,
Alexia Myers [aut],
Irene Luwabelwa [aut]| Title: | Floristic Quality Assessment Tools for R |
|---|---|
| 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. |
| Authors: | Andrew Gard [aut, cre] ,
Alexia Myers [aut],
Irene Luwabelwa [aut] |
| Maintainer: | Andrew Gard <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.5.4 |
| Built: | 2024-11-05 05:58:56 UTC |
| Source: | https://github.com/equitable-equations/fqar |
assessment_coccurrences() accepts a list of species inventories
downloaded from universalfqa.org and
returns a complete listing of all co-occurrences. Repeated co-occurrences
across multiple assessments are included, but self co-occurrences are not,
allowing for meaningful summary statistics to be computed.
assessment_cooccurrences(inventory_list)assessment_cooccurrences(inventory_list)
inventory_list |
A list of site inventories having the format of
|
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)
# 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)# 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)
assessment_coccurrences_summary() accepts a list of species
inventories downloaded from
universalfqa.org and returns a summary of
the co-occurrences of each target species. Repeated co-occurrences across
multiple assessments are included in summary calculations, but self
co-occurrences are not.
assessment_cooccurrences_summary(inventory_list)assessment_cooccurrences_summary(inventory_list)
inventory_list |
A list of site inventories having the format of
|
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)
# 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)# 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)
assessment_glance() tidies a floristic quality assessment data set
obtained from universalfqa.org.
assessment_glance(data_set)assessment_glance(data_set)
data_set |
A data set downloaded from
universalfqa.org either manually or using
|
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_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)
# 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)# 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)
assessment_inventory() returns a data frame of all plant species
included in a floristic quality assessment obtained from
universalfqa.org.
assessment_inventory(data_set)assessment_inventory(data_set)
data_set |
A data set downloaded from
universalfqa.org either manually or using
|
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)
# 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)# 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)
assessment_list_glance() tidies a list of floristic quality assessment
data sets obtained from universalfqa.org,
returning summary information as a single data frame.
assessment_list_glance(assessment_list)assessment_list_glance(assessment_list)
assessment_list |
A list of data sets downloaded from
universalfqa.org, typically using
|
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_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)
# 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)# 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)
assessment_list_inventory() returns a list of data frames, each of
which consists of all plant species included in a floristic quality
assessment obtained from universalfqa.org.
assessment_list_inventory(assessment_list)assessment_list_inventory(assessment_list)
assessment_list |
A list of data sets downloaded from
universalfqa.org, typically using
|
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)
# 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)# 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)
A data set summarizing 786 floristic quality assessments using the 2017 Chicago Region USACE database.
chicagochicago
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)
database_glance() tidies a floristic quality database obtained from
universalfqa.org.
database_glance(database)database_glance(database)
database |
A database downloaded from
universalfqa.org either manually or using
|
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)
# 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)# 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)
database_inventory() returns a data frame of all plant species
included in a floristic quality database obtained from
universalfqa.org.
database_inventory(database)database_inventory(database)
database |
A database downloaded from
universalfqa.org either manually or using
|
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)
# 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)# 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_assessment() retrieves a specified floristic quality
assessment from universalfqa.org. ID
numbers for assessments in various databases can be found using the
index_fqa_assessments() function.
download_assessment(assessment_id)download_assessment(assessment_id)
assessment_id |
A numeric identifier of the desired floristic quality
assessment, as specified by
universalfqa.org. ID numbers for
assessments in specified databases can be viewed with the
|
An untidy data frame in the original format of the Universal FQA
website. Use assessment_glance() for a
tidy summary and
assessment_inventory() for
species-level data.
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) edison_species <- assessment_inventory(edison)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) edison_species <- assessment_inventory(edison)
download_assessment_list() searches a specified floristic quality
assessment database and retrieves all matches from
universalfqa.org. Download speeds from that
website may be slow, causing delays in the evaluation of this function.
download_assessment_list(database_id, ...)download_assessment_list(database_id, ...)
database_id |
Numeric identifier of the desired floristic quality
assessment database, as specified by
universalfqa.org. Database id numbers can
be viewed with the
|
... |
|
A list of data frames matching the search criteria. Each is an untidy
data frame in the original format of the Universal FQA website. Use
assessment_list_glance() for a tidy
summary.
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)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_database() retrieves a specified floristic quality database
from universalfqa.org. A list of available
databases can be found using the
index_fqa_databases() function.
download_database(database_id)download_database(database_id)
database_id |
A numeric identifier of the desired floristic quality
database, as specified by
universalfqa.org. ID numbers for
databases recognized this site can be viewed with the
|
An untidy data frame in the original format of the Universal FQA
website. Use database_glance() for a tidy
summary and database_inventory() for
species-level data.
databases <- index_fqa_databases() # Database 1 is the original 1994 Chicago edition. chicago_database <- download_database(1)databases <- index_fqa_databases() # Database 1 is the original 1994 Chicago edition. chicago_database <- download_database(1)
download_transect() retrieves a specified floristic quality transect
assessment from universalfqa.org. ID
numbers for transect assessments in various databases can be found using the
index_fqa_transects() function.
download_transect(transect_id)download_transect(transect_id)
transect_id |
A numeric identifier of the desired floristic quality
transect assessment, as specified by
universalfqa.org. ID numbers for transect
assessments in specified databases can be viewed with the
|
An untidy data frame in the original format of the Universal FQA
website. Use transect_glance() for a tidy
summary, transect_phys() for a
physiognometric overview, and
transect_inventory() for species-level
data.
