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Type:	Package
Title:	Floristic Quality Assessment Tools for R
Version:	0.5.6
Description:	Tools for downloading and analyzing floristic quality assessment data. See Freyman et al. (2015) <doi:10.1111/2041-210X.12491> for more information about floristic quality assessment and the associated database.
License:	MIT + file LICENSE
Encoding:	UTF-8
Language:	en-US
LazyData:	true
Imports:	dplyr, ggplot2, httr, jsonlite, memoise, rlang, tidyr,tidyselect
RoxygenNote:	7.3.2
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Depends:	R (≥ 4.1.0)
VignetteBuilder:	knitr
URL:	https://github.com/equitable-equations/fqar/
BugReports:	https://github.com/equitable-equations/fqar/issues
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-08-20 17:52:57 UTC; eloise
Author:	Andrew Gard [aut, cre], Alexia Myers [aut], Irene Luwabelwa [aut]
Maintainer:	Andrew Gard <agard@lakeforest.edu>
Repository:	CRAN
Date/Publication:	2025-08-20 18:40:02 UTC

Generate a species co-occurrence matrix from assessment inventories

Description

assessment_coccurrences() accepts a list of species inventoriesdownloaded fromuniversalfqa.org andreturns a complete listing of all co-occurrences. Repeated co-occurrencesacross multiple assessments are included, but self co-occurrences are not,allowing for meaningful summary statistics to be computed.

Usage

assessment_cooccurrences(inventory_list)

Arguments

Value

A data frame with 13 columns:

• target_species (character)

• target_species_c (numeric)

• target_species_nativity (character)

• target_species_n (numeric)

• cospecies_scientific_name (character)

• cospecies_family (character)

• cospecies_acronym (character)

• cospecies_nativity (character)

• cospecies_c (numeric)

• cospecies_w (numeric)

• cospecies_physiognomy (character)

• cospecies_duration (character)

• cospecies_common_name (character)

Examples

# assessment_cooccurrences is best used in combination with# download_assessment_list() and assessment_list_inventory().maine <- download_assessment_list(database = 56)maine_invs <- assessment_list_inventory(maine)maine_cooccurrences <- assessment_cooccurrences(maine_invs)

Generate a summary of co-occurrences in various assessment inventories

Description

assessment_coccurrences_summary() accepts a list of speciesinventories downloaded fromuniversalfqa.org and returns a summary ofthe co-occurrences of each target species. Repeated co-occurrences acrossmultiple assessments are included in summary calculations, but selfco-occurrences are not.

Usage

assessment_cooccurrences_summary(inventory_list)

Arguments

Value

A data frame with 16 columns:

• target_species (character)

• target_species_c (numeric)

• target_species_nativity (character)

• target_species_n (numeric)

• cospecies_n (numeric)

• cospecies_native_n (numeric)

• cospecies_mean_c (numeric)

• cospecies_native_mean_c (numeric)

• cospecies_std_dev_c (numeric)

• cospecies_native_std_dev_c (numeric)

• percent_native (numeric)

• percent_nonnative (numeric)

• percent_native_low_c (numeric)

• percent_native_med_c (numeric)

• percent_native_high_c (numeric)

• discrepancy_c (numeric)

Examples

# assessment_cooccurrences_summary is best used in combination with# download_assessment_list() and assessment_list_inventory().maine <- download_assessment_list(database = 56)maine_invs <- assessment_list_inventory(maine)maine_cooccurrences_summary <- assessment_cooccurrences_summary(maine_invs)

Obtain tidy summary information for a floristic quality assessment

Description

assessment_glance() tidies a floristic quality assessment data setobtained fromuniversalfqa.org.

Usage

assessment_glance(data_set)

Arguments

Value

A data frame with 53 columns:

• assessment_id (numeric)

• title (character)

• date (date)

• site_name (character)

• city (character)

• county (character)

• state (character)

• country (character)

• fqa_db_region (character)

• fqa_db_publication_year (character)

• fqa_db_description (character)

• custom_fqa_db_name (character)

• custom_fqa_db_description (character)

• practitioner (character)

• latitude (character)

• longitude (character)

• weather_notes (character)

• duration_notes (character)

• community_type_notes (character)

• other_notes (character)

• private_public (character)

• total_mean_c (numeric)

• native_mean_c (numeric)

• total_fqi (numeric)

