manualOutlierFindeR {BeeBDC}

Finds outliers, and their duplicates, as determined by experts

Description

Uses expert-identified outliers with source spreadsheets that may be edited by users. The function will also use the duplicates file made using 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.

Usage

manualOutlierFindeR(
  data = NULL,
  DataPath = NULL,
  PaigeOutliersName = "removedBecauseDeterminedOutlier.csv",
  newOutliersName = "All_outliers_ANB.xlsx",
  ColombiaOutliers_all = "All_Colombian_OutlierIDs.csv",
  duplicates = NULL,
  NearTRUE = NULL,
  NearTRUE_threshold = 5
)

Arguments

data

A data frame or tibble. Occurrence records as input.

DataPath

A character path to the directory that contains the outlier spreadsheets.

PaigeOutliersName

A character patch. Should lead to outlier spreadsheet from Paige Chesshire (csv file).

newOutliersName

A character path. Should lead to appropriate outlier spreadsheet (xlsx file).

ColombiaOutliers_all

A character path. Should lead to spreadsheet of bee outliers from Colombia (csv file).

duplicates

A data frame or tibble. The duplicate file produced by dupeSummary().

NearTRUE

Optional. A character file name to the csv file. If you want to remove expert outliers that are too close to TRUE points, use the name of the NearTRUE.csv. Note: This implementation is only basic for now unless there is a greater need in the future.

NearTRUE_threshold

Numeric. The threshold (in km) for the distance to TRUE points to keep expert outliers.

Value

Returns the data with a new column, .expertOutlier where records that are FALSE are the expert outliers.

Examples

## Not run: 
  # Read example data
  data(beesFlagged)
# Read in the most-recent duplicates file as well
if(!exists("duplicates")){
  duplicates <- fileFinder(path = DataPath,
                            fileName = "duplicateRun_") %>%
    readr::read_csv()}
# identify the outliers and get a list of their database_ids
beesFlagged_out <- manualOutlierFindeR(
  data = beesFlagged,
  DataPath = DataPath,
  PaigeOutliersName = "removedBecauseDeterminedOutlier.csv",
  newOutliersName = "^All_outliers_ANB_14March.xlsx",
  ColombiaOutliers_all = "All_Colombian_OutlierIDs.csv",
  duplicates = duplicates)

## End(Not run)