Using geosphere/haversine matrix function with dplyr mutate operations

geosphere-haversine-example.R

library(geosphere)
library(tidyverse)

# Simple dataframe and use case:
ref_ll <- c(-95.0, 45.0)

df__ <- tribble(
   ~lon,  ~lat,
   -95.0, 45.0,
   -96.0, 45.0,
   -95.0, 44.0,
   -96.0, 44.0
  )

# This will do it naturally
distHaversine(ref_ll, df__ %>% as.matrix())
#> [1]      0.00  78714.27 111319.49 136731.40

# This will do the lon/lat rows against the single reference latitude/longitude:
df__ %>%
   mutate(ttl_distance = distHaversine(ref_ll, matrix(c(lon, lat), ncol=2)))
#> # A tibble: 4 x 3
#>     lon   lat ttl_distance
#>   <dbl> <dbl>        <dbl>
#> 1   -95    45           0 
#> 2   -96    45       78714.
#> 3   -95    44      111319.
#> 4   -96    44      136731.


# If the reference lat/lons are in the dataframe, this will do it
# Not handling missing values here at all...
df__ <- tribble(
   ~lon,  ~lat, ~reflon, ~reflat,
   -95.0, 45.0, -95.0, 45.0,
   -96.0, 45.0, -95.0, 45.0,
   -95.0, 44.0, -97.0, 43.0,
   -97.0, 43.0, -97.0, 43.0,
   -96.0, 44.0, -97.0, 43.0,
  )

# Make a m x 2 matrix out of the reference lon/lat and target lon/lat, and feed those results in.
# `matrix()` will create the appropriate matrix.
df__ %>%
   mutate(ttl_distance = distHaversine(matrix(c(reflon, reflat), ncol=2), matrix(c(lon, lat), ncol=2)))
#> # A tibble: 5 x 5
#>     lon   lat reflon reflat ttl_distance
#>   <dbl> <dbl>  <dbl>  <dbl>        <dbl>
#> 1   -95    45    -95     45           0 
#> 2   -96    45    -95     45       78714.
#> 3   -95    44    -97     43      196136.
#> 4   -97    43    -97     43           0 
#> 5   -96    44    -97     43      137520.