BeeBDC

Bee Biodiversity Data Cleaning.
https://github.com/jbdorey/beebdc

Category: Biosphere
Sub Category: Biodiversity Data Cleaning and Standardization

Last synced: about 14 hours ago
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Repository metadata

Repo for the BeeBDC package

• Host: GitHub
• URL: https://github.com/jbdorey/beebdc
• Owner: jbdorey
• Created: 2023-03-28T02:16:07.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2026-02-05T22:16:04.000Z (about 2 months ago)
• Last Synced: 2026-03-21T14:11:34.213Z (6 days ago)
• Language: R
• Homepage: https://jbdorey.github.io/BeeBDC/
• Size: 69.6 MB
• Stars: 4
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Releases: 0
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md

README.Rmd

          ---
output: 
   rmarkdown::github_document:
     html_preview: true
pkgdown:
  as_is: true
urlcolor: blue
editor_options: 
  markdown: 
    wrap: 80

vignette: >
  %\VignetteIndexEntry{BeeBDC}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
library(BeeBDC)
```



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# 

## **BeeBDC: an occurrence data cleaning package**



[![CRANstatus](https://www.r-pkg.org/badges/version/BeeBDC)](https://CRAN.R-project.org/package=BeeBDC)
[![downloads](https://cranlogs.r-pkg.org/badges/grand-total/BeeBDC)](https://cranlogs.r-pkg.org:443/badges/grand-total/BeeBDC)
[![R-CMD-check](https://github.com/jbdorey/BeeBDC/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/jbdorey/BeeBDC/actions/workflows/R-CMD-check.yaml)
[![License](https://img.shields.io/badge/license-GPL%20(%3E=%203)-lightgrey.svg?style=flat)](http://www.gnu.org/licenses/gpl-3.0.html)



#### **Overview**

The consistent implementation of biodiversity data continues to be a challenge
for researchers. We present the **BeeBDC** package which provides novel and
updated functions for flagging, cleaning, and visualising occurrence datasets.
Our functions are mostly general in regards to taxon; however, we also provide
some functions and data that are specific for use with bee occurrence data;
specifically due to their input data. We build upon functions and conventions in
other fantastic R packages, especially [bdc](https://brunobrr.github.io/bdc/)
and
[CoordinateCleaner](https://ropensci.github.io/CoordinateCleaner/articles/Cleaning_GBIF_data_with_CoordinateCleaner.html),
while also removing many dependencies on sp-related packages. Hence, our package
name is **Bee** **B**iodiversity **D**ata **C**leaning (**BeeBDC**).

We provide a full workflow that uses BeeBDC, bdc, and CoordinateCleaner to clean
occurrence data in our Articles page and encourage users to read and also cite
this primary [publication](https://doi.org/10.1101/2023.06.30.547152). For our
parallelised implementation of iChao and iNEXT species richness estimations,
cite this [primary publication](https://doi.org/10.21203/rs.3.rs-6372769/v1).

#### **Structure of *BeeBDC***

The **BeeBDC** toolkit is organized using the conventions similar to **bdc** and
**CoordinateCleaner**.

> Like in the **bdc** package, we provide a suggested workflow here. While our
> functions can mostly be run out of order, there are a few exceptions mentioned
> throughout the documentation. Additionally, many functions require the
> database_id column that is generated early on in the **BeeBDC** or **bdc**
> workflows. When running very large datasets (e.g., the global bee occurrence
> dataset) you may require a machine that has a minimum amount of RAM (\~32 GB).
> However, we do try to provide work-arounds, especially by alowing some
> functions to be broken into consumable chunks. Paper DOI -
> ; Package GitHub -
> 

#### ![Workflow figure from Dorey et al. 2023](https://photos.smugmug.com/photos/i-V37Vg2w/4/X4/i-V37Vg2w-X4.jpg)

#### **Installation**

You can install *BeeBDC* from CRAN or GitHub.

