Drone Sighting Vis

README.md

Flight Data Vis

Spots represent Cities where drones have been sighted by pilots. Size of circle represents number of sightings.

flight_data.R

library(dplyr)
library(RMySQL)
library(ggplot2)
library(lubridate)
library(tidyr)
library(readxl)
library(stringr)
library(leaflet)
library(ggmap)

#thanks to bob for the code that gets the excel and turns it into a nice format!!
URL1 <- "http://www.faa.gov/uas/media/UAS_Sightings_report_21Aug-31Jan.xlsx"
URL2 <- "http://www.faa.gov/uas/media/UASEventsNov2014-Aug2015.xls"

fil1 <- basename(URL1)
fil2 <- basename(URL2)

if (!file.exists(fil1)) download.file(URL1, fil1)
if (!file.exists(fil2)) download.file(URL2, fil2)

xl1 <- read_excel(fil1)
xl2 <- read_excel(fil2)

flight_data <- setNames(bind_rows(xl2[,1:3],
                             xl1[,c(1,3,4)]), 
                        c("event_time", "city", "state"))
flight_data$city_state <- paste(flight_data$city, ', ', flight_data$state, sep = '')


#get list of unique cities
cities <- data.frame(flight_data %>% count(city_state))

#initialise empty data and loop through unique city names to get longlat from the geocode
#function in ggmap (just an easy way of interacting with the Google Maps API)
#don't run unless you want to make 400+ calls to the google maps API (takes ~10minutes)
longlats <- data.frame()
for (i in 1:nrow(cities)){
  city_state <- cities[i,1]
  ll <- geocode(city_state)
  record <- cbind(city_state, ll)
  longlats <- rbind(longlats, record)
} 

#add long/lats to flight data
flight_data <- left_join(flight_data, longlats)

#group data by city
flight_data_grouped <- flight_data %>% group_by(lon, lat) %>% summarise(ct = n())

#create map
leaflet(flight_data_grouped) %>% 
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(radius = flight_data_grouped$ct/2, color = "steelblue", stroke = FALSE, fillOpacity = 0.6) %>% 
  setView(lng = mean(flight_data$lon, na.rm = T), lat = mean(flight_data$lat, na.rm = T), zoom = 5)

flight_data.png