How to do cumulative logical operations on mutliple columns

Question

I've a number of columns in an xts object, and I want to find the percentage in the first column above a certain number, the percentage in either first or second column above a certain number, the percentage in any of the first three columns above a certain number, etc.

I'm currently doing it manually, as follows:

library(xts)
set.seed(69) 
x = xts( cbind( v.1 = runif(20)*100,  v.2 = runif(20)*100,   v.3 = runif(20)*100,   v.4 = runif(20)*100), Sys.Date()-20:1 )

c(
  mean( x$v.1 > 50),
  mean( x$v.1 > 50 | x$v.2 > 50) ,
  mean( x$v.1 > 50 | x$v.2 > 50 | x$v.3 > 50) ,
  mean( x$v.1 > 50 | x$v.2 > 50 | x$v.3 > 50 | x$v.4 > 50)
  )

Which gives this example output:

[1] 0.50 0.70 0.80 0.95

But now I want to generalize to any number of columns, not just v.1 to v.4. So I'm looking for a single function something like this:

this_is_mean( x, c('v.1','v.2','v.3','v.4'), 50)

or maybe it would look like:

mean ( foo( x, c('v.1','v.2','v.3','v.4'), 50) )

(I'll be using paste('v',1:N,sep='.') for the column names, of course)

Answer 1

Here's an another alternative that seems to be a bit faster than @AnadaMahto's solution (on this example). You might also find it a bit more straightforward.

R> rowMeans(apply(x > 50, 1, cumsum) >= 1)
 v.1  v.2  v.3  v.4 
0.50 0.70 0.80 0.95

Though do note that rowMeans only makes one pass over the data. Unlike mean, which makes 2 passes (one is for floating point arithmetic error-correction).

Answer 2

It seems like you should be able to use sapply and rowSums (if I understand your problem correctly):

sapply(1:ncol(x), function(y) mean(rowSums(x[, seq(y)] > 50) >= 1))
## [1] 0.50 0.70 0.80 0.95

---

Use vapply(1:ncol(x), function(y) mean(rowSums(x[, seq(y)] > 50) >= 1), numeric(1L)) to eek out a little bit more speed, if required.