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Floristic Quality Assessment (FQA) is a standardized method for rating the ecological value of natural areas based on the plant species found within them. The \({\tt fqar}\) package provides tools to download and analyze floristic quality assessments from universalfqa.org. .


The \({\tt fqar}\) package is available on CRAN.

{r install} install.packages("fqar")

Alternatively, the development version can be installed from GitHub.

{r github} devtools::install_github("equitable-equations/fqar")


The \({\tt fqar}\) package consists of four categories of functions: indexing, downloading, tidying, and analytic functions. \({\tt fqar}\) also includes two sample data sets. NOTE: analytic functions are currently available only in the developmental version.

Indexing functions

```{r indexing} # download a list of all fqa databases: databases <- index_fqa_databases()

download a list of all assessments in a specific database:

chicago_fqas <- index_fqa_assessments(database_id = 149)

download a list of all transect assessments in a specific database:

chicago_transects <- index_fqa_transects(database_id = 149)

### Downloading functions

Floristic quality assessments can be downloaded individually by ID number or collectively using `dplyr::filter` syntax.

```{r downloading}
# download a single assessment:
woodland <- download_assessment(assessment_id = 25640)

# download multiple assessments:
mcdonald_fqas <- download_assessment_list(database_id = 149, 
                                          site == "McDonald Woods")

\({\tt fqar}\) also provides functions for downloading transect assessments.

```{r downloading2} # download a single transect assessment: rock_garden <- download_transect(transect_id = 6875)

download multiple transect assessments:

lord_fqas <- download_transect_list(database = 63, practitioner == “Sam Lord”)

Unfortunately, the [universalfqa.org](https://universalfqa.org/) server is often slow, and downloads (especially for transect assessments) may take some time. 

### Tidying functions

Data sets obtained from universalfqa.org are quite messy. ${\tt fqar}$ provides tools for converting such sets into a more convenient tidy format.

```{r tidying}
# obtain a data frame with species data for a downloaded assessment:
woodland_species <- assessment_inventory(woodland)

# obtain a data frame with summary information for a downloaded assessment:
woodland_summary <- assessment_glance(woodland)

# obtain a data frame with summary information for multiple downloaded assessments:
mcdonald_summary <- assessment_list_glance(mcdonald_fqas)

Similar functions are provided for handling transect assessments. For those sets, physiognometric information can also be extracted.

```{r tidying2} # obtain a data frame with species data for a downloaded transect assessment: survey_species <- transect_inventory(rock_garden)

obtain a data frame with physiognometric data for a downloaded transect assessment:

survey_phys <- transect_phys(rock_garden)

obtain a data frame with summary information for a downloaded transect assessment:

rock_garden_summary <- transect_glance(rock_garden)

obtain a data frame with summary information for multiple downloaded transect assessments:

lord_summary <- transect_list_glance(lord_fqas)

### Analytic functions

The developmental version of ${\tt fqar}$ provides tools for analyzing species co-occurrence across multiple floristic quality assessments. A typical workflow consists of downloading a list of assessments, extracting inventories from each, then enumerating and summarizing co-occurrences of the species of interest.

```{r analysis}
# Obtain a tidy data frame of all co-occurrences in the 1995 Southern Ontario database:
ontario <- download_assessment_list(database = 2)

# Extract inventories as a list:
ontario_invs <- assessment_list_inventory(ontario)

# Enumerate all co-occurrences in this database:
ontario_cooccurrences <- assessment_cooccurrences(ontario_invs)

# Sumamrize co-occurrences in this database, one row per target species:
ontario_cooccurrences <- assessment_cooccurrences_summary(ontario_invs)

Of particular note is the species_profile() function, which returns the frequency distribution of C-values of co-occurring species for a given target species.

{r profile} aster_profile <- species_profile("Aster lateriflorus", ontario_invs)

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