Calculate Distance using Latitude and Longitude data in Different Data frames of different lengths with loop

Question

I have 2 data frames of different lengths, each with a longitude and latitude coordinate. I would like to connect the two data frames by calculating the distance between the lat/long points.

For simplicity, Data frame A (starting point) has the following structure

ID     long      lat 
1 -89.92702 44.19367 
2 -89.92525 44.19654 
3 -89.92365 44.19756 
4 -89.91949 44.19848 
5 -89.91359 44.19818  

And Data frame B (end point) has a similar structure but shorter

ID      LAT       LON
1  43.06519 -87.91446
2  43.14490 -88.07172
3  43.08969 -87.91202

I would like to calculate the distance between each point such that I would end with a data frame, merged to A, that has the distances between A1 and B1, A1 and B2, A1 and B3. Furthermore, this should repeat for all values of A in A$ID with all values of B$ID

A$ID   B$ID
1      1
2      2
3      3
4
5

Prior to posting this, I consulted several Stack Overflow threads (including this one and This Medium post but I am not sure how to approach the looping, especially since the lists are of different lengths.

Thank you!

Answer 1

Here's a solution using two packages: sf and tidyverse. The first one is used to convert the data into simple features and calculate the distance; while, the second one is used to put the data in the desired format.

library(tidyverse)
library(sf)

# Transform data into simple features
sfA <- st_as_sf(A, coords = c("long","lat"))
sfB <- st_as_sf(B, coords = c("LON","LAT"))

# Calculate distance between all entries of sf1 and sf2
distances <- st_distance(sfA, sfB, by_element = F)
# Set colnames for distances matrix
colnames(distances) <- paste0("B",1:3)

# Put the results in the desired format
# Transform distances matrix into a tibble
as_tibble(distances) %>%
  # Get row names and add them as a column
  rownames_to_column() %>%
  # Set ID as the column name for the row numbers
  rename("ID" = "rowname") %>%
  # Transform ID to numeric
  mutate_at(vars(ID), as.numeric) %>%
  # Join with the original A data frame
  right_join(A, by = "ID") %>%
  # Change the order of columns
  select(ID, long, lat, everything()) %>%
  # Put data into long format
  pivot_longer(cols = starts_with("B"),
               names_to = "B_ID",
               names_pattern = "B(\\d)",
               values_to = "distance")