Geocoding is the process of converting an address into geographic coordinates (latitude and longitude), while reverse geocoding is the process of converting geographic coordinates (latitude and longitude) into an address.
Note that the georeference system used is WGS84 projection.
Prepare the workflow environment
Make sure you have the ‘rtry’ package installed. If not, you may refer to the vignette “Introduction to rtry” (rtry-introduction).
To start, set the work directory to the desired location:
# Set the working directory
setwd("<path_to_dir>")
# Check the working directory
getwd()
Note: The character “\” is used as escape character in R to give the following character special meaning (e.g. “\n” for newline, “\t” for tab, “\r” for carriage return and so on). Therefore, for Windows users, it is important to use the “\” in the file path of the command instead of “/” in order for R to correctly understand the input path.
Load the required packages using the commands:
# Load the rtry package
library(rtry)
# Check the version of rtry
packageVersion("rtry")
# Load the dplyr package which is used for piping (%>%)
library(dplyr)
Use ‘rtry’ for geocoding
rtry_geocoding() takes two parameters address and email, and returns a data frame that contains latitude (lat) and longitude (lon) in WGS84 projection.
rtry_geocoding(address = NULL, email = NULL)
address |
String of an address |
email |
String of an email address |
In the context of this example workflow, we will use the location data provided within the ‘rtry’ package. In this specific case the input argument for the file data_locations.csv can be obtained via system.file() that finds the full file path to the ‘rtry’ package:
# Obtain and print the path to the sample dataset within the rtry package
path_to_data <- system.file("testdata", "data_locations.csv", package = "rtry")
path_to_data
## [1] "C:/Program Files/R/R-4.0.5/library/rtry/testdata/data_locations.csv"
To load the .csv file with location information, use rtry_import():
# Load the locations from a .csv file
input_locations <- rtry_import(path_to_data, separator = ",", encoding = "UTF-8", quote = "\"")
# View the location data in the data viewer
View(input_locations)
## input: C:/Program Files/R/R-4.0.5/library/rtry/testdata/data_locations.csv
## dim: 20 3
## col: Country code Country Location
Then, the location data should be converted into the required format for address, i.e. <location>, <country>:
# Extract and combine the location and country names
input_addresses <- paste(input_locations$Location, input_locations$Country, sep = ", ")
# Display the first six rows
head(input_addresses)
## [1] "Hajdúdorog, Hungary" "Diósd, Hungary" "Fót, Hungary" "Bőcs, Hungary"
## [5] "Regéc, Hungary" "Sáska, Hungary"
Note that file encoding UTF-8 is used, and it is normal for the RStudio console to display character in Unicode character (<U+0000> semantic) depending on the system language setting. For example, “Bőcs” might be displayed as “B<U+0151>cs”.
In order to apply the function rtry_geocoding() to the list input_addresses, use lapply(), and please remember to change the email address into your own email address.
Since OSM is an absolute maximum of 1 request per second, in the following example, a 2 second delay has been set between each search.
# Prepare counter for printed progress messages
counter <- 1
output_coordinates <- NULL # somethings received error messages 'no object found'
# Use lapply to apply function to the list of addresses
output_coordinates <- lapply(input_addresses, function(address) {
# Calling the Nominatim OpenStreetMap API
# Please change the email address into your own email address
geocode_output <- rtry_geocoding(address, email = "john.doe@example.com")
# No heavy uses (an absolute maximum of 1 request per second)
# Here set to 2 seconds between each search
Sys.sleep(2)
# Print message in console to see the progress
message("Geocoding ", counter, "/", nrow(input_locations), " completed.")
counter <<- counter + 1
# Return data.frame with the input address, output of the rtry_geocoding function
return(data.frame(address = address, geocode_output))
}) %>%
# Stack the list output into data.frame
bind_rows() %>% data.frame()
## Geocoding 1/20 completed.
## Geocoding 2/20 completed.
## Geocoding 3/20 completed.
## ...
## Geocoding 20/20 completed.
The progress of the geocoding can be seen in the console. Once the geocoding is completed, view the output_coordinates using the View function.
The output_coordinates would look like the following. Note that the location which is unknown to OSM, the resulting latitude and longitude will remain or marked as NA.
Substitute the coordinates into the corresponding columns of the input data.
