Package 'certegis'

Determine (Hospital) Adherence Area

Description

Looks up the nearest reference name for a given zipcode (4 digits) based on the shortest distance in the postcodes4_afstanden dataset.

Usage

adherence_area(
  zipcode,
  reference = c(Antonius = "8601", `Frisius Heerenveen` = "8441", `Frisius Leeuwarden` =
    "8934", Martini = "9728", `Nij Smellinghe` = "9202", Ommelander = "9679", Treant =
    "7824", Wilhelmina = "9401")
)

Arguments

zipcode	A character or numeric vector of zipcodes. Six-character zipcodes are automatically truncated to the first 4 digits using substr().
reference	A character vector of zipcodes to use as reference, preferably named. Defaults to hospitals in the region.

Details

The function looks up the distance from each input zipcode to all zipcodes of reference in postcodes4_afstanden. The names with the shortest distance is returned. In case of equal distances, the first name in definition order is chosen.

Zipcodes not found in postcodes4_afstanden return NA.

Value

A character vector of names of reference, the same length as zipcode.

Examples

adherence_area("9700")
adherence_area(c("9713", "7702", "8896"))

# six-character zipcodes are automatically truncated
adherence_area("9251AB")

if (requireNamespace("certeplot2", quietly = TRUE)) {
  geo_postcodes4 |>
     crop_certe() |>
     plot2(category = suppressMessages(adherence_area(postcode)),
           datalabels = FALSE) |>
     plot2::add_sf(geo_provincies |> crop_certe(),
            colour_fill = NA, colour = "black", linewidth = 0.5)
}

if (requireNamespace("certeplot2", quietly = TRUE)) {
  hospitals <- c("Antonius"           = "8601",
                 "Frisius Heerenveen" = "8441",
                 "Frisius Leeuwarden" = "8934",
                 "Martini"            = "9728",
                 "Nij Smellinghe"     = "9202",
                 "Ommelander"         = "9679",
                 "Treant"             = "7824",
                 "Wilhelmina"         = "9401")
  zip4 <- geo_postcodes4 |> crop_certe()

  zip4 |>
    plot2(category = adherence_area(postcode, reference = hospitals),
          datalabels = FALSE) |>
    plot2::add_sf(geo_provincies |> crop_certe(),
           colour_fill = "#FFFFFF77", colour = "black", linewidth = 0.5) |>
    plot2::add_sf(zip_to_sf(hospitals),
           datalabels = names(hospitals),
           colour = "black")
}

---

Check Cases Within Radius

Description

Based on the postcodes4_afstanden data set, this function determines the specified minimum number of cases within a certain radius.

Usage

cases_within_radius(
  data,
  radius_km = 10,
  minimum_cases = 10,
  column_count = NULL,
  ...
)

Arguments

data	data set containing a column 'postcode'
radius_km	radius in kilometres from each zip code. The search diameter is twice this number (since zip codes e.g. to the west and to the east are searched).
minimum_cases	minimum number of cases to search for
column_count	column name in data with the number of case counts, defaults to the first column with numeric values
...	ignored, allows for future extensions

Value

This function adds two columns ("cases_within_radius" ⁠<dbl>⁠ and "minimum_met" ⁠<lgl>⁠) to the input data.

Examples

library(dplyr, warn.conflicts = FALSE)

postcodes_friesland <- geo_postcodes4 |> 
  filter_geolocation(provincie == "Friesland") |> 
  pull(postcode)

# example with Norovirus cases:
noro <- data.frame(postcode = postcodes_friesland,
                   n = floor(runif(length(postcodes_friesland),
                                   min = 0, max = 3)))
head(noro)

radial_check <- cases_within_radius(noro, radius_km = 10, minimum_cases = 10)
head(radial_check)


# dplyr group support:
mdro <- data.frame(type = rep(c("ESBL", "MRSA", "VRE"), 20),
                   pc4 = postcodes_friesland[1:20],
                   n = floor(runif(60, min = 0, max = 3)))
mdro |> 
  group_by(type) |> 
  cases_within_radius()
  
  
# plotting support:
if (require("certeplot2")) {

  radial_check |>
    add_map() |>
    filter_geolocation(provincie == "Friesland") |>
    plot2(category = cases_within_radius,
          category.title = "Cases",
          datalabels = FALSE,
          colour_fill = "viridis")

}

---

Data Sets with Geometries of Dutch Provinces, Municipalities and Zip Codes

Description

Data Sets with Geometries of Dutch Provinces, Municipalities and Zip Codes

Usage

geo_gemeenten

geo_ggdregios

geo_nuts3

geo_postcodes2

geo_postcodes3

geo_postcodes4

geo_postcodes6

geo_provincies

Format

An object of class sf (inherits from data.frame) with 345 rows and 4 columns.

