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

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

library(r5r)
library(sf)
library(data.table)
library(ggplot2)
library(patchwork)
library(dplyr)
library(h3jsr)

## -----------------------------------------------------------------------------
# setup and load Porto Alegre multimodal network into memory

# system.file returns the directory with example data inside the r5r package
# set data path to directory containing your own data if not using the examples
data_path <- system.file("extdata/poa", package = "r5r")

r5r_network <- build_network(data_path)

# load transit network as an SF
transit_network <- transit_network_to_sf(r5r_network)

# map
ggplot() +
  geom_sf(data=transit_network$routes, aes(color=mode)) +
  theme_void()

## -----------------------------------------------------------------------------
fare_structure <- setup_fare_structure(r5r_network, 
                                       base_fare = 4.8,
                                       by = "MODE")

## -----------------------------------------------------------------------------
head(fare_structure, n=7)


## -----------------------------------------------------------------------------
fare_structure$max_discounted_transfers
fare_structure$transfer_time_allowance <- 60 # update transfer_time_allowance
fare_structure$fare_cap

## -----------------------------------------------------------------------------
fare_structure$fares_per_type

## -----------------------------------------------------------------------------
fare_structure$fares_per_type[type == "RAIL", unlimited_transfers := TRUE]
fare_structure$fares_per_type[type == "RAIL", fare := 4.50]
fare_structure$fares_per_type[type == "RAIL", allow_same_route_transfer := TRUE]

## -----------------------------------------------------------------------------
fare_structure$fares_per_type

## -----------------------------------------------------------------------------
fare_structure$fares_per_transfer

## -----------------------------------------------------------------------------
# conditional update fare value
fare_structure$fares_per_transfer[first_leg == "BUS" & second_leg == "BUS", fare := 7.2]

## -----------------------------------------------------------------------------
# conditional update fare value
fare_structure$fares_per_transfer[first_leg != second_leg, fare := 8.37]

# use fcase instead ?
fare_structure$fares_per_transfer[, fare := fcase(first_leg == "BUS" & second_leg == "BUS", 7.2,
                                                 first_leg != second_leg, 8.37)]


## -----------------------------------------------------------------------------
# remove row
fare_structure$fares_per_transfer <- fare_structure$fares_per_transfer[!(first_leg == "RAIL" & second_leg == "RAIL")]


## -----------------------------------------------------------------------------
fare_structure$fares_per_transfer

## -----------------------------------------------------------------------------
tail(fare_structure$fares_per_route)

## -----------------------------------------------------------------------------
## load input data
points <- read.csv(system.file("extdata/poa/poa_hexgrid.csv", package = "r5r"))

# calculate travel times function
calculate_travel_times <- function(fare) {
  ttm_df <- travel_time_matrix(
    r5r_network,
    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"
    ),
    time_window = 1,
    fare_structure = fare_structure,
    max_fare = fare,
    max_trip_duration = 40,
    max_walk_time = 20
  )

  return(ttm_df)
}


# calculate travel times, and combine results
ttm <- calculate_travel_times(fare = Inf)
ttm_500 <- calculate_travel_times(fare = 5)

# merge results
ttm[ttm_500, on = .(from_id, to_id), travel_time_500 := i.travel_time_p50]
ttm[, travel_time_unl := travel_time_p50]
ttm[, travel_time_p50 := NULL]

## -----------------------------------------------------------------------------
tail(ttm, 10)

## -----------------------------------------------------------------------------
# plot of overall travel time differences between limited and unlimited cost travel time matrices 
time_difference = ttm[!is.na(travel_time_500), .(count = .N), 
                      by = .(travel_time_unl, travel_time_500)]

p1 <- ggplot(time_difference, aes(y = travel_time_unl, x = travel_time_500)) +
  geom_point(size = 0.7) +
  coord_fixed() +
  scale_x_continuous(breaks = seq(0, 45, 5)) +
  scale_y_continuous(breaks = seq(0, 45, 5)) +
  theme_light() +
  theme(legend.position = "none") +
  labs(y = "travel time (minutes)\nunestricted monetary cost",
       x = "travel time (minutes)\nmonetary cost restricted to BRL 5.00"
       )

# plot of unreachable destinations when the monetary cost limit is too low
unreachable <- ttm[, .(count = .N), by = .(travel_time_unl, is.na(travel_time_500))]
unreachable[, perc := count / sum(count, na.rm = T), by = .(travel_time_unl)]
unreachable <- unreachable[is.na == TRUE]
unreachable <- na.omit(unreachable)

p2 <- ggplot(unreachable, aes(x=travel_time_unl, y=perc)) +
  geom_col() +
  coord_flip() +
  scale_x_continuous(breaks = seq(0, 45, 5)) +
  scale_y_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.2), 
                     labels = paste0(seq(0, 100, 20), "%")) +
  theme_light() +
  labs(x = "travel time (minutes)\nwithout monetary cost restriction",
       y = "% of unreachable destinations\nconsidering a R$ 5.00 monetary cost limit")

# combine both plots using patchwork
p1 + p2 + plot_annotation(subtitle = "Comparing travel times with and without monetary cost restriction")


## -----------------------------------------------------------------------------
# calculate accessibility function
calculate_accessibility <- function(fare, fare_string) {
  access_df <- accessibility(
    r5r_network,
    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"
    ),
    time_window = 1,
    opportunities_colname = "healthcare",
    cutoffs = 40,
    fare_structure = fare_structure,
    max_fare = fare,
    max_trip_duration = 40,
    max_walk_time = 20,
    progress = FALSE)

  access_df$max_fare <- fare_string

  return(access_df)
}

# calculate accessibility, combine results, and convert to SF
access_500 <- calculate_accessibility(fare=5, fare_string="R$ 5.00 budget")
access_unl <- calculate_accessibility(fare=Inf, fare_string="Unlimited budget")

access <- rbind(access_500, access_unl)

# bring geometry
access$geometry <- h3jsr::cell_to_polygon(access$id)
access <- st_as_sf(access)


## -----------------------------------------------------------------------------
# plot accessibility maps
ggplot(data = access) +
  geom_sf(aes(fill = accessibility), color=NA, size = 0.2) +
  scale_fill_distiller(palette = "Spectral") +
  facet_wrap(~max_fare) +
  labs(subtitle = "Effect of monetary cost on accessibility") +
  theme_minimal() +
  theme(legend.position = "bottom",
        axis.text = element_blank())


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

