In my continued exploration of London’s meetups I wanted to calculate the distance from meetup venues to a centre point in London.

I’ve created a gist containing the coordinates of some of the venues that host NoSQL meetups in London town if you want to follow along:

Now to do the calculation. I’ve chosen the Centre Point building in Tottenham Court Road as our centre point. We can use the distHaversine function in the geosphere library allows us to do the calculation:

Now we can calculate the distance from Skillsmatter to our centre point:</pr> _~r distHaversine(c(aVenue$lon, aVenue$lat), centre) ## [1] 2302 _~

That works pretty well so now we want to apply it to every row in the venues data frame and add an extra column containing that value.

This was my first attempt... _~r venues %>% mutate(distHaversine(c(lon,lat),centre)) ## Error in .pointsToMatrix(p1): Wrong length for a vector, should be 2 _~

...which didn’t work quite as I’d imagined!

I eventually found my way to the by function which allows you to 'apply a function to a data frame split by factors'. In this case I wouldn’t be grouping rows by a factor - I’d apply the function to each row separately.

I wired everything up like so: _~r distanceFromCentre = by(venues, 1:nrow(venues), function(row) { distHaversine(c(row$lon, row$lat), centre) }) distanceFromCentre %>% head() 1:nrow(venues) 1 2 3 4 5 6 2301.6 3422.6 957.5 2280.6 1974.1 1509.5 _~ We can now add the distances to our venues data frame: _~r venuesWithCentre = venues %>% mutate(distanceFromCentre = by(venues, 1:nrow(venues), function(row) { distHaversine(c(row$lon, row$lat), centre) })) venuesWithCentre %>% head() venue lat lon distanceFromCentre 1 Skills Matter 51.52 -0.09911 2301.6 2 Skinkers 51.50 -0.08387 3422.6 3 Theodore Bullfrog 51.51 -0.12375 957.5 4 The Skills Matter eXchange 51.52 -0.09923 2280.6 5 The Guardian 51.53 -0.12234 1974.1 6 White Bear Yard 51.52 -0.10980 1509.5 _~

Et voila!