gesundheitsaemter

The goal of gesundheitsaemter is to provide data sets for the analysis of German Health Authorities (Gesundheitsaemter). The package includes five different data sets collected from three different sources.

Data set name	Content	data source URL
health_departments	General overview of German Health Authorities (Gesundheitsaemter)	link
authority_zip_range	All possible zip codes that are assigned to the health authority	link
communities	All possible community names, be reminded that one community can have several zip codes and vice versa	link
population_zip	Population and area assigned to every German zip code	link
metadata_rki	Contact information and further information about the health authority	link
metadata_google	Geo location in the health department data set is generated using the google maps api with the R package ggmap, this file includes or meta data of this interface	link

Installation or Import

You can install the released version of gesundheitsaemter from Github with:

devtools::install_github("gstephan30/gesundheitsaemter")

For users not wanting to install the data, direct download links are:

Merging the data

Every data set has an individual identifier for merging, this can be seen here:

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Explore Data

The primary data set with an overview of all German Health Authorities is health_departments:

library(gesundheitsaemter)
health_departments
#> # A tibble: 399 x 8
#>    authority_id name      department      street    postalcode place  long   lat
#>    <chr>        <chr>     <chr>           <chr>     <chr>      <chr> <dbl> <dbl>
#>  1 1            Stadt Fl~ Fachbereich 2 ~ Norderst~ 24939      Flen~  9.43  54.8
#>  2 2            Landesha~ Amt für Gesund~ Fleethör~ 24103      Kiel  10.1   54.3
#>  3 3            Hansesta~ Gesundheitsamt  Sophiens~ 23560      Lübe~ 10.7   53.9
#>  4 4            Stadt Ne~ Gesundheitsamt  Meßtorff~ 24534      Neum~  9.99  54.1
#>  5 5            Kreis Di~ Fachdienst Ges~ Esmarchs~ 25746      Heide  9.08  54.2
#>  6 6            Kreis No~ Fachdienst Ges~ Damm 8    25813      Husum  9.05  54.5
#>  7 7            Kreis Os~ Fachdienst Ges~ Holstens~ 23701      Eutin 10.6   54.1
#>  8 8            Kreisver~ Fachdienst Ges~ Kurt-Wag~ 25337      Elms~  9.70  53.7
#>  9 9            Kreis Re~ Fachdienst 4.3~ Kaiserst~ 24768      Rend~  9.67  54.3
#> 10 10           Kreis Sc~ Fachdienst Ges~ Moltkest~ 24837      Schl~  9.56  54.5
#> # ... with 389 more rows

With merging you can inspect the population covered by each health authority. For demonstration purposes we are just interested in health authorities of assigned to the city of Berlin.

library(dplyr)

berlin_departments <- health_departments %>% 
  filter(place == "Berlin") %>% 
  select(authority_id, name, long, lat) %>% 
  left_join(
    authority_zip_range
  ) %>% 
  left_join(
    population_zip
  ) %>% 
  group_by(authority_id, name, long, lat) %>% 
  summarise(total_population = sum(population, na.rm = TRUE)) %>% 
  arrange(desc(total_population))

berlin_departments %>% 
  knitr::kable("html") %>% 
  kableExtra::kable_styling("striped")

authority_id	name	long	lat	total_population
191	Bezirksamt Pankow von Berlin	13.40958	52.56722	427902
189	Bezirksamt Mitte von Berlin	13.32961	52.56222	386250
195	Bezirksamt Tempelhof-Schöneberg von Berlin	13.37914	52.44589	360322
192	Bezirksamt Charlottenburg-Wilmersdorf von Berlin	13.31146	52.48909	338414
196	Bezirksamt Neukölln von Berlin	13.44643	52.45255	314923
194	Bezirksamt Steglitz-Zehlendorf von Berlin	13.32567	52.45743	305501
190	Bezirksamt Friedrichshain-Kreuzberg von Berlin	13.40984	52.49247	256021
200	Bezirksamt Reinickendorf von Berlin	13.34767	52.57113	254399
199	Bezirksamt Lichtenberg von Berlin	13.52422	52.50767	252169
198	Bezirksamt Marzahn-Hellersdorf von Berlin	13.60615	52.53863	243939
197	Bezirksamt Treptow-Köpenick von Berlin	13.53302	52.43471	238613
193	Bezirksamt Spandau von Berlin	13.20109	52.53530	215305
399	Zentrum für tuberkulosekranke und -gefährdete Menschen, Berlin	13.47689	52.51557	0

Plotting in relation to the covered population:

library(leaflet)

berlin_departments %>% 
  leaflet() %>% 
  addTiles() %>% 
  addCircleMarkers(
    lng = ~long, 
    lat = ~lat, 
    fill = ~total_population, 
    radius = ~total_population/10000, 
    label = ~paste0(name, ", Population: ", total_population))