library(tidyverse)
library(plotly)

#reading
flight<-read.csv('flight.csv')
airports<-read.csv('airports.csv')
flighsort<-flight[order(flight$airline),]


#airports locations
airports<-read.csv('airports.csv')


#clearning data for map
#converting lon_Dest_airport from factor to character to map it
AirportsMap <- flight %>%
  mutate(lon_Dest_airport = parse_number(as.character(lon_Dest_airport)))

#cleaning the original file to get the number of flights between each two airports
AirportsMap<-AirportsMap %>% group_by(origin_airport,dest_airport,lon_origin_airport,lat_origin_airport,
                                 lon_Dest_airport,lat_Dest_airport) %>% tally()
#drawing the map
geo <- list(
  scope = 'usa',
  projection = list(type = 'world'),
  showland = TRUE,
  landcolor = toRGB("gray95"),
  countrycolor = toRGB("gray80")
)

#adding marker for the three origin airports
plot_geo(locationmode = 'USA-states') %>%
  add_markers(
    data=airports, x = ~lon, y = ~lat, text=~airport,size = 0.1,
    hoverinfo = "text",alpha = 0.5) %>%
#adding flights routes
  add_segments(
    data = AirportsMap,
    x = ~lon_origin_airport, xend = ~lon_Dest_airport,
    y = ~lat_origin_airport, yend = ~lat_Dest_airport,
    alpha = 0.3
  ) %>%
#adding a title
  layout(
    title = 'NYC Flights 2013<br>(Hover for airport names)',
    geo = geo, showlegend = FALSE
  )