An object of class sf (inherits from data.frame) with 25 rows and 4 columns.

An object of class sf (inherits from data.frame) with 40 rows and 4 columns.

An object of class sf (inherits from data.frame) with 90 rows and 4 columns.

An object of class sf (inherits from data.frame) with 798 rows and 4 columns.

An object of class sf (inherits from data.frame) with 4068 rows and 4 columns.

An object of class sf (inherits from data.frame) with 58481 rows and 4 columns.

An object of class sf (inherits from data.frame) with 12 rows and 4 columns.

Details

These data.frames are of additional class sf and contain 3 variables:

• ...
  name of the area, these are: –geo_gemeenten$gemeente–, –geo_ggdregios$ggdregio–, –geo_nuts3$nuts3–, –geo_postcodes2$postcode–, –geo_postcodes3$postcode–, –geo_postcodes4$postcode–, –geo_postcodes6$postcode–, –geo_provincies$provincie–

• inwoners
  number of inhabitants in the area

• oppervlakte_km2
  area in square kilometres

• geometry
  multipolygonal object of the area

All data sets have the coordinate reference system (CRS) set to EPSG:28992 ('RD New'), following the sphere of Earth. They can be flattened to e.g. EPSG:4326 ('WGS 84') using st_transform().

See the repository file to update these data sets.

NOTE: all data sets contains all areas of the whole country of the Netherlands, except for geo_postcodes6 which was cropped to only cover the Certe region (using crop_certe()).

Source

The data in these data.frames are retrieved from, and publicly available at, Statistics Netherlands:

• Centraal Bureau voor de Statistiek (CBS), 'Gebiedsindelingen', GPKG 2022 v1, https://www.cbs.nl

• Centraal Bureau voor de Statistiek (CBS), 'Kerncijfers per postcode', ZIP 2020 v1, https://www.cbs.nl

Examples

if (require("certeplot2")) {

  geo_postcodes6 |>
    filter_geolocation(plaats == "Groningen") |>
    plot2(category = inwoners / oppervlakte_km2,
          datalabels = FALSE,
          title = "City of Groningen (PC6 level)")
  
}

if (require("certeplot2")) {

  geo_postcodes4 |>
    filter_geolocation(plaats == "Groningen") |>
    plot2(category = inwoners / oppervlakte_km2,
          datalabels = FALSE,
          title = "City of Groningen (PC4 level)")
  
}

if (require("sf")) {

  head(geo_gemeenten)

}

---

Geocoding to Find Coordinates and Addresses

Description

Geocoding is the process of retrieving geographic coordinates based on text, such as an address or the name of a place (Wikipedia page). On the other hand, reverse geocoding is the process of retrieving the name and address from geographic coordinates (Wikipedia page).

Usage

geocode(
  place,
  as_coordinates = FALSE,
  only_netherlands = TRUE,
  api_key = read_secret("gis.api_key"),
  api_requests_per_second = 1,
  crs = NULL
)

reverse_geocode(
  sf_data,
  api_key = read_secret("gis.api_key"),
  api_requests_per_second = 1
)

Arguments

place	a (vector of) names or addresses of places
as_coordinates	a logical to indicate whether the result should be returned as coordinates (i.e., class sfc_POINT)
only_netherlands	a logical to indicate whether only Dutch places should be searched
api_key	free API key created at https://geocode.maps.co
api_requests_per_second	number of requests per second
crs	Coordinate Reference System of the output. Use 4326 for a regular longitude/latitude.
sf_data	an 'sf' object or an 'sfc' object (i.e., a vector with geometric sfc_POINTs). Can also be a character vector, in which case geocode() will be called first.

