# devtools::install_github("njtierney/maxcovr")
library(geodist)
library(maxcovr)
library(microbenchmark)
library(ggplot2)
library(geosphere)
Rcpp::sourceCpp("pargeodist.cpp")# Create some dummy data to use in calculations
set.seed(10)
n <- 5000
x <- cbind (-10 + 20 * stats::runif (n), -10 + 20 * stats::runif (n))
y <- cbind (-10 + 20 * stats::runif (2 * n), -10 + 20 * stats::runif (2 * n))
colnames (x) <- colnames (y) <- c ("x", "y")
geodist_res <- geodist::geodist(x, y, measure = "haversine")
maxcovr_res <- maxcovr::distance_matrix_cpp(y, x)
geosphere_res <- geosphere::distm(x, y, fun = geosphere::distHaversine)
parallel_res <- rcpp_parallel_distm_C(x, y) * 1609.34
all.equal(geodist_res, parallel_res)
#> [1] TRUE
all.equal(geodist_res, maxcovr_res)
#> [1] "Mean relative difference: 0.002858014"
all.equal(maxcovr_res, parallel_res)
#> [1] "Mean relative difference: 0.00286122"
all.equal(geodist_res, geosphere_res)
#> [1] TRUE
all.equal(parallel_res, geosphere_res)
#> [1] TRUE
mb1 <-
microbenchmark(
geodist = geodist::geodist(x, y, measure = "haversine"),
maxcovr = maxcovr::distance_matrix_cpp(y, x),
geosphere = geosphere::distm(x, y, fun = geosphere::distHaversine),
parallel = rcpp_parallel_distm_C(x, y) * 1609.34
)
mb1
#> Unit: seconds
#> expr min lq mean median uq max
#> geodist 8.370764 8.610591 8.750009 8.735267 8.876369 9.282945
#> maxcovr 10.096687 10.299988 10.691648 10.583608 11.017114 11.627757
#> geosphere 19.944979 20.426758 21.037501 20.990131 21.516573 23.482105
#> parallel 1.728775 1.758184 1.878221 1.824284 1.945294 2.494596
#> neval cld
#> 100 b
#> 100 c
#> 100 d
#> 100 a
autoplot(mb1)
set.seed(10)
n_users <- 20000
n_sites <- 250
x <- cbind (-10 + 20 * runif (n_users), -10 + 20 * runif (n_users))
y <- cbind (-10 + 20 * runif (2 * n_sites), -10 + 20 * runif (2 * n_sites))
colnames (x) <- colnames (y) <- c ("x", "y")
head(maxcovr::nearest_facility_dist(y, x))
#> [,1] [,2] [,3]
#> [1,] 1 143 66317.70
#> [2,] 2 321 55602.46
#> [3,] 3 338 69220.63
#> [4,] 4 241 59111.24
#> [5,] 5 228 100389.84
#> [6,] 6 275 25261.91
# The distances are off since this uses the same Haversine formula, but
# the idea is the same and the nearest site (at least in these limited cases)
# end up being the same
head(rcpp_parallel_nearest_facility(x, y))
#> [,1] [,2] [,3]
#> [1,] 1 143 65976.83
#> [2,] 2 321 55665.52
#> [3,] 3 338 69315.58
#> [4,] 4 241 59162.38
#> [5,] 5 228 100673.45
#> [6,] 6 275 25368.55
mb2 <- microbenchmark::microbenchmark(
maxcovr = maxcovr::nearest_facility_dist(y, x),
parallel = rcpp_parallel_nearest_facility(x, y)
)
mb2
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> maxcovr 2063.5472 2097.4554 2131.3308 2124.951 2140.3765 2355.9278 100
#> parallel 309.2106 317.2172 328.0342 329.000 336.7175 359.1827 100
#> cld
#> b
#> a
autoplot(mb2)