inst/doc/r5r.R

In r5r: Rapid Realistic Routing with 'R5'

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = identical(tolower(Sys.getenv("NOT_CRAN")), "true"),
  out.width = "100%"
)

## removes files previously created by 'build_network()'
#data_path <- system.file("extdata/poa", package = "r5r")
#existing_files <- list.files(data_path)
#files_to_keep <- c(
#  "poa_hexgrid.csv",
#  "poa_osm.pbf",
#  "poa_points_of_interest.csv",
#  "poa_eptc.zip",
#  "poa_trensurb.zip",
#  'fares'
#  )
#files_to_remove <- existing_files[! existing_files %in% files_to_keep]
#invisible(file.remove(file.path(data_path, files_to_remove)))

## ----eval = FALSE-------------------------------------------------------------
# # from CRAN
# install.packages('r5r')
# 
# # dev version with latest features
# devtools::install_github("ipeaGIT/r5r", subdir = "r-package")

## ----eval = FALSE-------------------------------------------------------------
# # install {rJavaEnv} from CRAN
# install.packages("rJavaEnv")
# 
# # check version of Java currently installed (if any)
# rJavaEnv::java_check_version_rjava()
# 
# ## if this is the first time you use {rJavaEnv}, you might need to run this code
# ## below to consent the installation of Java.
# # rJavaEnv::rje_consent(provided = TRUE)
# 
# # install Java 21
# rJavaEnv::java_quick_install(version = 21)
# 
# # check if Java was successfully installed
# rJavaEnv::java_check_version_rjava()
# 

## ----message = FALSE----------------------------------------------------------
options(java.parameters = "-Xmx2G")

# By default, {r5r} uses all CPU cores available. If you want to limit the 
# number of CPUs to 4, for example, you can run:  
options(java.parameters = c("-Xmx2G", "-XX:ActiveProcessorCount=4"))

## ----message = FALSE, warning = FALSE-----------------------------------------
library(r5r)
library(sf)
library(data.table)
library(ggplot2)

## -----------------------------------------------------------------------------
data_path <- system.file("extdata/poa", package = "r5r")
list.files(data_path)

## -----------------------------------------------------------------------------
poi <- fread(file.path(data_path, "poa_points_of_interest.csv"))
head(poi)

## -----------------------------------------------------------------------------
points <- fread(file.path(data_path, "poa_hexgrid.csv"))

# sample points
sampled_rows <- sample(1:nrow(points), 200, replace=TRUE)
points <- points[ sampled_rows, ]
head(points)

## ----message = FALSE----------------------------------------------------------
# Indicate the path where OSM and GTFS data are stored
r5r_network <- build_network(data_path = data_path)

## ----message = FALSE----------------------------------------------------------
# set departure datetime input
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
                                 format = "%d-%m-%Y %H:%M:%S")

# calculate accessibility
access <- accessibility(
  r5r_network,
  origins = points,
  destinations = points,
  opportunities_colnames = c("schools", "healthcare"),
  mode = c("WALK", "TRANSIT"),
  departure_datetime = departure_datetime,
  decay_function = "step",
  cutoffs = 60
  )

head(access)

## ----message = FALSE----------------------------------------------------------
# set inputs
mode <- c("WALK", "TRANSIT")
max_walk_time <- 30 # minutes
max_trip_duration <- 120 # minutes
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
                                 format = "%d-%m-%Y %H:%M:%S")

# calculate a travel time matrix
ttm <- travel_time_matrix(
  r5r_network,
  origins = poi,
  destinations = poi,
  mode = mode,
  departure_datetime = departure_datetime,
  max_walk_time = max_walk_time,
  max_trip_duration = max_trip_duration
  )

head(ttm)

## ----ttm head, echo=FALSE, message=FALSE, out.width='100%', eval = FALSE------
# knitr::include_graphics("https://github.com/ipeaGIT/r5r/blob/master/r-package/inst/img/vig_output_ttm.png?raw=true")

## ----message = FALSE----------------------------------------------------------
# calculate a travel time matrix
ettm <- expanded_travel_time_matrix(
  r5r_network,
  origins = poi,
  destinations = poi,
  mode = mode,
  departure_datetime = departure_datetime,
  breakdown = TRUE,
  max_walk_time = max_walk_time,
  max_trip_duration = max_trip_duration
  )

head(ettm)

## ----message = FALSE----------------------------------------------------------
# set inputs
origins <- poi[10,]
destinations <- poi[12,]
mode <- c("WALK", "TRANSIT")
max_walk_time <- 60 # minutes
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
                                 format = "%d-%m-%Y %H:%M:%S")

# calculate detailed itineraries
det <- detailed_itineraries(
  r5r_network,
  origins = origins,
  destinations = destinations,
  mode = mode,
  departure_datetime = departure_datetime,
  max_walk_time = max_walk_time,
  shortest_path = FALSE
  )

head(det)

## ----detailed head, echo = FALSE, out.width='100%', message = FALSE, eval = FALSE----
# knitr::include_graphics("https://github.com/ipeaGIT/r5r/blob/master/r-package/inst/img/vig_output_detailed.png?raw=true")

## ----message = FALSE----------------------------------------------------------
# extract OSM network
street_net <- r5r::street_network_to_sf(r5r_network)

# extract public transport network
transit_net <- r5r::transit_network_to_sf(r5r_network)

# plot
ggplot() +
  geom_sf(data = street_net$edges, color='gray85') +
  geom_sf(data = det, aes(color=mode)) +
  facet_wrap(.~option) + 
  theme_void()


## ----ggplot2 output, echo = FALSE, out.width='100%', message = FALSE, eval = FALSE----
# knitr::include_graphics("https://github.com/ipeaGIT/r5r/blob/master/r-package/inst/img/vig_detailed_ggplot.png?raw=true")

## ----message = FALSE----------------------------------------------------------
r5r::stop_r5(r5r_network)
rJava::.jgc(R.gc = TRUE)

## ----eval = TRUE, include = FALSE, message = FALSE----------------------------
# clean cache (CRAN policy)
r5r::r5r_cache(delete_file = 'all')