# How to use this package

    library(eudata)

    library(dplyr)
    #>
    #> Attaching package: 'dplyr'
    #> The following objects are masked from 'package:stats':
    #>
    #>     filter, lag
    #> The following objects are masked from 'package:base':
    #>
    #>     intersect, setdiff, setequal, union
    library(purrr)
    library(ggplot2)

The data on GISCO is divided into topics.

    get_topics()
    #> # A tibble: 7 × 2
    #>   title                      titleMultilinual
    #>   <chr>                      <named list>
    #> 1 Coastal lines              <named list [3]>
    #> 2 Communes                   <named list [3]>
    #> 3 Countries                  <named list [3]>
    #> 4 Local Administrative Units <named list [3]>
    #> 5 NUTS                       <named list [3]>
    #> 6 Postal codes               <named list [3]>
    #> 7 Urban Audit                <named list [3]>

Select the topics that you are interested in. Within each topic there are numerous files. These may differ in in the year they are associated with, spatial resolution, coordinate reference system, data format, among other things.

This package provides an easy access to the latest files. The example below selects the highest resolution file, where the coordinate system is the usual lat/long.

    api <- get_topic("NUTS")

    file_list <- get_latest_files(api)$gpkg |>
      grep(pattern = "01M_.*_4326_", value = TRUE)

    file_list
    #>        NUTS_BN_01M_2024_4326_LEVL_0.gpkg
    #> "gpkg/NUTS_BN_01M_2024_4326_LEVL_0.gpkg"
    #>        NUTS_BN_01M_2024_4326_LEVL_1.gpkg
    #> "gpkg/NUTS_BN_01M_2024_4326_LEVL_1.gpkg"
    #>        NUTS_BN_01M_2024_4326_LEVL_2.gpkg
    #> "gpkg/NUTS_BN_01M_2024_4326_LEVL_2.gpkg"
    #>        NUTS_BN_01M_2024_4326_LEVL_3.gpkg
    #> "gpkg/NUTS_BN_01M_2024_4326_LEVL_3.gpkg"
    #>        NUTS_RG_01M_2024_4326_LEVL_0.gpkg
    #> "gpkg/NUTS_RG_01M_2024_4326_LEVL_0.gpkg"
    #>        NUTS_RG_01M_2024_4326_LEVL_1.gpkg
    #> "gpkg/NUTS_RG_01M_2024_4326_LEVL_1.gpkg"
    #>        NUTS_RG_01M_2024_4326_LEVL_2.gpkg
    #> "gpkg/NUTS_RG_01M_2024_4326_LEVL_2.gpkg"
    #>        NUTS_RG_01M_2024_4326_LEVL_3.gpkg
    #> "gpkg/NUTS_RG_01M_2024_4326_LEVL_3.gpkg"

Be aware, that these files can be huge. The `get_content_length` function returns the size of a file without downloading it. It is not vectorized, so you have to use a `map` like construct if you have a list of files.

    to_tibble <- function(x, column_name = "value")
      tibble::tibble(names = names(x), `:=`(!!column_name, x))

    file_sizes <-
      map_int(file_list, get_content_length, api = api) |>
      to_tibble(column_name = "size")
      # tibble::as_tibble_col()

    file_sizes |>
      knitr::kable(
        format.args = list(big.mark = "_", scientific = FALSE)
      )

names | size
--- | ---
NUTS_BN_01M_2024_4326_LEVL_0.gpkg | 4_108_288
NUTS_BN_01M_2024_4326_LEVL_1.gpkg | 1_036_288
NUTS_BN_01M_2024_4326_LEVL_2.gpkg | 1_171_456
NUTS_BN_01M_2024_4326_LEVL_3.gpkg | 2_957_312
NUTS_RG_01M_2024_4326_LEVL_0.gpkg | 4_096_000
NUTS_RG_01M_2024_4326_LEVL_1.gpkg | 5_357_568
NUTS_RG_01M_2024_4326_LEVL_2.gpkg | 7_012_352
NUTS_RG_01M_2024_4326_LEVL_3.gpkg | 11_915_264

Suppose we selected a file to download. Then you can save it to a local file using the `get_content` function. It also save a copy into a cache under your cache folder. The place of this folder is OS dependent, use `rappdirs::user_cache_dir("eudata")` to locate it.

