performance - R Calculate overlapping section (polygon intersection) the fast way -


i looking way calculate percentage base-polygon overlayed (selection) polygon.

take example. want calculate area of red , blue on area of red. 

group_a <- data.frame(x = c(1, 4, 1),                       y = c(2, 4, 4),                       group = "base") group_b <- data.frame(x = c(2, 5, 2),                       y = c(3, 3, 5),                       group = "selection")  dat <- rbind(group_a, group_b) dat x y     group 1 1 2      base 2 4 4      base 3 1 4      base 4 2 3 selection 5 5 3 selection 6 2 5 selection  library(ggplot2)  ggplot(dat, aes(x = x, y = y, fill = group)) + geom_polygon(alpha = 0.5)

one way change data.frames polygon using rgdal- , rgeos-packages, , use garea , gintersection find neccessary values. however, assume might not fastest way this. know faster way calculate areas/intersections? delighted if there rcpp-way it.

i hope haven't asked much; , always: thank ideas/hints!

plot1

edit: how other packages:

library(rgdal) library(rgeos) base <- group_a[, c("x", "y")] sel <- group_b[, c("x", "y")]  base_pol <- polygons(list(polygon(base)), "base") sel_pol <- polygons(list(polygon(sel)), "sel")  shape <- spatialpolygons(list(base_pol, sel_pol)) plot(shape)  base_area <- garea(shape["base"]) intersections <- gintersection(shape["base"], shape["sel"])  garea(intersections)/base_area # [1] 0.4027778

plot2

speed benchmark: running microbenchmark, obtained following results:

ovperc <- function(base, sel) {   base <- base[, c("x", "y")]   sel <- sel[, c("x", "y")]    base_pol <- polygons(list(polygon(base)), "base")   sel_pol <- polygons(list(polygon(sel)), "sel")    shape <- spatialpolygons(list(base_pol, sel_pol))   # plot(shape)    base_area <- garea(shape["base"])   intersections <- gintersection(shape["base"], shape["sel"])    return(garea(intersections)/base_area) }  library(microbenchmark) microbenchmark(ovperc(group_a, group_b), times = 1000) # unit: milliseconds #                    expr      min       lq     mean   median       uq      max neval # ovperc(group_a, group_b) 3.680386 3.970394 4.932217 4.157952 4.745398 67.13696  1000


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