Iterate in R with a function that requires four vectors - r

Question

I'm trying to find the distance between multiple cities using the distHaversine function in the geosphere package. This code requires a variety of arguments:
The longitude and latitude of the first place.
The longitude and latitude of the second place.
The radius of the earth in whatever unit (I'm using r = 3961 for miles).
When I input this as a vector, it works easily:
HongKong <- c(114.17, 22.31)
GrandCanyon <- c(-112.11, 36.11)
library(geosphere)
distHaversine(HongKong, GrandCanyon, r=3961)
#[1] 7399.113 distance in miles
However, my actual datasets look like this:
library(dplyr)
location1 <- tibble(person = c("Sally", "Jane", "Lisa"),
current_loc = c("Bogota Colombia", "Paris France", "Hong Kong China"),
lon = c(-74.072, 2.352, 114.169),
lat = c(4.710, 48.857, 22.319))
location2 <- tibble(destination = c("Atlanta United States", "Rome Italy", "Bangkok Thailand", "Grand Canyon United States"),
lon = c(-84.388, 12.496, 100.501, -112.113),
lat = c(33.748, 41.903, 13.756, 36.107))
What I want is for there to be rows that say how far each destination is from the person's current location.
I know there has to be a way using purrr's pmap_dbl(), but I'm unable to figure it out.
Bonus points if your code uses the tidyverse and if there's any easy way to make a column that identifies the closest destination. Thank you!
In an ideal world, I would get this:
solution <- tibble(person = c("Sally", "Jane", "Lisa"),
current_loc = c("Bogota Colombia", "Paris France", "Hong Kong China"),
lon = c(-74.072, 2.352, 114.169),
lat = c(4.710, 48.857, 22.319),
dist_Atlanta = c(1000, 2000, 7000),
dist_Rome = c(2000, 500, 3000),
dist_Bangkok = c(7000, 5000, 1000),
dist_Grand = c(1500, 4000, 7500),
nearest = c("Atlanta United State", "Rome Italy", "Bangkok Thailand"))
Note: The numbers in the dist columns are random; however, they would be the output from the distHaversine() function. The name of those columns is arbitrary--it does not need to be called that. Also, if the nearest column is out of the scope of this question, I think that I can figure that one out.

Answer 1

distHaversine accepts only one pair of lat and lon values at a time so we need to send all combinations of location1 and location2 rows one by one to the function. One way using sapply would be
library(geosphere)
location1[paste0("dist_", stringr::word(location2$destination))] <-
t(sapply(seq_len(nrow(location1)), function(i)
sapply(seq_len(nrow(location2)), function(j) {
distHaversine(location1[i, c("lon", "lat")], location2[j, c("lon", "lat")], r=3961)
})))
location1$nearest <- location2$destination[apply(location1[5:8], 1, which.min)]
location1
# A tibble: 3 x 9
# person current_loc lon lat dist_Atlanta dist_Rome dist_Bangkok dist_Grand nearest
# <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#1 Sally Bogota Colombia -74.1 4.71 2114. 5828. 11114. 3246. Atlanta United States
#2 Jane Paris France 2.35 48.9 4375. 687. 5871. 5329. Rome Italy
#3 Lisa Hong Kong China 114. 22.3 8380. 5768. 1075. 7399. Bangkok Thailand

Answer 2

Using the tidyverse an map fuction form purrr as you asked, I found a solution, all in one pipe line.
library(tidyverse)
library(geosphere)
# renaming lon an lat variables in each df
location1 <- location1 %>%
rename(lon.act = lon, lat.act = lat)
location2 <- location2 %>%
rename(lon.dest = lon, lat.dest = lat)
# geting distances
merge(location1, location2, all = TRUE) %>%
group_by(person,current_loc, destination) %>%
nest() %>%
mutate( act = map(data, `[`, c("lon.act", "lat.act")) %>%
map(as.numeric),
dest = map(data, `[`, c("lon.dest", "lat.dest")) %>%
map(as.numeric),
dist = map2(act, dest, ~distHaversine(.x, .y, r = 3961))) %>%
unnest(data, dist) %>%
group_by(person) %>%
mutate(mindis = dist == min(dist))