R: Draw a polygon with conditional colour

If you want two different colors, you need two different polygons. You can either call polygon multiple times, or you can add NA values in your x and y vectors to indicate a new polygon. R will not automatically calculate the intersection for you. You must do that yourself. Here's how you could draw that with different colors.

x <- c(1,2,2.5,NA,2.5,3,4)
y <- c(0,1,0,NA,0,-1,0)

#calculate color based on most extreme y value
g <- cumsum(is.na(x))
gc <- ifelse(tapply(y, g, 
    function(x) x[which.max(abs(x))])>0, 
    "red","green")

plot(c(1, 4),c(-1,1), type = "n")
polygon(x, y, col = gc)
abline(h=0)

In the more general case, it might not be as easy to split a polygon into different regions. There seems to be some support for this type of operation in GIS packages, where this type of thing is more common. However, I've put together a somewhat general case that may work for simple polygons.

First, I define a closure that will define a cutting line. The function will take a slope and y-intercept for a line and will return the functions we need to cut a polygon.

getSplitLine <- function(m=1, b=0) {
    force(m); force(b)
    classify <- function(x,y) {
        y >= m*x + b
    }
    intercepts <- function(x,y, class=classify(x,y)) {
        w <- which(diff(class)!=0)
        m2 <- (y[w+1]-y[w])/(x[w+1]-x[w])
        b2 <- y[w] - m2*x[w]

        ix <- (b2-b)/(m-m2)
        iy <- ix*m + b
        data.frame(x=ix,y=iy,idx=w+.5, dir=((rank(ix, ties="first")+1) %/% 2) %% 2 +1)
    }
    plot <- function(...) {
    abline(b,m,...)
    }
    list(
        intercepts=intercepts,
        classify=classify,
        plot=plot
    )
}

Now we will define a function to actually split a polygon using the splitter we've just defined.

splitPolygon <- function(x, y, splitter) {
    addnullrow <- function(x) if (!all(is.na(x[nrow(x),]))) rbind(x, NA) else x
    rollup <- function(x,i=1) rbind(x[(i+1):nrow(x),], x[1:i,])
    idx <- cumsum(is.na(x) | is.na(y))
    polys <- split(data.frame(x=x,y=y)[!is.na(x),], idx[!is.na(x)])
    r <- lapply(polys, function(P) {
        x <- P$x; y<-P$y
        side <- splitter$classify(x, y)
        if(side[1] != side[length(side)]) {
            ints <- splitter$intercepts(c(x,x[1]), c(y, y[1]), c(side, side[1]))
        } else {
            ints <- splitter$intercepts(x, y, side)
        }
        sideps <- lapply(unique(side), function(ss) {
            pts <- data.frame(x=x[side==ss], y=y[side==ss], 
                idx=seq_along(x)[side==ss], dir=0)
            mm <- rbind(pts, ints)
            mm <- mm[order(mm$idx), ]
            br <- cumsum(mm$dir!=0 & c(0,head(mm$dir,-1))!=0 & 
                c(0,diff(mm$idx))>1)
            if (length(unique(br))>1) {
                mm<-rollup(mm, sum(br==br[1]))
            }
            br <- cumsum(c(FALSE,abs(diff(mm$dir*mm$dir))==3))
            do.call(rbind, lapply(split(mm, br), addnullrow))
        })
        pss<-rep(unique(side), sapply(sideps, nrow))
        ps<-do.call(rbind, lapply(sideps, addnullrow))[,c("x","y")]
        attr(ps, "side")<-pss
        ps
   })
   pss<-unname(unlist(lapply(r, attr, "side")))
   src <- rep(seq_along(r), sapply(r, nrow))
   r <- do.call(rbind, r)
   attr(r, "source")<-src
   attr(r, "side")<-pss
   r
}

The input is just the values of x and y as you would pass to polygon along with the cutter. It will return a data.frame with x and y values that can be used with polygon.

For example

x <- c(1,2,2.5,NA,2.5,3,4)
y <- c(1,-2,2,NA,-1,2,-2)

sl<-getSplitLine(0,0)

plot(range(x, na.rm=T),range(y, na.rm=T), type = "n")
p <- splitPolygon(x,y,sl)
g <- cumsum(c(F, is.na(head(p$y,-1))))
gc <- ifelse(attr(p,"side")[is.na(p$y)],  
    "red","green")
polygon(p, col=gc)
sl$plot(lty=2, col="grey")

This should work for simple concave polygons as well with sloped lines. Here's another example

x <- c(1,2,3,4,5,4,3,2)
y <- c(-2,2,1,2,-2,.5,-.5,.5)

sl<-getSplitLine(.5,-1.25)

plot(range(x, na.rm=T),range(y, na.rm=T), type = "n")
p <- splitPolygon(x,y,sl)
g <- cumsum(c(F, is.na(head(p$y,-1))))
gc <- ifelse(attr(p,"side")[is.na(p$y)],  
    "red","green")
polygon(p, col=gc)
sl$plot(lty=2, col="grey")

Right now things can get a bit messy when the the vertex of the polygon falls directly on the splitting line. I may try to correct that in the future.

A faster, but not very accurate solution is to split data frame to list according to grouping variable (e.g. above=red and below=blue). This is a pretty nice workaround for rather big (I would say > 100 elements) datasets. For smaller chunks some discontinuity may be visible:

x <- 1:100
y1 <- sin(1:100/10)*0.8
y2 <- sin(1:100/10)*1.2
plot(x, y2, type='l')
lines(x, y1, col='red')

df <- data.frame(x=x, y1=y1, y2=y2)

df$pos_neg <- ifelse(df$y2-df$y1>0,1,-1) # above (1) or below (-1) average

# create the number for chunks to be split into lists:
df$chunk <- c(1,cumsum(abs(diff(df$pos_neg)))/2+1) # first element needs to be added`
df$colors <- ifelse(df$pos_neg>0, "red","blue") # colors to be used for filling the polygons
# create lists to be plotted:
l <- split(df, df$chunk) # we should get 4 sub-lists
lapply(l, function(x) polygon(c(x$x,rev(x$x)),c(x$y2,rev(x$y1)),col=x$colors))

As I said, for smaller dataset some discontinuity may be visible if sharp changes occur between positive and negative areas, but if horizontal line distinguishes between those two, or more elements are plotted then this effect is neglected: