# Basic workflow

This workflow is meant to be a basic example workflow of how a user might take a flagged version of our (or some other) occurrence dataset and filter for specific taxa or countries, re-apply flagging functions, re-filter the data, or make maps based of those data.

# 0.0 Script preparation

## 0.1 Working directory

Choose the path to the root folder in which all other folders can be found.

    RootPath <- paste0("/your/path/here")

    # Create the working directory in the RootPath if it doesn't exist already
    if (!dir.exists(paste0(RootPath, "/Data_acquisition_workflow"))) {
        dir.create(paste0(RootPath, "/Data_acquisition_workflow"), recursive = TRUE)
    }
    # Set the working directory
    setwd(paste0(RootPath, "/Data_acquisition_workflow"))

For the first time that you run BeeBDC, and if you want to use the renv package to manage your packages, you can install renv…

            install.packages("renv", repos = "http://cran.us.r-project.org")

and then initialise renv the project.

            renv::init(project = paste0(RootPath,"/Data_acquisition_workflow"))

If you have already initialised a project, you can instead just activate it.

    renv::activate(project = paste0(RootPath, "/Data_acquisition_workflow"))

## 0.2 Install packages (if needed)

You may need to install gdal on your computer. This can be done on a Mac by using Homebrew in the terminal and the command “brew install gdal”.

To start out, you will need to install BiocManager, devtools, ComplexHeatmap, and rnaturalearthhires to then install and fully use BeeBDC.

    if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager", repos = "http://cran.us.r-project.org")

    BiocManager::install("ComplexHeatmap")

    # Install remotes if needed
    if (!require("remotes", quietly = TRUE)) install.packages("remotes", repos = "http://cran.us.r-project.org")
    # Download and then load rnaturalearthhires
    remotes::install_github("ropensci/rnaturalearthhires")
    install.packages("rnaturalearthhires", repos = "https://ropensci.r-universe.dev",
        type = "source")
    library(rnaturalearthhires)

Now install BeeBDC.

    install.packages("BeeBDC")
    library(BeeBDC)

Snapshot the renv environment.

    renv::snapshot(project = paste0(RootPath, "/Data_acquisition_workflow"), prompt = FALSE)
    ## - The lockfile is already up to date.

Set up the directories used by BeeBDC. These directories include where the data, figures, reports, etc. will be saved. The RDoc needs to be a path RELATIVE to the RootPath; i.e., the file path from which the two diverge.

    BeeBDC::dirMaker(RootPath = RootPath, RDoc = "vignettes/BeeBDC_main.Rmd") %>%
        # Add paths created by this function to the environment()
    list2env(envir = parent.env(environment()))

## 0.3 Load packages

Load packages.

    lapply(c("ComplexHeatmap", "magrittr"), library, character.only = TRUE)

# 2.0 Taxon example

If you want to filter the dataset to a particular taxon of interest, you can do so quite easily using dplyr from the tidyverse group of packages. To filter to a selected bee genus, in our case Anthophorini…

    # Load some package data — the taxonomy and a flagged example dataset Download
    # the full beesTaxonomy file
    taxonomyFile <- BeeBDC::beesTaxonomy()

    # load in the small test dataset in the background
    system.file("extdata", "testTaxonomy.rda", package = "BeeBDC") |>
        load()
    # Rename the file
    taxonomyFile <- testTaxonomy
    rm(testTaxonomy)

    # Load the example beesFlagged dataset
    beesFlagged <- BeeBDC::beesFlagged

    selectedGenera <- taxonomyFile %>%
        # Select only tribe anthophorini (for example)
    dplyr::filter(tolower(tribe) == tolower("anthophorini")) %>%
        distinct(genus)

    # Filter the data
    taxonData <- beesFlagged %>%
        dplyr::filter(genus %in% selectedGenera$genus)
    # View the data
    taxonData

    ## # A tibble: 2 × 124
    ##   database_id  scientificName family subfamily genus subgenus subspecies species
    ##   <chr>        <chr>          <chr>  <chr>     <chr> <chr>    <lgl>      <chr>
    ## 1 Dorey_data_… Habropoda mis… Apidae Apinae    Habr… <NA>     NA         Habrop…
    ## 2 Dorey_data_… Deltoptila el… Apidae Apinae    Delt… <NA>     NA         Deltop…
    ## # ℹ 116 more variables: specificEpithet <chr>, infraspecificEpithet <chr>,
    ## #   acceptedNameUsage <lgl>, taxonRank <chr>, scientificNameAuthorship <chr>,
    ## #   identificationQualifier <lgl>, higherClassification <chr>,
    ## #   identificationReferences <lgl>, typeStatus <chr>,
    ## #   previousIdentifications <chr>, verbatimIdentification <chr>,
    ## #   identifiedBy <chr>, dateIdentified <chr>, decimalLatitude <dbl>,
    ## #   decimalLongitude <dbl>, stateProvince <chr>, continent <chr>, …

# 3.0 Country example

Similarly to the above you can filter for only countries of interest. Keep in mind, that sometimes the country column may not hold all of the records that fall in that country, if it, or the coordinates, have been entered incorrectly.

    # Select your study area
    studyArea <- c("Canada", "United states", "Mexico", "Guatemala")
    # Filter the data to that area
    countryData <- beesFlagged %>%
        dplyr::filter(country %in% studyArea)
    # View the data
    countryData

    ## # A tibble: 49 × 124
    ##    database_id scientificName family subfamily genus subgenus subspecies species
    ##    <chr>       <chr>          <chr>  <chr>     <chr> <chr>    <lgl>      <chr>
    ##  1 Dorey_data… Macrotera arc… Andre… Panurgin… Macr… <NA>     NA         Macrot…
    ##  2 Dorey_data… Exomalopsis s… Apidae Apinae    Exom… <NA>     NA         Exomal…
    ##  3 Paige_data… Augochlorella… Halic… Halictin… Augo… <NA>     NA         Augoch…
    ##  4 Dorey_data… Svastra duplo… Apidae Apinae    Svas… <NA>     NA         Svastr…
    ##  5 Dorey_data… Agapostemon f… Halic… Halictin… Agap… <NA>     NA         Agapos…
    ##  6 Dorey_data… Melissodes tr… Apidae Apinae    Meli… <NA>     NA         Meliss…
    ##  7 Dorey_data… Osmia pumila … Megac… Megachil… Osmia <NA>     NA         Osmia …
    ##  8 Dorey_data… Perdita bisho… Andre… Panurgin… Perd… <NA>     NA         Perdit…
    ##  9 Dorey_data… Melissodes lu… Apidae Apinae    Meli… <NA>     NA         Meliss…
    ## 10 Paige_data… Melecta thora… Apidae Apinae    Mele… <NA>     NA         Melect…
    ## # ℹ 39 more rows
    ## # ℹ 116 more variables: specificEpithet <chr>, infraspecificEpithet <chr>,
    ## #   acceptedNameUsage <lgl>, taxonRank <chr>, scientificNameAuthorship <chr>,
    ## #   identificationQualifier <lgl>, higherClassification <chr>,
    ## #   identificationReferences <lgl>, typeStatus <chr>,
    ## #   previousIdentifications <chr>, verbatimIdentification <chr>,
    ## #   identifiedBy <chr>, dateIdentified <chr>, decimalLatitude <dbl>, …

# 4.0 Filtering example

## 4.1 Simple filter

The BeeBDC package provides a simple function that can re-build the .summary column based off of the filtering columns that are present in the dataset (those starting with “.”). you can also choose which filters you DO NOT want to implement using the dontFilterThese argument. In this example, we are also removing all of the filtering columns in the output dataset (removeFilterColumns = TRUE) and filtering to only completely clean occurrences (filterClean = TRUE). For the latter, we are only keeping .summary == TRUE.

    filteredData <-
      BeeBDC::summaryFun(data = beesFlagged,
       # Choose the columns to NOT filter (or NULL to filter all columns)
       dontFilterThese = c(".gridSummary", ".lonFlag", ".latFlag", ".uncer_terms",
                          ".uncertaintyThreshold"),
        # In the output, do you want to REMOVE all filtering columns (TRUE), or keep them (FALSE)
       removeFilterColumns = TRUE,
       # In the output, do you want to only keep clean data according to your filtering (TRUE),
        # Or keep all data and simply update the .summary column (FALSE)
      filterClean = TRUE)

    ##  - We will NOT flag the following columns. However, they will remain in the data file.
    ## .gridSummary, .lonFlag, .latFlag, .uncer_terms, .uncertaintyThreshold

    ##  - summaryFun:
    ## Flagged 74
    ##   The .summary column was added to the database.

    ##  - REMOVED all occurrences that were FALSE for the 'summary' column.

## 4.2 Uncertainty threshold

You may also want to change the .uncertaintyThreshold as we have chosen a somewhat strict default of 1 km in our dataset. Here, we will instead flag to 10 km (threshold = 10000 [m]). Additionally, we use the magrittr package pipe (%>%) to feed the outputs directly into `summaryFun()` to filter our data in one action!

    filteredData <- beesFlagged %>%
      # Remove any exiting .uncertaintyThreshold column
      dplyr::select(!tidyselect::any_of(".uncertaintyThreshold")) %>%
        # Chose the coordinate uncertainty to filter to...
      BeeBDC::coordUncerFlagR(data = .,
                      uncerColumn = "coordinateUncertaintyInMeters",
                        # 10 km here
                      threshold = 10000) %>%
        # Now re-do the .summary column and filter the data using this new value
      BeeBDC::summaryFun(
      data = .,
      dontFilterThese = c(".gridSummary", ".lonFlag", ".latFlag", ".uncer_terms"),
      removeFilterColumns = TRUE,
      filterClean = TRUE)

    ## \coordUncerFlagR:
    ##  Flagged 3 geographically uncertain records:
    ##  The column '.uncertaintyThreshold' was added to the database.

    ##  - We will NOT flag the following columns. However, they will remain in the data file.
    ## .gridSummary, .lonFlag, .latFlag, .uncer_terms

    ##  - summaryFun:
    ## Flagged 75
    ##   The .summary column was added to the database.

    ##  - REMOVED all occurrences that were FALSE for the 'summary' column.

## 4.2 Date filter

### a. bdc_year_outOfRange

Another column that users are likely to want to pay close attention to is the .year_outOfRange column that is set at 1950 in our dataset. In this case, bdc provides the function where users can change the year_threshold argument to, in this case, 1970. As with above, we then use `summaryFun()` to get results in one go.

    filteredData <- beesFlagged %>%
        # Remove any exisitng .year_outOfRange column
    dplyr::select(!".year_outOfRange") %>%
        # Chose the minimum year to filter to...
    bdc::bdc_year_outOfRange(data = ., eventDate = "year", year_threshold = 1970) %>%
        # Now re-do the .summary column and filter the data using this new value
    BeeBDC::summaryFun(data = ., dontFilterThese = c(".gridSummary", ".lonFlag", ".latFlag",
        ".uncer_terms", ".uncertaintyThreshold"), removeFilterColumns = TRUE, filterClean = TRUE)

    ##
    ## bdc_year_outOfRange:
    ## Flagged 23 records.
    ## One column was added to the database.

    ##  - We will NOT flag the following columns. However, they will remain in the data file.
    ## .gridSummary, .lonFlag, .latFlag, .uncer_terms, .uncertaintyThreshold

    ##  - summaryFun:
    ## Flagged 76
    ##   The .summary column was added to the database.

    ##  - REMOVED all occurrences that were FALSE for the 'summary' column.

### b. year range

Or, if you’re interested in a particular time period, again dplyr comes to the rescue with some very straight forward filtering within a year range.

    filteredData <-
      # The input dataset
      beesFlagged %>%
      # Chose the year range...
      dplyr::filter(year > 1950 & year < 1970) %>%
      # Now re-do the .summary column and filter the data using this new value
      BeeBDC::summaryFun(
        # Select the input dataset to filter
        data = .,
        # Choose the columns to NOT filter (or NULL to filter all columns)
        dontFilterThese = c(".gridSummary", ".lonFlag", ".latFlag", ".uncer_terms",
                            ".uncertaintyThreshold"),
        # In the output, do you want to REMOVE all filtering columns (TRUE), or keep them (FALSE)
        removeFilterColumns = TRUE,
        # In the output, do you want to only keep clean data according to your filtering (TRUE),
        # Or keep all data and simply update the .summary column (FALSE)
        filterClean = TRUE)

    ##  - We will NOT flag the following columns. However, they will remain in the data file.
    ## .gridSummary, .lonFlag, .latFlag, .uncer_terms, .uncertaintyThreshold

    ##  - summaryFun:
    ## Flagged 8
    ##   The .summary column was added to the database.

    ##  - REMOVED all occurrences that were FALSE for the 'summary' column.

Users may choose any number of filtering steps form the main workflow to include above `summaryFun()`, just use pipes ‘%>%’ between the function and use ‘.’ as the data input because this will feed in the data aoutput from the above function into the proceeding one.

# 5. Summary figures

Now, if you wanted to rebuild some figures, say after you’ve added or filtered data, then you can use some of the below processes.

## 5.1 Duplicate chordDiagrams

Our `chordDiagramR()` function is very useful and it relies on two great packages, circlize and ComplexHeatmap. Unfortunately, the latter is not available on CRAN and so must be downloaded using BiocManager.

    if (!require("BiocManager", quietly = TRUE)) {
        install.packages("BiocManager")
    }
    BiocManager::install("ComplexHeatmap", force = TRUE)
    renv::snapshot()

We don’t actually have an example duplicates dataset with the package, so I’ll magic one up behind the scences!

    duplicates <- fileFinder(path = "PATH TO A FOLDER CONTAINING THE duplicateRun_ — could be supp. materials folder",
        fileName = "duplicateRun_") %>%
        readr::read_csv() %>%
        # Select only the stingless bee data
    dplyr::filter(database_id %in% stinglessData$database_id | database_id_match %in%
        stinglessData$database_id)

Then, set some parameters for figure borders and run your data through `chordDiagramR()`.

    # Choose the global figure parameters
      par(mar = c(2, 2, 2, 2)/2, mfrow = c(1,1))

    # Create the chorDiagram. You can leave many of the below values out but we show here
    # the defaults

    BeeBDC::chordDiagramR(
      # The duplicate data from the dupeSummary function output
      dupeData = duplicates,
      outPath = OutPath_Figures,
      fileName = "ChordDiagram.pdf",
      # These can be modified to help fit the final pdf that's exported.
      width = 9,
      height = 7.5,
      bg = "white",
      # How few distinct dataSources should a group have to be listed as "other"
      smallGrpThreshold = 3,
      title = "Duplicated record sources",
      # The default list of colour palettes to choose from usign the paleteer package
      palettes = c("cartography::blue.pal", "cartography::green.pal",
                   "cartography::sand.pal", "cartography::orange.pal", "cartography::red.pal",
                   "cartography::purple.pal", "cartography::brown.pal"),
      canvas.ylim = c(-1.0,1.0),
      canvas.xlim = c(-0.6, 0.25),
      text.col = "black",
      legendX = grid::unit(6, "mm"),
      legendY = grid::unit(18, "mm"),
      legendJustify = c("left", "bottom"),
      niceFacing = TRUE)

## 5.2 Duplicate histogram

In this example, we will use one of the example datasets to show you how this works. We will use beesFlagged, which has been filtered from a larger dataset and contains duplicates from that larger dataset. To print the plot in R, you need to specify returnPlot = TRUE, otherwise it will only save to the disk

    data("beesFlagged", package = "BeeBDC")

    # Create a figure shoring the total number of duplicates, kept duplicates, and unique
    # records for each datasource (simplified to the text before the first underscore) and
    # the proportion of the above for each data source
    BeeBDC::dupePlotR(
      data = beesFlagged,
      # The outPath to save the plot as
      outPath = tempdir(),
      fileName = "Fig3_duplicatePlot.pdf",
      # Colours in order: duplicate, kept duplicate, unique
      dupeColours = c("#F2D2A2","#B9D6BC", "#349B90"),
      # Plot size and height
      base_height = 7, base_width = 7,
      legend.position = c(0.85, 0.8),
      # Extra variables can be fed into forcats::fct_recode() to change names on plot
      GBIF = "GBIF", SCAN = "SCAN", iDigBio = "iDigBio", USGS = "USGS", ALA = "ALA",
      ASP = "ASP",
      returnPlot = TRUE
    )

## 5.3 Flags by source

The `plotFlagSummary()` function is one of the most important for quickly summarising and checking that your data and flags have worked together correctly. It can be a good starting point for error-checking. You will also see in `plotFlagSummary()` that you can filter to particular species and also output quick point maps of those species.

