Matrix of booleans based on quantile in R

Question

I have a matrix whose columns are stock returns and whose rows are dates, which looks like this:

              ES1.Index    VG1.Index   TY1.Comdty    RX1.Comdty   GC1.Comdty
1999-01-05  0.009828476  0.012405717 -0.003058466 -0.0003480884 -0.001723317
1999-01-06  0.021310816  0.027030061  0.001883240  0.0017392317  0.002425398
1999-01-07 -0.001952962 -0.016130850 -0.002826191 -0.0011591516  0.013425435
1999-01-08  0.007989946 -0.004071275 -0.005913678  0.0016224363 -0.001363540

I'd like to have a function that returns a matrix with the same column-names and row-names filled with 1s and 0s based on whether each observation within each row-vector belongs or not to some group within two given quantiles.

For example, I may want to divide each row vector into 3 groups and have 1s for all observations falling within the 2nd group and 0s elsewhere. The result being something looking like:

           ES1.Index VG1.Index TY1.Comdty RX1.Comdty GC1.Comdty
1999-01-05         0         0          1          1          0
1999-01-06         1         0          0          1          0
1999-01-07         0         1          0          0          1
1999-01-08         0         0          1          0          1

(The 1s and 0s in my example are meant to be just a visual outcome, the numbers aren't accurate)

Which would be the least verbose way to get to that?

Answer 1

Taking the intermediate steps of finding the quantiles and testing against them is not necessary. Only the ordinal properties of each vector matter.

# set bounds
lb = 1/3
ub = 2/3

# find ranks
p = t(apply(m,1,rank))/ncol(m)

# test ranks against bounds
+( p >= lb & p <= ub )


           ES1.Index VG1.Index TY1.Comdty RX1.Comdty GC1.Comdty
1999-01-05         0         0          0          1          1
1999-01-06         0         0          1          0          1
1999-01-07         1         0          1          0          0
1999-01-08         0         1          0          0          1

Answer 2

We can use apply with MARGIN=1 to loop over the rows, cut each row vector with breaks specified by the quantile , transpose the output to get an output.

t(apply(df1, 1, function(x) {
       x1 <- cut(x, breaks= quantile(x, seq(0, 1,1/3)))
       +(levels(x1)[2]== x1 & !is.na(x1))}))