Add/Retrieve location coordinates

Adds/Retrieves longitude/latitude values to location data within an epiflows object. Coordinates are added to object's locations slot as lon and lat columns.

add_coordinates(x, coordinates = c("lon", "lat"), loc_column = "id",
  overwrite = FALSE)

get_coordinates(x, ...)

# S3 method for epiflows
get_coordinates(x, location = NULL, ...)

Arguments

x	An epiflows object.
coordinates	Either names of the appended columns with longitudes and latitudes, respectively (default: "lon" and "lat") or a data frame with longitude and latitude columns.
loc_column	Name of the column where location names are stored (default: "country").
overwrite	If TRUE, retrieves all geocodes, even those already retrieved. If FALSE (default), overwrites only NAs.
...	unused
location	a character specifying a single location to return as a vector of coordinates. You cannot specify multiple locations with this parameter. Defaults to `NULL`, indicating all locations.

Value

An updated epiflows object.

See also

map_epiflows(); plot.epiflows(); get_locations(); get_vars(); global_vars()

Examples

# Setting up the data
data("Brazil_epiflows")
data("YF_coordinates")
get_coordinates(Brazil_epiflows) # no coordinates yet
#> NULL
ef <- add_coordinates(Brazil_epiflows, YF_coordinates[-1])
get_coordinates(ef)
#>                          id        lon       lat
#> 1            Espirito Santo -40.308863 -19.18342
#> 2              Minas Gerais -44.555031 -18.51218
#> 3            Rio de Janeiro -43.172897 -22.90685
#> 4                 Sao Paulo -46.633309 -23.55052
#> 5          Southeast Brazil -46.209155 -20.33318
#> 6                 Argentina -63.616672 -38.41610
#> 7                     Chile -71.542969 -35.67515
#> 8                   Germany  10.451526  51.16569
#> 9                     Italy  12.567380  41.87194
#> 10                 Paraguay -58.443832 -23.44250
#> 11                 Portugal  -8.224454  39.39987
#> 12                    Spain  -3.749220  40.46367
#> 13           United Kingdom  -3.435973  55.37805
#> 14 United States of America -95.712891  37.09024
#> 15                  Uruguay -55.765835 -32.52278
get_coordinates(ef, location = "Espirito Santo") # coordinates for Espirito Santo
#> [1] -40.30886 -19.18342
  # You can use google maps' geocode functionality if you have a decent 
  # internet connection
  ef2 <- add_coordinates(Brazil_epiflows, loc_column = "id")
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Espirito%20Santo&sensor=false
#> Warning: geocode failed with status OVER_QUERY_LIMIT, location = "Espirito Santo"
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Minas%20Gerais&sensor=false
#> Warning: geocode failed with status OVER_QUERY_LIMIT, location = "Minas Gerais"
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Rio%20de%20Janeiro&sensor=false
#> Warning: geocode failed with status OVER_QUERY_LIMIT, location = "Rio de Janeiro"
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Sao%20Paulo&sensor=false
#> Warning: geocode failed with status OVER_QUERY_LIMIT, location = "Sao Paulo"
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Southeast%20Brazil&sensor=false
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Argentina&sensor=false
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Chile&sensor=false
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Germany&sensor=false
#> Warning: geocode failed with status OVER_QUERY_LIMIT, location = "Germany"
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Italy&sensor=false
#> Warning: geocode failed with status OVER_QUERY_LIMIT, location = "Italy"
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Paraguay&sensor=false
#> Warning: geocode failed with status OVER_QUERY_LIMIT, location = "Paraguay"
#> .
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Portugal&sensor=false
#> .
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Spain&sensor=false
#> Warning: geocode failed with status OVER_QUERY_LIMIT, location = "Spain"
#> .
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=United%20Kingdom&sensor=false
#> Warning: geocode failed with status OVER_QUERY_LIMIT, location = "United Kingdom"
#> .
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=United%20States%20of%20America&sensor=false
#> .
#> Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Uruguay&sensor=false
  ef2
#> 
#> /// Epidemiological Flows //
#> 
#>   // class: epiflows, epicontacts
#>   // 15 locations; 100 flows; directed
#>   // optional variables: pop_size, duration_stay, num_cases, first_date, last_date, coordinates 
#> 
#>   // locations
#> 
#> # A tibble: 15 x 8
#>    id    location_popula… num_cases_time_… first_date_cases last_date_cases
#>  * <chr>            <dbl>            <dbl> <fct>            <fct>          
#>  1 Espi…          3973697             2600 2017-01-04       2017-04-30     
#>  2 Mina…         20997560             4870 2016-12-19       2017-04-20     
#>  3 Rio …         16635996              170 2017-02-19       2017-05-10     
#>  4 Sao …         44749699              200 2016-12-17       2017-04-20     
#>  5 Sout…         86356952             7840 2016-12-17       2017-05-10     
#>  6 Arge…               NA               NA <NA>             <NA>           
#>  7 Chile               NA               NA <NA>             <NA>           
#>  8 Germ…               NA               NA <NA>             <NA>           
#>  9 Italy               NA               NA <NA>             <NA>           
#> 10 Para…               NA               NA <NA>             <NA>           
#> 11 Port…               NA               NA <NA>             <NA>           
#> 12 Spain               NA               NA <NA>             <NA>           
#> 13 Unit…               NA               NA <NA>             <NA>           
#> 14 Unit…               NA               NA <NA>             <NA>           
#> 15 Urug…               NA               NA <NA>             <NA>           
#> # ... with 3 more variables: length_of_stay <dbl>, lon <dbl>, lat <dbl>
#> 
#>   // flows
#> 
#> # A tibble: 100 x 3
#>    from             to         n
#>    <chr>            <chr>  <dbl>
#>  1 Espirito Santo   Italy  2828.
#>  2 Minas Gerais     Italy 15714.
#>  3 Rio de Janeiro   Italy  8164.
#>  4 Sao Paulo        Italy 34039.
#>  5 Southeast Brazil Italy 76282.
#>  6 Espirito Santo   Spain  3270.
#>  7 Minas Gerais     Spain 18176.
#>  8 Rio de Janeiro   Spain  9443.
#>  9 Sao Paulo        Spain 39371.
#> 10 Southeast Brazil Spain 88231.
#> # ... with 90 more rows
#>