In ropensci/stplanr: Sustainable Transport Planning

devtools::load_all()
library(tidyverse)

if(!file.exists("routes.geojson")) {
  routes = pct::get_pct_routes_fast("isle-of-wight")
  routes = routes %>% 
    slice(1:1000)
  sf::write_sf(routes, "routes.geojson", delete_dsn = TRUE)
}

routes = geojsonsf::geojson_sf("routes.geojson")
nrow(routes)

res1 = overline_old(routes, attrib = "foot")

res2 = overline3(routes, attrib = "foot")

summary(res2)
summary(res2)

Small test

res = bench::mark(time_unit = "s", check = FALSE,
  original = {o1 <<- overline_old(routes, attrib = "foot")},
  new = {o2 <<- overline3(routes, attrib = "foot")}
)

The results are as follows:

res |>
  dplyr::select(expression, median, mem_alloc) |>
  mutate(routes_per_second = nrow(routes) / median) |>
  knitr::kable()

Large test

routes = readRDS("/tmp/uptake_commute_fastest.Rds")
r = routes |> 
  slice(seq(10000))
names(r)
system.time({
rnet = overline_old(r,
                            attrib = c("bicycle","bicycle_go_dutch","bicycle_ebike","quietness","gradient_smooth"),
                            fun = list(sum = sum, max = first),
                            ncores = 1,
                            regionalise = 1e9)
})

system.time({
rnet = overline3(r,
                            attrib = c("bicycle","bicycle_go_dutch","bicycle_ebike","quietness","gradient_smooth"),
                            fun = list(sum = sum, max = first),
                            ncores = 1,
                            regionalise = 1e9)
})
bench::mark(check = FALSE, iterations = 1,
            old = {res1 <<- overline_old(r,
                            attrib = c("bicycle","bicycle_go_dutch","bicycle_ebike","quietness","gradient_smooth"),
                            fun = list(sum = sum, max = first),
                            ncores = 1,
                            regionalise = 1e9)
            },
new = {res2 <<- overline3(r,
                            attrib = c("bicycle","bicycle_go_dutch","bicycle_ebike","quietness","gradient_smooth"),
                            fun = list(sum = sum, max = first),
                            ncores = 1,
                            regionalise = 1e9)
}
            )