• native_fqi (numeric)

• adjusted_fqi (numeric)

• c_value_zero (numeric) Percent of c-values 0

• c_value_low (numeric) Percent of c-values 1-3

• c_value_mid (numeric) Percent of c-values 4-6

• c_value_high (numeric) Percent of c-values 7-10

• native_tree_mean_c (numeric)

• native_shrub_mean_c (numeric)

• native_herbaceous_mean_c (numeric)

• total_species (numeric)

• native_species (numeric)

• non_native_species (numeric)

• mean_wetness (numeric)

• native_mean_wetness (numeric)

• tree (numeric)

• shrub (numeric)

• vine (numeric)

• forb (numeric)

• grass (numeric)

• sedge (numeric)

• rush (numeric)

• fern (numeric)

• bryophyte (numeric)

• annual (numeric)

• perennial (numeric)

• biennial (numeric)

• native_annual (numeric)

• native_perennial (numeric)

• native_biennial (numeric)

Examples

# While assessment_glance can be used with a .csv file downloaded manually# from the universal FQA website, it is most typically used in combination# with download_assessment().edison <- download_assessment(25002)assessment_glance(edison)

Obtain species details for a floristic quality assessment

Description

assessment_inventory() returns a data frame of all plant speciesincluded in a floristic quality assessment obtained fromuniversalfqa.org.

Usage

assessment_inventory(data_set)

Arguments

Value

A data frame with 9 columns:

• scientific_name (character)

• family (character)

• acronym (character)

• nativity (character)

• c (numeric)

• w (numeric)

• physiognomy (character)

• duration (character)

• common_name (character)

Examples

# While assessment_glance can be used with a .csv file downloaded# manually from the universal FQA website, it is most typically used# in combination with download_assessment().edison <- download_assessment(25002)assessment_inventory(edison)

Obtain tidy summary information for multiple floristic quality assessments

Description

assessment_list_glance() tidies a list of floristic quality assessmentdata sets obtained fromuniversalfqa.org,returning summary information as a single data frame.

Usage

assessment_list_glance(assessment_list)

Arguments

Value

A data frame with 53 columns:

• assessment_id (numeric)

• title (character)

• date (date)

• site_name (character)

• city (character)

• county (character)

• state (character)

• country (character)

• fqa_db_region (character)

• fqa_db_publication_year (character)

• fqa_db_description (character)

• custom_fqa_db_name (character)

• custom_fqa_db_description (character)

• practitioner (character)

• latitude (character)

• longitude (character)

• weather_notes (character)

• duration_notes (character)

• community_type_notes (character)

• other_notes (character)

• private_public (character)

• total_mean_c (numeric)

• native_mean_c (numeric)

• total_fqi (numeric)

• native_fqi (numeric)

• adjusted_fqi (numeric)

• c_value_zero (numeric) Percent of c-values 0

• c_value_low (numeric) Percent of c-values 1-3

• c_value_mid (numeric) Percent of c-values 4-6

• c_value_high (numeric) Percent of c-values 7-10

• native_tree_mean_c (numeric)

• native_shrub_mean_c (numeric)

• native_herbaceous_mean_c (numeric)

• total_species (numeric)

• native_species (numeric)

• non_native_species

• mean_wetness (numeric)

• native_mean_wetness (numeric)

• tree (numeric)

• shrub (numeric)

• vine (numeric)

• forb (numeric)

• grass (numeric)

• sedge (numeric)

• rush (numeric)

• fern (numeric)

• bryophyte (numeric)

• annual (numeric)

• perennial (numeric)

• biennial (numeric)

• native_annual (numeric)

• native_perennial (numeric)

• native_biennial (numeric)

Examples

# While assessment_list_glance can be used with a list of .csv file downloaded# manually from the universal FQA website, it is most typically used# in combination with download_assessment_list().maine <- download_assessment_list(database = 56)assessment_list_glance(maine)

Obtain species details for a list of floristic quality assessments

Description

assessment_list_inventory() returns a list of data frames, each ofwhich consists of all plant species included in a floristic qualityassessment obtained fromuniversalfqa.org.

