Access flow data

This accessor extract location data from an epiflows object. get_locations is a generic with a method defined for epiflows objects.

get_locations(x, ...)

# S3 method for epiflows
get_locations(x, ...)

Arguments

x	An epiflows object.
...	unused

Value

A data.frame with at least 1 column called id, specifying the id of the location used in the flows data frame.

See also

get_flows(); get_id(); get_pop_size(); get_vars(); get_coordinates(); global_vars()

Examples

data("Brazil_epiflows")
get_locations(Brazil_epiflows)
#>                          id location_population num_cases_time_window
#> 1            Espirito Santo             3973697                  2600
#> 2              Minas Gerais            20997560                  4870
#> 3            Rio de Janeiro            16635996                   170
#> 4                 Sao Paulo            44749699                   200
#> 5          Southeast Brazil            86356952                  7840
#> 6                 Argentina                  NA                    NA
#> 7                     Chile                  NA                    NA
#> 8                   Germany                  NA                    NA
#> 9                     Italy                  NA                    NA
#> 10                 Paraguay                  NA                    NA
#> 11                 Portugal                  NA                    NA
#> 12                    Spain                  NA                    NA
#> 13           United Kingdom                  NA                    NA
#> 14 United States of America                  NA                    NA
#> 15                  Uruguay                  NA                    NA
#>    first_date_cases last_date_cases length_of_stay
#> 1        2017-01-04      2017-04-30             NA
#> 2        2016-12-19      2017-04-20             NA
#> 3        2017-02-19      2017-05-10             NA
#> 4        2016-12-17      2017-04-20             NA
#> 5        2016-12-17      2017-05-10             NA
#> 6              <NA>            <NA>           10.9
#> 7              <NA>            <NA>           10.3
#> 8              <NA>            <NA>           22.3
#> 9              <NA>            <NA>           30.1
#> 10             <NA>            <NA>            7.3
#> 11             <NA>            <NA>           27.2
#> 12             <NA>            <NA>           27.2
#> 13             <NA>            <NA>           19.5
#> 14             <NA>            <NA>           18.5
#> 15             <NA>            <NA>            8.0