Determining the Distance Between Two Zip Codes (Alternatives to Mapdist)

Determining the distance between two ZIP codes (alternatives to mapdist)

taRifx.geo::georoute (only available here until I push out another update, at which point it will be available via install.packages) can use Bing Maps (which supports I believe 25k per day) and can return a distance.

georoute( c("3817 Spruce St, Philadelphia, PA 19104", 
            "9000 Rockville Pike, Bethesda, Maryland 20892"), 
             verbose=TRUE, returntype="time", 
             service="bing" )

You'll have to get a Bing Maps API key and set it in your R global options (ideal placement is in .Rprofile), but the key is free:

options(BingMapsKey="whateverBingGivesYouForYourKey")

Calculating the distance between ZIP Codes in R with the mapdist function

It could be that the zip code on its own is ambiguous. If you include 'USA' in the search string it works

library(ggmap)

mapdist(from = c("19111, USA"), to = c("19187, USA"))

#         from         to     m    km    miles seconds  minutes     hours
# 1 19111, USA 19187, USA 21420 21.42 13.31039    1976 32.93333 0.5488889

Or

library(googleway)

set_key("your_api_key")

google_distance(origins = c("19111, USA"), 
                destinations = c("19187, USA"))

# $destination_addresses
# [1] "Philadelphia, PA 19187, USA"
# 
# $origin_addresses
# [1] "Philadelphia, PA 19111, USA"
# 
# $rows
# elements
# 1 21.4 km, 21420, 33 mins, 1976, 35 mins, 2101, OK
# 
# $status
# [1] "OK"

Determining the distance between multiple ZIP codes from one point

library(ggmap)

Building an example data.frame

geoData <- data.frame(FROM = c('95077', 'Manchester Deaf Institute'),
                  TO = c('06473', 'Birmingham O2 Academy 1'), 
                  stringsAsFactors = FALSE) 

passing columns as args

mapdist(from = geoData[['FROM']], 
    to   = geoData[['TO']], 
    mode = 'driving')

result

                       from                      to       m       km      miles seconds   minutes     hours
1                     95077                   06473 4932333 4932.333 3064.95173  161558 2692.6333 44.877222
2 Manchester Deaf Institute Birmingham O2 Academy 1  141330  141.330   87.82246    6569  109.4833  1.824722

How to calculate Distance between Two Zipcodes in Miles Using R

You can find online a table that has longitudes and latitudes for 29,000+ US cities, along with their zip codes. with it you could then convert your columns of zip codes to coordinates.

Then, the geosphere package offers eight functions for calculating distances (great circle, Haversine, Rhumb, etc) based on coordinates. It also converts meters to miles.

Distance between two zipcodes using zipcodeR package not working

The zip code "32610" is not valid as per is_zcta (which returns true if the given ZIP code is also a ZIP code tabulation area)

is_zcta("32610")
[1] FALSE

Use mapdist() to calculate distance between beijing and new york

If you look at R's help option it gives you a nice example of what is going on.

#import ggmap

    library(ggmap)

#get set of longitudinal and latitudinal coordinates

    (wh <- as.numeric(geocode("the white house, dc")))
    (lm <- as.numeric(geocode("lincoln memorial washington dc")))

You have to also set a mode for walking, bicycling or driving.

mapdist(wh, lm, mode = "walking")

As Alistaire mentions, for geographic distances, you must calculate distances according to the earth's spherical shape. Even though the earth isn't perfectly circular, there are formulas that are reasonably accurate.

The following is a formula I took directly from this website:

http://www.r-bloggers.com/great-circle-distance-calculations-in-r/

gcd.hf <- function(long1, lat1, long2, lat2) {
  R <- 6371 # Earth mean radius [km]
  delta.long <- (long2 - long1)
  delta.lat <- (lat2 - lat1)
  a <- sin(delta.lat/2)^2 + cos(lat1) * cos(lat2) * sin(delta.long/2)^2
  c <- 2 * asin(min(1,sqrt(a)))
  d = R * c
  return(d) # Distance in km
}

I'd read the link if this you are asking for geographical distance.

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