Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Get area description from vector of area codes

Description

Get area description from vector of area codes

Usage

get_area_name(area_codes)

Arguments

Details

Usually used as an inverse function for get_wgsrpd3_codes. Useful for condensing sets of codes for e.g. file names, plotting and table formatting.

Value

Character. Either a vector of length one, with a name for the set of Level 3 areas, or (if no name exists for that set of areas) the input vector of codes.

Examples

get_area_name(get_wgsrpd3_codes("Brazil"))

Extract WGSRPD Level 3 (area) codes.

Description

Extract WGSRPD Level 3 (area) codes.

Usage

get_wgsrpd3_codes(geography, include_equatorial = NULL)

Arguments

Details

Country mapping follows Gallagher et al. (2020). Importantly, this means that some overseas territories are not considered part of the country in this system, e.g. the Canary Islands are designated as their own Level 3 area, rather than part of Spain in this mapping. Where this is ambiguous, the mapping can be explored using View(wgsrpd_mapping).

Gallagher, R. V., Allen, S., Rivers, M. C., Allen, A. P., Butt, N., Keith, D., & Adams, V. M. (2020). Global shortfalls in extinction risk assessments for endemic flora. bioRxiv, 2020.2003.2012.984559. https://doi.org/10.1101/2020.03.12.984559

Value

Character with area codes (Level 3) that fall within the geography.

Examples

get_wgsrpd3_codes("Brazil")

Plot a POWO style map for given range and range centroids.

Description

Plot a POWO style map for given range and range centroids.

Usage

powo_map(range_sf, centroids_sf)

Arguments

Value

A ggplot map of the range

POWO colour palette for range maps

Description

Range maps displayed on the POWO website have a fixed, discrete colour palette based on the type of taxon occurrence in a region.

Usage

powo_pal()

scale_color_powo(...)

scale_colour_powo(...)

scale_fill_powo(...)

Arguments

Value

Character. Vector of names and HEX values to match those of POWO.

Example dataset for name matching

Description

A dataset containing 20 sampled Red List assessments for name-matching

Usage

redlist_example

Format

A data frame with 20 rows and 4 variables:

assessmentId

Red List identifier

scientificName

Taxon name.

redlistCategory

Red List threat category

authority

Taxon author/s.

Source

Downloaded and sampled from https://www.iucnredlist.org/

Data for mapping plant family to order or higher classification

Description

A dataset containing the higher classification (Angiosperms, Gymnosperms, Ferns and Lycophytes) and Order for each family in the WCVP.

Usage

taxonomic_mapping

Format

A data frame with 457 rows and 3 variables: family, order and higher_classification

Source

Fern and lycophyte taxonomy from PPG I (2016; doi:10.1111/jse.12229). Angiosperm taxonomy from APG IV (2016; doi:10.1111/boj.12385). Gymnosperm taxonomy from Forest et al (2018; doi:10.1038/s41598-018-24365-4)

Generate a species checklist from WCVP

Description

Generate a species checklist from WCVP

Usage

wcvp_checklist(
  taxon = NULL,
  taxon_rank = c("species", "genus", "family", "order", "higher"),
  area_codes = NULL,
  synonyms = TRUE,
  render_report = FALSE,
  native = TRUE,
  introduced = TRUE,
  extinct = TRUE,
  location_doubtful = TRUE,
  hybrids = FALSE,
  infraspecies = TRUE,
  report_filename = NULL,
  report_dir = NULL,
  report_type = c("alphabetical", "taxonomic"),
  wcvp_names = NULL,
  wcvp_distributions = NULL
)

Arguments

Details

The synonyms argument can be used to limit names to those that are Accepted. If synonyms = TRUE then invalid, illegitimate and other non-accepted names are also included (i.e., the checklist is not limited to names for which taxon_status == "Synonym"). Two styles of checklist are supported in rWCVP - alphabetical and taxonomic. In an alphabetical checklist, all names are arranged alphabetically with accepted names in bold, and synonyms are followed by their accepted name. For a taxonomic checklist, names are grouped by their accepted names, and synonyms are listed beneath. Both types of checklist include author, publication and distribution information, though note that family headings are only supported in alphabetical checklists (due to the additional grouping requirement of the taxonomic format).

