Chain subset conditions

Description

Chain subset conditions

Usage

chainsubset(..., out.vars)

Arguments

Details

A set of logical conditions are chained, not and'ed. That is, each argument to chainsubset is used as a filter to create a smaller dataset. Each subsequent argument filters further. For independent conditions this will be the same as and'ing them. I.e. chainsubset(x < 0 , y < 0) will yield the same subset as (x < 0) & (y < 0). However, for e.g. aggregate filters like chainsubset(x < mean(x), y < mean(y)) we first find all the observations with x < mean(x), then among these we find the ones with y < mean(y). The mean(y) is now conditional on x < mean(x).

Value

Expression that can be eval'ed to yield a logical subset mask.

Examples

N <- 10000
dat <- data.frame(y=rnorm(N), x=rnorm(N),id=factor(sample(N/100,N,replace=TRUE)))
# It's not the same as and'ing the conditions:
ss <- chainsubset(x < mean(y), y < 3*mean(x))
sum(eval(ss,dat))
sum(evalq(x < mean(y) & y < 3*mean(x), dat))
ss2 <- chainsubset(x < mean(y), y < a*mean(x), out.vars='a')
a <- 3; sum(eval(ss2, dat))
a <- 2; sum(eval(ss2, dat))
# Among observations with x < y, find entire id's with more than
# one fifth of their x's larger than 1/2
ss3 <- chainsubset( x < y, tapply(x,id,function(.xx) {sum(.xx > 1/2) > length(.xx)/5} )[id])
 sum(eval(ss3,dat))

Results