Distance calculation based on latitude and longitude

Distance from lat and long

# Here are codes to calculate distance of points based on their lat and long coordinations.
# The Python version is faster than R version.
# Data are presented within R code. For 9 points, in my old laptop, 
# R took 0.011 seconds while Python used 0.00595 seconds.
# For a dataset with 1,000 million points, Python version finished in 20 mins,
# but R needs ~62 hours!

## R version. ============================================================
library(dplyr)
library(sp)

pp = read.csv("~/Documents/points.csv")
# pts pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude
#   1        -73.97349        40.77980         -73.97342         40.75856
#   2        -73.97534        40.75577         -73.95689         40.77539
#   3        -73.95567        40.77672         -73.98109         40.78081
#   4        -73.98205        40.77904         -73.95519         40.77434
#   5        -73.97197        40.76250         -73.97358         40.79469
#   6        -73.98004        40.78619         -73.97630         40.79461
#   7        -73.99150        40.76628         -73.98544         40.76862
#   8        -73.98467        40.76813         -73.99794         40.74097
#   9        -73.99796        40.74096         -73.99930         40.74418

get_dist = function(x) data.frame(actual_dist = spDistsN1(
  matrix(c(x$pickup_longitude, x$pickup_latitude), ncol = 2), 
  c(x$dropoff_longitude, x$dropoff_latitude), longlat = TRUE) * 0.621)
  # in miles

system.time(
pp$act_dist <- 
  pp %>% 
  rowwise() %>% 
  do(get_dist(.)))
# user  system elapsed 
# 0.011   0.000   0.011 

pp
# pts pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude actual_dist
#   1        -73.97349        40.77980         -73.97342         40.75856   1.4628708
#   2        -73.97534        40.75577         -73.95689         40.77539   1.6619633
#   3        -73.95567        40.77672         -73.98109         40.78081   1.3621336
#   4        -73.98205        40.77904         -73.95519         40.77434   1.4445393
#   5        -73.97197        40.76250         -73.97358         40.79469   2.2189531
#   6        -73.98004        40.78619         -73.97630         40.79461   0.6119867
#   7        -73.99150        40.76628         -73.98544         40.76862   0.3563861
#   8        -73.98467        40.76813         -73.99794         40.74097   1.9960550
#   9        -73.99796        40.74096         -73.99930         40.74418   0.2327583

## Python version =============================================================
import geopy

from geopy.distance import vincenty

## function to get distance in miles for one point
def get_dist_mile(x):
    p1 = (x[2], x[1])
    p2 = (x[4], x[3])
    return vincenty(p1, p2).miles
    
# read data
import pandas as pd
pp = pd.read_csv("/home/dli/Documents/points.csv", sep = ",")
# calculate distance for all points.

import timeit
timeit.Timer("pp.loc[:,'adist'] = pp.apply(get_dist_mile, axis = 1)","from __main__ import pp, get_dist_mile").timeit(1)
# 0.0059549808502197266

Try

library(raster)
cbind(
  pp, 
  actual_dist = pointDistance(pp[,2:3], pp[, 4:5], lonlat= TRUE) * .621 / 1000
)