Details

These functions use OpenStreetMap (OSM), by using the API of https://geocode.maps.co.

geocode() provides geocoding and returns an 'sf' data.frame at default. In case of multiple results, the distance from the main Certe building in Groningen is leading.

reverse_geocode() provides reversed geocoding and returns a data.frame with the columns "name", "address", "zipcode" and "city".

For both functions, the https://geocode.maps.co API will only be called on unique input values, to increase speed.

Source

Data © OpenStreetMap contributors, ODbL 1.0. https://osm.org/copyright

Examples

## Not run: 

# geocoding: retrieve 'sf' data.frame based on place names
coord <- geocode("Van Swietenlaan 2, Groningen")
coord

# reverse geocoding: get the name and address
reverse_geocode(coord)

# places can be any text, and the results are prioritised based on
# the distance from the main Certe building, so:
reverse_geocode(c("Certe", "IKEA"))

hospitals <- geocode(c("Martini ziekenhuis",
                       "Medisch Centrum Leeuwarden",
                       "Tjongerschans Heerenveen",
                       "Scheper Emmen"))
hospitals

if (require("certeplot2")) {
  geo_gemeenten |>
    crop_certe() |>
    plot2(datalabels = FALSE) |>
    plot2::add_sf(hospitals, colour = "certeroze", datalabels = place)
}


## End(Not run)

---

Geodata Functions

Description

These are functions to work with geographical data. To determine coordinates based on a location (or vice versa), use geocode() / reverse_geocode().

Usage

get_map(maptype = "postcodes4")

add_map(data, maptype = NULL, by = NULL, crop_certe = TRUE)

is.sf(sf_data)

as.sf(data)

crop_certe(sf_data)

filter_geolocation(sf_data, ...)

filter_sf(sf_data, xmin = NULL, xmax = NULL, ymin = NULL, ymax = NULL)

convert_to_degrees_CRS4326(sf_data)

convert_to_metre_CRS28992(sf_data)

zip_to_sf(zipcode)

degrees_to_sf(longitudes, latitudes, crs = 28992)

latitude(sf_data)

longitude(sf_data)

Arguments

maptype	type of geometric data, must be one of: "gemeenten", "ggdregios", "nuts3", "postcodes2", "postcodes3", "postcodes4", "postcodes6", "provincies". For add_map(), this is determined automatically if left blank.
data	data set to join left to the geodata
by	column to join by
crop_certe	logical to keep only the Certe region
sf_data	a data set of class 'sf'
...	filters to set
xmin, xmax, ymin, ymax	coordination filters for sf_data, given in degrees following EPSG:4326 ('WGS 84')
zipcode	zipcode to transform to an sf point
longitudes	vector of longitudes
latitudes	vector of latitudes
crs	the coordinate reference system (CRS) to use as output

Details

All of these functions will check if the sf package is installed, and will load its namespace (but not attach the package).

crop_certe() cuts any geometry to the Certe region (more of less): the Northern three provinces of the Netherlands and municipalities of Noordoostpolder, Urk, and Steenwijkerland. This will be based on postcodes.

filter_geolocation() filters an sf object on qualitative values such as 'gemeente' and 'provincie'. The input data sf_data will be joined with postcodes and filtering can thus be done on any of these columns: postcode, inwoners, inwoners_man, inwoners_vrouw, plaats, gemeente, provincie, nuts3, ggdregio.

filter_sf() filters an sf object on coordinates, and is internally used by crop_certe().

convert_to_degrees_CRS4326() will transform SF data to WGS 84 – WGS84 - World Geodetic System 1984, used in GPS, CRS 4326.

convert_to_metre_CRS28992() will transform SF data to Amersfoort / RD New – Netherlands - Holland - Dutch, CRS 28992.

zip_to_sf() converts a zipcode to sf data as an sf point, using adherence_area() and the geo_postcodes4 data set internally. It returns the midpoint (centroid) of the zipcode on the map.

latitude() specifies the north-south position ('y axis') and longitude() specifies the east-west position ('x axis'). They return the numeric coordinate of the centre of a simple feature.

Value

An sf model. The column with geodata is always called "geometry".

