Caching
caching.RmdDownloading large amounts of complex geometries from the BKG servers can take some amount of time. Whether or not this time can be reduced depends on three factors:
- Whether the data is retrieved from a WFS or the download server
- Whether the data has already been downloaded
- Whether the data is already pre-loaded in the package
WFS versus bulk downloads
Some functions like bkg_admin() or
bkg_dlm() interface WFS servers while others like
bkg_nuts() or bkg_admin_archive() download
entire ZIP files from a download server. While WFS servers allow for
more flexibility, they do not allow for easy caching. ffm
supports caching of static files. By default, files are cached in a
temporary directory that is removed after the R session ends. You can
set a permanent cache directory using
options(ffm_cache_dir = ...).
Repeated data downloads
In case of a static data download, the first call will always download fresh data.
system.time(bkg_nuts(level = "3"))
#> user system elapsed
#> 0.05 0.03 4.41Subsequent calls will have significantly reduced time because the downloaded file is directly read from the cache.
system.time(bkg_nuts(level = "3"))
#> user system elapsed
#> 0.05 0.00 0.09In case you need to re-download the data, you can use the
update_cache argument.
system.time(bkg_nuts(level = "3", update_cache = TRUE))
#> user system elapsed
#> 0.05 0.02 4.72Pre-loaded datasets
The most common datasets are already included in ffm.
These include 2023 data from bkg_admin() and
bkg_nuts() at a scale of 1:5,000,000.
bkg_krs
#> Simple feature collection with 400 features and 24 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 4031295 ymin: 2684102 xmax: 4672497 ymax: 3551313
#> Projected CRS: ETRS89-extended / LAEA Europe
#> # A tibble: 400 × 25
#> objid beginn ade gf bsg ars ags sdv_ars gen bez ibz bem nbd sn_l sn_r sn_k sn_v1 sn_v2 sn_g
#> <chr> <date> <int> <int> <int> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 DEBKG… 2019-10-03 4 9 1 01001 01001 010010… Flen… Krei… 40 -- ja 01 0 01 00 00 000
#> 2 DEBKG… 2019-10-03 4 9 1 01002 01002 010020… Kiel Krei… 40 -- ja 01 0 02 00 00 000
#> 3 DEBKG… 2019-10-03 4 9 1 01003 01003 010030… Lübe… Krei… 40 -- ja 01 0 03 00 00 000
#> 4 DEBKG… 2019-10-03 4 9 1 01004 01004 010040… Neum… Krei… 40 -- ja 01 0 04 00 00 000
#> 5 DEBKG… 2019-10-03 4 9 1 01051 01051 010510… Dith… Kreis 42 -- ja 01 0 51 00 00 000
#> 6 DEBKG… 2021-06-19 4 9 1 01053 01053 010530… Herz… Kreis 42 -- ja 01 0 53 00 00 000
#> 7 DEBKG… 2019-10-03 4 9 1 01054 01054 010540… Nord… Kreis 42 -- ja 01 0 54 00 00 000
#> 8 DEBKG… 2019-10-03 4 9 1 01055 01055 010550… Osth… Kreis 42 -- ja 01 0 55 00 00 000
#> 9 DEBKG… 2019-10-03 4 9 1 01056 01056 010560… Pinn… Kreis 42 -- ja 01 0 56 00 00 000
#> 10 DEBKG… 2019-10-03 4 9 1 01057 01057 010570… Plön Kreis 42 -- ja 01 0 57 00 00 000
#> # ℹ 390 more rows
#> # ℹ 6 more variables: fk_s3 <chr>, nuts <chr>, ars_0 <chr>, ags_0 <chr>, wsk <date>, geometry <MULTIPOLYGON [m]>
bkg_nuts2
#> Simple feature collection with 38 features and 6 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 280353.1 ymin: 5235878 xmax: 921261.6 ymax: 6101302
#> Projected CRS: ETRS89 / UTM zone 32N
#> # A tibble: 38 × 7
#> OBJID BEGINN GF NUTS_LEVEL NUTS_CODE NUTS_NAME geometry
#> <chr> <date> <int> <int> <chr> <chr> <MULTIPOLYGON [m]>
#> 1 DEBKGNU5000000B6 2021-10-04 9 2 DE11 Stuttgart (((533236 5377030, 532410.6 5376422, 531497.3 53768…
#> 2 DEBKGNU5000000B7 2021-10-04 9 2 DE12 Karlsruhe (((471339.8 5493433, 470661.9 5493366, 470318.8 549…
#> 3 DEBKGNU5000000B8 2021-10-04 9 2 DE13 Freiburg (((429398.8 5394070, 430815.5 5395489, 431522.4 539…
#> 4 DEBKGNU5000000B9 2021-10-04 9 2 DE14 Tübingen (((582536.8 5314052, 581292.7 5313301, 581305.1 531…
#> 5 DEBKGNU5000000BA 2021-10-04 9 2 DE21 Oberbayern (((794293.6 5298232, 795275.6 5298403, 795573.8 529…
#> 6 DEBKGNU5000000BB 2021-10-04 9 2 DE22 Niederbayern (((827554.6 5386808, 828451 5385530, 828141.5 53843…
#> 7 DEBKGNU5000000BC 2023-12-06 9 2 DE23 Oberpfalz (((729727.5 5418506, 729341.6 5419385, 728572 54189…
#> 8 DEBKGNU5000000BD 2023-12-06 9 2 DE24 Oberfranken (((702619 5531689, 705057.5 5530518, 704989.1 55300…
#> 9 DEBKGNU5000000BE 2021-10-04 9 2 DE25 Mittelfranken (((631678.8 5422644, 629641.9 5420651, 630270.2 541…
#> 10 DEBKGNU5000000BF 2021-10-04 9 2 DE26 Unterfranken (((566109.9 5492367, 565932.7 5492922, 565005.2 549…
#> # ℹ 28 more rows