Define and apply custom bins on a dataframe

Another cut answer that takes into account extrema:

dat <- read.table("clipboard", header=TRUE)

cuts <- apply(dat, 2, cut, c(-Inf,seq(0.5, 1, 0.1), Inf), labels=0:6)
cuts[cuts=="6"] <- "0"
cuts <- as.data.frame(cuts)

  cosinFcolor cosinEdge cosinTexture histoFcolor histoEdge histoTexture jaccard
1           3         0            0           1         1            0       0
2           0         0            5           0         2            2       0
3           1         0            2           0         0            1       0
4           0         0            3           0         1            1       0
5           1         3            1           0         4            0       0
6           0         0            1           0         0            0       0

Explanation

The cut function splits into bins depending on the cuts you specify. So let’s take 1:10 and split it at 3, 5 and 7.

cut(1:10, c(3, 5, 7))
 [1] <NA>  <NA>  <NA>  (3,5] (3,5] (5,7] (5,7] <NA>  <NA>  <NA> 
Levels: (3,5] (5,7]

You can see how it has made a factor where the levels are those in between the breaks. Also notice it doesn’t include 3 (there’s an include.lowest argument which will include it). But these are terrible names for groups, let’s call them group 1 and 2.

cut(1:10, c(3, 5, 7), labels=1:2)
 [1] <NA> <NA> <NA> 1    1    2    2    <NA> <NA> <NA>

Better, but what’s with the NAs? They are outside our boundaries and not counted. To count them, in my solution, I added -infinity and infinity, so all points would be included. Notice that as we have more breaks, we’ll need more labels:

x <- cut(1:10, c(-Inf, 3, 5, 7, Inf), labels=1:4)
 [1] 1 1 1 2 2 3 3 4 4 4
Levels: 1 2 3 4

Ok, but we didn’t want 4 (as per your problem). We wanted all the 4s to be in group 1. So let’s get rid of the entries which are labelled ‘4’.

x[x=="4"] <- "1"
 [1] 1 1 1 2 2 3 3 1 1 1
Levels: 1 2 3 4

This is slightly different to what I did before, notice I took away all the last labels at the end before, but I’ve done it this way here so you can better see how cut works.

Ok, the apply function. So far, we’ve been using cut on a single vector. But you want it used on a collection of vectors: each column of your data frame. That’s what the second argument of apply does. 1 applies the function to all rows, 2 applies to all columns. Apply the cut function to each column of your data frame. Everything after cut in the apply function are just arguments to cut, which we discussed above.

Hope that helps.