General note: the below vignette contains frozen output of 12 May 2021. This makes it possible to build the package with vignettes without access to the data sources.
This vignette learns you:
read_types()
);The below N2KHAB data sources are available. For practical
information on data storage and locations, see
vignette("v020_datastorage")
.
habitatmap
(Zenodo-link):
geospatial polygons of BWK and Natura 2000 habitat types in the Flemish
Region, originally published by the Research Institute for Nature and
Forest (INBO) (De Saeger et al. 2020) and
distributed by ‘Informatie Vlaanderen’.habitatstreams
(Zenodo-link):
geospatial lines of the Natura 2000 habitat type 3260
that
correspond with its presence in streaming water segments in the Flemish
Region, originally published by INBO (Leyssen,
Smeekens, and Denys 2020) and distributed by ‘Informatie
Vlaanderen’.habitatsprings
(Zenodo-link):
geospatial points hat correspond with the presence or absence of the
Natura 2000 habitat type 7220
in springs and streaming
water segments in the Flemish Region. The data source is produced, owned
and administered by INBO.habitatquarries
(Zenodo-link):
geospatial polygons that correspond with the presence or absence of the
Natura 2000 habitat type 8310
in underground marl quarries
in the Flemish Region (and border areas). The data source is produced,
owned and administered by INBO.habitatmap_stdized
(Zenodo-link): derived
from habitatmap
. This datasource only contains polygons
with habitat (sub)type or RIB codes. All codes conform to the
types
reference list (see read_types()
). The
datasource is a GeoPackage with essentially two objects: 1) the
geospatial polygons; 2) a long-formatted table with the associated types
(multiple records can occur per polygon_id
). For more
details see read_habitatmap_stdized()
.habitatmap_terr
(Zenodo-link): the
further interpreted, terrestrial part of habitatmap_stdized
(‘terrestrial’ includes ‘semi-terrestrial’). Amongst other properties,
it excludes polygons without terrestrial types, it excludes
rows which most probably are no habitat or RIB at all, and several main
type codes were translated to a corresponding subtype which they almost
always represent. The datasource is a GeoPackage, organized in the same
way as habitatmap_stdized
. For more details see
read_habitatmap_terr()
.watersurfaces_hab
(Zenodo-link): a
combination of the environmental data source watersurfaces
(Leyssen et al. (2020); see
read_watersurfaces()
) and the above data source
habitatmap_stdized
. It represents polygons (from
watersurfaces
or habitatmap_stdized
) that
completely or partly contain standing water habitat types or RIBs
(i.e. 2190_a
, ‘31..
’ types or
rbbah
; types 3260
and 7220
are
not included). The datasource is a GeoPackage, organized in the same way
as habitatmap_stdized
. For more details see
read_watersurfaces_hab()
.read_...()
functionsIn the below R code, it is supposed that a n2khab_data
folder is present in your working directory or a directory up to 10
levels higher, with the relevant data sources in the right place. See
vignette("v020_datastorage")
for the setup.
The advantage of using the read_...()
functions is the
uniform approach to read the above data sources in R, enhancing
collaboration and speeding up your work.
sf
object, in
the coordinate reference system (CRS) ‘Belge 1972 / Belgian Lambert 72’
(EPSG-code 31370).type
variable (if present) is a factor with the
same levels as the type
variable of the types
reference list (see read_types()
).The functions do just basic preprocessing, in order to return (by default) an object that well reflects the structure of the raw data source and returns all its records.
habitatmap <- read_habitatmap()
habitatmap
#> Simple feature collection with 646589 features and 30 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 21991.38 ymin: 153058.3 xmax: 258871.8 ymax: 244027.3
#> Projected CRS: Belge 1972 / Belgian Lambert 72
#> # A tibble: 646,589 x 31
#> polygon_id eval eenh1 eenh2 eenh3 eenh4 eenh5 eenh6 eenh7 eenh8 v1 v2
#> * <chr> <fct> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 000098_v20… m b <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 000132_v20… m bl <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 000135_v20… m bl <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 000136_v20… m bl <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 000142_v20… m bl <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 000150_v20… m bl <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 000297_v20… m bl <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 8 000991_v20… m bl <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 9 000999_v20… m bl <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 001000_v20… m bl <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> # … with 646,579 more rows, and 19 more variables: v3 <chr>, source <chr>,
#> # info <chr>, bwk_label <chr>, hab1 <chr>, phab1 <int>, hab2 <chr>,
#> # phab2 <int>, hab3 <chr>, phab3 <int>, hab4 <chr>, phab4 <int>, hab5 <chr>,
#> # phab5 <int>, source_hab <chr>, source_phab <chr>, hab_legend <fct>,
#> # area_m2 <dbl>, geometry <MULTIPOLYGON [m]>
The habitatmap
object is a very wide data frame which is
not so easy to handle in R. For analytical work on habitat types and
RIBs, you’re advised to use the tidied data source
habitatmap_stdized
; see below.