If you do not specify a `dest` file, the data will be downloaded into a temporary file. The path to this file is the `body` element of the result of the call.

    file_to_download <- grep(pattern = "RG.*LEVL_3", file_list, value = TRUE)
    file_to_download
    #>        NUTS_RG_01M_2024_4326_LEVL_3.gpkg
    #> "gpkg/NUTS_RG_01M_2024_4326_LEVL_3.gpkg"

    result <- get_content(
      api = api,
      end_point = file_to_download,
      save_to_file = TRUE
    )

    result
    #> <httr2_response>
    #> GET https://gisco-services.ec.europa.eu/distribution/v2/nuts/gpkg/NUTS_RG_01M_2024_4326_LEVL_3.gpkg
    #> Status: 304 Not Modified
    #> Content-Type: application/geopackage+sqlite3
    #> Body: On disk '/tmp/RtmpAMg8OU/Rbuild1651cf74b465cf/eudata/vignettes/NUTS_RG_01M_2024_4326_LEVL_3.gpkg' (11915264 bytes)

The selected data format `dpkg` can be read into memory with the `sf` package. First only the first five records are shown.

    db_file <- result$body
    layer <- sf::st_layers(db_file)
    layer
    #> Driver: GPKG
    #> Available layers:
    #>                          layer_name geometry_type features fields crs_name
    #> 1 NUTS_RG_01M_2024_4326_LEVL_3.gpkg Multi Polygon     1345      8   WGS 84

    sample <- sf::st_read(
      db_file,
      query = glue::glue("select * from \"{layer}\" limit 5")
    )
    #> Reading query `select * from "NUTS_RG_01M_2024_4326_LEVL_3.gpkg" limit 5'
    #> from data source `/tmp/RtmpAMg8OU/Rbuild1651cf74b465cf/eudata/vignettes/NUTS_RG_01M_2024_4326_LEVL_3.gpkg'
    #>   using driver `GPKG'
    #> Simple feature collection with 5 features and 8 fields
    #> Geometry type: POLYGON
    #> Dimension:     XY
    #> Bounding box:  xmin: 19.3143 ymin: 40.36304 xmax: 20.62595 ymax: 42.55856
    #> Geodetic CRS:  WGS 84

    sample
    #> Simple feature collection with 5 features and 8 fields
    #> Geometry type: POLYGON
    #> Dimension:     XY
    #> Bounding box:  xmin: 19.3143 ymin: 40.36304 xmax: 20.62595 ymax: 42.55856
    #> Geodetic CRS:  WGS 84
    #>   NUTS_ID LEVL_CODE CNTR_CODE NAME_LATN NUTS_NAME MOUNT_TYPE URBN_TYPE
    #> 1   AL011         3        AL     Dibër     Dibër         NA        NA
    #> 2   AL012         3        AL    Durrës    Durrës         NA        NA
    #> 3   AL013         3        AL     Kukës     Kukës         NA        NA
    #> 4   AL031         3        AL     Berat     Berat         NA        NA
    #> 5   AL032         3        AL      Fier      Fier         NA        NA
    #>   COAST_TYPE                           geom
    #> 1         NA POLYGON ((20.3461 41.8761, ...
    #> 2         NA POLYGON ((19.8072 41.5662, ...
    #> 3         NA POLYGON ((20.11943 42.52485...
    #> 4         NA POLYGON ((19.96849 40.86741...
    #> 5         NA POLYGON ((19.62572 41.06464...