### a. all taxa in dataset

# Visualise all flags for each dataSource (simplified to the text before the first underscore)

BeeBDC::plotFlagSummary( data = beesFlagged, # Colours in order of pass (TRUE), fail (FALSE), and NA flagColours = c(“#127852”, “#A7002D”, “#BDBABB”), fileName = paste0(“Fig4_FlagsPlot_”, Sys.Date(),“.pdf”), outPath = tempdir(), width = 15, height = 9, # Extra variables can be fed into forcats::fct_recode() to change names on plot GBIF = “GBIF”, SCAN = “SCAN”, iDigBio = “iDigBio”, USGS = “USGS”, ALA = “ALA”, ASP = “ASP”, returnPlot = TRUE ) ```

###### b. Single sp. summary ####

In fact, lets build one of these single-species example below using the same data and the omnipresent Apis mellifera.

    # Visualise all flags for each dataSource (simplified to the text before the first underscore)
      # A clever user might also realise the potential to summarise and produce outputs in other columns
    BeeBDC::plotFlagSummary(
      # WARNING: alternate path if wanting to produce figures for the selected taxonData (2.0 above)
      # Select only the taxonData data
      data = beesFlagged,
      # Colours in order of pass (TRUE), fail (FALSE), and NA
      flagColours = c("#127852", "#A7002D", "#BDBABB"),
      fileName = paste0("FlagsPlot_Amell", Sys.Date(),".pdf"),
      outPath = tempdir(),
      width = 15, height = 9,
      # OPTIONAL:
             #  # Filter to species
               speciesName = "Apis mellifera Linnaeus, 1758",
                 # column to look in
               nameColumn = "scientificName",
               # Save the filtered data
               saveFiltered = FALSE,
         # Filter column to display on map
               filterColumn = ".summary",
               plotMap = TRUE,
           # amount to jitter points if desired, e.g. 0.25 or NULL
         jitterValue = NULL,
           # Map opacity value for points between 0 and 1
         mapAlpha = 1,
      returnPlot = TRUE,
      # Extra variables can be fed into forcats::fct_recode() to change names on plot
      GBIF = "GBIF", SCAN = "SCAN", iDigBio = "iDigBio", USGS = "USGS", ALA = "ALA",
      ASP = "ASP", CAES = "CAES", 'B. Mont.' = "BMont", 'B. Minkley' = "BMin", Ecd = "Ecd",
      Gaiarsa = "Gai", EPEL = "EPEL"
    )

    ##  - Filtering to selected species...
    ##  - Selected species has 8 occurrences.
    ##  - Preparing data to plot...
    ##  - Building plot...

## 5.4 Maps

We can also make some overall summary maps at the country level using `summaryMaps()`. If you get an error about breaks not being unique, then reduce class_n.

    BeeBDC::summaryMaps(data = beesFlagged, width = 10, height = 10, class_n = 3, class_Style = "jenks",
        outPath = tempdir(), fileName = "CountryMaps_jenks.pdf", returnPlot = TRUE)

    ##  - Extracting country data from points...

    ## although coordinates are longitude/latitude, st_intersects assumes that they
    ## are planar
    ## although coordinates are longitude/latitude, st_intersects assumes that they
    ## are planar

    ## Extraction complete.
    ##  - Buffering naturalearth map by pointBuffer...

    ## dist is assumed to be in decimal degrees (arc_degrees).
    ## although coordinates are longitude/latitude, st_intersects assumes that they
    ## are planar
    ## although coordinates are longitude/latitude, st_intersects assumes that they
    ## are planar

# 6.0 Save data

    mapData %>%
        readr::write_excel_csv(paste0(DataPath, "/Output/Intermediate/", "cleanTaxon_",
            Sys.Date(), ".csv"))