read_habitatmap(filter_hab = TRUE)
will only retain the
polygons that occur in habitatmap_stdized
, i.e. those that
contain habitat types or RIBs.
The data sources habitatstreams
,
habitatsprings
and habitatquarries
have a
straightforward data structure. There was no need to generate derived
data sources. The meaning of their columns is described in the function
documentation. Not all function arguments are discussed in this
vignette: do take the time to look at the documentation!
habitatstreams <- read_habitatstreams()
habitatstreams
#> Simple feature collection with 560 features and 3 fields
#> Geometry type: LINESTRING
#> Dimension: XY
#> Bounding box: xmin: 33097.92 ymin: 157529.6 xmax: 254039 ymax: 243444.6
#> Projected CRS: Belge 1972 / Belgian Lambert 72
#> # A tibble: 560 x 4
#> river_name source_id type geometry
#> <fct> <fct> <fct> <LINESTRING [m]>
#> 1 Wolfputbeek VMM 3260 (127857.1 167681.2, 127854.9 167684.5, 127844 1…
#> 2 Oude Kale VMM 3260 (95737.01 196912.9, 95732.82 196912.4, 95710.38…
#> 3 Venloop EcoInv 3260 (169352.7 209314.9, 169358.8 209290.5, 169326.2…
#> 4 Venloop EcoInv 3260 (169633.6 209293.5, 169625 209289.2, 169594.4 2…
#> 5 Kleine Nete EcoInv 3260 (181087.1 208607.2, 181088.6 208608.1, 181089 2…
#> 6 Kleine Nete EcoInv 3260 (180037.4 208360.4, 180038.3 208377.5, 180038.3…
#> 7 Kleine Nete EcoInv 3260 (180520 208595.7, 180540.5 208607.4, 180541.2 2…
#> 8 Kleine Nete EcoInv 3260 (187379.9 209998.8, 187381.3 209998.5, 187381.6…
#> 9 Raamdonkseb… extrapol 3260 (183545.5 192409, 183541.9 192406.7, 183541.9 1…
#> 10 Kleine Nete EcoInv 3260 (183516.4 208261.7, 183567.3 208279.2, 183567.3…
#> # … with 550 more rows
With read_habitatstreams(source_text = TRUE)
a second
object sources
is returned with the meaning of the
source_id
codes:
read_habitatstreams(source_text = TRUE) %>%
.$sources
#> # A tibble: 7 x 2
#> source_id source_text
#> <fct> <fct>
#> 1 VMM "Gegevens afgeleid van macrofyteninventarisaties uitgevoer…
#> 2 EcoInv "Tijdens ecologische inventarisatiestudies uitgevoerd in o…
#> 3 extrapol "De conclusie van het nabijgelegen geïnventariseerde segme…
#> 4 Van Belleghem (20… "Macrofytengegevens afgeleid van Van Belleghem S., Bal K.,…
#> 5 Leyssen ea (2005) "Macrofytengegevens afgeleid van Leyssen A., Adriaens P., …
#> 6 WrnBe "Waarnemingen afkomstig van Waarnemingen.be, de website vo…
#> 7 INBO "Tijdens veldbezoeken werd de aan- of afwezigheid van het …
habitatsprings <- read_habitatsprings()
habitatsprings
#> Simple feature collection with 104 features and 11 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 36407.84 ymin: 155249.7 xmax: 258371.1 ymax: 179732
#> Projected CRS: Belge 1972 / Belgian Lambert 72
#> # A tibble: 104 x 12
#> point_id name system_type code_orig type certain unit_id area_m2 year
#> <dbl> <chr> <fct> <chr> <fct> <lgl> <dbl> <dbl> <dbl>
#> 1 1 Steenputb… rivulet 7220 7220 TRUE 1 50 2014
#> 2 2 Steenputb… rivulet 7220 7220 TRUE 1 20 2014
#> 3 3 Duling <NA> 7230 7230 TRUE NA NA 2014
#> 4 4 Kapittelb… <NA> gh <NA> TRUE NA NA 2014
#> 5 5 Remersdaa… rivulet 7220 7220 TRUE 2 200 2014
#> 6 6 Remersdaa… rivulet 7220 7220 TRUE 2 500 2014
#> 7 7 Kesterbeek unknown 7220,gh 7220 FALSE 32 NA NA
#> 8 8 Krindaal rivulet 7220 7220 TRUE 3 800 2014
#> 9 9 Bois de B… rivulet 7220 7220 TRUE 4 50 2014
#> 10 10 KwintenHo… mire 7220 7220 TRUE 5 10 2014
#> # … with 94 more rows, and 3 more variables: in_sac <lgl>, source <chr>,
#> # geometry <POINT [m]>
Do note that both the habitatsprings
and
habitatquarries
data sources also contain records which are
no habitat type. You can filter the habitat types by setting the
filter_hab
argument as TRUE
(not shown).