Usage

assessment_list_inventory(assessment_list)

Arguments

Value

A list of data frames, each with 9 columns:

• scientific_name (character)

• family (character)

• acronym (character)

• nativity (character)

• c (numeric)

• w (numeric)

• physiognomy (character)

• duration (character)

• common_name (character)

Examples

# While assessment_list_inventory can be used with a list of .csv file downloaded# manually from the universal FQA website, it is most typically used# in combination with download_assessment_list().maine <- download_assessment_list(database = 56)maine_invs <- assessment_list_inventory(maine)

Chicagoland floristic quality assessment data

Description

A data set summarizing 786 floristic quality assessments using the 2017Chicago Region USACE database.

Usage

chicago

Format

A data frame with 52 columns:

• Title (character)

• Date (date)

• Site Name (character)

• City (character)

• County (character)

• State (character)

• Country (character)

• FQA DB Region (character)

• FQA DB Publication Year (character)

• FQA DB Description (character)

• Custom FQA DB Name (character)

• Custom FQA DB Description (character)

• Practitioner (character)

• Latitude (character)

• Longitude (character)

• Weather Notes (character)

• Duration Notes (character)

• Community Type Notes (character)

• Other Notes (character)

• Private/Public (character)

• Total Mean C (numeric)

• Native Mean C (numeric)

• Total FQI: (numeric)

• Native FQI (numeric)

• Adjusted FQI (numeric)

• % C value 0 (numeric)

• % C value 1-3 (numeric)

• % C value 4-6 (numeric)

• % C value 7-10 (numeric)

• Native Tree Mean C (numeric)

• Native Shrub Mean C (numeric)

• Native Herbaceous Mean C (numeric)

• Total Species (numeric)

• Native Species (numeric)

• Non-native Species

• Mean Wetness (numeric)

• Native Mean Wetness (numeric)

• Tree (numeric)

• Shrub (numeric)

• Vine (numeric)

• Forb (numeric)

• Grass (numeric)

• Sedge (numeric)

• Rush (numeric)

• Fern (numeric)

• Bryophyte (numeric)

• Annual (numeric)

• Perennial (numeric)

• Biennial (numeric)

• Native Annual (numeric)

• Native Perennial (numeric)

• Native Biennial (numeric)

Source

universalfqa.org

Obtain tidy summary information for a floristic quality database

Description

database_glance() tidies a floristic quality database obtained fromuniversalfqa.org.

Usage

database_glance(database)

Arguments

Value

A data frame with 8 columns:

• region (character)

• year (numeric)

• description (character)

• total_species (numeric)

• native_species (numeric)

• non_native_species (numeric)

• total_mean_c (numeric)

• native_mean_c (numeric)

Examples

# While database_glance can be used with a .csv file downloaded manually# from the universal FQA website, it is most typically used in combination# with download_database().chicago_db <- download_database(database_id = 1)chicago_db_summary <- database_glance(chicago_db)

Obtain species details for a floristic quality database

Description

database_inventory() returns a data frame of all plant speciesincluded in a floristic quality database obtained fromuniversalfqa.org.

Usage

database_inventory(database)

Arguments

Value

A data frame with 9 columns:

• scientific_name (character)

• family (character)

• acronym (character)

• nativity (character)

• c (numeric)

• w (numeric)

• physiognomy (character)

• duration (character)

• common_name (character)

Examples

# While database_glance can be used with a .csv file downloaded# manually from the universal FQA website, it is most typically used# in combination with download_database().chicago_db <- download_database(database_id = 1)chicago_species <- database_inventory(chicago_db)

Download a single floristic quality assessment

Description

download_assessment() retrieves a specified floristic qualityassessment fromuniversalfqa.org. IDnumbers for assessments in various databases can be found using theindex_fqa_assessments() function.

Usage

download_assessment(assessment_id, timeout = 4)

Arguments

Value

An untidy data frame in the original format of the Universal FQAwebsite, except that the assessment id number has been appended in thefirst row. Useassessment_glance() for atidy summary andassessment_inventory() forspecies-level data.

Examples

databases <- index_fqa_databases() # Database 1 is the original 1994 Chicago edition.chicago_assessments <- index_fqa_assessments(1) # Edison dune and swale has id number 25002.edison <- download_assessment(25002)edison_tidy <- assessment_glance(edison)

Download multiple floristic quality assessments

Description

download_assessment_list() searches a specified floristic qualityassessment database and retrieves all matches fromuniversalfqa.org. Download speeds from thatwebsite may be slow, causing delays in the evaluation of this function.

