Error when binning data using `cut` in R

Question

I am trying to bin a variable with value between 1 to 100,000 into ten groups by 10,000. I am using the following code and getting an error.

cut(x, breaks = quantile(x, probs=seq(0, 100000, 10000)), include.lowest = TRUE)

What am I doing wrong?

Answer 1

Well, at first I saw this as a typo, but after some discussion in comments I decided to write an answer.

The error occurs to quantile , as probs should be between 0 and 1 (read ?quantile ).

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It looks like you have been confused with the following two:

cut(x, breaks = seq(0, 100000, 10000), include.lowest = TRUE)
cut(x, breaks = quantile(x, prob = seq(0, 1, 0.1)), include.lowest = TRUE)

As I said, they will give different result, especially when your data are not uniformly distributed.

As a representative example, consider non-uniformly distributed data, say Beta distributed:

set.seed(0)
x <- rbeta(10000, 3, 5)

b1 <- seq(0, 1, 0.1)

b2 <- quantile(x, prob = seq(0, 1, 0.1), names = FALSE)
round(b2, 2)
# [1] 0.01 0.17 0.23 0.28 0.32 0.37 0.41 0.46 0.52 0.60 0.94

Note, the difference between b2 and b1 are significant. You can inspect the (empirical) quantile-quantile plot:

plot(b1, b2); abline(0, 1)

You will see the dots deviates strongly from the line.

In above, b1 gives uniform bin cells, while b2 gives ragged bin cells. Now consider bin counts:

table(cut(x, breaks = b1, include.lowest = TRUE))
#  [0,0.1] (0.1,0.2] (0.2,0.3] (0.3,0.4] (0.4,0.5] (0.5,0.6] (0.6,0.7] (0.7,0.8] 
#      256      1239      2011      2242      1948      1323       685       245 
#(0.8,0.9]   (0.9,1] 
#       48         3 

table(cut(x, breaks = b2, include.lowest = TRUE))
#[0.0101,0.169]  (0.169,0.228]  (0.228,0.276]  (0.276,0.321]  (0.321,0.365] 
#          1000           1000           1000           1000           1000 
# (0.365,0.412]  (0.412,0.463]  (0.463,0.519]  (0.519,0.598]  (0.598,0.935] 
#          1000           1000           1000           1000           1000 

Have you seen the difference? If we place break points by quantile, we will have uniform counts over bins.