install.packages("tidyverse")
install.packages("leaflet")
install.packages("rgdal")
install.packages("readr")
install.packages("RCurl")
install.packages("htmlwidgets")
install.packages("leaflet")

require(leaflet)
require(readr)
require(RCurl)
require(tidyverse)
require(lubridate)
require(rgdal)
require(htmlwidgets)
require(leaflet)

# We load the data directly from tubCloud and open the data set

download <- getURL("https://hessenbox-a10.rz.uni-frankfurt.de/dl/fi88asBdwcm9oRT5tWy3rFy9")
tracks <- read_delim(download, delim = ',')

# Let's inspect the data and do some basic statistics
# The column "type" tells us that we have tracks and stays in the data. At the moment
# we are only interested in tracks. Therefore we select the tracks. We do this by
# using the fact that stays obviously don't have a distance (length)

tracks <- tracks[!(is.na(tracks$length)),]

# Let's find out how many user participated, how many tracks they produced and
# which distances they travelled.

cnt.user <- length(unique(tracks$user_id))
cnt.user

cnt.tracks <- nrow(tracks)
cnt.tracks

distance <- sum(tracks$length)/1000
distance

# Modal split by tracks

modal_split <- tracks %>%
  group_by(started_on, mode) %>%
  summarise(distance = round(sum(length)/1000, 2),
            n_mode = length(mode)) %>%
  mutate(prop_distance = (round(distance / sum(distance), 3) * 100),
         prop_count = (round(n_mode / sum(n_mode), 3) * 100))

# Diurnal curve: Number of tracks per hour
# use package lubridate to get values per hour

diurnal_curve <- tracks %>%
  subset(select = c(mode, started_at)) %>%
  mutate(started_at = hour(started_at)) %>%
  group_by(started_at) %>%
  summarise(tracks_per_hour = n())

# Plot modal split
# Let's use some nice colors. But which modes do we have in the dataset?

levels(as.factor(modal_split$mode))

# Ok, let's define the colors for these modes

mode_colors <- c("Mode::Bicycle" = "#90A72F",
                 "Mode::Bus" = "#5175AE",
                 "Mode::Car" = "#D57F0E",
                 "Mode::Motorbike" = "#D57F0E",
                 "Mode::LightRail" = "#35AD9C",
                 "Mode::Subway" = "#3E8EB6",
                 "Mode::RegionalTrain" = "#D6393A",
                 "Mode::Tram" = "#D6393A",
                 "Mode::Walk" = "#CEC11D")

p_modal_split_distance_day <-
  modal_split %>%
  ggplot(aes(x = started_on, y = distance, fill = mode)) +
  theme_bw() +
  geom_bar(stat = "identity", width = .7) +
  labs(x = "", y = "Distances (km)",
       title = "Modal split per day",
       subtitle = "Share of distances per mode per day",
       caption = "GUFM Test user | Period: 01.06. - 15.06.2021") +
  geom_text(aes(x = started_on, label = prop_distance), colour = "white",
            size = 2, position = position_stack(vjust = 0.6)) +
  scale_fill_manual(values = mode_colors,
                    limits = c("Mode::Bicycle", "Mode::Bus", "Mode::Car",
                               "Mode::Motorbike","Mode::LightRail", "Mode::Subway",
                               "Mode::RegionalTrain", "Mode::Tram",
                               "Mode::Walk"),
                    labels = c("Bicycle", "Bus", "Car", "Motorbike", "Lightrail", "Subway",
                               "Train", "Tram", "Walk"))

# Plot diurnal curve

diurnal_curve %>%
  ggplot(aes(x = started_at, y = tracks_per_hour)) +
  geom_line(size = 1, stat = "identity") +
  geom_point() +
  theme_bw() +
  labs(x = "Time", y = "Percentage of trips",
       title = "Diurnal curves",
       subtitle = "Diurnal curves of test user",
       caption = "GUFM Test user | Period: 01.06. - 15.06.2021")

# Plot a map
# I prepared a geojson. Let's download it

download <- getURL("https://hessenbox-a10.rz.uni-frankfurt.de/dl/fizfxmvtQ87MXYmT4EeeiB7/tracks.geojson")
s.tracks <- readOGR(download, verbose = FALSE)

### Visualize tracks on a map

factpal <- colorFactor(rainbow(10), s.tracks$mode) # choose a color palette

#### Plot map
# For other background maps go to:
# https://leaflet-extras.github.io/leaflet-providers/preview/

map <- leaflet() %>%
  addProviderTiles(providers$CartoDB.DarkMatter) %>%
  addPolylines(data = s.tracks, stroke = TRUE,
               fillOpacity = 0.2, smoothFactor = 0.5,
               color = ~factpal(mode), group = "modes") %>%
  addLegend(pal = factpal, values = s.tracks$mode, group = "modes", position = "bottomleft") %>%
  addLayersControl(overlayGroups = c("modes")
  )

map

# saveWidget(map, file = "/Users/robert.schoenduwe/Documents/New/map.html")