```{r eval=FALSE}
  # Install BeeBDC from CRAN
install.packages("BeeBDC")

  # Or using the development version from GitHub (keeping in mind this may not be as stable)
remotes::install_github("https://github.com/jbdorey/BeeBDC.git", 
                          # To use the development version use "devel"; otherwise choose "main"
                        ref = "devel", force = TRUE)
```

Two optional packages can also be downloaded prior to starting your workflow, if
desired. But, these are not essential. The packages BiocManager and devtools may
also be required to download the two extra packages.

1.  The first package, **rnaturalearthhires**, is a data package that allows the
    usage of higher-resolution country maps and is very useful for multiple
    **BeeBDC** functions.
2.  The second package, **ComplexHeatmap**, is only used for one **BeeBDC**
    function (`chordDiagramR()`) and is less critical.

When either of these packages are called, the user will be prompted to install
them. However, the latter may try to restart your R session.

```{r eval=FALSE}
  # These two packages may need to be installed in order to install the actual required packages 
    # below.
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
if (!require("devtools", quietly = TRUE))
    install.packages("devtools")

  # Install ComplexHeatmap and rnaturalearthhires
devtools::install_github("ropensci/rnaturalearthhires")
BiocManager::install("ComplexHeatmap")
```

> ### First time using the *sf* or *terra* packages?
>
> The first time that you use **terra** or **sf** on a new computer you may need
> to install some dependencies. Try to install the **terra** and **sf** packages
> first but then come back here if that doesn't work.
>
> ##### Windows:
>
> On **Windows**, you need to first install
> [Rtools](https://cran.r-project.org/bin/windows/Rtools/) to get a C++ compiler
> that **R** can use. You need a recent version of **Rtools42**
> (rtools42-5355-5357).
>
> ##### MacOS:
>
> On **macOS**, you can use [MacPorts](https://www.macports.org/) or
> [Homebrew](https://brew.sh/).
>
> With **MacPorts** you can do
>
> `sudo port install R-terra`
>
> With **Homebrew**, you need to first install GDAL:
>
> `brew install pkg-config`
>
> `brew install gdal`
>
> Followed by (note the additional configuration argument needed for Homebrew)
>
> ```{r, eval = FALSE}
>   # Install terra
> install.packages("terra", type = "source", configure.args = "--with-proj-lib=$(brew --prefix)/lib/")
>   # install sf
> install.packages("sf", type = "source", configure.args = "--with-proj-lib=$(brew --prefix)/lib/")
>
> library(terra)
> library(sf)
> ```

Load the package with:

