R calculate distance in miles using 2 latitude & 2 longitude vectors in a data frame for 18k rows

Question

I have a data frame with 6 columns:

- location id 1
- latitude 1
- longitude 1
- location id 2
- latitude 2
- longitude 2

I would like to calculate the distance between each of the 2 points in miles and add it as a new column. I'm struggling to find a function that does this. The closest I found is here: https://blog.exploratory.io/calculating-distances-between-two-geo-coded-locations-358e65fcafae but it fails because it can't find a function called ' list_extract '.

Here's a sample of the data:

structure(list(df1_location_number = c(5051, 5051, 5051, 5051, 
5051), df1_Latitude = c(34.7171375, 34.7171375, 34.7171375, 34.7171375, 
34.7171375), df1_Longitude = c(-118.9107316, -118.9107316, -118.9107316, 
-118.9107316, -118.9107316), df2_location_number = c(3051, 3085, 
3022, 3041, 3104), df2_Latitude = c(34.7171375, 39.53404, 31.626788, 
35.247982, 39.33425), df2_Longitude = c(-118.9107316, -93.292373, 
-88.330116, -84.804119, -123.713064)), row.names = c(NA, 5L), class = "data.frame")

Any suggestions?

Answer 1

library(geodist) is a good & fast library for calculating distances, and the geodist_vec() function is vectorised to work on 'columns' of data

library(geodist)

## calcualte distance in metres using Haversine formula
df$dist_m <- geodist::geodist_vec(
  x1 = df$df1_Longitude
  , y1 = df$df1_Latitude
  , x2 = df$df2_Longitude
  , y2 = df$df2_Latitude
  , paired = TRUE
  , measure = "haversine"
)

## convert to miles
df$dist_miles <- df$dist_m / 1609

#   df1_location_number df1_Latitude df1_Longitude df2_location_number df2_Latitude df2_Longitude    dist_m dist_miles
# 1                5051     34.71714     -118.9107                3051     34.71714    -118.91073       0.0     0.0000
# 2                5051     34.71714     -118.9107                3085     39.53404     -93.29237 2327593.8  1446.6089
# 3                5051     34.71714     -118.9107                3022     31.62679     -88.33012 2859098.6  1776.9413
# 4                5051     34.71714     -118.9107                3041     35.24798     -84.80412 3095858.6  1924.0886
# 5                5051     34.71714     -118.9107                3104     39.33425    -123.71306  667849.7   415.0713