Once you have the structure of the database, it is easy to filter, for example, for Hungarian data only.

    hu_data <- sf::st_read(
      db_file,
      query = glue::glue("select * from \"{layer}\" where CNTR_CODE = \"HU\"")
    )
    #> Reading query `select * from "NUTS_RG_01M_2024_4326_LEVL_3.gpkg" where CNTR_CODE = "HU"'
    #> from data source `/tmp/RtmpAMg8OU/Rbuild1651cf74b465cf/eudata/vignettes/NUTS_RG_01M_2024_4326_LEVL_3.gpkg'
    #>   using driver `GPKG'
    #> Simple feature collection with 20 features and 8 fields
    #> Geometry type: POLYGON
    #> Dimension:     XY
    #> Bounding box:  xmin: 16.11385 ymin: 45.73807 xmax: 22.89627 ymax: 48.58464
    #> Geodetic CRS:  WGS 84

    hu_data |>
      knitr::kable()

NUTS_ID | LEVL_CODE | CNTR_CODE | NAME_LATN | NUTS_NAME | MOUNT_TYPE | URBN_TYPE | COAST_TYPE | geom
--- | --- | --- | --- | --- | --- | --- | --- | ---
HU213 | 3 | HU | Veszprém | Veszprém | NA | 2 | NA | POLYGON ((17.44675 47.44017…
HU221 | 3 | HU | Győr-Moson-Sopron | Győr-Moson-Sopron | NA | 2 | NA | POLYGON ((17.24179 48.02247…
HU222 | 3 | HU | Vas | Vas | NA | 2 | NA | POLYGON ((17.04417 47.39973…
HU223 | 3 | HU | Zala | Zala | NA | 3 | NA | POLYGON ((17.07991 47.04601…
HU233 | 3 | HU | Tolna | Tolna | NA | 3 | NA | POLYGON ((18.99883 46.6876,…
HU231 | 3 | HU | Baranya | Baranya | NA | 2 | NA | POLYGON ((18.32133 46.41398…
HU232 | 3 | HU | Somogy | Somogy | NA | 3 | NA | POLYGON ((18.20145 46.88144…
HU110 | 3 | HU | Budapest | Budapest | NA | 1 | NA | POLYGON ((19.14045 47.34944…
HU120 | 3 | HU | Pest | Pest | NA | 2 | NA | POLYGON ((18.89655 48.05398…
HU211 | 3 | HU | Fejér | Fejér | NA | 2 | NA | POLYGON ((18.68845 47.57705…
HU212 | 3 | HU | Komárom-Esztergom | Komárom-Esztergom | NA | 2 | NA | POLYGON ((18.92296 47.75898…
HU311 | 3 | HU | Borsod-Abaúj-Zemplén | Borsod-Abaúj-Zemplén | NA | 2 | NA | POLYGON ((21.46748 48.57032…
HU312 | 3 | HU | Heves | Heves | NA | 3 | NA | POLYGON ((20.40755 48.15312…
HU313 | 3 | HU | Nógrád | Nógrád | NA | 3 | NA | POLYGON ((19.63712 48.24557…
HU321 | 3 | HU | Hajdú-Bihar | Hajdú-Bihar | NA | 2 | NA | POLYGON ((21.19624 47.95697…
HU322 | 3 | HU | Jász-Nagykun-Szolnok | Jász-Nagykun-Szolnok | NA | 2 | NA | POLYGON ((20.82797 47.65902…
HU323 | 3 | HU | Szabolcs-Szatmár-Bereg | Szabolcs-Szatmár-Bereg | NA | 3 | NA | POLYGON ((22.31386 48.32577…
HU331 | 3 | HU | Bács-Kiskun | Bács-Kiskun | NA | 2 | NA | POLYGON ((19.26903 47.13413…
HU332 | 3 | HU | Békés | Békés | NA | 2 | NA | POLYGON ((21.01366 47.22622…
HU333 | 3 | HU | Csongrád-Csanád | Csongrád-Csanád | NA | 2 | NA | POLYGON ((20.50795 46.77311…

A map with `ggplot2`.

    hu_data |>
      ggplot() +
      geom_sf()

Another example, now for postal codes.

