Indexing functions
Each floristic quality assessment is tied to a specific databases of
native plants that has been compiled by experts in local flora. A
listing of all databases accepted by universalFQA.org can be viewed with
the \({\tt index\_fqa\_databases()}\)
function.
databases <- index_fqa_databases()
head(databases)
#> # A tibble: 6 × 4
#> database_id region year descr…¹
#> <dbl> <chr> <dbl> <chr>
#> 1 206 "Allegheny Plateau, Glaciated" 2021 Faber-…
#> 2 70 "Appalachian Mtn (EPA Ecoregions 66,67,68,69) of KY… 2013 Gianop…
#> 3 108 "Atlantic Coastal Pine Barrens (8.5.4) " 2017 NEIWPC…
#> 4 136 "Atlantic Coastal Pine Barrens (8.5.4)" 2018 Nature…
#> 5 204 "Atlantic Coastal Pine Barrens (8.5.4)" 2021 Faber-…
#> 6 1 "Chicago Region" 1994 Swink,…
#> # … with abbreviated variable name ¹description
To see a listing of all public floristic quality assessments using a
given database, use the \({\tt
index\_fqa\_assessments()}\) function.
missouri_fqas <- index_fqa_assessments(database_id = 63)
head(missouri_fqas)
#> # A tibble: 6 × 5
#> id assessment date site practitioner
#> <dbl> <chr> <date> <chr> <chr>
#> 1 26735 Oakwood Bottoms Survey 2022-08-18 Oakwood Bottoms Jonathan Jackson and…
#> 2 26527 1 2022-08-04 Fremont GRB
#> 3 26528 2 2022-08-04 Fremont grb
#> 4 26530 3 2022-08-04 Fremont grb
#> 5 26531 4 2022-08-04 Fremont grb
#> 6 26532 5 2022-08-04 Fremont grb
Similarly, the \({\tt
index\_fqa\_transects()}\) function returns a listing of all
public transect assessments using the specified database.
missouri_transects <- index_fqa_transects(database_id = 63)
head(missouri_transects)
#> # A tibble: 6 × 5
#> id assessment date site practitioner
#> <dbl> <chr> <date> <chr> <chr>
#> 1 6875 Post Burn Survey 2022-06-14 Rock Garden Glade Colatskie, Scherrer, Nels…
#> 2 6736 Plot 24 2022-02-11 kaintuck GRB
#> 3 6737 Plot 25 2022-02-11 kaintuck GRB
#> 4 6738 Plot 26 2022-02-11 kaintuck GRB
#> 5 6739 Plot 27 2022-02-11 kaintuck GRB
#> 6 6740 Plot 28 2022-02-11 kaintuck GRB
Downloading functions
Floristic quality assessments can be downloaded individually by id
number or in batches according to specified search criteria using the
\({\tt download\_assessment()}\) and
\({\tt download\_assessment\_list()}\)
functions, respectively.
The first of these accepts an assessment ID number as its sole input
and returns a data frame. For instance, the Grasshopper Hollow survey
has \({\tt assessment\_id} = 25961\)
according to the listing obtained using \({\tt
index\_fqa\_assessments()}\). The following code downloads this
assessment.
grasshopper <- download_assessment(assessment_id = 25961)
Multiple assessments from a specified database can be downloaded
simultaneously using \({\tt
download\_assessment\_list()}\), which makes use of \({\tt dplyr::filter}\) syntax on the
variables \({\tt id, assessment, date,
site}\) and \({\tt
practitioner}\). For instance, the following code downloads all
assessments performed using the 2015 Missouri database at the Ambrose
Farm site.
ambrose <- download_assessment_list(database_id = 63,
site == "Ambrose Farm")
For even mid-sized requests, this command may run slowly due to the
limited speed of the universalFQA.org website. For this
reason, a progress bar has been added to the \({\tt download\_assessment\_list()}\)
function when \(n\ge 5\).
As the name suggests, the output of \({\tt
download\_assessment\_list()}\) is a list of data frames.
class(ambrose)
#> [1] "list"
length(ambrose)
#> [1] 3
Transect assessment data data stored on universalFQA.org is accessible to
analysts using the \({\tt fqar}\)
package via the functions \({\tt
download\_transect()}\) and \({\tt
download\_transect\_list()}\), which work exactly like their
counterparts, \({\tt
download\_assessment()}\) and \({\tt
download\_assessment\_list()}\).
rock_garden <- download_transect(transect_id = 6875)
golden <- download_transect_list(database_id = 63,
site == "Golden Prairie")
Tidying functions
The data frames obtained from these downloading functions are all
highly untidy, respecting the default structure of the website from
which they are obtained. The \({\tt
fqar}\) package provides tools for efficiently re-formatting
these sets.
Each floristic quality assessments on universalFQA.org includes two types
of information: details about the species observed during data
collection and summary information about the assessment as a whole. The
\({\tt fqar}\) functions \({\tt assessment\_inventory()}\) and \({\tt assessment\_glance()}\) extract and
tidy these two types of information.