Usage

download_assessment_list(database_id, ..., timeout = 4)

Arguments

Value

A list of data frames matching the search criteria. Each is an untidydata frame in the original format of the Universal FQA website. Useassessment_list_glance() for a tidysummary.

Examples

databases <- index_fqa_databases() # Database 1 is the original 1994 Chicago edition.somme_assessments <- download_assessment_list(1, site == "Somme Woods")somme_summary <- assessment_list_glance(somme_assessments)

Download a single floristic quality database

Description

download_database() retrieves a specified floristic quality databasefromuniversalfqa.org. A list of availabledatabases can be found using theindex_fqa_databases() function.

Usage

download_database(database_id, timeout = 4)

Arguments

Value

An untidy data frame in the original format of the Universal FQAwebsite. Usedatabase_glance() for a tidysummary anddatabase_inventory() forspecies-level data.

Examples

databases <- index_fqa_databases() # Database 1 is the original 1994 Chicago edition.chicago_database <- download_database(1)

Download a single floristic quality transect assessment

Description

download_transect() retrieves a specified floristic quality transectassessment fromuniversalfqa.org. IDnumbers for transect assessments in various databases can be found using theindex_fqa_transects() function.

Usage

download_transect(transect_id, timeout = 4)

Arguments

Value

An untidy data frame in the original format of the Universal FQAwebsite, except that the transect id number has been appended in thefirst row.. Usetransect_glance() for a tidysummary,transect_phys() for aphysiognometric overview, andtransect_inventory() for species-leveldata.

Examples

databases <- index_fqa_databases() # Database 1 is the original 1994 Chicago edition.chicago_transects <- index_fqa_transects(1) # CBG Sand prairie swale fen A has id number 5932.cbg <- download_transect(5932, timeout = 10)

Download multiple floristic quality transect assessments

Description

download_transect_list() searches a specified floristic qualityassessment database and retrieves all matches fromuniversalfqa.org. Download speeds from thatwebsite may be slow, causing delays in the evaluation of this function.

Usage

download_transect_list(database_id, ..., timeout = 4)

Arguments

Value

A list of data frames matching the search criteria. Each is an untidydata frame in the original format of the Universal FQA website. Usetransect_list_glance() for a tidysummary.

Examples

databases <- index_fqa_databases() # Database 1 is the original 1994 Chicago edition.dupont <- download_transect_list(1, site == "DuPont Natural Area")

List all available public floristic quality assessments

Description

For any given database,index_fqa_assessments() produces a data frameof all floristic quality assessments publicly available atuniversalfqa.org.

Usage

index_fqa_assessments(database_id, timeout = 4)

Arguments

Value

A data frame with 5 columns:

• id (numeric)

• assessment (character)

• date (date)

• site (character)

• practitioner (character)

Examples

databases <- index_fqa_databases() # The 2017 Chicago database has id_number 149chicago_2017_assessments <- index_fqa_assessments(149)

List all available floristic quality assessment databases

Description

index_fqa_databases() produces a data frame showing all floristicquality assessment databases publicly available atuniversalfqa.org.

Usage

index_fqa_databases(timeout = 4)

Arguments

Value

A data frame with 4 columns:

• database_id (numeric)

• region (character)

• year (numeric)

• description (character)

Examples

databases <- index_fqa_databases()

List all available public floristic quality transect assessments

Description

For any given database,index_fqa_transects() produces a data frameof all floristic quality transect assessments publicly available atuniversalfqa.org.

Usage

index_fqa_transects(database_id, timeout = 4)

Arguments

Value

A data frame with 5 columns:

• id (numeric)

• assessment (character)

• date (date)

• site (character)

• practitioner (character)

Examples

databases <- index_fqa_databases() # The 2017 Chicago database has id_number 149chicago_2017_transects <- index_fqa_transects(149)

Missouri floristic quality assessment data

Description

A data set summarizing 216 floristic quality assessments using the 2015Missouri database.

