library(r5r)
#> Please make sure you have already allocated some memory to Java by running:
#> options(java.parameters = '-Xmx2G').
#> You should replace '2G' by the amount of memory you'll require. Currently, Java memory is set to
library(accessibility)
library(data.table)
path <- system.file("extdata/poa", package = "r5r")
r5r_core <- setup_r5(path, verbose = FALSE)
#> Using cached R5 version from /home/dhersz/R/x86_64-pc-linux-gnu-library/4.2/r5r/jar/r5-v6.7-all.jar
#>
#> Using cached network.dat from /home/dhersz/R/x86_64-pc-linux-gnu-library/4.2/r5r/extdata/poa/network.dat
points <- fread(file.path(path, "poa_hexgrid.csv"))
ttm <- travel_time_matrix(
r5r_core,
origins = points,
destinations = points,
mode = c("WALK", "TRANSIT"),
departure_datetime = as.POSIXct(
"13-05-2019 14:00:00",
format = "%d-%m-%Y %H:%M:%S"
),
max_walk_dist = 800,
max_trip_duration = 120,
verbose = FALSE,
progress = FALSE
)
min_cost <- cost_to_closest(
ttm,
points,
opportunity = "schools",
travel_cost = "travel_time_p50"
)
#> Warning in `[.data.table`(filled_access_df, is.na(get(access_col)),
#> `:=`(eval(access_col, : inf (type 'double') at RHS position 1 truncated
#> (precision lost) when assigning to type 'integer' (column 2 named 'min_cost')
min_cost
#> id travel_time_p50
#> 1: 89a9012124fffff 0
#> 2: 89a9012126bffff 16
#> 3: 89a9012127bffff 14
#> 4: 89a90128003ffff 7
#> 5: 89a90128007ffff 16
#> ---
#> 1218: 89a90e934cbffff 13
#> 1219: 89a90e934cfffff 9
#> 1220: 89a90e934d3ffff 7
#> 1221: 89a90e934d7ffff 0
#> 1222: 89a90e934dbffff 23
Created on 2022-09-23 by the reprex package (v2.0.1)
Created on 2022-09-23 by the reprex package (v2.0.1)