For instance, the following code creates a data frame of species
found in the 2021 Grasshopper Hollow survey downloaded earlier.
grasshopper_species <- assessment_inventory(grasshopper)
glimpse(grasshopper_species)
#> Rows: 300
#> Columns: 9
#> $ `Scientific Name` <chr> "Acer rubrum var. rubrum", "Acer saccharum subsp. sa…
#> $ Family <chr> "Sapindaceae", "Sapindaceae", "Asteraceae", "Acorace…
#> $ Acronym <chr> "ACERUR", "ACESUG", "ACHMIL", "ACOCAL", "ACTPAC", "A…
#> $ `Native?` <chr> "native", "native", "native", "non-native", "native"…
#> $ C <dbl> 5, 5, 1, 0, 8, 2, 5, 4, 4, 2, 7, 6, 4, 5, 4, 8, 5, 6…
#> $ W <dbl> 0, 3, 3, -5, 3, 3, -3, 5, 3, 3, -5, 3, 0, 0, 3, 5, 5…
#> $ Physiognomy <chr> "tree", "tree", "forb", "forb", "forb", "forb", "for…
#> $ Duration <chr> "perennial", "perennial", "perennial", "perennial", …
#> $ `Common Name` <chr> "red maple", "sugar maple", "yarrow", "sweet flag", …
A tidy summary of the assessment can be obtained with \({\tt assessment\_glance()}\). The output is
a data frame with a single row and 53 columns, including \({\tt Native\ Mean\ C}\), \({\tt Native\ Species}\), and \({\tt Native\ FQI}\).
grasshopper_summary <- assessment_glance(grasshopper)
names(grasshopper_summary)
#> [1] "Title" "Date"
#> [3] "Site Name" "City"
#> [5] "County" "State"
#> [7] "Country" "FQA DB Region"
#> [9] "FQA DB Publication Year" "FQA DB Description"
#> [11] "Custom FQA DB Name" "Custom FQA DB Description"
#> [13] "Practitioner" "Latitude"
#> [15] "Longitude" "Weather Notes"
#> [17] "Duration Notes" "Community Type Notes"
#> [19] "Other Notes" "Private/Public"
#> [21] "Total Mean C" "Native Mean C"
#> [23] "Total FQI" "Native FQI"
#> [25] "Adjusted FQI" "% C value 0"
#> [27] "% C value 1-3" "% C value 4-6"
#> [29] "% C value 7-10" "Native Tree Mean C"
#> [31] "Native Shrub Mean C" "Native Herbaceous Mean C"
#> [33] "Total Species" "Native Species"
#> [35] "Non-native Species" "Mean Wetness"
#> [37] "Native Mean Wetness" "Tree"
#> [39] "Shrub" "Vine"
#> [41] "Forb" "Grass"
#> [43] "Sedge" "Rush"
#> [45] "Fern" "Bryophyte"
#> [47] "Annual" "Perennial"
#> [49] "Biennial" "Native Annual"
#> [51] "Native Perennial" "Native Biennial"
The tidy format provided by \({\tt
assessment\_glance()}\) is most useful when applied to multiple
data sets at once, for instance in the situation where the analyst wants
to consider statistics from many different assessments simultaneously.
The \({\tt
assessment\_list\_glance()}\) function provides a shortcut when
those data frames are housed in a list like that returned by \({\tt download\_assessment\_list()}\). For
instance, the following code returns a data frame with 52 columns and 3
rows, one per assessment.
ambrose_summary <- assessment_list_glance(ambrose)
The \({\tt fqar}\) package also
provides functions for handling transect assessment data. \({\tt transect\_inventory()}\), \({\tt transect\_glance()}\), and \({\tt transect\_list\_glance()}\) work just
like their counterparts, \({\tt
assessment\_inventory()}\), \({\tt
assessment\_glance()}\), and \({\tt
assessment\_list\_glance()}\).
rock_garden_species <- transect_inventory(rock_garden)
rock_garden_summary <- transect_glance(rock_garden)
golden_summary <- transect_list_glance(golden)
Additionally, transect assessments usually include physiognometric
metrics like relative frequency and relative coverage. These can be
extracted with the \({\tt
trasect\_phys()}\) function.
rock_garden_phys <- transect_phys(rock_garden)
glimpse(rock_garden_phys)
#> Rows: 6
#> Columns: 6
#> $ Physiognomy <chr> "Native forb", "Native grass", "Native sed…
#> $ Frequency <dbl> 115, 53, 20, 6, 4, 1
#> $ Coverage <dbl> 628, 413, 180, 125, 78, 1
#> $ `Relative Frequency (%)` <dbl> 51.6, 23.8, 9.0, 2.7, 1.8, 0.4
#> $ `Relative Coverage (%)` <dbl> 26.1, 17.2, 7.5, 5.2, 3.2, 0.0
#> $ `Relative Importance Value` <dbl> 38.9, 20.5, 8.3, 4.0, 2.5, 0.2
Data sets
Two tidy data sets of floristic quality data, \({\tt missouri}\) and \({\tt chicago}\), are included with the
\({\tt fqar}\) package. Produced with
\({\tt assessment\_list\_glance()}\),
these show summary information for every floristic quality assessment
that used databases 63 and 149, respectively, prior to August 14, 2022.
These sets may be useful for visualization or machine-learning purposes.
For instance, one might consider the relationship between richness and
native mean C in sites assessed using the 2015 Missouri database:
ggplot(missouri, aes(x = `Native Species`,
y = `Native Mean C`)) +
geom_point() +
geom_smooth() +
scale_x_continuous(trans = "log10") +
labs(x = "Native Species (logarithmic scale)",
y = "Native Mean C") +
theme_minimal()
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