```{r, message=FALSE, warning=FALSE,echo=TRUE,eval=FALSE}
library(BeeBDC)
```

#### 1. [**Data merge**](https://jbdorey.github.io/BeeBDC/articles/BeeBDC_main.html#data-merge)


Integrate and merge different datasets from major the data repositories - GBIF,
SCAN, iDigBio, the USGS, and ALA.

-   `atlasDownloader()` Downloads ALA data and creates a new file in the path to
    put those data. This function can also request downloads from other atlases
    ([see here](https://galah.ala.org.au/R/articles/choosing_an_atlas.html)).
    However, it will only send the download to your email and you must do the
    rest yourself at this point.
-   `repoMerge()` Locates data from GBIF, ALA, iDigBio, and SCAN within a
    directory and reads it in along with its eml metadata.
-   `repoFinder()` Find GBIF, ALA, iDigBio, and SCAN files in a directory.
-   `importOccurrences()` Looks for and imports the most-recent version of the
    occurrence data created by the `BeeBDC::repoMerge()` function.
-   `USGS_formatter()` The function finds, imports, formats, and creates
    metadata for the USGS dataset.
-   `formattedCombiner()` Merges the Darwin Core version of the USGS dataset
    that was created using `BeeBDC::USGS_formatter()` with the main dataset.
-   `dataSaver()` Used at the end of 1.x in the example workflow in order to
    save the occurrence dataset and its associated eml metadata.

#### 2. [**Data preperation**](https://jbdorey.github.io/BeeBDC/articles/BeeBDC_main.html#data-preparation)

The reading in and formatting of the major and minor [bee] occurrence
repositories as well as some data modifications. This section is mostly, but not
entirely, related to bee occurrence data.

-   `fileFinder()` A function which can be used to find files within a
    user-defined directory based on a user-provided character string.
-   `PaigeIntegrater()` Replaces publicly available data with data that has been
    manually cleaned and error-corrected for use in the paper Chesshire, P. R.,
    Fischer, E. E., Dowdy, N. J., Griswold, T., Hughes, A. C., Orr, M. J., . . .
    McCabe, L. M. (In Press). Completeness analysis for over 3000 United States
    bee species identifies persistent data gaps. Ecography.
-   `readr_BeeBDC()` Read in a variety of data files that are specific to
    certain smaller data providers. There is an internal readr function for each
    dataset and each one of these functions is called by readr_BeeBDC. While
    these functions are internal, they are displayed in the documentation of
    readr_BeeBDC for clarity.
-   `idMatchR()` This function attempts to match database_ids from a prior bdc
    or BeeBDC run in order to keep this column somewhat consistent between
    iterations. However, not all records contain sufficient information for this
    to work flawlessly.

#### 3. [**Initial flags**](https://jbdorey.github.io/BeeBDC/articles/BeeBDC_main.html#initial-flags)

Flagging and carpentry of several, mostly general, data issues. See bdc's
[pre-filter](https://brunobrr.github.io/bdc/articles/prefilter.html) for more
related functions.

-   `countryNameCleanR()` This is a basic function for a user to manually fix
    some country name inconsistencies.
-   `jbd_CfC_chunker()` Because the `BeeBDC::jbd_country_from_coordinates()`
    function is very RAM-intensive, this wrapper allows a user to specify
    chunk-sizes and only analyse a small portion of the occurrence data at a
    time. The prefix jbd\_ is used to highlight the difference between this
    function and the original `bdc::bdc_country_from_coordinates()`.
-   `jbd_Ctrans_chunker()` Because the `BeeBDC::jbd_coordinates_transposed()`
    function is very RAM-intensive, this wrapper allows a user to specify
    chunk-sizes and only analyse a small portion of the occurrence data at a
    time. The prefix jbd\_ is used to highlight the difference between this
    function and the original bdc::bdc_coordinates_transposed(). These functions
    will preferably use the countryCode column generated by
    `bdc::bdc_country_standardized()`.
-   `jbd_coordCountryInconsistent()` Compares stated country name in an
    occurrence record with record's coordinates using rnaturalearth data. The
    prefix, jbd\_ is meant to distinguish this function from the original
    `bdc::bdc_coordinates_country_inconsistent()`. This functions will
    preferably use the countryCode and country_suggested columns generated by
    `bdc::bdc_country_standardized()`; please run it on your dataset prior to
    running this function.
-   `flagAbsent()` Flags occurrences that are "ABSENT" for the occurrenceStatus
    (or some other user-specified) column.
-   `GBIFissues()` This function will flag records which are subject to a
    user-specified vector of GBIF issues.
-   `flagRecorder()` This function is used to save the flag data for your
    occurrence data as you run the BeeBDC script. It will read and append
    existing files, if asked to. Your flags should also be saved in the
    occurrence file itself automatically.

#### 4. [**Taxonomy**](https://jbdorey.github.io/BeeBDC/articles/BeeBDC_main.html#taxonomy)

Harmonisation of scientific names against a taxonomy downloaded from **taxadb**,
from the provided [Discover Life](https://www.discoverlife.org) website's taxonomic reference, or a custom taxonomy.

