Calculate distance between latitude and longitude points?

Question

I have a data frame that contains the longitude/latitude of various patients' city locations. I am curious about each patient's distance traveled to the hospital. Is there a way I can somehow find out the distance each patient traveled to the hospital with the geodist package?

Lets say the hospital is located here:

Latitude: 36.840 Longitude: -119.850

I want to calculate the distance between the hospital and where each patient lives. Here is my dataset:

> dput(d) structure(list(major_city = c("Mountain View", "Watsonville", "Honolulu", "Los Altos", "Morgan Hill", "Fulton", "Oak Grove", "Port Kent", "Bedford", "San Jose"), latitude = c(37.39, 36.9, 21.31, 37.36, 37.1, 43.3, 36.69, 44.526, NA, 37.27), longitude = c(-122.07, -121.7, -157.85, -122.15, -121.7, -76.4, -87.44, -73.409, NA, -121.84)), row.names = c(NA, -10L), class = c("data.table", "data.frame" ), .internal.selfref = <pointer: 0x7f98af80bae0>, index = integer(0))

I want to do this for the entire dataset rather than just one patient.

Does these answers address your question? stackoverflow.com/questions/19847833/… and stackoverflow.com/questions/67477686/… — Jon Spring
– Jon Spring, Commented Jan 25, 2023 at 0:38

clsantos · Accepted Answer · 2023-01-25 00:47:32Z

You could try this. I used the geosphere package instead of geodist but should be good enough

df <- data.frame(major_city = c("Mountain View", "Watsonville", "Honolulu", "Los Altos", "Morgan Hill", "Fulton", "Oak Grove", "Port Kent", "Bedford", "San Jose"), latitude = c(37.39, 36.9, 21.31, 37.36, 37.1, 43.3, 36.69, 44.526, NA, 37.27), longitude = c(-122.07, -121.7, -157.85, -122.15, -121.7, -76.4, -87.44, -73.409, NA, -121.84)) df$distance_m <- apply(df, MARGIN = 1, function(x) { geosphere::distGeo(as.numeric(c(x['longitude'], x['latitude'])), c(-119.85, 36.84)) }) df$distance_km <- df$distance_m / 1000

This is the output

 major_city latitude longitude distance_m distance_km 1 Mountain View 37.390 -122.070 206527.4 206.5274 2 Watsonville 36.900 -121.700 165083.2 165.0832 3 Honolulu 21.310 -157.850 4045683.3 4045.6833 4 Los Altos 37.360 -122.150 212440.1 212.4401 5 Morgan Hill 37.100 -121.700 167241.2 167.2412 6 Fulton 43.300 -76.400 3731976.3 3731.9763 7 Oak Grove 36.690 -87.440 2879678.0 2879.6780 8 Port Kent 44.526 -73.409 3962114.4 3962.1144 9 Bedford NA NA NA NA 10 San Jose 37.270 -121.840 183321.3 183.3213

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