# Add the output coordinates to the corresponding columns in the input data
input_locations$Latitude <- output_coordinates$lat
input_locations$Longitude <- output_coordinates$lon
# If necessary, re-arrange the columns
input_locations <- rtry_select_col(input_locations, "Country code", Country, Location, Latitude, Longitude, showOverview = FALSE)
# View data
head(input_locations)
# Export into .csv
output_file = file.path(tempdir(), "locations_to_coordinates.csv")
rtry_export(input_locations, output_file)
## File saved at: C:/Users/user/AppData/Local/Temp/Rtmp4wJAvQ/locations_to_coordinates.csv
Use ‘rtry’ for reverse geocoding
The rtry_revgeocoding() takes two parameters lat_lon and email, and returns a data frame that contains the corresponding location.
rtry_revgeocoding(lat_lon = NULL, email = NULL)
lat_lon |
A data frame containing latitude and longitude in WGS84 projection |
email |
String of an email address |
Here, we will use the coordinates data provided within the ‘rtry’ package. In this specific case the input argument for the file data_coordinates.csv can be obtained via system.file() that finds the full file path to the ‘rtry’ package:
# Obtain and print the path to the sample dataset within the rtry package
path_to_data <- system.file("testdata", "data_coordinates.csv", package = "rtry")
path_to_data
## [1] "C:/Program Files/R/R-4.0.5/library/rtry/testdata/data_coordinates.csv"
To load the .csv file with coordinates information, use rtry_import():
input_coordinates <- rtry_import(path_to_data, separator = ",", encoding = "UTF-8", quote = "\"")
## input: C:/Program Files/R/R-4.0.5/library/rtry/testdata/data_coordinates.csv
## dim: 20 2
## col: Latitude Longitude
Then, the coordinates data should be converted into a data.frame:
# Extract and converted the coordinates into a data frame
input_lat_lon <- data.frame(lat = input_coordinates$Latitude, lon = input_coordinates$Longitude)
In order to apply the function rtry_revgeocoding to the input_lat_lon, use apply(), and please remember to change the email address into your own email address.
Since OSM is an absolute maximum of 1 request per second, in the following example, a 2 second delay has been set between each search.
# Prepare counter for printed progress messages
counter <- 1
output_locations <- NULL # somethings received error messages 'no object found'
# Use apply to apply function to the data.frame that contains the coordinates
# Please change the email address to your own email address
output_locations <- apply(input_lat_lon, 1, function(lat_lon) {
# Calling the Nominatim OpenStreetMap API
rev_geocode_output <- rtry_revgeocoding(lat_lon, email = "john.doe@example.com")
# No heavy uses (an absolute maximum of 1 request per second)
# Here set to 2 seconds between each search
Sys.sleep(2)
# Print message in console to see the progress
message("Reverse Geocoding ", counter, "/", length(input_lat_lon$lat), " completed.")
counter <<- counter + 1
# Return data.frame with the input coordinates, output of the rtry_revgeocoding function
return(data.frame(lat = lat_lon[1], lon = lat_lon[2], rev_geocode_output))
}) %>%
# Stack the list output into data.frame
bind_rows() %>% data.frame()
## Reverse Geocoding 1/20 completed.
## Reverse Geocoding 2/20 completed.
## Reverse Geocoding 3/20 completed.
## ...
## Reverse Geocoding 20/20 completed.
The progress of the reverse geocoding can be seen in the console. Once the reverse geocoding is completed, view the output_locations using the View function.
The output location information would look like the following. Note that for some coordinates, OpenStreetMap might not have the town/city information, in such case, those columns will be marked as NA.
Substitute the country_code and country into the corresponding columns of the input list, while the location information is extracted from either town or city.
# Add the output location information to the corresponding columns in the input data
input_coordinates$'Country code' <- output_locations$country_code
input_coordinates$Country <- output_locations$country
input_coordinates$Location <- ifelse(!is.na(output_locations$town), output_locations$town, output_locations$city)
# If necessary, re-arrange the columns
input_coordinates <- rtry_select_col(input_coordinates, Latitude, Longitude, "Country code", Country, Location, showOverview = FALSE)
# View data
head(input_coordinates)
# Export into .csv
output_file = file.path(tempdir(), "coordinates_to_locations.csv")
rtry_export(input_coordinates, output_file)
## File saved at: C:/Users/user/AppData/Local/Temp/Rtmp4wJAvQ/locations_to_coordinates.csv