-   `taxadbToBeeBDC()` Uses the **taxadb** to download a species taxonomy from
    any of their sources and transforms it into the BeeBDC format that can then
    be exported as a .csv or into the R enviornment to be be fed directly into
    `BeeBDC::harmoniseR()`. This means that the taxonomy from ANY taxon can be
    used. See also `BeeBDC::beesTaxonomy()` for the best global bee taxonomy.
-   `harmoniseR()` Uses the Discover Life taxonomy to harmonise bee occurrences
    and flag those that do not match the checklist. This function could be
    hijacked to service other taxa if a user matched the format of the
    beesTaxonomy file. `BeeBDC::harmoniseR()` prefers to use the names_clean
    columns that is generated by `bdc::bdc_clean_names()`. While this is not
    required, you may find better results by running that function on your
    dataset first.

#### 5. [**Space**](https://jbdorey.github.io/BeeBDC/articles/BeeBDC_main.html#space)

Flagging of erroneous, suspicious, and low-precision geographic coordinates.

-   `jbd_coordinates_precision()` This function flags occurrences where BOTH
    latitude and longitude values are rounded. This contrasts with the original
    function, `bdc::bdc_coordinates_precision()` that will flag occurrences
    where only one of latitude OR longitude are rounded. The BeeBDC approach
    saves occurrences that may have had terminal zeros rounded in one coordinate
    column.
-   `diagonAlley()` A simple function that looks for potential latitude and
    longitude fill-down errors by identifying consecutive occurrences with
    coordinates at regular intervals. This is accomplished by using a sliding
    window with the length determined by minRepeats.
-   `coordUncerFlagR()` To use this function, the user must choose a column,
    probably "coordinateUncertaintyInMeters" and a threshold above which
    occurrences will be flagged for geographic uncertainty.
-   `countryOutlieRs()` This function flags country-level outliers using the
    checklist provided with this package. For additional context and column
    names, see `beesChecklist`.
-   `continentOutlieRs()` This function flags continent-level outliers using the
    checklist provided with this package. This function works much the same as
    `countryOutlieRs()`, but at a lower resolution. For additional context and
    column names, see `beesChecklist`.
-   `jbd_create_figures()` Creates figures (i.e., bar plots, maps, and
    histograms) reporting the results of data quality tests implemented the bdc
    and BeeBDC packages. Works like `bdc::bdc_create_figures()`, but it allows
    the user to specify a save path.

#### 6. [**Time**](https://jbdorey.github.io/BeeBDC/articles/BeeBDC_main.html#time)

Flagging and, whenever possible, correction of inconsistent collection date.

-   `dateFindR()` A function made to search other columns for dates and add them
    to the eventDate column. The function searches the columns locality,
    fieldNotes, locationRemarks, and verbatimEventDate for the relevant
    information.

#### 7. [**De-duplication**](https://jbdorey.github.io/BeeBDC/articles/BeeBDC_main.html#de-duplication)

-   `dupeSummary()` This function uses user-specified inputs and columns to
    identify duplicate occurrence records. Duplicates are identified iteratively
    and will be tallied up, duplicate pairs clustered, and sorted at the end of
    the function. The function is designed to work with Darwin Core data with a
    database_id column, but it is also modifiable to work with other columns.

#### 8. [**Filtering**](https://jbdorey.github.io/BeeBDC/articles/BeeBDC_main.html#data-filtering)

-   `manualOutlierFindeR()` Uses expert-identified outliers with source
    spreadsheets that may be edited by users. The function will also use the
    duplicates file made using `BeeBDC::dupeSummary()` to identify duplicates of
    the expert-identified outliers and flag those as well. The function will add
    a flagging column called .expertOutlier where records that are FALSE are the
    expert outliers.
-   `summaryFun()` Using all flag columns (column names starting with "."), this
    function either creates or updates the .summary flag column which is FALSE
    when ANY of the flag columns are FALSE. Columns can be excluded and removed
    after creating the .summary column. Additionally, the occurrence dataset can
    be filtered to only those where .summary = TRUE at the end of the function.

#### 9. [**Figures and tables**](https://jbdorey.github.io/BeeBDC/articles/BeeBDC_main.html#figures-and-tables)

-   `chordDiagramR()` This function outputs a figure which shows the relative
    size and direction of occurrence points duplicated between data providers,
    such as, SCAN, GBIF, ALA, etc. This function requires the outputs generated
    by `BeeBDC::dupeSummary()`.
-   `dupePlotR()` Creates a plot with two bar graphs. One shows the absolute
    number of duplicate records for each data source while the other shows the
    proportion of records that are duplicated within each data source. This
    function requires a dataset that has been run through
    `BeeBDC::dupeSummary()`.
-   `plotFlagSummary()` Creates a compound bar plot that shows the proportion of