Value

Data frame with filtered data and, if render_report=TRUE. a report HTML file.

Examples

# These examples take >10 seconds to run and require 'rWCVPdata'

if(requireNamespace("rWCVPdata")){
wcvp_checklist(taxon = "Myrtaceae", taxon_rank = "family", area = get_wgsrpd3_codes("Brazil"))
wcvp_checklist(taxon = "Ferns", taxon_rank = "higher", area = get_wgsrpd3_codes("New Zealand"))
}

Generate spatial distribution objects for species, genera or families

Description

Generate spatial distribution objects for species, genera or families

Usage

wcvp_distribution(
  taxon,
  taxon_rank = c("species", "genus", "family", "order", "higher"),
  native = TRUE,
  introduced = TRUE,
  extinct = TRUE,
  location_doubtful = TRUE,
  wcvp_names = NULL,
  wcvp_distributions = NULL
)

Arguments

Details

Where taxon_rank is higher than species, the distribution of the whole group will be returned, not individual species within that group. This also applies when toggling options - for example, introduced occurrences will only be included if they are outside the native range, regardless of whether native=TRUE or native=FALSE. To identify extinctions, introductions or doubtful occurrences within the native range, the wcvp_summary and wcvp_occ_mat functions can be used.

Value

Simple features (sf) data frame containing the range polygon/s of the taxon.

Examples

 # this example requires 'rWCVPdata'
if(requireNamespace("rWCVPdata")){
r <- wcvp_distribution("Callitris", taxon_rank = "genus")
p <- wcvp_distribution_map(r)
p
}

Plot distribution maps for species, genera or families

Description

Plot distribution maps for species, genera or families

Usage

wcvp_distribution_map(
  range,
  crop_map = FALSE,
  native = TRUE,
  introduced = TRUE,
  extinct = TRUE,
  location_doubtful = TRUE
)

Arguments

Details

The colour scheme mirrors that used by Plants of the World (POWO; https://powo.science.kew.org/), where green is native, purple is introduced, red is extinct and orange is doubtful. See Examples for how to use custom colours.

Value

A ggplot2::ggplot of the distribution.

Examples

 # these examples require 'rWCVPdata'
if(requireNamespace("rWCVPdata")){
p <- wcvp_distribution_map(wcvp_distribution("Callitris", taxon_rank = "genus"))
p
# now only the native range, and cropped to range extent
p <- wcvp_distribution_map(wcvp_distribution("Callitris", taxon_rank = "genus"),
  introduced = FALSE, crop_map = TRUE
)
p
# now with different colours
# note that this taxon only has native and introduced occurrences, so only two colours are needed
p <- wcvp_distribution_map(wcvp_distribution("Callitris", taxon_rank = "genus"))
p +
  # for polygons
  ggplot2::scale_fill_manual(values = c("red", "blue")) +
  # for points (islands)
  ggplot2::scale_colour_manual(values = c("red", "blue"))
  }
  

Exact matching to WCVP.

Description

Exact matching of names to the WCVP, optionally using the author string to refine results.

Usage

wcvp_match_exact(names_df, wcvp_names, name_col, author_col = NULL, id_col)

Arguments

Value

Match results from WCVP bound to the original data from names_df.

See Also

Other name matching functions: wcvp_match_fuzzy(), wcvp_match_names()

Examples

 # these examples require 'rWCVPdata'
if(requireNamespace("rWCVPdata")){
wcvp_names <- rWCVPdata::wcvp_names

# including author string
wcvp_match_exact(redlist_example, wcvp_names, "scientificName",
  author_col = "authority",
  id_col = "assessmentId"
)

# without author string
wcvp_match_exact(redlist_example, wcvp_names, "scientificName", id_col = "assessmentId")
}

Fuzzy (approximate) matching to the WCVP.