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)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)
download_transect_list() searches a specified floristic quality
assessment database and retrieves all matches from
universalfqa.org. Download speeds from that
website may be slow, causing delays in the evaluation of this function.
download_transect_list(database_id, ...)download_transect_list(database_id, ...)
database_id |
Numeric identifier of the desired floristic quality
assessment database, as specified by
universalfqa.org. Database id numbers can
be viewed with the
|
... |
|
A list of data frames matching the search criteria. Each is an untidy
data frame in the original format of the Universal FQA website. Use
transect_list_glance() for a tidy
summary.
databases <- index_fqa_databases() # Database 1 is the original 1994 Chicago edition. dupont <- download_transect_list(1, site == "DuPont Natural Area")databases <- index_fqa_databases() # Database 1 is the original 1994 Chicago edition. dupont <- download_transect_list(1, site == "DuPont Natural Area")
For any given database, index_fqa_assessments() produces a data frame
of all floristic quality assessments publicly available at
universalfqa.org.
index_fqa_assessments(database_id)index_fqa_assessments(database_id)
database_id |
A numeric identifier of the desired database, as specified
by universalfqa.org. The id numbers can
be viewed with the
|
A data frame with 5 columns:
id (numeric)
assessment (character)
date (date)
site (character)
practitioner (character)
databases <- index_fqa_databases() # The 2017 Chicago database has id_number 149 chicago_2017_assessments <- index_fqa_assessments(149)databases <- index_fqa_databases() # The 2017 Chicago database has id_number 149 chicago_2017_assessments <- index_fqa_assessments(149)
index_fqa_databases() produces a data frame showing all floristic
quality assessment databases publicly available at
universalfqa.org.
index_fqa_databases()index_fqa_databases()
A data frame with 4 columns:
database_id (numeric)
region (character)
year (numeric)
description (character)
databases <- index_fqa_databases()databases <- index_fqa_databases()
For any given database, index_fqa_transects() produces a data frame
of all floristic quality transect assessments publicly available at
universalfqa.org.
index_fqa_transects(database_id)index_fqa_transects(database_id)
database_id |
A numeric identifier of the desired database, as specified
by universalfqa.org. The id numbers can
be viewed with the
|
A data frame with 5 columns:
id (numeric)
assessment (character)
date (date)
site (character)
practitioner (character)
databases <- index_fqa_databases() # The 2017 Chicago database has id_number 149 chicago_2017_transects <- index_fqa_transects(149)databases <- index_fqa_databases() # The 2017 Chicago database has id_number 149 chicago_2017_transects <- index_fqa_transects(149)
A data set summarizing 216 floristic quality assessments using the 2015 Missouri database.
missourimissouri
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)
species_acronym() accepts a species and a database inventory and
returns the acronym of the species within that database. Either a numeric
database ID from universalfqa.org or a
homemade inventory with the same format may be specified.
species_acronym(species, database_id = NULL, database_inventory = NULL)species_acronym(species, database_id = NULL, database_inventory = NULL)
species |
The scientific name of the plant species of interest |
database_id |
ID number of an existing database on
universalfqa.org. Use
|
database_inventory |
An inventory of species having the same form as one
created using
|
The acronym of the given species within the given database.
species_acronym("Anemone canadensis", database_id = 149)species_acronym("Anemone canadensis", database_id = 149)
species_c() accepts a species and a database inventory and returns the
c-value of that species. Either a numeric database ID from
universalfqa.org or a homemade inventory
with the same format may be specified.
species_c(species, database_id = NULL, database_inventory = NULL)species_c(species, database_id = NULL, database_inventory = NULL)
species |
The scientific name of the plant species of interest |
database_id |
ID number of an existing database on
universalfqa.org. Use
|
database_inventory |
An inventory of species having the same form as one
created using
|
The C-value of the given species within the given database.
species_c("Anemone canadensis", database_id = 149)species_c("Anemone canadensis", database_id = 149)
species_common name() accepts the scientific name of a species and a
database inventory and returns the common name of that species. Either a numeric
database ID from universalfqa.org or a
homemade inventory with the same format may be specified.
species_common_name(species, database_id = NULL, database_inventory = NULL)species_common_name(species, database_id = NULL, database_inventory = NULL)
species |
The scientific name of the plant species of interest |
database_id |
ID number of an existing database on
universalfqa.org. Use
|
database_inventory |
An inventory of species having the same form as one
created using
|
The common name of the given species within the given database.