habitatquarries <- read_habitatquarries()
habitatquarries
#> Simple feature collection with 45 features and 6 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 221427.3 ymin: 160393.5 xmax: 243211.1 ymax: 168965.1
#> Projected CRS: Belge 1972 / Belgian Lambert 72
#> # A tibble: 45 x 7
#> polygon_id unit_id name code_orig type extra_reference
#> <int> <int> <chr> <chr> <fct> <fct>
#> 1 4 4 Avergat - … gh <NA> Lahaye 2018
#> 2 6 6 Avergat - … 8310 8310 Lahaye 2018
#> 3 5 5 Avergat - … gh <NA> Lahaye 2018
#> 4 20 20 Coolen 8310 8310 Limburgs Landschap 2020; pers…
#> 5 21 21 Coolen gh <NA> Limburgs Landschap 2020
#> 6 29 29 Groeve Lin… 8310 8310 <NA>
#> 7 31 31 Grote berg… 8310 8310 De Haan & Lahaye 2018
#> 8 37 37 Grote berg… 8310 8310 De Haan & Lahaye 2018
#> 9 24 24 Henisdael … 8310 8310 Dusar et al. 2007
#> 10 34 34 Henisdael … 8310 8310 Dusar et al. 2007
#> # … with 35 more rows, and 1 more variable: geom <POLYGON [m]>
The habitatquarries data source also includes the literature references. These can be added as a second data frame – in that case a list is returned:
habitatquarries2$extra_references
#> # A tibble: 9 x 23
#> category bibtexkey address author booktitle journal month note number pages
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 BOOK de_haan_… Brusse… De Ha… <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 INCOLLE… dusar_me… Genk Dusar… Likona j… <NA> <NA> <NA> <NA> 6-13
#> 3 BOOK jenneken… Riemst Jenne… <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 INCOLLE… lahaye_g… Riemst Lahay… <NA> <NA> <NA> <NA> <NA> 12
#> 5 BOOK verhoeve… Weert Verho… <NA> <NA> <NA> <NA> 1769 <NA>
#> 6 BOOK walschot… <NA> Walsc… <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 MISC wikipedi… <NA> {Wiki… <NA> <NA> jan Page… <NA> <NA>
#> 8 MISC limburgs… <NA> {Limb… <NA> <NA> apr Libr… <NA> <NA>
#> 9 ARTICLE silverta… <NA> Silve… <NA> Natuur… <NA> <NA> 12 334-…
#> # … with 13 more variables: publisher <chr>, series <chr>, title <chr>,
#> # volume <chr>, year <dbl>, url <chr>, isbn <chr>, copyright <chr>,
#> # abstract <chr>, language <chr>, urldate <chr>, issn <chr>, keywords <chr>
These extra references can also be printed to the R console in BibTeX
format, when specifying bibtex = TRUE
.
The reading functions for the three processed data sources will always return a list:
Let’s see how this works!
