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Analysis and Metrics","monthly_downloads":204,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# fqar \n\n\u003cimg src=\"man/figures/logo.png\" align=\"right\" height=\"138\" /\u003e\n\n  \u003c!-- badges: start --\u003e\n  [![R-CMD-check](https://github.com/equitable-equations/fqar/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/equitable-equations/fqar/actions/workflows/R-CMD-check.yaml)\n  [![CRAN status](https://www.r-pkg.org/badges/version/fqar)](https://CRAN.R-project.org/package=fqar)\n  [![DOI](https://joss.theoj.org/papers/10.21105/joss.06366/status.svg)](https://doi.org/10.21105/joss.06366)\n\n\n  \u003c!-- badges: end --\u003e\n  \nFloristic 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](https://universalfqa.org/), an online database maintained by [Openlands](https://openlands.org/).\n\n## Installation\n \nThe ${\\tt fqar}$ package is available on CRAN.\n\n```{r install}\ninstall.packages(\"fqar\")\n```\n\nAlternatively, the development version can be installed from [GitHub](https://github.com/equitable-equations/fqar).\n\n```{r github}\ndevtools::install_github(\"equitable-equations/fqar\")\n```\n\n## Usage \n\nThe ${\\tt fqar}$ package consists of four categories of functions: indexing, downloading, tidying, and analytic functions. ${\\tt fqar}$ also includes two sample data sets. \n\n### Indexing functions\n\nAt the simplest level, `fqar` allows users to obtain specific information about the databases, assessments, and transect assessments available from [universalfqa.org](https://universalfqa.org/). \n\n```{r indexing}\n# download a list of all fqa databases:\ndatabases \u003c- index_fqa_databases()\n\n# download a list of all assessments in a specific database:\nchicago_fqas \u003c- index_fqa_assessments(database_id = 149) \n\n# download a list of all transect assessments in a specific database:\nchicago_transects \u003c- index_fqa_transects(database_id = 149)\n```\n\n### Downloading functions\n\nFloristic quality assessments can be downloaded individually by ID number or collectively using `dplyr::filter` syntax.\n\n```{r downloading}\n# download a single assessment using the `assessment_id` assigned by\n# [universalfqa.org](https://universalfqa.org/). These identifiers \n# can be found using `index_fqa_assessments`.\nwoodland \u003c- download_assessment(assessment_id = 25640)\n\n# download multiple assessments:\nmcdonald_fqas \u003c- download_assessment_list(database_id = 149, \n                                          site == \"McDonald Woods\")\n```\n\n${\\tt fqar}$ also provides functions for downloading transect assessments.\n\n```{r downloading2}\n# download a single transect assessment:\nrock_garden \u003c- download_transect(transect_id = 6875)\n\n# download multiple transect assessments:\nlord_fqas \u003c- download_transect_list(database = 63,\n                                    practitioner == \"Sam Lord\")\n```\n\nUnfortunately, the [universalfqa.org](https://universalfqa.org/) server is often slow, and downloads (especially for transect assessments) may take some time. \n\n### Tidying functions\n\nData sets obtained from universalfqa.org are quite messy. ${\\tt fqar}$ provides tools for converting such sets into a more convenient tidy format.\n\n```{r tidying}\n# obtain a data frame with species data for a downloaded assessment:\nwoodland_species \u003c- assessment_inventory(woodland)\n\n# obtain a data frame with summary information for a downloaded assessment:\nwoodland_summary \u003c- assessment_glance(woodland)\n\n# obtain a data frame with summary information for multiple downloaded assessments:\nmcdonald_summary \u003c- assessment_list_glance(mcdonald_fqas)\n```\n\nSimilar functions are provided for handling transect assessments. For those sets, physiognometric information can also be extracted.\n\n```{r tidying2}\n# obtain a data frame with species data for a downloaded transect assessment:\nsurvey_species \u003c- transect_inventory(rock_garden)\n\n# obtain a data frame with physiognometric data for a downloaded transect assessment:\nsurvey_phys \u003c- transect_phys(rock_garden)\n\n# obtain a data frame with summary information for a downloaded transect assessment:\nrock_garden_summary  \u003c- transect_glance(rock_garden)\n\n# obtain a data frame with summary information for multiple downloaded transect assessments:\nlord_summary \u003c- transect_list_glance(lord_fqas)\n```\n\n### Analytic functions\n\nAs of version 0.3.0, ${\\tt fqar}$ includes 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.\n\n```{r analysis}\n# Obtain a tidy data frame of all co-occurrences in the 1995 Southern Ontario database:\nontario \u003c- download_assessment_list(database = 2)\n\n# Extract inventories as a list:\nontario_invs \u003c- assessment_list_inventory(ontario)\n\n# Enumerate all co-occurrences in this database:\nontario_cooccurrences \u003c- assessment_cooccurrences(ontario_invs)\n\n# Sumamrize co-occurrences in this database, one row per target species:\nontario_cooccurrences \u003c- assessment_cooccurrences_summary(ontario_invs)\n```\n\nOf particular note is the `species_profile()` function, which returns the frequency distribution of C-values of co-occurring species for a given target species.\n\n```{r profile}\naster_profile \u003c- species_profile(\"Aster lateriflorus\", ontario_invs)\n```\n\n## Learn More \n\n* Read the ${\\tt fqar}$ [vignette](https://github.com/equitable-equations/fqar/blob/main/vignettes/fqar.Rmd) to learn how to download and analyze FQAs with fqar.\n* View the help files of any function in the ${\\tt fqar}$ package for more examples. \n\n## Contribute\n\nTo contribute to ${\\tt fqar}$ you can fork this repository and create pull requests to add features you think will be useful for users. 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