Usage

missouri

Format

A data frame with 52 columns:

• Title (character)

• Date (date)

• Site Name (character)

• City (character)

• County (character)

• State (character)

• Country (character)

• FQA DB Region (character)

• FQA DB Publication Year (character)

• FQA DB Description (character)

• Custom FQA DB Name (character)

• Custom FQA DB Description (character)

• Practitioner (character)

• Latitude (character)

• Longitude (character)

• Weather Notes (character)

• Duration Notes (character)

• Community Type Notes (character)

• Other Notes (character)

• Private/Public (character)

• Total Mean C (numeric)

• Native Mean C (numeric)

• Total FQI: (numeric)

• Native FQI (numeric)

• Adjusted FQI (numeric)

• % C value 0 (numeric)

• % C value 1-3 (numeric)

• % C value 4-6 (numeric)

• % C value 7-10 (numeric)

• Native Tree Mean C (numeric)

• Native Shrub Mean C (numeric)

• Native Herbaceous Mean C (numeric)

• Total Species (numeric)

• Native Species (numeric)

• Non-native Species

• Mean Wetness (numeric)

• Native Mean Wetness (numeric)

• Tree (numeric)

• Shrub (numeric)

• Vine (numeric)

• Forb (numeric)

• Grass (numeric)

• Sedge (numeric)

• Rush (numeric)

• Fern (numeric)

• Bryophyte (numeric)

• Annual (numeric)

• Perennial (numeric)

• Biennial (numeric)

• Native Annual (numeric)

• Native Perennial (numeric)

• Native Biennial (numeric)

Source

universalfqa.org

Acronym of a species in a specified database

Description

species_acronym() accepts a species and a database inventory andreturns the acronym of the species within that database. Either a numericdatabase ID fromuniversalfqa.org or ahomemade inventory with the same format may be specified.

Usage

species_acronym(species, database_id = NULL, database_inventory = NULL)

Arguments

Value

The acronym of the given species within the given database.

Examples

species_acronym("Anemone canadensis", database_id = 149)

C-value of a species in a specified database

Description

species_c() accepts a species and a database inventory and returns thec-value of that species. Either a numeric database ID fromuniversalfqa.org or a homemade inventorywith the same format may be specified.

Usage

species_c(species, database_id = NULL, database_inventory = NULL)

Arguments

Value

The C-value of the given species within the given database.

Examples

species_c("Anemone canadensis", database_id = 149)

Common name of a species in a specified database

Description

species_common name() accepts the scientific name of a species and adatabase inventory and returns the common name of that species. Either a numericdatabase ID fromuniversalfqa.org or ahomemade inventory with the same format may be specified.

Usage

species_common_name(species, database_id = NULL, database_inventory = NULL)

Arguments

Value

The common name of the given species within the given database.

Examples

species_common_name("Anemone canadensis", database_id = 149)

Nativity of a species in a specified database

Description

species_nativity() accepts a species and a database inventory and returns thenativity of that species. Either a numeric database ID fromuniversalfqa.org or a homemade inventorywith the same format may be specified.

Usage

species_nativity(species, database_id = NULL, database_inventory = NULL)

Arguments

Value

The nativity of the given species within the given database, eithernative or non-native.

Examples

species_nativity("Anemone canadensis", database_id = 149)

Physiognomy of a species in a specified database

Description

species_phys() accepts a species and a database inventory and returns thephysiognomy of that species. Either a numeric database ID fromuniversalfqa.org or a homemade inventorywith the same format may be specified.

Usage

species_phys(species, database_id = NULL, database_inventory = NULL)

Arguments

Value

The physiognomy of the given species within the given database

Examples

species_phys("Anemone canadensis", database_id = 149)

Generate the co-occurrence profile for a species

Description

species_profile() accepts a species and list of inventories like thosegenerated byassessment_list_inventory() andreturns the co-occurrence profile of that species. Repeated co-occurrencesacross multiple assessments are included in summary calculations but selfco-occurrences are not.

Usage

species_profile(species, inventory_list, native = FALSE)

Arguments

Value

A data frame with 14 columns:

• target_species (character)

• target_species_c (numeric)

• cospecies_n (numeric)

• cospecies_native_n (numeric)

• cospecies_mean_c (numeric)

• cospecies_native_mean_c (numeric)

• cospecies_std_dev_c (numeric)

• cospecies_native_std_dev_c (numeric)

• percent_native (numeric)

• percent_nonnative (numeric)

• percent_native_low_c (numeric)

• percent_native_med_c (numeric)

• percent_native_high_c (numeric)

• discrepancy_c (numeric)

Examples

# species_profile() is best used in combination with# download_assessment_list() and assessment_list_inventory().ontario <- download_assessment_list(database = 2)ontario_invs <- assessment_list_inventory(ontario)species_profile("Aster lateriflorus", ontario_invs)

Plot the co-occurrence profile of a species

Description

species_profile_plot() accepts a species and list of inventories likethose generated byassessment_list_inventory() andgenerates a histogram of the co-occurrence profile of that species. Repeatedco-occurrences across multiple assessments are included in summarycalculations but self co-occurrences are not.