Description

Fuzzy matching to names in the WCVP using phonetic matching and edit distance. The WCVP can be loaded for matching from rWCVPdata::wcvp_names.

Usage

wcvp_match_fuzzy(names_df, wcvp_names, name_col, progress_bar = TRUE)

phonetic_match(names_df, wcvp_names, name_col)

edit_match(names_df, wcvp_names, name_col)

Arguments

Details

The wcvp_match_fuzzy function uses phonetic matching first and then finds the closest match based on edit distance for any remaining names.

Phonetic matching uses phonics::metaphone encoding with a maximum code length of 20.

Edit distance matching finds the closest match based on Levenshtein similarity, calculated by RecordLinkage::levenshteinSim.

Value

Match results from WCVP bound to the original data from names_df.

See Also

Other name matching functions: wcvp_match_exact(), wcvp_match_names()

Examples

 # this example requires 'rWCVPdata'
if(requireNamespace("rWCVPdata")){
wcvp_names <- rWCVPdata::wcvp_names
wcvp_match_fuzzy(redlist_example, wcvp_names, "scientificName")
}


 # this example requires 'rWCVPdata'
if(requireNamespace("rWCVPdata")){
wcvp_names <- rWCVPdata::wcvp_names
phonetic_match(redlist_example, wcvp_names, "scientificName")
}


 # this example requires 'rWCVPdata'
if(requireNamespace("rWCVPdata")){
wcvp_names <- rWCVPdata::wcvp_names
edit_match(redlist_example, wcvp_names, "scientificName")
}

Match names to the WCVP.

Description

Match names to WCVP, first using exact matching and then using fuzzy matching on any remaining unmatched names.

Usage

wcvp_match_names(
  names_df,
  wcvp_names = NULL,
  name_col = NULL,
  id_col = NULL,
  author_col = NULL,
  join_cols = NULL,
  fuzzy = TRUE,
  progress_bar = TRUE
)

Arguments

Details

By default, exact matching uses only the taxon name (supplied by name_col) unless a column specifying the author string is provided (as author_col).

Columns setting out name parts can be supplied as join_cols in place of a taxon name, but must be supplied in the order you want them joined (e.g. c("genus", "species", "infra_rank", "infra")).

Fuzzy matching uses a combination of phonetic and edit distance matching, and can optionally be turned off using fuzzy=FALSE.

The WCVP can be loaded for matching from rWCVPdata::wcvp_names.

See here for an example workflow.

Value

Match results from WCVP bound to the original data from names_df.

See Also

Other name matching functions: wcvp_match_exact(), wcvp_match_fuzzy()

Examples

 # these examples require 'rWCVPdata'
if(requireNamespace("rWCVPdata")){
wcvp_names <- rWCVPdata::wcvp_names

# without author
wcvp_match_names(redlist_example, wcvp_names,
  name_col = "scientificName",
  id_col = "assessmentId"
)

# with author
wcvp_match_names(redlist_example, wcvp_names,
  name_col = "scientificName",
  id_col = "assessmentId", author_col = "authority"
)
}

Generate occurrence matrix for taxa and areas

Description

Generate occurrence matrix for taxa and areas

Usage

wcvp_occ_mat(
  taxon = NULL,
  taxon_rank = c("species", "genus", "family", "order", "higher"),
  area_codes = NULL,
  native = TRUE,
  introduced = TRUE,
  extinct = TRUE,
  location_doubtful = TRUE,
  wcvp_names = NULL,
  wcvp_distributions = NULL
)

Arguments

Details

See here for an example of how this output can be formatted for publication.

Value

A data.frame containing the taxon_name and plant_name_id of all species that are present in the area, plus one variable for each WGSPRD level 3 region in area, with species presences marked as 1 and absences marked as 0.