species_common_name("Anemone canadensis", database_id = 149)species_common_name("Anemone canadensis", database_id = 149)
species_nativity() accepts a species and a database inventory and returns the
nativity of that species. Either a numeric database ID from
universalfqa.org or a homemade inventory
with the same format may be specified.
species_nativity(species, database_id = NULL, database_inventory = NULL)species_nativity(species, database_id = NULL, database_inventory = NULL)
species |
The scientific name of the plant species of interest |
database_id |
ID number of an existing database on
universalfqa.org. Use
|
database_inventory |
An inventory of species having the same form as one
created using
|
The nativity of the given species within the given database, either native or non-native.
species_nativity("Anemone canadensis", database_id = 149)species_nativity("Anemone canadensis", database_id = 149)
species_phys() accepts a species and a database inventory and returns the
physiognomy of that species. Either a numeric database ID from
universalfqa.org or a homemade inventory
with the same format may be specified.
species_phys(species, database_id = NULL, database_inventory = NULL)species_phys(species, database_id = NULL, database_inventory = NULL)
species |
The scientific name of the plant species of interest |
database_id |
ID number of an existing database on
universalfqa.org. Use
|
database_inventory |
An inventory of species having the same form as one
created using
|
The physiognomy of the given species within the given database
species_phys("Anemone canadensis", database_id = 149)species_phys("Anemone canadensis", database_id = 149)
species_profile() accepts a species and list of inventories like those
generated by
assessment_list_inventory() and
returns the co-occurrence profile of that species. Repeated co-occurrences
across multiple assessments are included in summary calculations but self
co-occurrences are not.
species_profile(species, inventory_list, native = FALSE)species_profile(species, inventory_list, native = FALSE)
species |
The scientific name of the target plant species |
inventory_list |
A list of site inventories having the format of
|
native |
Logical indicating whether only native co-occurrences should be considered. |
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)
# 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)# 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)
species_profile_plot() accepts a species and list of inventories like
those generated by
assessment_list_inventory() and
generates a histogram of the co-occurrence profile of that species. Repeated
co-occurrences across multiple assessments are included in summary
calculations but self co-occurrences are not.
species_profile_plot(species, inventory_list, native = FALSE)species_profile_plot(species, inventory_list, native = FALSE)
species |
The scientific name of the target plant species |
inventory_list |
A list of site inventories having the format of
|
native |
Logical indicating whether only native co-occurrences should be considered. |
# 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)# 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)
species_w() accepts a species and a database inventory and returns the
wetness value of that species. Either a numeric database ID from
universalfqa.org or a homemade inventory
with the same format may be specified.
species_w(species, database_id = NULL, database_inventory = NULL)species_w(species, database_id = NULL, database_inventory = NULL)
species |
The scientific name of the plant species of interest |
database_id |
ID number of an existing database on
universalfqa.org. Use
|
database_inventory |
An inventory of species having the same form as one
created using
|
The wetness value of the given species within the given database.
species_w("Anemone canadensis", database_id = 149)species_w("Anemone canadensis", database_id = 149)
transect_glance() tidies a floristic quality transect assessment data
set obtained from universalfqa.org.
transect_glance(data_set)transect_glance(data_set)
data_set |
A data set downloaded from
universalfqa.org either manually or using
|
A data frame with 1 row and 54 columns:
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)
# 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)# 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)
transect_inventory() returns a data frame of all plant species
included in a floristic quality transect assessment obtained from
universalfqa.org.
transect_inventory(data_set)transect_inventory(data_set)
data_set |
A data set downloaded from
universalfqa.org either manually or using
|
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)
# 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)# 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)
transect_list_glance() tidies a list of floristic quality transect
assessment data sets obtained from
universalfqa.org, returning summary
information as a single data frame.
transect_list_glance(transect_list)transect_list_glance(transect_list)
transect_list |
A list of data sets downloaded from
universalfqa.org, typically using
|
A data frame with 1 row and 54 columns:
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)
# 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)# 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)
transect_list_inventory() returns a list of data frames, each of which
consists of all plant species included in a floristic quality assessment of a
transect obtained from universalfqa.org.
transect_list_inventory(transect_list)transect_list_inventory(transect_list)
transect_list |
A list of data sets downloaded from
universalfqa.org, typically using
|
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)
# 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)# 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)
transect_phys() returns a data frame with physiognometric information
for a floristic quality transect assessment obtained from
universalfqa.org.
transect_phys(data_set)transect_phys(data_set)
data_set |
A data set downloaded from
universalfqa.org either manually or using
|
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)
# 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)# 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)
transect_subplot_inventories() accepts a floristic quality transect
assessment data set obtained from
universalfqa.org and returns a list of
species inventories, one per quadrat/subplot.
transect_subplot_inventories(transect)transect_subplot_inventories(transect)
transect |
A data set downloaded from
universalfqa.org either manually or using
|
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)
cbg_fen <- download_transect(5932) cbg_inventories <- transect_subplot_inventories(cbg_fen)cbg_fen <- download_transect(5932) cbg_inventories <- transect_subplot_inventories(cbg_fen)