    api <- get_topic("Postal")

    file_to_download <- grep("_4326", get_latest_files(api)$gpkg, value = TRUE)

    result <- get_content(api, file_to_download, save_to_file = TRUE)
    result
    #> <httr2_response>
    #> GET https://gisco-services.ec.europa.eu/distribution/v2/pcode/gpkg/PCODE_PT_2024_4326.gpkg
    #> Status: 304 Not Modified
    #> Content-Type: application/geopackage+sqlite3
    #> Body: On disk '/tmp/RtmpAMg8OU/Rbuild1651cf74b465cf/eudata/vignettes/PCODE_PT_2024_4326.gpkg' (145338368 bytes)

    db_file <- result$body
    layer <- sf::st_layers(db_file)
    layer
    #> Driver: GPKG
    #> Available layers:
    #>                layer_name geometry_type features fields crs_name
    #> 1 PCODE_PT_2024_4326.gpkg         Point   820156     14   WGS 84

    sample <- sf::st_read(
      db_file,
      query = glue::glue("select * from \"{layer}\" limit 5")
    )
    #> Reading query `select * from "PCODE_PT_2024_4326.gpkg" limit 5'
    #> from data source `/tmp/RtmpAMg8OU/Rbuild1651cf74b465cf/eudata/vignettes/PCODE_PT_2024_4326.gpkg'
    #>   using driver `GPKG'
    #> Simple feature collection with 5 features and 14 fields
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: 21.7416 ymin: 62.1132 xmax: 23.1452 ymax: 62.8429
    #> Geodetic CRS:  WGS 84

    sample
    #> Simple feature collection with 5 features and 14 fields
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: 21.7416 ymin: 62.1132 xmax: 23.1452 ymax: 62.8429
    #> Geodetic CRS:  WGS 84
    #>   POSTCODE CNTR_ID  PC_CNTR NUTS3_2024    CODE GISCO_ID_2021 NSI_CODE_2021
    #> 1    64701      FI FI_64701    'FI199' '64701'        FI_846           846
    #> 2    64901      FI FI_64901    'FI199' '64901'        FI_151           151
    #> 3    60060      FI FI_60060    'FI199' '60060'        FI_743           743
    #> 4    60101      FI FI_60101    'FI199' '60101'        FI_743           743
    #> 5    60110      FI FI_60110    'FI199' '60110'        FI_743           743
    #>     LAU_NAT LAU_LATIN COASTAL CITY_ID GREATER_CITY_ID FUA_ID DGURBA
    #> 1     Teuva      <NA>    <NA>    <NA>            <NA>   <NA>      3
    #> 2   Isojoki      <NA>    <NA>    <NA>            <NA>   <NA>      3
    #> 3 Seinäjoki      <NA>    <NA>    <NA>            <NA>   <NA>      2
    #> 4 Seinäjoki      <NA>    <NA>    <NA>            <NA>   <NA>      2
    #> 5 Seinäjoki      <NA>    <NA>    <NA>            <NA>   <NA>      2
    #>                      geom
    #> 1 POINT (21.7416 62.4819)
    #> 2 POINT (21.9588 62.1132)
    #> 3  POINT (22.954 62.8429)
    #> 4 POINT (22.8636 62.7652)
    #> 5 POINT (23.1452 62.5424)

    hu_data <- sf::st_read(
      db_file,
      query = glue::glue("select * from \"{layer}\" where CNTR_ID = \"HU\"")
    )
    #> Reading query `select * from "PCODE_PT_2024_4326.gpkg" where CNTR_ID = "HU"'
    #> from data source `/tmp/RtmpAMg8OU/Rbuild1651cf74b465cf/eudata/vignettes/PCODE_PT_2024_4326.gpkg'
    #>   using driver `GPKG'
    #> Simple feature collection with 3155 features and 14 fields
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: 16.16701 ymin: 45.77374 xmax: 22.86451 ymax: 48.56046
    #> Geodetic CRS:  WGS 84