Examples

 # this example requires 'rWCVPdata'
if(requireNamespace("rWCVPdata")){
wcvp_occ_mat(
  taxon = "Poa", taxon_rank = "genus",
  area = c("TAS", "VIC", "NSW"), introduced = FALSE
)
}

Reformat local versions of WCVP

Description

Reformat local versions of WCVP

Usage

wcvp_reformat(wcvp_local, version = NULL)

Arguments

Details

Note that not all of the original variables are preserved during reformatting. For example, publication is a single variable in v9, but split over multiple in the data package. It is therefore not possible to simply rename this variable. Variables that are present in the data package but not in v9 are filled with NA.

Value

A data.frame with the same variable structure as the WCVP that is included in the data package rWCVPdata.

Generate a summary table from the WCVP

Description

Generate a summary table from the WCVP

Usage

wcvp_summary(
  taxon = NULL,
  taxon_rank = c("species", "genus", "family", "order", "higher"),
  area_codes = NULL,
  grouping_var = c("area_code_l3", "genus", "family", "order", "higher"),
  hybrids = FALSE,
  wcvp_names = NULL,
  wcvp_distributions = NULL
)

Arguments

Details

Valid values for rank 'higher' are 'Angiosperms', 'Gymnosperms', 'Ferns' and 'Lycophytes'. Note that grouping variable (if taxonomic) should be of a lower level than taxon and taxon_rank to produce a meaningful summary (i.e., it does not make sense to group a genus by genus, family or higher classification). Additionally, if the grouping variable is taxonomic then species occurrences are aggregated across the input area. This means that if a species is native to any of the input area (even if it is introduced or extinct in other parts) it is counted as 'Native'. Similarly, introduced occurrences take precedence over extinct occurrences. Note that in this type of summary table, 'Endemic' means endemic to the input area, not necessarily to a single WGSRPD Level 3 Area within the input area.

Value

Data.frame with filtered data, or a gt table

Examples

 # this example requires 'rWCVPdata'
if(requireNamespace("rWCVPdata")){
ferns <- wcvp_summary("Ferns", "higher", get_wgsrpd3_codes("New Zealand"), grouping_var = "family")
wcvp_summary_gt(ferns)
}

Render a summary table from wcvp_summary

Description

Render a summary table from wcvp_summary

Usage

wcvp_summary_gt(x)

Arguments

Value

gt table

Examples

 # this example requires 'rWCVPdata'
if(requireNamespace("rWCVPdata")){
ferns <- wcvp_summary("Ferns", "higher", get_wgsrpd3_codes("New Zealand"), grouping_var = "family")
wcvp_summary_gt(ferns)
}

Biodiversity Information Standards (TDWG) World Geographical Scheme for Recording Plant Distributions (WGSRPD)

Description

Spatial data for WGSRPD Level 3, for plotting maps

Usage

wgsrpd3

Format

An 'sf' object with 20 rows and 4 variables:

LEVEL3_NAM

Region name

LEVEL3_COD

Region code

LEVEL2_COD

Level 2 code

LEVEL1_COD

Level 1 code (continent)

geometry

sf geometry

fillcol

Used for mapping.

Source

https://github.com/tdwg/wgsrpd/tree/master/level3

Data for mapping WGSRPD geography to other levels

Description

A dataset containing the area (Level 3), #' region (Level 2), continent (Level 1), country (political) and hemisphere category for each Level 3 area. Country mapping follows Gallagher et al. (2020).

Usage

wgsrpd_mapping

Format

A data frame with 370 rows and 7 variables:

HEMISPHERE

Northern, Southern or Equatorial (spanning the equator).

LEVEL1_COD

Continent code.

LEVEL1_NAM

Continent.

LEVEL2_COD

Region code.

LEVEL2_NAM

Region.

COUNTRY

Country (political; from Gallagher et al., 2020)

LEVEL3_COD

Area code.

LEVEL3_NAM

Area.

Source

Modified from data available at https://github.com/tdwg/wgsrpd