Usage

species_profile_plot(species, inventory_list, native = FALSE)

Arguments

Examples

# species_profile_plot() is best used in combination with# download_assessment_list() and assessment_list_inventory().ontario <- download_assessment_list(database = 2)ontario_invs <- assessment_list_inventory(ontario)species_profile_plot("Aster lateriflorus", ontario_invs, native = TRUE)

Wetness value of a species in a specified database

Description

species_w() accepts a species and a database inventory and returns thewetness value of that species. Either a numeric database ID fromuniversalfqa.org or a homemade inventorywith the same format may be specified.

Usage

species_w(species, database_id = NULL, database_inventory = NULL)

Arguments

Value

The wetness value of the given species within the given database.

Examples

species_w("Anemone canadensis", database_id = 149)

Obtain tidy summary information for a floristic quality transect assessment

Description

transect_glance() tidies a floristic quality transect assessment dataset obtained fromuniversalfqa.org.

Usage

transect_glance(data_set)

Arguments

Value

A data frame with 1 row and 55 columns:

• transect_id (numeric)

• title (character)

• date (date)

• site_name (character)

• city (character)

• county (character)

• state (character)

• country (character)

• omernik_level_three_ecoregion (character)

• fqa_db_region (character)

• fqa_db_publication_year (character)

• fqa_db_description (character)

• fqa_db_selection_name (character)

• custom_fqa_db_name (character)

• custom_fqa_db_description (character)

• practitioner (character)

• latitude (character)

• longitude (character)

• community_code (character)

• community_name (character)

• community_type_notes (character)

• weather_notes (character)

• duration_notes (character)

• environment_description (character)

• other_notes (character)

• transect_plot_type (character)

• plot_size (numeric) Plot size in square meters

• quadrat_subplot_size (numeric) Quadrat or subplot size in square meters

• transect_length (numeric) Transect length in meters

• sampling_design_description (character)

• cover_method (character)

• private_public (character)

• total_mean_c (numeric)

• cover_weighted_mean_c (numeric)

• native_mean_c (numeric)

• total_fqi (numeric)

• native_fqi (numeric)

• cover_weighted_fqi (numeric)

• cover_weighted_native_fqi (numeric)

• adjusted_fqi (numeric)

• c_value_zero (numeric) Percent of c-values 0

• c_value_low (numeric) Percent of c-values 1-3

• c_value_mid (numeric) Percent of c-values 4-6

• c_value_high (numeric) Percent of c-values 7-10

• total_species (numeric)

• native_species (numeric)

• non_native_species (numeric)

• mean_wetness (numeric)

• native_mean_wetness (numeric)

• annual (numeric)

• perennial (numeric)

• biennial (numeric)

• native_annual (numeric)

• native_perennial (numeric)

• native_biennial (numeric)

Examples

# While transect_glance can be used with a .csv file downloaded manually# from the universal FQA website, it is most typically used in combination# with download_transect().tyler <- download_transect(6352)transect_glance(tyler)

Obtain species details for a floristic quality transect assessment

Description

transect_inventory() returns a data frame of all plant speciesincluded in a floristic quality transect assessment obtained fromuniversalfqa.org.

Usage

transect_inventory(data_set)

Arguments

Value

A data frame with 13 columns:

• species (character)

• family (character)

• acronym (character)

• nativity (character)

• c (numeric)

• w (numeric)

• physiognomy (character)

• duration (character)

• frequency (numeric)

• coverage (numeric)

• relative_frequency_percent (numeric)

• relative_coverage_percent (numeric)

• relative_importance_value (numeric)

Examples

# while transect_glance can be used with a .csv file downloaded# manually from the universal FQA website, it is most typically used# in combination with download_transect().tyler <- download_transect(6352)transect_inventory(tyler)

Obtain tidy summary information for multiple floristic quality transectassessments

Description

transect_list_glance() tidies a list of floristic quality transectassessment data sets obtained fromuniversalfqa.org, returning summaryinformation as a single data frame.