    records that pass or fail each flag (rows) and for each data source
    (columns). The function can also optionally return a point map for a
    user-specified species when plotMap = TRUE. This function requires that your
    dataset has been run through some filtering functions - so that is can
    display logical columns starting with ".".
-   `summaryMaps()` Builds an output figure that shows the number of species and
    the number of occurrences per country. Breaks the data into classes for
    visualisation. Users may filter data to their taxa of interest to produce
    figures of interest.
-   `interactiveMapR()` Uses the occurrence data (preferably uncleaned) and
    outputs interactive .html maps that can be opened in your browser to a
    specific directory. The maps can highlight if an occurrence has passed all
    filtering (.summary == TRUE) or failed at least one filter (.summary ==
    FALSE). This can be modified by first running `BeeBDC::summaryFun()` to set
    the columns that you want to be highlighted. It can also highlight
    occurrences flagged as expert-identified or country outliers.
-   `dataProvTables()` This function will attempt to find and build a table of
    data providers that have contributed to the input data, especially using the
    'institutionCode' column. It will also look for a variety of other columns
    to find data providers using a an internally set sequence of if-else
    statements. Hence, this function is quite specific for bee data, but should
    work for other taxa in similar institutions.
-   `flagSummaryTable()` Takes a flagged dataset and returns the total number of
    fails (FALSE) per flag (columns starting with ".") and per species. Users
    may define the column to group the summary by. While it is intended to work
    with the scientificName column, users may select any grouping column (e.g.,
    country).

#### 10. [Species richness estimation](https://jbdorey.github.io/BeeBDC/articles/speciesRichness_example.html)

-   `diversityPrepR()` Takes your occurrence dataset along with a taxonomy and
    checklist in order to produce a file that's ready to be passed into the
    `BeeBDC::richnessEstimateR()` function in order to estimate species richness
    using iChao (non-parametric species richness; `BeeBDC::ChaoWrapper()`) and
    iNEXT (hill numbers; `BeeBDC::iNEXTwrapper()`) for countries, continents, or
    the entire globe. 
-   `richnessEstimateR()` Takes an output dataset from
    `BeeBDC::diversityPrepR()` to estimate species richness using iChao
    (non-parametric species richness; `BeeBDC::ChaoWrapper()`) and iNEXT (hill
    numbers; `BeeBDC::iNEXTwrapper()`) for countries, continents, and/or the
    entire globe. Has parallel functionality.
-   `iNEXTwrapper()` A wrapper for `iNEXT::iNEXT()` to interpolate and
    extrapolate Hill numbers with order q (rarify species richness). The wrapper
    has the ability to estimate species richness for multiple sites (or
    countries) at once and to do this using multiple cores.
-   `ChaoWrapper()` A wrapper for `SpadeR::ChaoSpecies()` to non-parametrically
    estimate species richness. The wrapper has the ability to estimate species
    richness for multiple sites (or countries) at once and to do this using
    multiple cores at once.

#### 11. **Datasets**

We provide two full datasets that are downloadable using the below two functions

-   `beesTaxonomy()` Downloads the taxonomic information for the bees of the
    world. Source of taxonomy is listed under "source" but are mostly derived
    from the Discover Life website. The data will be sourced from the BeeBDC
    article's Figshare. Please see also `BeeBDC::taxadbToBeeBDC()` for the
    download of any other taxonomy (for any taxa or for bees).
-   `beesChecklist()` Download the table contains taxonomic and country
    information for the bees of the world based on data collated on Discover
    Life. The data will be sourced from the BeeBDC article's Figshare.

We further provide five test datasets that are available with BeeBDC

-   `BeeBDC::bees3sp` This test dataset includes 105 random occurrence records
    from three bee species. The included species are: "*Agapostemon tyleri*
    Cockerell, 1917", "*Centris rhodopus* Cockerell, 1897", and "*Perdita
    octomaculata* (Say, 1824)".
-   `BeeBDC::beesRaw` A small bee occurrence dataset with flags generated by
    BeeBDC used to run example script and test functions. For data types, see
    `ColTypeR()`.
-   `BeeBDC::beesFlagged` A small bee occurrence dataset with flags generated by
    BeeBDC used to run example script and test functions. For data types, see
    `ColTypeR()`.
-   `BeeBDC::beesCountrySubset` A very small bee occurrence dataset with the
    columns "scientificName" and "country_suggested" and data for four
    countries, Fiji, Uganda, Vietnam, and Zambia. This is the test dataset for
    the species richness functions.
-   There are also two small test datasets of the beesTaxonomy and beesChecklist
    in the system files of the package that are filtered to include *only* those
    species that occur in bees3sp, beesRaw, and beesFlagged. These are
    accessible as follows but are only used internally for tests.