Examples

# Retrieving and joining maps ------------------------------------------

get_map() # defaults to the geo_postcodes4 data set

# adding a map applies a RIGHT JOIN to get all relevant geometric data
data.frame(postcode = 7753, number_of_cases = 3) |> 
  add_map()


# Cropping to Certe region ---------------------------------------------

# Note: provinces do not include Flevoland
geo_provincies |> crop_certe()

# but other geometries do, such as geo_gemeenten
if (require("certeplot2")) {
  geo_gemeenten |> crop_certe() |>    # cropped municipalities
    plot2(title = "Certe Region") |>
    plot2::add_sf(
      geo_provincies |> crop_certe(), # cropped provinces
      colour_fill = NA,
      colour = "black",
      linewidth = 0.5)
}


# Filtering geometries -------------------------------------------------

geo_gemeenten |>
  crop_certe() |>
  # notice that the `provincie` column is not even in `geo_gemeenten`
  filter_geolocation(provincie == "Flevoland")
  
geo_gemeenten |>
  crop_certe() |>
  filter_geolocation(inwoners_vrouw >= 50000)

if (require("certeplot2")) {
  geo_postcodes4 |> 
    filter_geolocation(gemeente == "Tytsjerksteradiel") |> 
    plot2(category = inwoners,
          datalabels = postcode)

}

# filter on a latitude of 52.5 degrees and higher
geo_provincies |> filter_sf(ymin = 52.5)


# Transforming Coordinate Reference System (CRS) -----------------------

geo_provincies |> convert_to_degrees_CRS4326()

geo_provincies |> convert_to_metre_CRS28992()


# Converting zip codes to points on a map ------------------------------

if (require("certeplot2")) {
  geo_provincies |>
    crop_certe() |> 
    plot2(category = NULL, colour_fill = NA, datalabels = FALSE) |> 
    plot2::add_sf(
      zip_to_sf(c(9201, 9713)),
      datalabels = c("Dit is nou Drachten", "En dit Groningen"),
      colour= "red"
    )
}


# Other functions ------------------------------------------------------

degrees_to_sf(4.5, 54)

if (require("certeplot2")) {
  geo_provincies |>
    crop_certe() |> 
    plot2(category = NULL, colour_fill = NA) |> 
    plot2::add_sf(
      degrees_to_sf(6.5, 53),
      datalabels = "Dit is (6.5, 53)!"
    )
}

latitude(geo_provincies)
longitude(geo_provincies)

---

Number of Inhabitants per Zip Code and Age

Description

Number of Inhabitants per Zip Code and Age

Usage

inwoners_per_postcode_leeftijd

Format

A data.frame with 99,260 observations and 5 variables:

• postcode
  zip code, contains PC2, PC3 and PC4

• leeftijd
  age group per 5 years: 0-4, 5-9, ..., 90-94, 95+

• inwoners
  total number of inhabitants

• inwoners_man
  total number of male inhabitants

• inwoners_vrouw
  total number of female inhabitants

Details

See the repository file to update this data set.

Source

The data in this data.frame are retrieved from, and publicly available at, Statistics Netherlands: StatLine, Centraal Bureau voor de Statistiek (CBS), 'Bevolking en leeftijd per postcode' (data set 83502NED), 1 januari 2021, https://opendata.cbs.nl.

Examples

head(inwoners_per_postcode_leeftijd)
str(inwoners_per_postcode_leeftijd)

---

Data Set with Dutch Zip Codes, Cities, Municipalities and Province

Description

Data Set with Dutch Zip Codes, Cities, Municipalities and Province

Usage

postcodes

Format

A data.frame with 4,963 observations and 9 variables:

• postcode
  zip code, contains PC2, PC3 and PC4

• inwoners
  total number of inhabitants

• inwoners_man
  total number of male inhabitants

• inwoners_vrouw
  total number of female inhabitants

• plaats
  formal Dutch city name

• gemeente
  formal Dutch municipality name

• provincie
  formal Dutch province name

• nuts3
  Nomenclature of Territorial Units for Statistics, level 3 (in Dutch: COROP region, Coordinatie Commissie Regionaal OnderzoeksProgramma)

• ggdregio
  name of the regional GGD service (public healthcare service)

Details

See the repository file to update this data set.

Source

The data in this data.frame are retrieved from, and publicly available at, Statistics Netherlands: StatLine, Centraal Bureau voor de Statistiek (CBS), 'Bevolking per geslacht per postcode' (data set 83503NED), 1 januari 2021, https://opendata.cbs.nl.