Usage

transect_list_glance(transect_list)

Arguments

Value

A data frame with 1 row and 55 columns:

• transect_id (numeric)

• title (character)

• date (date)

• site_name (character)

• city (character)

• county (character)

• state (character)

• country (character)

• omernik_level_three_ecoregion (character)

• fqa_db_region (character)

• fqa_db_publication_year (character)

• fqa_db_description (character)

• fqa_db_selection_name (character)

• custom_fqa_db_name (character)

• custom_fqa_db_description (character)

• practitioner (character)

• latitude (character)

• longitude (character)

• community_code (character)

• community_name (character)

• community_type_notes (character)

• weather_notes (character)

• duration_notes (character)

• environment_description (character)

• other_notes (character)

• transect_plot_type (character)

• plot_size (numeric) Plot size in square meters

• quadrat_subplot_size (numeric) Quadrat or subplot size in square meters

• transect_length (numeric) Transect length in meters

• sampling_design_description (character)

• cover_method (character)

• private_public (character)

• total_mean_c (numeric)

• cover_weighted_mean_c (numeric)

• native_mean_c (numeric)

• total_fqi (numeric)

• native_fqi (numeric)

• cover_weighted_fqi (numeric)

• cover_weighted_native_fqi (numeric)

• adjusted_fqi (numeric)

• c_value_zero (numeric) Percent of c-values 0

• c_value_low (numeric) Percent of c-values 1-3

• c_value_mid (numeric) Percent of c-values 4-6

• c_value_high (numeric) Percent of c-values 7-10

• total_species (numeric)

• native_species (numeric)

• non_native_species (numeric)

• mean_wetness (numeric)

• native_mean_wetness (numeric)

• annual (numeric)

• perennial (numeric)

• biennial (numeric)

• native_annual (numeric)

• native_perennial (numeric)

• native_biennial (numeric)

Examples

# While transect_list_glance can be used with a list of .csv file downloaded# manually from the universal FQA website, it is most typically used in# combination with download_transect_list().transect_list <- download_transect_list(149, id %in% c(3400, 3427))transect_list_glance(transect_list)

Obtain species details for a list of transect assessments

Description

transect_list_inventory() returns a list of data frames, each of whichconsists of all plant species included in a floristic quality assessment of atransect obtained fromuniversalfqa.org.

Usage

transect_list_inventory(transect_list)

Arguments

Value

A list of data frames, each with 13 columns:

• species (character)

• family (character)

• acronym (character)

• nativity (character)

• c (numeric)

• w (numeric)

• physiognomy (character)

• duration (character)

• frequency (numeric)

• coverage (numeric)

• relative_frequency_percent (numeric)

• relative_coverage_percent (numeric)

• relative_importance_value (numeric)

Examples

# While transect_list_inventory can be used with a list of .csv file downloaded# manually from the universal FQA website, it is most typically used# in combination with download_transect_list()chicago <- download_transect_list(database = 149)chicago_invs <- transect_list_inventory(chicago)

Obtain physiognometric information for a floristic quality transect assessment

Description

transect_phys() returns a data frame with physiognometric informationfor a floristic quality transect assessment obtained fromuniversalfqa.org.

Usage

transect_phys(data_set)

Arguments

Value

A data frame with 6 columns:

• physiognomy (character)

• frequency (numeric)

• coverage (numeric)

• relative_frequency_percent (numeric)

• relative_coverage_percent (numeric)

• relative_importance_value_percent (numeric)

Examples

# While transect_phys can be used with a .csv file downloaded# manually from the universal FQA website, it is most typically used# in combination with download_transect().tyler <- download_transect(6352)transect_phys(tyler)

Extract quadrat/subplot-level inventories from a transect assessment

Description

transect_subplot_inventories() accepts a floristic quality transectassessment data set obtained fromuniversalfqa.org and returns a list ofspecies inventories, one per quadrat/subplot.

Usage

transect_subplot_inventories(transect)

Arguments

Value

A list of data frames, each with 9 columns:

• scientific_name (character)

• family (character)

• acronym (character)

• nativity (character)

• c (numeric)

• w (numeric)

• physiognomy (character)

• duration (character)

• common_name (character)

Examples

cbg_fen <- download_transect(5932)cbg_inventories <- transect_subplot_inventories(cbg_fen)