```{r testData, eval = FALSE}
  # Access the test taxonomy file
system.file("extdata", "testTaxonomy.rda", package="BeeBDC") |> load()
  # View the file
View(testTaxonomy)
  # Access the test checklist file
system.file("extdata", "testChecklist.rda", package="BeeBDC") |> load()
  # View the file
View(testChecklist)
```

#### **Package website**

See *BeeBDC* package website
() for detailed
explanation on each module.

#### **Getting help**

> This package is maintained by Dr James B Dorey, Lecturer in Biological Sciences at the University of Wollongong, Australia.

> If you encounter a clear bug, please file an issue
> [**here**](https://github.com/jbdorey/BeeBDC/issues). For questions or
> suggestion, flick us an email (jdorey\@uow.edu.au).

#### **Citations**

-   **Paper, dataset, and package citation:** Dorey, J.B., Fischer, E.E.,
    Chesshire, P.R., Nava-Bolaños, A., O’Reilly, R.L., Bossert, S., Collins,
    S.M., Lichtenberg, E.M., Tucker, E., Smith-Pardo, A., Falcon-Brindis, A.,
    Guevara, D.A., Ribeiro, B.R., de Pedro, D., Hung, J.K.-L., Parys, K.A.,
    McCabe, L.M., Rogan, M.S., Minckley, R.L., Velzco, S.J.E., Griswold, T.,
    Zarrillo, T.A., Jetz, W., Sica, Y.V., Orr, M.C., Guzman, L.M., Ascher, J.,
    Hughes, A.C. & Cobb, N.S. (2023) A globally synthesised and flagged bee
    occurrence dataset and cleaning workflow. Scientific Data, 10, 1–17.
    
    -   **Figshare live data link:**
        
-   **Species richness estimation citation:** Dorey J. B., Gilpin, A.-M., Johnson, N.,
    Esquerre, D., Hughes, A. C., Ascher, J. S., & Orr, M. C. (Accepted). Estimating global bee species richness and taxonomic gaps. Nature Communications. https://doi.org/10.21203/rs.3.rs-6372769/v1
-   **Package citation:** Dorey, J. B., O'Reilly, R. L., Bossert, S. &
    Fischer, E. (2023). BeeBDC: an occurrence data cleaning package. R package
    version 1.3.3. url: 
-   **Discover Life citation (for use of bee taxonomy and checklist):** Ascher,
    J.S. & Pickering, J. (2026) Discover Life bee species guide and world
    checklist (Hymenoptera: Apoidea: Anthophila).
    

This package and its data sets were created with the support, and as a part, of
the iDigBees project




        

---

Owner metadata

• Name: James B Dorey
• Login: jbdorey
• Email:
• Kind: user
• Description: Mostly a bee researcher looking at evolution, ecology, phylogenetics, and conservation
• Website:
• Location: Adelaide, South Australia
• Twitter: JamesBeeDorey
• Company:
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• Last ynced at: 2023-09-06T08:35:38.219Z
• Profile URL: https://github.com/jbdorey

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---

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