vignettes/v010_reference_lists.Rmd
v010_reference_lists.RmdWith reference lists we mean complete and authorative lists of all items or categories that constitute some collection. Their purpose is typically to promote standardization and thereby to ease collaborative work.
The n2khab package provides the following built-in
reference lists, relevant to N2KHAB projects 1:
types: checklist of types (habitat (sub)types and
regionally important biotopes) (documentation links: this website / installed package), represented by their
current codesenv_pressures: checklist of environmental pressures,
represented by codes (documentation links: this website / installed package)Additionally, namelist provides names and (optionally)
shortnames for IDs/codes used in the above lists (documentation links:
this website / installed package).
Beside enlisting all items, the reference lists provide additional information on them, sometimes in a generic way with variables like ‘attribute_1’, ‘attribute_2’, ‘tag_1’ and so on (explained in the documentation files). This information may be of a defining nature (and obligate), or may just provide useful categories and tags to filter by.
Reading functions of the n2khab package return the
reference lists as tibbles, with appropriate text from
namelist added. A tibble is a data frame that makes working
in the tidyverse a little easier.
In the data source on disk, each item envisaged by a reference list is always represented by a code (sometimes a combination of two codes) – not a name. The same approach is often followed for other attributes (use of codes, not names or descriptions). However for some variables English has been used directly in the data source.
The splitting between code and explanatory names, shortnames and
other language-dependent text made it possible to store the latter in
multiple languages in namelist, in the variables
name and shortname. Currently, this list
systematically provides English and Dutch text for each code. This can
be extended in future versions of the package (not necessarily in a
systematic way).
Making the types reference list available in the R
environment is as easy as:
read_types()
#> # A tibble: 110 × 25
#> type typelevel main_type type_name type_shortname typeclass typeclass_name
#> <fct> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 1130 main_type 1130 Estuaries Estuaries CH Coastal and h…
#> 2 1140 main_type 1140 Mudflats… Mud- and sand… CH Coastal and h…
#> 3 1310 main_type 1310 Salicorn… Brackish pion… CH Coastal and h…
#> 4 1310_p… subtype 1310 Salicorn… Salicornia ha… CH Coastal and h…
#> 5 1310_zk subtype 1310 Low salt… Low saltmarsh… CH Coastal and h…
#> 6 1310_zv subtype 1310 High sal… High saltmars… CH Coastal and h…
#> 7 1320 main_type 1320 Spartina… Spartina swar… CH Coastal and h…
#> 8 1330 main_type 1330 Atlantic… Atlantic salt… CH Coastal and h…
#> 9 1330_da subtype 1330 Saltmars… Saltmarshes d… CH Coastal and h…
#> 10 1330_h… subtype 1330 Halophyt… Halophytic gr… CH Coastal and h…
#> # ℹ 100 more rows
#> # ℹ 18 more variables: hydr_class <fct>, hydr_class_name <fct>,
#> # hydr_class_shortname <fct>, groundw_dep <fct>, groundw_dep_name <fct>,
#> # groundw_dep_shortname <fct>, flood_dep <fct>, flood_dep_name <fct>,
#> # flood_dep_shortname <fct>, tag_1 <chr>, tag_1_name <chr>,
#> # tag_1_shortname <chr>, tag_2 <chr>, tag_2_name <chr>,
#> # tag_2_shortname <chr>, tag_3 <chr>, tag_3_name <chr>, …By default, English is used. But, you can also choose to get a tibble in another language:
read_types(lang = "nl")
#> # A tibble: 110 × 25
#> type typelevel main_type type_name type_shortname typeclass typeclass_name
#> <fct> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 1130 main_type 1130 Estuaria estuaria CH Kust- en zilt…
#> 2 1140 main_type 1140 Bij eb d… bij eb droogv… CH Kust- en zilt…
#> 3 1310 main_type 1310 Eenjarig… zilte pionier… CH Kust- en zilt…
#> 4 1310_p… subtype 1310 Binnendi… binnendijkse … CH Kust- en zilt…
#> 5 1310_zk subtype 1310 Buitendi… buitendijks l… CH Kust- en zilt…
#> 6 1310_zv subtype 1310 Buitendi… buitendijks h… CH Kust- en zilt…
#> 7 1320 main_type 1320 Schorren… schorren met … CH Kust- en zilt…
#> 8 1330 main_type 1330 Atlantis… Atlantische s… CH Kust- en zilt…
#> 9 1330_da subtype 1330 Buitendi… buitendijks s… CH Kust- en zilt…
#> 10 1330_h… subtype 1330 Binnendi… zilte graslan… CH Kust- en zilt…
#> # ℹ 100 more rows
#> # ℹ 18 more variables: hydr_class <fct>, hydr_class_name <fct>,
#> # hydr_class_shortname <fct>, groundw_dep <fct>, groundw_dep_name <fct>,
#> # groundw_dep_shortname <fct>, flood_dep <fct>, flood_dep_name <fct>,
#> # flood_dep_shortname <fct>, tag_1 <chr>, tag_1_name <chr>,
#> # tag_1_shortname <chr>, tag_2 <chr>, tag_2_name <chr>,
#> # tag_2_shortname <chr>, tag_3 <chr>, tag_3_name <chr>, …The lang argument is available in the below functions as
well.
env_pressures is made available with:
read_env_pressures()
#> # A tibble: 35 × 7
#> ep_code ep_abbrev ep_name ep_class ep_class_name explanation remarks
#> <fct> <fct> <fct> <fct> <fct> <chr> <chr>
#> 1 ep_011 011_struct 11 Chang… ep_clas… 1 Physical m… <NA> <NA>
#> 2 ep_012 012_soildyn_incr 12 Soil … ep_clas… 1 Physical m… <NA> <NA>
#> 3 ep_013 013_soildyn_decr 13 Soil … ep_clas… 1 Physical m… <NA> <NA>
#> 4 ep_014 014_aqconn 14 Aquat… ep_clas… 1 Physical m… <NA> <NA>
#> 5 ep_015 015_terrconn 15 Terre… ep_clas… 1 Physical m… <NA> <NA>
#> 6 ep_03.1 03.1_eutr_air 3.1 Eutr… ep_clas… 3 Eutrophica… <NA> <NA>
#> 7 ep_03.2 03.2_eutr_soil 3.2 Eutr… ep_clas… 3 Eutrophica… <NA> <NA>
#> 8 ep_03.3 03.3_eutr_gw 3.3 Eutr… ep_clas… 3 Eutrophica… <NA> <NA>
#> 9 ep_03.4 03.4_eutr_sw 3.4 Eutr… ep_clas… 3 Eutrophica… <NA> <NA>
#> 10 ep_04.1 04.1_acidif_air 4.1 Acid… ep_clas… 4 Acidificat… <NA> <NA>
#> # ℹ 25 more rowsWhen actually using these reading functions, you will – of course – assign its result to an object.
With N2KHAB projects, we mean scientific monitoring programmes and research projects regarding Flemish Natura 2000 habitats and regionally important biotopes (RIBs).↩︎