A tidy representation of the habitatmap
data, restricted
to the polygons that contain habitat types or RIBs, is returned by
read_habitatmap_stdized()
:
hms_pol <- hms$habitatmap_polygons
hms_pol
#> Simple feature collection with 87781 features and 2 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 22003.2 ymin: 153084.4 xmax: 258871.8 ymax: 243446.1
#> Projected CRS: Belge 1972 / Belgian Lambert 72
#> # A tibble: 87,781 x 3
#> polygon_id description_orig geom
#> * <fct> <chr> <MULTIPOLYGON [m]>
#> 1 130153_v20… 70% 9120_qb; 30% … (((150669.4 227248.6, 150668.1 227242.9, 1505…
#> 2 130815_v20… 70% gh; 30% 9160 (((258338.4 158696.1, 258336.5 158693.5, 2583…
#> 3 137826_v20… 100% 6430,rbbhf (((181587.2 234938, 181613.1 234933.1, 181646…
#> 4 170624_v20… 100% rbbmr (((145876.8 229686.8, 145701.7 229680.6, 1456…
#> 5 203261_v20… 70% gh; 30% rbbmr (((117137.2 210307.9, 117136.3 210288.3, 1171…
#> 6 204352_v20… 80% gh; 20% rbbsp (((116357.3 159278.3, 116340.5 159259.8, 1163…
#> 7 204376_v20… 90% gh; 10% rbbsg (((116110.1 210545.5, 116102.4 210541.1, 1160…
#> 8 205188_v20… 60% rbbsp; 40% gh (((232114.7 161594.5, 232122.7 161590.5, 2321…
#> 9 205291_v20… 70% gh; 30% rbbsp (((191253.5 160641.5, 191254.8 160636.1, 1912…
#> 10 205756_v20… 70% gh; 30% rbbsf (((216258.8 156749, 216260.9 156750, 216287.7…
#> # … with 87,771 more rows
hms_occ <- hms$habitatmap_types
hms_occ
#> # A tibble: 110,485 x 5
#> polygon_id type certain code_orig phab
#> <fct> <fct> <lgl> <chr> <int>
#> 1 000038_v2016 91E0_va TRUE 91E0_va 100
#> 2 000043_v2016 9130_end TRUE 9130_end 100
#> 3 000064_v2020 9130_end TRUE 9130_end 100
#> 4 000132_v2016 9130_end TRUE 9130_end 100
#> 5 000204_v2016 91E0_vn TRUE 91E0_vn 100
#> 6 000255_v2016 91E0_vc TRUE 91E0_vc 100
#> 7 000297_v2016 rbbsp TRUE rbbsp 10
#> 8 000311_v2016 9130_end TRUE 9130_end 70
#> 9 000311_v2016 rbbsp TRUE rbbsp 30
#> 10 000390_v2016 91E0_vn TRUE 91E0_vn 70
#> # … with 110,475 more rows
Let’s estimate the surface area per type, including uncertain
occurrences of types and taking into account cover percentage per
polygon (phab
):
hms_pol %>%
mutate(area = st_area(.)) %>%
st_drop_geometry() %>%
inner_join(hms_occ, by = "polygon_id") %>%
# area of type within polygon:
mutate(area_type = area * phab / 100) %>%
group_by(type) %>%
summarise(area = sum(area_type) %>%
set_units("ha") %>%
round(2))
#> # A tibble: 101 x 2
#> type area
#> <fct> [ha]
#> 1 1130 5678.87
#> 2 1140 2098.03
#> 3 1310_pol 17.39
#> 4 1310_zk 34.02
#> 5 1310_zv 10.62
#> 6 1320 2.41
#> 7 1330_da 117.61
#> 8 1330_hpr 138.12
#> 9 2110 27.14
#> 10 2120 462.54
#> # … with 91 more rows
read_habitatmap_terr()
behaves exactly the same way:
hmt$habitatmap_terr_polygons
#> Simple feature collection with 78602 features and 4 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 22003.2 ymin: 153084.4 xmax: 258871.8 ymax: 243351.8
#> Projected CRS: Belge 1972 / Belgian Lambert 72
#> # A tibble: 78,602 x 5
#> polygon_id description_orig description source geom
#> * <fct> <chr> <chr> <fct> <MULTIPOLYGON [m]>
#> 1 130153_v20… 70% 9120_qb; 30%… 70% 9120_q… habita… (((150669.4 227248.6, 1506…
#> 2 130815_v20… 70% gh; 30% 9160 70% gh; 30… habita… (((258338.4 158696.1, 2583…