    hu_data |>
      select(POSTCODE, LAU_NAT)
    #> Simple feature collection with 3155 features and 2 fields
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: 16.16701 ymin: 45.77374 xmax: 22.86451 ymax: 48.56046
    #> Geodetic CRS:  WGS 84
    #> First 10 features:
    #>    POSTCODE      LAU_NAT                      geom
    #> 1      2181        Iklad  POINT (19.43659 47.6654)
    #> 2      2182       Domony POINT (19.41373 47.64118)
    #> 3      2183   Galgamácsa POINT (19.39007 47.69573)
    #> 4      2184     Vácegres POINT (19.36604 47.67584)
    #> 5      2185 Váckisújfalu POINT (19.34847 47.70234)
    #> 6      2191          Bag POINT (19.48168 47.63521)
    #> 7      2192   Hévízgyörk  POINT (19.52681 47.6225)
    #> 8      2193   Galgahévíz POINT (19.54952 47.61575)
    #> 9      2194         Tura  POINT (19.59443 47.6041)
    #> 10     2200        Monor POINT (19.44099 47.34713)

    EOV <- "EPSG:23700"

    hu_data |>
      filter(grepl("^Gyöngyös$", LAU_NAT)) |>
      ggplot() +
      geom_sf() +
      coord_sf(crs = EOV, datum = EOV)

