels=labels)
  </code></pre>
  
  <p>I get the error: "Error in cut.default(x[!flag], breaks = newquant, include.lowest = TRUE,  : 
    'breaks' are not unique". I thought this was because there are ties in the quantiles, but the documentation for quantcut specifically shows an example of how the function can handle ties by using fewer intervals. The error occurs regardless of whether I specify the labels argument.</p>
  
  <p>Any advice would be greatly appreciated.</p>
  
  <p>EDIT: Here is code to enter the variable X:</p>
  
  <pre><code>X = c(6L, 4L, 9L, 6L, 1L, 2L, 5L, 3L, 5L, 7L, 10L, 7L, 2L, 7L, 7L, 
  5L, 6L, 6L, 3L, 4L, 6L, 4L, 2L, 7L, 6L, 7L, 4L, 3L, 5L, 3L, 7L, 
  6L, 8L, 12L, 4L, 4L, 0L, 1L, 7L, 6L, 7L, 4L, 7L, 1L, 1L, 1L, 
  2L, 3L, 3L, 1L, 1L, 6L, 5L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 1L, 
  3L, 1L, 1L, 1L, 3L, 3L, 0L, 1L, 3L, 1L, 8L, 5L, 3L, 0L, 0L, 2L, 
  1L, 3L, 8L, 0L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 4L, 
  1L, 5L, 5L, 12L, 7L, 2L, 6L, 6L, 2L, 6L, 0L, 1L, 4L, 1L, 4L, 
  0L, 7L, 3L, 2L, 1L, 1L, 8L, 5L, 5L, 3L, 0L, 5L, 6L, 2L, 4L, 2L, 
  2L, 2L, 6L, 4L, 2L, 2L, 2L, 2L, 6L, 8L, 5L, 1L, 2L, 8L, 3L, 2L, 
  7L, 4L, 6L, 6L, 6L, 7L, 5L, 1L, 5L, 5L, 6L, 1L, 4L, 4L, 5L, 6L, 
  2L, 4L, 7L, 2L, 4L, 10L, 6L, 3L, 5L, 2L, 2L, 6L, 6L, 2L, 4L, 
  5L, 7L, 4L, 5L, 11L, 6L, 6L, 8L, 2L, 4L, 4L, 6L, 12L, 16L, 9L, 
  7L, 14L, 13L, 11L, 5L, 5L, 2L, 2L, 7L, 7L, 6L, 4L, 3L, 4L, 3L, 
  5L, 4L, 5L, 7L, 9L, 4L, 3L, 12L, 4L, 4L, 4L, 8L, 7L, 6L, 1L, 
  3L, 6L, 7L, 5L, 5L, 6L, 9L, 6L, 4L, 7L, 8L, 5L, 6L, 3L, 6L, 4L, 
  7L, 3L, 3L, 4L, 7L, 5L, 7L, 5L, 9L, 5L, 8L, 3L, 4L, 3L, 2L, 5L, 
  2L, 4L, 3L, 8L, 4L, 2L, 2L, 1L, 5L, 3L, 5L, 8L, 5L, 6L, 4L, 5L, 
  1L, 1L, 2L, 6L, 2L, 7L, 2L, 4L, 4L, 3L, 3L, 4L, 10L, 5L, 6L, 
  10L, 2L, 5L, 5L, 0L, 1L, 6L, 2L, 5L, 4L, 6L, 6L, 9L, 5L, 5L, 
  6L, 3L, 8L, 1L, 5L, 1L, 8L, 5L, 2L, 5L, 2L, 4L, 2L, 4L, 4L)
  </code></pre>