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:

library(dplyr)
 
# https://gist.github.com/mneedham/7e926a213bf76febf5ed
venues = read.csv("/tmp/venues.csv")
 
venues %>% head()
##                        venue      lat       lon
## 1              Skills Matter 51.52482 -0.099109
## 2                   Skinkers 51.50492 -0.083870
## 3          Theodore Bullfrog 51.50878 -0.123749
## 4 The Skills Matter eXchange 51.52452 -0.099231
## 5               The Guardian 51.53373 -0.122340
## 6            White Bear Yard 51.52227 -0.109804

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:

options("scipen"=100, "digits"=4)
library(geosphere)
 
centre = c(-0.129581, 51.516578)
aVenue = venues %>% slice(1)
aVenue
##           venue   lat      lon
## 1 Skills Matter 51.52 -0.09911

Now we can calculate the distance from Skillsmatter to our centre point:

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…

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:

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:

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!