Avoiding empty and small groups when using pretty_breaks with cut2 - r

Question

I'm working with variables resembling the data val values created below:
# data --------------------------------------------------------------------
data("mtcars")
val <- c(mtcars$wt, 10.55)
I'm cutting this variable in the following manner:
# Cuts --------------------------------------------------------------------
cut_breaks <- pretty_breaks(n = 10, eps.correct = 0)(val)
res <- cut2(x = val, cuts = cut_breaks)
which produces the following results:
> table(res)
res
[ 1, 2) [ 2, 3) [ 3, 4) [ 4, 5) [ 5, 6) 6 7 8 9 [10,11]
4 8 16 1 3 0 0 0 0 1
In the created output I would like to change the following:
I'm not interested in creating grups with one value. Ideally, I would like to for each group to have at least 3 / 4 values. Paradoxically, I can leave with groups having 0 values as those will dropped later on when mergining on my real data
Any changes to the cutting mechanism, have to work on a variable with integer values
The cuts have to be pretty. I'm trying to avoid something like 1.23 - 2.35. Even if those values would be most sensible considering the distribution.
In effect, what I'm trying to achieve is this: try to make more or less even pretty group and if getting a really tiny group then bump it together with the next group, do not worry about empty groups.
Full code
For convenience, the full code is available below:
# Libs --------------------------------------------------------------------
Vectorize(require)(package = c("scales", "Hmisc"),
character.only = TRUE)
# data --------------------------------------------------------------------
data("mtcars") val <- c(mtcars$wt, 10.55)
# Cuts --------------------------------------------------------------------
cut_breaks <- pretty_breaks(n = 10, eps.correct = 0)(val) res <-
cut2(x = val, cuts = cut_breaks)
What I've tried
First approach
I tried to play with the eps.correct = 0 value in the pretty_breaks like in the code:
cut_breaks <- pretty_breaks(n = cuts, eps.correct = 0)(variable)
but none of the values gets me anwhere were close
Second approach
I've also tried using the m= 5 argument in the cut2 function but I keep on arriving at the same result.
Comment replies
My breaks function
I tried the mybreaks function but I would have to put some work into it to get nice cuts for more bizzare variables. Broadly speaking, pretty_breaks cuts well for me, juts the tiny groups that occur from time to time are not desired.
> set.seed(1); require(scales)
> mybreaks <- function(x, n, r=0) {
+ unique(round(quantile(x, seq(0, 1, length=n+1)), r))
+ }
> x <- runif(n = 100)
> pretty_breaks(n = 5)(x)
[1] 0.0 0.2 0.4 0.6 0.8 1.0
> mybreaks(x = x, n = 5)
[1] 0 1

Answer 1

You could use the quantile() function as a relatively easy way to get similar numbers of observations in each of your groups.
For example, here's a function that takes a vector of values x, a desired number of groups n, and a desired rounding off point r for the breaks, and gives you suggested cut points.
mybreaks <- function(x, n, r=0) {
unique(round(quantile(x, seq(0, 1, length=n+1)), r))
}
cut_breaks <- mybreaks(val, 5)
res <- cut(val, cut_breaks, include.lowest=TRUE)
table(res)
[2,3] (3,4] (4,11]
8 16 5