Hey folks,

I was recently asked how to visualize flight connections with R, so I decided to make a blog post with a short tutorial on this topic: The aim is to map all possible flight connections from the NYC JFK Airport to other major airports in the US. For this we will be using the following packages:

• nycflights13: Dataset with flights departing from NYC in 2013.
• dplyr: package for manipulating datasets.
• maps: package for mapping.
• geosphere: package for spherical trigonometry.

Make sure to install and load these beforehand using the install.packages() and library() functions.

Data:

Lets get  a quick overview on the dataset first. Once you loaded the package nycflights13 you have access to the dataset airports:

library(nycflights13)
airports
Source: local data frame [1,397 x 7]

   faa                           name      lat        lon  alt tz dst
1  04G              Lansdowne Airport 41.13047  -80.61958 1044 -5   A
2  06A  Moton Field Municipal Airport 32.46057  -85.68003  264 -5   A
3  06C            Schaumburg Regional 41.98934  -88.10124  801 -6   A
4  06N                Randall Airport 41.43191  -74.39156  523 -5   A
5  09J          Jekyll Island Airport 31.07447  -81.42778   11 -4   A
6  0A9 Elizabethton Municipal Airport 36.37122  -82.17342 1593 -4   A
7  0G6        Williams County Airport 41.46731  -84.50678  730 -5   A
8  0G7  Finger Lakes Regional Airport 42.88356  -76.78123  492 -5   A
9  0P2   Shoestring Aviation Airfield 39.79482  -76.64719 1000 -5   U
10 0S9          Jefferson County Intl 48.05381 -122.81064  108 -8   A
.. ...                            ...      ...        ...  ... .. ...

The dataset contains useful metadata on airports. For our map however we only want airports that are located in the US. Therefore we will make a couple of data filtering steps using the dplyr package. We will select only those airports which have a latitude < 48.5 and a longitude > -130. Then we will exclude the JFK airport from this dataset and create a new dataframe just containing the JFK airport:

usairports <- filter(airports, lat < 48.5)
usairports <- filter(usairports, lon > -130)
usairports <- filter(usairports, faa!="JFK") #filter out jfk
jfk <- filter(airports, faa=="JFK") #separate df for jfk

Create flight connection map:

The data is now prepared for our final flight connection map. The first step will be to create a basemap for the US using the maps package:

#create basemap
map("world", regions=c("usa"), fill=T, col="grey8", bg="grey15", ylim=c(21.0,50.0), xlim=c(-130.0,-65.0))
#overlay airports
points(usairports$lon,usairports$lat, pch=3, cex=0.1, col="chocolate1")

The map should now look like this:

The next and final step is to add the flight connectins from NYC to all other airports using the gesophere package and the gcIntermediate() function.

for (i in (1:dim(usairports)[1])) { 
inter <- gcIntermediate(c(jfk$lon[1], jfk$lat[1]), c(usairports$lon[i], usairports$lat[i]), n=200)
lines(inter, lwd=0.1, col="turquoise2")    
}

The gcIntermediate() function takes two points and creates a circular connection between them. With the lines() function you add this circle to the plot. In this example this is done for all airports in the dataframe with a for loop. The final map can be seen below:

Enjoy the final result! If you have any questions related to dplyr or the gesophere package, feel free to ask.

Cheers

Martin