The summer just begins and a lot of people start to experience flight delay. I am also interested in seeing which cites in the US are well connected by flights. The OpenFlights project provides free flight information. Then I used R to download the data and loaded them into a SQLite database, since I try to keep them as the persistent data.
RSQLitefacilities R to join and query tables in the database. Finally the flight routes and the airports were visualized by
|Rank||IATA code||City||Arriving flight routes|
Wiki says that Atlanta is the busiest airport in the US according to total passenger boardings. However, from the number of the incoming flight routs, it only ranks 5th, following Chicago, Los Angeles, Denver and New York. Possibly Atlanta is the hub mostly for passengers to do connection. If somebody really loves air traveling, Chicago(with ORD) and New York(with both JFK and EWR) are the two most convenient cities to stay with, because they have the most options.
# Import libraries and set up directory library(ggmap) library(RSQLite) setwd("C:/Google Drive/Codes") # Read data directly from URLs airport <- read.csv("http://openflights.svn.sourceforge.net/viewvc/openflights/openflights/data/airports.dat", header = F) route <- read.csv("http://openflights.svn.sourceforge.net/viewvc/openflights/openflights/data/routes.dat", header = F) # Remove the airports without IATA codes and rename the variables airport <- airport[airport$V5!='', c('V3', 'V4', 'V5','V7','V8','V9')] colnames(airport) <- c("City", "Country", "IATA", "lantitude", "longitude", "altitude") route <- route[c('V3', 'V5')] colnames(route) <- c("Departure", "Arrival") # Store data to SQLite database conn <- dbConnect("SQLite", dbname = "air.db") dbSendQuery(conn, "drop table if exists airport;") dbWriteTable(conn, "airport", airport) dbSendQuery(conn, "drop table if exists route;") dbWriteTable(conn, "route", route) dbDisconnect(conn) # Manipulate data in SQLite database conn <- dbConnect("SQLite", dbname = "air.db") sqlcmd01 <- dbSendQuery(conn, " select a.type, a.city as iata, a.frequency, b.city, b.country, b.lantitude, b.longitude from (select 'depart' as type, departure as city, count(departure) as frequency from route group by departure order by frequency desc, type) as a left join airport as b on a.city = b.iata order by frequency desc ;") top <- combine <- fetch(sqlcmd01, n = -1) sqlcmd02 <- dbSendQuery(conn, " select route.rowid as id, route.departure as point, airport.lantitude as lantitude, airport.longitude as longitude from route left join airport on route.departure = airport.iata union select route.rowid as id, route.arrival as point, airport.lantitude as lantitude, airport.longitude as longitude from route left join airport on route.arrival = airport.iata order by id ;") combine <- fetch(sqlcmd02, n = -1) dbDisconnect(conn) # Draw the flight routes and the airports on Google map ggmap(get_googlemap(center = 'us', zoom = 4, maptype = 'roadmap'), extent = 'device') + geom_line(data = combine, aes(x = longitude, y = lantitude, group = id), size = 0.1, alpha = 0.05,color = 'red4') + geom_point(data = top, aes(x = longitude, y = lantitude, size = frequency), colour = "blue", alpha = 0.3) + scale_size(range=c(0,15))