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?
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