assessment_glance: Obtain tidy summary information for a floristic quality...

In fqar: Floristic Quality Assessment Tools for R

Obtain tidy summary information for a floristic quality assessment

Description

assessment_glance() tidies a floristic quality assessment data set obtained from universalfqa.org.

Usage

assessment_glance(data_set)

Arguments

data_set

A data set downloaded from universalfqa.org either manually or using download_assessment()

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)

fqar documentation built on June 22, 2025, 1:06 a.m.