#> 3 137826_v20… 100% 6430,rbbhf 100% 6430_… habita… (((181587.2 234938, 181613…
#> 4 170624_v20… 100% rbbmr 100% rbbmr habita… (((145876.8 229686.8, 1457…
#> 5 203261_v20… 70% gh; 30% rbbmr 70% gh; 30… habita… (((117137.2 210307.9, 1171…
#> 6 204352_v20… 80% gh; 20% rbbsp 80% gh; 20… habita… (((116357.3 159278.3, 1163…
#> 7 204376_v20… 90% gh; 10% rbbsg 90% gh; 10… habita… (((116110.1 210545.5, 1161…
#> 8 205188_v20… 60% rbbsp; 40% gh 60% rbbsp;… habita… (((232114.7 161594.5, 2321…
#> 9 205291_v20… 70% gh; 30% rbbsp 70% gh; 30… habita… (((191253.5 160641.5, 1912…
#> 10 205756_v20… 70% gh; 30% rbbsf 70% gh; 30… habita… (((216258.8 156749, 216260…
#> # … with 78,592 more rows
hmt$habitatmap_terr_types
#> # A tibble: 99,784 x 6
#> polygon_id type certain code_orig phab source
#> <fct> <fct> <lgl> <chr> <int> <fct>
#> 1 000038_v2016 91E0_va TRUE 91E0_va 100 habitatmap_stdized
#> 2 000043_v2016 9130_end TRUE 9130_end 100 habitatmap_stdized
#> 3 000064_v2020 9130_end TRUE 9130_end 100 habitatmap_stdized
#> 4 000132_v2016 9130_end TRUE 9130_end 100 habitatmap_stdized
#> 5 000204_v2016 91E0_vn TRUE 91E0_vn 100 habitatmap_stdized
#> 6 000255_v2016 91E0_vc TRUE 91E0_vc 100 habitatmap_stdized
#> 7 000297_v2016 rbbsp TRUE rbbsp 10 habitatmap_stdized
#> 8 000311_v2016 9130_end TRUE 9130_end 70 habitatmap_stdized
#> 9 000311_v2016 rbbsp TRUE rbbsp 30 habitatmap_stdized
#> 10 000390_v2016 91E0_vn TRUE 91E0_vn 70 habitatmap_stdized
#> # … with 99,774 more rows
Compared to habitatmap_stdized
, purely aquatic
or non-habitat/RIB polygons were omitted, and a part of the type data
were interpreted in a more specific way. Further, while type
7220
is present in habitatmap_terr
, the
read_habitatmap_terr()
function drops it by default because
the habitatsprings
data source is recommended for that.
This can be controlled by the drop_7220
argument.
As a consequence, some type codes are completely absent from
habitatmap_terr_types
:
hms_occ %>%
distinct(type) %>%
anti_join(
hmt$habitatmap_terr_types %>%
distinct(type),
by = "type"
) %>%
arrange(type)
#> # A tibble: 7 x 1
#> type
#> <fct>
#> 1 2190
#> 2 6410
#> 3 6430
#> 4 6510
#> 5 7140
#> 6 7220
#> 7 9130
About 3% of all type occurrences received a new type code:
A similar story, this time for polygons that (could) have aquatic types:
wsh$watersurfaces_polygons
#> Simple feature collection with 3233 features and 4 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 22546.57 ymin: 159273.1 xmax: 253896.9 ymax: 242960.1
#> Projected CRS: Belge 1972 / Belgian Lambert 72
#> # A tibble: 3,233 x 5
#> polygon_id polygon_id_ws polygon_id_habitatm… description_orig
#> * <fct> <fct> <fct> <chr>
#> 1 ANTANT0082 ANTANT0082 596466_v2014 60% 3150; 20% rbbmr; 20% rbbsf
#> 2 ANTANT0234 ANTANT0234 633396_v2020 100% 3130_na
#> 3 ANTANT0251 ANTANT0251 113978_v2014 100% 3150
#> 4 ANTANT0253 ANTANT0253 111606_v2014 100% 3150
#> 5 ANTANT0297 ANTANT0297 409153_v2014+409153… 85% 3140; 15% 3150+85% 3140; 1…
#> 6 ANTANT0315 ANTANT0315 519082_v2018 100% 3140
#> 7 ANTANT0319 ANTANT0319 601958_v2014 100% 3150,gh
#> 8 ANTANT0381 ANTANT0381 644003_v2014 85% gh; 15% 3140
#> 9 ANTANT0383 ANTANT0383 631879_v2014+593522… 50% 3150; 40% rbbmr; 10% rbbsf…
#> 10 ANTANT0384 ANTANT0384 644003_v2014 85% gh; 15% 3140