Examples

head(postcodes)
str(postcodes)

---

Distance from Zip Code to Zip Code

Description

This data set was obtained by calculating the difference from the middle point of a zip code geometry to another zip code geometry (using the geo_postcodes4 data set and the sf package).

Usage

postcodes4_afstanden

Format

A data.frame with 1,123,600 observations and 3 variables:

• postcode.x
  zip code (PC4)

• postcode.y
  zip code (PC4)

• afstand_km
  distance in kilometres

Source

The data in this data.frame are retrieved from, and publicly available at, Statistics Netherlands:

• Centraal Bureau voor de Statistiek (CBS), 'Gebiedsindelingen', GPKG 2022 v1, https://www.cbs.nl

Examples

head(postcodes4_afstanden)

---

Get Hourly/Daily Weather from KNMI Through Open-Meteo

Description

Retrieves hourly weather data from the Open-Meteo API using the KNMI HARMONIE AROME weather model via the ⁠/v1/forecast⁠ endpoint. The KNMI model provides high-resolution forecasts (2 km for the Netherlands, 5.5 km for Europe) updated hourly. The "knmi_seamless" model (default) blends KNMI HARMONIE AROME with ECMWF IFS beyond the 2.5-day KNMI horizon.

Usage

get_weather_hourly(
  latitude = 53.22,
  longitude = 6.57,
  metrics = "temperature_2m",
  past_days = 30,
  forecast_days = 0,
  model = "knmi_seamless",
  timezone = "Europe/Amsterdam",
  api_url = "https://api.open-meteo.com/v1/forecast"
)

get_weather_daily(min_hour = 8, max_hour = 17, fun = base::mean, ...)

Arguments

latitude	Latitude in decimal degrees (WGS84). Default: 53.22 (Groningen). Use negative values for the Southern Hemisphere.
longitude	Longitude in decimal degrees (WGS84). Default: 6.57 (Groningen). Use negative values for locations west of Greenwich.
metrics	Character vector of one or more hourly weather metrics to retrieve (default: "temperature_2m"). Multiple metrics can be specified, e.g. c("temperature_2m", "wind_speed_10m"). Common metrics include: "temperature_2m": air temperature at 2 m above ground (degrees Celsius) "relative_humidity_2m": relative humidity at 2 m (%) "precipitation": rain + snow total (mm) "wind_speed_10m": wind speed at 10 m (km/h) "cloud_cover": total cloud cover (%) See the KNMI API documentation for the full list of available variables.
past_days	Integer number of past days to include (default: 30, range: 0–92).
forecast_days	Integer number of forecast days to include (default: 0, range: 0–16). The native KNMI model provides up to 2.5 days; beyond that, the seamless model falls back to ECMWF IFS.
model	Character string specifying the weather model. One of "knmi_seamless" (default, KNMI + ECMWF blend), "knmi_harmonie_arome_europe" (5.5 km, Central & Northern Europe), or "knmi_harmonie_arome_netherlands" (2 km, Netherlands & Belgium).
timezone	Character string for the timezone of returned timestamps (default: "Europe/Amsterdam").
api_url	Base URL of the Open-Meteo API endpoint.
min_hour	First hour of the day to include on a 0-23 scale. Default: 8.
max_hour	Last hour of the day to include on a 0-23 scale. Default: 17.
fun	Function to use for calculation, defaults to mean().
...	Arguments passed on to get_weather_hourly().

Value

A data.frame with columns:

• time: POSIXct timestamp in the requested timezone

• One column per requested weather variable, named as given in metrics

Examples

## Not run: 
# temperature in Groningen over the past 30 days
get_weather_hourly()
get_weather_daily() # default: between 08:00-17:59


assen <- geocode("Assen", as_coordinates = TRUE, crs = 4326)
latitude(assen)
longitude(assen)
get_weather_daily(latitude = latitude(assen),
                  longitude = longitude(assen))


# wind speed with 3-day forecast
get_weather_hourly(metrics = "wind_speed_10m", past_days = 7, forecast_days = 3)

# multiple variables at once
get_weather_hourly(metrics = c("temperature_2m", "relative_humidity_2m"))

# high-resolution Netherlands-only model
get_weather_hourly(model = "knmi_harmonie_arome_netherlands",
                   past_days = 0,
                   forecast_days = 2)

## End(Not run)

---