knitr::opts_chunk$set(echo = TRUE)

library("opensensmapr")
library("dplyr")
library("lubridate")
library("units")
library("sf")
library("leaflet")
library("readr")
library("jsonlite")
library("here")
library("maps")

online <- TRUE # access online API or use local data backup?

analysis_date <- lubridate::as_datetime("2018-01-01 00:00:00")

if(online) {
  # retrieve data from openSenseMap API
  all_boxes <- osem_boxes()
  pm25_boxes <- osem_boxes(
    exposure = 'outdoor',
    date = analysis_date, # ±4 hours
    phenomenon = 'PM2.5'
  )

  # update local data
  all_json <- toJSON(all_boxes, digits = NA, pretty = TRUE)
  write(all_json, file = here("data/all_boxes.json"))
  pm25_json <- toJSON(pm25_boxes, digits = NA, pretty = TRUE)
  write(pm25_json, file = here("data/pm25_boxes.json"))
} else {
  # load data from file and fix column types
  all_boxes_file <- fromJSON(here("data/all_boxes.json"))
  all_boxes <- type_convert(all_boxes_file,
                                  col_types = cols(
                                    exposure = col_factor(levels = NULL),
                                    model = col_factor(levels = NULL),
                                    grouptag = col_factor(levels = NULL)))
  class(all_boxes) <- c("sensebox", class(all_boxes))
  pm25_boxes_file <- fromJSON(here("data/pm25_boxes.json"))
  pm25_boxes <- type_convert(pm25_boxes_file,
                                  col_types = cols(
                                    exposure = col_factor(levels = NULL),
                                    model = col_factor(levels = NULL),
                                    grouptag = col_factor(levels = NULL)))
  class(pm25_boxes) <- c("sensebox", class(pm25_boxes))
}

knitr::kable(data.frame(nrow(all_boxes), nrow(pm25_boxes)),
             col.names = c(
               "# senseBoxes",
               paste("# senseBoxes with PM2.5 measurements around", format(analysis_date, "%Y-%m-%d %T %Z"))))

plot(pm25_boxes)

ms <- st_sfc(st_point(c(7.62571, 51.96236)))
st_crs(ms) <- 4326

pm25_boxes_sf <- st_as_sf(pm25_boxes, remove = FALSE, agr = "identity")
names(pm25_boxes_sf) <- c(names(pm25_boxes), "geometry")

pm25_boxes_sf <- cbind(pm25_boxes_sf, dist_to_ms = st_distance(ms, pm25_boxes_sf))
max_dist <- set_units(7, km) # km from city center

ms_boxes <- pm25_boxes_sf[pm25_boxes_sf$dist_to_ms < max_dist,c("X_id", "name")]
ms_boxes

sense_icon <- awesomeIcons(
  icon = 'cube',
  iconColor = '#ffffff',
  library = 'fa',
  markerColor = 'green'
)

leaflet() %>%
  addTiles() %>%
  addAwesomeMarkers(data = ms_boxes,
             popup = ~paste0("<b>Name:</b> ", name, "<br><b>Id:</b> ",
                             "<a href='https://opensensemap.org/explore/", X_id, "' ",
                             "target='_blank'>", X_id, "</a>"),
             label = ~name,
             icon = sense_icon)

if(online) {
  class(ms_boxes) <- c("sensebox", class(ms_boxes))
  ms_data <- osem_measurements(ms_boxes, phenomenon = "PM2.5",
                             from = lubridate::as_datetime("2017-12-31 20:00:00"),
                             to = lubridate::as_datetime("2018-01-01 04:00:00"),
                             columns = c("value", "createdAt", "lat", "lon", "boxId",
                                         "boxName", "exposure", "sensorId",
                                         "phenomenon", "unit", "sensorType"))
  # update local data
  data_json <- toJSON(ms_data, digits = NA, pretty = TRUE)
  write(data_json, file = here("data/ms_data.json"))
} else {
  # load data from file and fix column types
  ms_data_file <- fromJSON(here("data/ms_data.json"))
  ms_data <- type_convert(ms_data_file,
                                  col_types = cols(
                                    sensorId = col_factor(levels = NULL),
                                    unit = col_factor(levels = NULL)))
  class(ms_data) <- c("sensebox", class(ms_data))
}

summary(ms_data %>%
                select(value,sensorId,unit))

plot(value~createdAt, ms_data,
     type = "p", pch = '*', cex = 2, # new year's style
     col = factor(ms_data$sensorId),
     xlab = NA,
     ylab = unique(ms_data$unit),
     main = "Particulates measurements (PM2.5) on New Year 2017/2018",
     sub = paste(nrow(ms_boxes), "stations in Münster, Germany\n",
                 "Data by openSenseMap.org licensed under",
                 "Public Domain Dedication and License 1.0"))

top_measurements <- ms_data %>%
  arrange(desc(value))
top_boxes <- top_measurements %>%
               distinct(sensorId, .keep_all = TRUE)
knitr::kable(x = top_boxes %>%
               select(value, createdAt, boxName) %>%
               head(n = 3),
             caption = "Top 3 boxes")

knitr::kable(top_boxes %>% filter(value == max(top_boxes$value)) %>%
               select(sensorId, boxName),
             col.names = c("Top sensor identifier", "Top box name"))

top_boxes_sf <- top_boxes %>%
  filter(value == max(top_boxes$value)) %>%
  st_as_sf(coords = c('lon', 'lat'), crs = 4326)
bbox <- sf::st_bbox(top_boxes_sf)

world <- map("world", plot = FALSE, fill = TRUE) %>%
  sf::st_as_sf() %>%
  sf::st_geometry()

plot(world,
     xlim = round(bbox[c(1,3)], digits = 1),
     ylim = round(bbox[c(2,4)], digits = 1),
     axes = TRUE, las = 1)
plot(top_boxes_sf, add = TRUE, col = "red", cex = 2)
title("senseBox stations in Münster with highest PM2.5 measurements")

fireworks_icon <- makeIcon(
  # icon source: https://commons.wikimedia.org/wiki/File:Fireworks_2.png
  iconUrl = "320px-Fireworks_2.png", iconWidth = 160)

leaflet(data = top_boxes_sf) %>%
  addTiles() %>%
  addMarkers(popup = ~as.character(boxName),
             label = ~as.character(boxName),
             icon = fireworks_icon)

devtools::session_info()