Stupid R tricks: using outer to create many data.frame subsets

Selecting subsets of a data.frame is easy in R if you define the predicates manually.
But if you need to define many conditions the standard slicing and subsetting methods
are cumbersome.

For this illustration I want to pick some large number of numerical ranges and label
all of the rows that match any of the predicates.

The key is using outer to match against many predicates and then checking that any of them was satisfied.

peaks <- pi*c(0,2,4,6,8,10)
low <- peaks - pi/4
high <- peaks + pi/4
ranges <- data.frame(low=low,high=high)

x<- seq(0,10*pi,0.01)
y<- cos(x)
df <- data.frame(x=x,y=y)

# given a vector x
# which elements are contained in one of the ranges
# defined by the high and low columns of the ranges data.frame
library(plyr)
inranges <- function(x, ranges)
{
  a<-outer(x,ranges$low, ">")
  b<-outer(x,ranges$high, "<")
  c<-a & b
  aaply(c,1,function(y) any(y) )
}

# I can now add a new column that indicates which rows matched
df$peaks <- inranges(df$x, ranges)

library(ggplot2)
p <- ggplot(df,aes(x=x,y=y))
p <- p + geom_point(aes(color=peaks))
p

#or I can subset the data to only the matching rows:

df.peaks <- subset(df,inranges(x,ranges))

p <- ggplot(df.peaks,aes(x=x,y=y))
p <- p + geom_point()
p