Cities with the highest number of associated postal codes

    hu_data |>
      sf::st_drop_geometry() |>
      count(LAU_NAT) |>
      arrange(-n) |>
      filter(n > 1)
    #>               LAU_NAT   n
    #> 1            Budapest 184
    #> 2             Miskolc  24
    #> 3                Pécs  20
    #> 4            Debrecen  19
    #> 5              Szeged  17
    #> 6                Győr  13
    #> 7         Nyíregyháza  11
    #> 8            Orosháza   8
    #> 9                 Ózd   8
    #> 10               Ajka   5
    #> 11        Nagykanizsa   5
    #> 12        Salgótarján   5
    #> 13           Gyöngyös   4
    #> 14    Hajdúböszörmény   4
    #> 15             Sopron   4
    #> 16          Sárbogárd   4
    #> 17    Sátoraljaújhely   4
    #> 18      Zalaszentgrót   4
    #> 19     Badacsonytomaj   3
    #> 20      Bátonyterenye   3
    #> 21         Békéscsaba   3
    #> 22              Dabas   3
    #> 23        Dunaújváros   3
    #> 24               Encs   3
    #> 25              Ercsi   3
    #> 26          Esztergom   3
    #> 27            Gárdony   3
    #> 28   Hódmezővásárhely   3
    #> 29         Jászberény   3
    #> 30           Kaposvár   3
    #> 31          Kecskemét   3
    #> 32            Komárom   3
    #> 33              Lenti   3
    #> 34            Lőrinci   3
    #> 35            Marcali   3
    #> 36             Mohács   3
    #> 37          Nyíradony   3
    #> 38           Polgárdi   3
    #> 39               Pápa   3
    #> 40             Pásztó   3
    #> 41             Sarród   3
    #> 42      Szentgotthárd   3
    #> 43  Szigetszentmiklós   3
    #> 44            Szolnok   3
    #> 45        Szombathely   3
    #> 46             Sárvár   3
    #> 47         Tiszafüred   3
    #> 48           Veszprém   3
    #> 49      Vásárosnamény   3
    #> 50                Aba   2
    #> 51        Almásfüzitő   2
    #> 52            Alsóörs   2
    #> 53  Badacsonytördemic   2
    #> 54               Baja   2
    #> 55       Bakonypéterd   2
    #> 56           Baktakék   2
    #> 57      Balatonalmádi   2
    #> 58      Balatonboglár   2
    #> 59       Balatonfűzfő   2
    #> 60            Balinka   2
    #> 61             Baracs   2
    #> 62              Barcs   2
    #> 63            Berhida   2
    #> 64            Bonyhád   2
    #> 65       Borsodnádasd   2
    #> 66                Bük   2
    #> 67             Cegléd   2
    #> 68         Celldömölk   2
    #> 69     Csehimindszent   2
    #> 70           Csikvánd   2
    #> 71              Csoma   2
    #> 72           Csongrád   2
    #> 73            Csákvár   2
    #> 74            Dabronc   2
    #> 75            Edelény   2
    #> 76          Egercsehi   2
    #> 77              Egyek   2
    #> 78   Egyházasharaszti   2
    #> 79             Enying   2
    #> 80         Fegyvernek   2
    #> 81             Fertőd   2
    #> 82           Fityeház   2
    #> 83             Fonyód   2
    #> 84         Fülöpjakab   2
    #> 85           Gesztely   2
    #> 86            Gyarmat   2
    #> 87        Gyomaendrőd   2
    #> 88              Gyula   2
    #> 89      Gávavencsellő   2
    #> 90                Göd   2
    #> 91              Gödre   2
    #> 92         Hajdúnánás   2
    #> 93              Harta   2
    #> 94        Hosszúpályi   2
    #> 95       Jánossomorja   2
    #> 96            Kapuvár   2
    #> 97              Kazár   2
    #> 98      Kehidakustány   2
    #> 99           Kenderes   2
    #> 100           Kerepes   2
    #> 101            Kisbér   2
    #> 102             Kondó   2
    #> 103    Kunszentmiklós   2
    #> 104    Kunszentmárton   2
    #> 105            Kákics   2
    #> 106               Kám   2
    #> 107           Körmend   2
    #> 108            Kövegy   2
    #> 109              Lázi   2
    #> 110        Létavértes   2
    #> 111     Magyarszecsőd   2
    #> 112              Makó   2
    #> 113     Medgyesbodzás   2
    #> 114    Medgyesegyháza   2
    #> 115     Mezőkeresztes   2
    #> 116  Mosonszentmiklós   2
    #> 117      Mosonszolnok   2
    #> 118             Mánfa   2
    #> 119        Mátranovák   2
    #> 120    Mátraszentimre   2
    #> 121      Mátraterenye   2
    #> 122              Mérk   2
    #> 123     Nemesgörzsöny   2
    #> 124         Nyírkércs   2
    #> 125          Nádudvar   2
    #> 126       Olaszliszka   2
    #> 127              Paks   2
    #> 128             Parád   2
    #> 129             Pilis   2
    #> 130 Pilisszentkereszt   2
    #> 131               Pér   2
    #> 132            Sarkad   2
    #> 133            Siófok   2
    #> 134            Sumony   2
    #> 135        Szerecseny   2
    #> 136          Szerencs   2
    #> 137         Szákszend   2
    #> 138    Székesfehérvár   2
    #> 139            Sántos   2
    #> 140           Sülysáp   2
    #> 141       Tahitótfalu   2
    #> 142           Takácsi   2
    #> 143           Tapolca   2
    #> 144              Tata   2
    #> 145        Tiszaalpár   2
    #> 146       Tiszaeszlár   2
    #> 147      Tiszaföldvár   2
    #> 148       Tiszakécske   2
    #> 149         Tiszakürt   2
    #> 150          Tiszalök   2
    #> 151     Tiszanagyfalu   2
    #> 152    Tiszaszentimre   2
    #> 153        Tiszatelek   2
    #> 154           Tiszaug   2
    #> 155      Tiszavárkony   2
    #> 156             Tolna   2
    #> 157        Töltéstava   2
    #> 158  Törökszentmiklós   2
    #> 159           Túrkeve   2
    #> 160          Verpelét   2
    #> 161          Visegrád   2
    #> 162           Visonta   2
    #> 163      Vámosszabadi   2
    #> 164      Zalaegerszeg   2
    #> 165         Zalakomár   2
    #> 166   Zalaszentlászló   2
    #> 167             Zsurk   2
    #> 168        Öregcsertő   2
    #> 169           Öreglak   2