#> # … with 3,223 more rows, and 1 more variable: geom <MULTIPOLYGON [m]>
wsh$watersurfaces_types
#> # A tibble: 3,669 x 4
#> polygon_id type certain code_orig
#> <fct> <fct> <lgl> <chr>
#> 1 ANTANT0082 3150 TRUE 3150
#> 2 ANTANT0234 3130_na TRUE 3130_na
#> 3 ANTANT0251 3150 TRUE 3150
#> 4 ANTANT0253 3150 TRUE 3150
#> 5 ANTANT0297 3140 TRUE 3140
#> 6 ANTANT0297 3150 TRUE 3150
#> 7 ANTANT0315 3140 TRUE 3140
#> 8 ANTANT0319 3150 FALSE 3150,gh
#> 9 ANTANT0381 3140 TRUE 3140
#> 10 ANTANT0383 3150 TRUE 3150
#> # … with 3,659 more rows
Let’s compute some statistics of standing water types (ignoring the
value certain
):
wsh$watersurfaces_polygons %>%
mutate(area = st_area(.)) %>%
st_drop_geometry() %>%
inner_join(wsh$watersurfaces_types,
by = "polygon_id"
) %>%
group_by(type) %>%
summarise(
nr_watersurfaces = n_distinct(polygon_id),
total_area = sum(area),
area_min = min(area),
area_Q1 = quantile(area, 0.25),
area_Q2 = quantile(area, 0.5),
area_Q3 = quantile(area, 0.75),
max = max(area)
) %>%
mutate_at(
vars(matches("area|max")),
function(x) {
set_units(x, "a") %>% round(1)
}
)
#> # A tibble: 9 x 8
#> type nr_watersurfaces total_area area_min area_Q1 area_Q2 area_Q3 max
#> <fct> <int> [a] [a] [a] [a] [a] [a]
#> 1 2190_a 318 3605.6 0.0 0.9 2.1 5.5 317.5
#> 2 3110 6 1876.8 111.7 139.7 216.9 330.3 849.3
#> 3 3130 219 33022.9 0.6 13.0 41.0 160.0 2807.2
#> 4 3130_aom 1267 107732.6 0.1 2.3 9.7 44.1 7062.7
#> 5 3130_na 354 120174.2 1.0 44.7 122.5 277.9 13668.2
#> 6 3140 104 72272.0 0.8 13.3 55.3 418.5 13668.2
#> 7 3150 475 105019.1 0.4 6.6 17.3 83.5 13668.2
#> 8 3160 379 27936.9 0.1 6.1 17.4 59.3 4179.1
#> 9 rbbah 517 33716.3 0.1 4.2 10.9 47.0 3748.6
A strong point of sf
objects is that the geometry has
explicit units. Consequently we can make use of tools like
set_units()
to convert units (e.g. surface area as
are (a)).
Because the main type code 3130
will mostly boil down to
3130_aom
in the field, a further interpreted flavour can be
generated with
read_watersurfaces_hab(interpreted = TRUE)
.
The expand_types()
function helps us to join a type
column of your own data frame with the results of
read_habitatmap_terr()
or
read_watersurfaces_hab()
.
For both of these datasets, the following conversions of your data frame are supported in order to create optimal joins:
An example (df
is our data frame):
df <-
tribble(
~mycode, ~obs,
"2130", 5,
"2190", 45,
"2330_bu", 8,
"2330_dw", 8,
"6410_mo", 78,
"6410_ve", 4,
"91E0_vn", 10
)
df
#> # A tibble: 7 × 2
#> mycode obs
#> <chr> <dbl>
#> 1 2130 5
#> 2 2190 45
#> 3 2330_bu 8
#> 4 2330_dw 8
#> 5 6410_mo 78
#> 6 6410_ve 4
#> 7 91E0_vn 10
With the type_var
argument you specify which variable of
your data frame represents type codes:
df_exp <-
expand_types(df, type_var = "mycode")
df_exp
#> # A tibble: 13 × 2
#> mycode obs
#> <chr> <dbl>
#> 1 2130 5
#> 2 2190 45
#> 3 2330_bu 8
#> 4 2330_dw 8
#> 5 6410_mo 78
#> 6 6410_ve 4
#> 7 91E0_vn 10
#> 8 2130_had 5
#> 9 2130_hd 5
#> 10 2190_a 45
#> 11 2190_mp 45
#> 12 2190_overig 45
#> 13 2330 8
More examples and features are explained in the documentation of
expand_types()
.
Obviously, more rows of habitatmap_terr
will be retained
by joining df_exp
:
When joining with df
: