Nothing
R/additionalData_readRs.R
In BeeBDC: Occurrence Data Cleaning
Defines functions
readr_EPEL
readr_BeeBDC
Documented in
readr_BeeBDC
readr_EPEL
# These functions were written by James B Dorey beginning on the 17th of June 2022 to read in,
# Format and save various datasets.
# For questions, please email jbdorey[at]me.com
#### x.0 readr_BeeBDC function ####
#' A wrapper for all of the data readr_functions
#'
#' 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.
#'
#' This function wraps several internal readr functions. Users may call
#' readr_BeeBDC and select the dataset name to import a certain dataset. These datasets include:
#'
#' Excel (.xlsx) formatted datasets: CAES, MABC, Col, Bal, MEPB, MUPJ, Arm, JoLa, and VicWam.
#'
#' CSV (.csv) formatted datasets: EPEL, ASP, BMin, BMont, Ecd, Gai, KP, EcoS, GeoL, EaCo, FSCA, SMC,
#' Lic, Dor, BBD, STRI, and PALA
#'
#' See Dorey et al. 2023 BeeBDC... for further details.
#'
#' @param dataset Character. The name of the dataset to be read in. For example readr_CAES can
#' be called using "readr_CAES" or "CAES". This is not caps sensitive.
#' @param path A character path. The path to the directory containing the data.
#' @param inFile Character or character path. The name of the file itself (can also be the
#' remainder of a path including the file name).
#' @param outFile Character or character path. The name of the Darwin Core format file to be saved.
#' @param dataLicense Character. The license to accompany each record in the Darwin Core 'license'
#' column.
#' @param sheet A character String. For those datasets read from an .xlsx format, provide the
#' sheet name.
#' NOTE: This will be ignored for .csv readr_ functions and required for .xlsx readr_ functions.
#'
#' @return A data frame that is in Darwin Core format.
#' @export
#'
#' @importFrom readr read_csv write_excel_csv
#' @importFrom dplyr rename mutate select if_else %>%
#' @importFrom lubridate ymd month
#' @importFrom stringr str_c
#'
#'
#' @examples
#' \dontrun{
#' # An example using a .xlsx file
#'Arm_Data <- readr_BeeBDC(
#' dataset = "Arm",
#' path = paste0(tempdir(), "/Additional_Datasets"),
#' inFile = "/InputDatasets/Bee database Armando_Final.xlsx",
#' outFile = "jbd_Arm_Data.csv",
#' sheet = "Sheet1",
#' dataLicense = "https://creativecommons.org/licenses/by-nc-sa/4.0/")
#'
#'
#' # An example using a .csv file
#'EPEL_Data <- readr_BeeBDC(
#' dataset = "readr_EPEL",
#' path = paste0(tempdir(), "/Additional_Datasets"),
#' inFile = "/InputDatasets/bee_data_canada.csv",
#' outFile = "jbd_EPEL_data.csv",
#' dataLicense = "https://creativecommons.org/licenses/by-nc-sa/4.0/")
#' }
readr_BeeBDC <- function(
dataset = NULL,
path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = NULL){
#### x.0 Prep ####
##### x.1 Errors ####
###### a. FATAL errors ####
if(is.null(dataset)){
stop(" - Please provide an argument for dataset. This can be from any of the readr_ functions")
}
if(is.null(path)){
stop(" - Please provide an argument for the path to a folder containing your file.")
}
if(is.null(inFile)){
stop(" - Please provide an argument for the inFile name to find.")
}
if(is.null(outFile)){
stop(" - Please provide an argument for outFile to save as.")
}
if(is.null(dataLicense)){
stop(" - Please provide an argument for dataLicense.")
}
##### x.2 Data types ####
# Create the lists of potential data types for .xlsx or .csv inputs
excelTypes <- c("CAES", "MABC", "Col", "Bal", "MEPB", "MPUJ", "Arm", "JoLa", "VicWam")
csvTypes <- c("EPEL", "ASP", "BMin", "BMont", "Ecd", "Gai", "KP", "EcoS", "GeoL",
"EaCo", "FSCA", "SMC", "Lic", "Dor", "BBD", "PALA", "STRI")
# Wrong entry test
if(!tolower(dataset) %in% tolower(c(paste0("readr_",csvTypes), csvTypes,
paste0("readr_",excelTypes), excelTypes)) ){
stop("The dataset does not match a readr_BeeBDC function")
}
#### x.0 Choose function ####
##### x.1 Excel functions ####
# EXCEL test
if(tolower(dataset) %in% tolower(c(paste0("readr_",excelTypes), excelTypes)) ){
message("A .xlsx data type was chosen...")
# If no sheet is provided
if(is.null(sheet)){
stop(" - No sheet argument was provided. Please check for the sheet name to import.")
}
###### a. CAES ####
if(tolower(dataset) %in% tolower(c("readr_CAES", "CAES")) ){
dataOut <- readr_CAES(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### b. MABC ####
if(tolower(dataset) %in% tolower(c("readr_MABC", "MABC")) ){
dataOut <- readr_MABC(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### c. Col ####
if(tolower(dataset) %in% tolower(c("readr_Col", "Col")) ){
dataOut <- readr_Col(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### d. Bal ####
if(tolower(dataset) %in% tolower(c("readr_Bal", "Bal")) ){
dataOut <- readr_Bal(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### e. MEPB ####
if(tolower(dataset) %in% tolower(c("readr_MEPB", "MEPB")) ){
dataOut <- readr_MEPB(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### f. MPUJ ####
if(tolower(dataset) %in% tolower(c("readr_MPUJ", "MPUJ")) ){
dataOut <- readr_MPUJ(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### f. Arm ####
if(tolower(dataset) %in% tolower(c("readr_Arm", "Arm")) ){
dataOut <- readr_Arm(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### g. JoLa ####
if(tolower(dataset) %in% tolower(c("readr_JoLa", "JoLa")) ){
dataOut <- readr_JoLa(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### h. VicWam ####
if(tolower(dataset) %in% tolower(c("readr_VicWam", "VicWam")) ){
dataOut <- readr_VicWam(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
}# END Excel functions
##### x.2 CSV functions ####
# CSV test
if(tolower(dataset) %in% tolower(c(paste0("readr_",csvTypes), csvTypes)) ){
message("A .csv data type was chosen...")
###### a. EPEL ####
if(tolower(dataset) %in% tolower(c("readr_EPEL", "EPEL")) ){
dataOut <- readr_EPEL(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### b. ASP ####
if(tolower(dataset) %in% tolower(c("readr_ASP", "ASP")) ){
dataOut <- readr_ASP(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### c. BMin ####
if(tolower(dataset) %in% tolower(c("readr_BMin", "BMin")) ){
dataOut <- readr_BMin(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### d. BMont ####
if(tolower(dataset) %in% tolower(c("readr_BMont", "BMont")) ){
dataOut <- readr_BMont(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### e. Ecd ####
if(tolower(dataset) %in% tolower(c("readr_Ecd", "Ecd")) ){
dataOut <- readr_Ecd(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### f. Gai ####
if(tolower(dataset) %in% tolower(c("readr_Gai", "Gai")) ){
dataOut <- readr_Gai(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### g. KP ####
if(tolower(dataset) %in% tolower(c("readr_KP", "KP")) ){
dataOut <- readr_KP(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### h. EcoS ####
if(tolower(dataset) %in% tolower(c("readr_EcoS", "EcoS")) ){
dataOut <- readr_EcoS(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### i. GeoL ####
if(tolower(dataset) %in% tolower(c("readr_GeoL", "GeoL")) ){
dataOut <- readr_GeoL(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### j. EaCO ####
if(tolower(dataset) %in% tolower(c("readr_EaCO", "EaCo")) ){
dataOut <- readr_EaCO(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### k. FSCA ####
if(tolower(dataset) %in% tolower(c("readr_FSCA", "FSCA")) ){
dataOut <- readr_FSCA(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### l. SMC ####
if(tolower(dataset) %in% tolower(c("readr_SMC", "SMC")) ){
dataOut <- readr_SMC(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### m. Lic ####
if(tolower(dataset) %in% tolower(c("readr_Lic", "Lic")) ){
dataOut <- readr_Lic(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### n. Dor ####
if(tolower(dataset) %in% tolower(c("readr_Dor", "Dor")) ){
dataOut <- readr_Dor(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### o. BBD ####
if(tolower(dataset) %in% tolower(c("readr_BBD", "BBD")) ){
dataOut <- readr_BBD(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### p. STRI ####
if(tolower(dataset) %in% tolower(c("readr_STRI", "STRI")) ){
dataOut <- readr_STRI(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### g. PALA ####
if(tolower(dataset) %in% tolower(c("readr_PALA", "PALA")) ){
dataOut <- readr_PALA(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
} #END csv
return(dataOut)
} # END readr_BeeBDC
#### 1.0 EPEL ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_EPEL <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
catalog_number<-pollinator_family<-pollinator_genus<-pollinator_species<-collection_method<-
day_collected<-month_collected<-year_collected<-location_description<-latitude<-longitude<-
basis_of_record<-genus<-specificEpithet<-year<-day<-eventDate<-collector_number<-
location_name<-habitat<-.<-catalogNumber <- NULL
#### 1.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 1.2 Read+ ####
EPEL_Data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
# Rename some columns to make the consistent with DarwinCore
dplyr::rename(
catalogNumber = catalog_number,
family = pollinator_family,
genus = pollinator_genus,
specificEpithet = pollinator_species,
samplingProtocol = collection_method,
day = day_collected,
month = month_collected,
year = year_collected,
locality = location_description,
decimalLatitude = latitude,
decimalLongitude = longitude,
basisOfRecord = basis_of_record) %>%
# Make new columns
dplyr::mutate(
# Merge to scientific name
scientificName = paste(genus, specificEpithet),
# Add data source
dataSource = "EPEL_Anthophila",
# Add eventDate
eventDate = lubridate::ymd(paste(year, month, day, sep = "-"),
truncated = 2),
month = lubridate::month(eventDate)) %>%
# Add fieldNotes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(collector_number),
paste0("Collector_number: ", collector_number), ""),
dplyr::if_else(!is.na(location_name),
paste0("location_name: ", location_name), ""),
dplyr::if_else(!is.na(habitat),
paste0("habitat: ", habitat), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Remove these input columns
dplyr::select(!c(collector_number, location_name, habitat)) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("EPEL_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Elle Pollination Ecology Lab",
datasetID = "EPEL",
institutionCode = "SFU"
)
#### 1.3 Out ####
# Save the dataset
readr::write_excel_csv(EPEL_Data, file = paste(path, outFile, sep = "/"))
# Return data
return(EPEL_Data)
} # END readr_EPEL
#### 2.0 ASP ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_ASP <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
Tribe <- Morphospecies <- Successional_Stage <- genus <- specificEpithet <- NULL
eventDate <- catalogNumber <- . <- elevation <- NULL
#### 2.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 2.2 Read+ ####
ASP_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::mutate(verbatimElevation = elevation) %>%
dplyr::mutate(
# Add previousIdentifications
previousIdentifications = stringr::str_c(
dplyr::if_else(!is.na(Tribe),
paste0("Tribe: ", Tribe), ""),
dplyr::if_else(!is.na(Morphospecies),
paste0("Morphospecies: ", Morphospecies), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
dplyr::mutate(
# Add locationRemarks
locationRemarks = paste0("Successional Stage:", Successional_Stage),
# Add scientificName
scientificName = paste(genus, specificEpithet),
# Add dataSource
dataSource = "ASP_Anthophila",
# Add eventDate
eventDate = lubridate::dmy(eventDate,
truncated = 2),
# I'm sorry but this catalogNumber is useless. I'm going to edit it more-unique
catalogNumber = dplyr::if_else(!is.na(catalogNumber),
paste0("ASP_", catalogNumber), "")) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("ASP_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Allan Smith-Pardo",
datasetID = "ASP"
)
#### 2.3 Out ####
# Save the dataset
readr::write_excel_csv(ASP_data, file = paste(path, outFile, sep = "/"))
# Return data
return(ASP_data)
} # END readr_ASP
#### 3.0 BMin ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_BMin <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
eventDate <- . <- catalogNumber <- NULL
catalog_number <- pollinator_family <- pollinator_genus <- pollinator_species <- NULL
collection_method <- day_collected <- month_collected <- year_collected <- NULL
location_description <- latitude <- longitude <- basis_of_record <- genus <- NULL
specificEpithet <- year <- day <- eventDate <- collector_number <- location_name <- NULL
habitat <- . <- catalogNumber <- NULL
#### 3.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 3.2 Read+ ####
BMin_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::mutate(
# Format eventDate
eventDate = lubridate::dmy(eventDate,
truncated = 2, quiet = TRUE),
dataSource = "BMin_Anthophila") %>%
# add the database_id column
dplyr::mutate(
database_id = paste("BMin_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Robert Minckley",
datasetID = "BMin",
institutionCode = "University of Rochester"
)
#### 3.3 Out ####
# Save the dataset
readr::write_excel_csv(BMin_data, file = paste(path, outFile, sep = "/"))
# Return data
return(BMin_data)
} # END readr_BMin
#### 4.0 BMont ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_BMont <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
occurence_lsid <- fieldNotes <- GPS_device <- . <- catalogNumber <- eventDate <- dateTest <- NULL
tempDate <- mdy <- VerbatimEventDate <- NULL
#### 4.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 4.2 Read+ ####
BMont_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::rename(
occurrenceID = occurence_lsid,
verbatimEventDate = VerbatimEventDate) %>%
dplyr::mutate(
# Add fieldNotes
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(fieldNotes),
paste0("fieldNotes: ", fieldNotes), ""),
dplyr::if_else(!is.na(GPS_device),
paste0("GPS_device: ", GPS_device), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
dplyr::mutate(dataSource = "BMont_Anthophila") %>%
# add the database_id column
dplyr::mutate(
database_id = paste("BMont_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Find the date ranges
dplyr::mutate(dateTest = dplyr::if_else(stringr::str_count(eventDate) > 11,
stringr::str_replace(string = eventDate,
pattern = "/", replacement = ","),
"FALSE")) %>%
# Append these to fieldNotes
dplyr::mutate(fieldNotes = dplyr::if_else(dateTest != FALSE & !is.na(dateTest),
paste0(fieldNotes,
"|dateRange: ", eventDate), fieldNotes)) %>%
# Now remove those eventDates with a range and instead insert the start date
dplyr::mutate(tempDate = dplyr::if_else(dateTest != FALSE & !is.na(dateTest),
paste0(stringr::str_replace(eventDate,
pattern = ".*/",
replacement = "")),
eventDate)) %>%
# Pick up dates of different formats and format together.
dplyr::mutate(mdy = lubridate::mdy(tempDate, quiet = TRUE)) %>%
dplyr::mutate(ymd = lubridate::ymd(tempDate, quiet = TRUE)) %>%
tidyr::unite(col = eventDate,
mdy, ymd, sep = "", na.rm = TRUE,
remove = FALSE) %>%
# Remove working columns
dplyr::select(!c(dateTest, tempDate, mdy, ymd)) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Bombus Montana",
datasetID = "BMon"
)
#### 4.3 Out ####
# Save the dataset
readr::write_excel_csv(BMont_data, file = paste(path, outFile, sep = "/"))
# Return data
return(BMont_data)
} # END readr_BMont
#### 5.0 Ecd ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Ecd <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
year <- day <- institutionCode <- id <- . <- catalogNumber <- recordID <- NULL
#### 5.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 5.2 Read+ ####
Ecd_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::rename(recordId = recordID) %>%
dplyr::mutate(
# Format eventDate
eventDate = lubridate::ymd(paste(year, month, day, sep = "-"),
truncated = 2, quiet = TRUE),
dataSource = "Ecd_Anthophila",
# I want to make sure id is unique...
id = paste(institutionCode, id, sep = "_")) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Ecd_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Ecdysis",
datasetID = "Ecd"
)
#### 5.3 Out ####
# Save the dataset
readr::write_excel_csv(Ecd_data, file = paste(path, outFile, sep = "/"))
# Return data
return(Ecd_data)
} # END readr_Ecd
###### 6.0 Gai ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Gai <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
species<-subspecies<-SpecimenLocation<-eventTime<-EndTime<-TempStart<-TempEnd<-WindStart<-
WindEnd<-SkyStart<-SkyEnd<-Site<-siteLocality<-syd<-eventDate<-.<-institutionCode <- NULL
#### 6.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 6.2 Read+ ####
Gai_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
# Make columns DarwinCore-compatible
dplyr::rename(
collectionCode = 'Collection Code',
otherCatalogNumbers = 'Other Catalog Number',
specificEpithet = species,
infraspecificEpithet = subspecies) %>%
# Add locationRemarks from a bunch of other columns
dplyr::mutate(
locationRemarks = stringr::str_c(
dplyr::if_else(!is.na(SpecimenLocation), paste0("SpecimenLocation: ", SpecimenLocation), ""),
dplyr::if_else(!is.na(eventTime),
paste0("StartTime: ", eventTime), ""),
dplyr::if_else(!is.na(EndTime),
paste0("EndTime: ", EndTime), ""),
dplyr::if_else(!is.na(TempStart),
paste0("TempStart: ", TempStart), ""),
dplyr::if_else(!is.na(TempEnd),
paste0("TempEnd: ", TempEnd), ""),
dplyr::if_else(!is.na(WindStart),
paste0("WindStart: ", WindStart), ""),
dplyr::if_else(!is.na(WindEnd),
paste0("WindEnd: ", WindEnd), ""),
dplyr::if_else(!is.na(SkyStart),
paste0("SkyStart: ", SkyStart), ""),
dplyr::if_else(!is.na(SkyEnd),
paste0("SkyEnd: ", SkyEnd), ""),
dplyr::if_else(!is.na(Site),
paste0("Site: ", Site), ""),
dplyr::if_else(!is.na(siteLocality),
paste0("siteLocality: ", siteLocality), ""),
dplyr::if_else(!is.na(syd),
paste0("syd: ", syd), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
dplyr::mutate(
# Format eventDate
eventDate = lubridate::mdy(eventDate,
truncated = 2, quiet = FALSE),
dataSource = "Gai_Anthophila") %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Gai_data_", 1:nrow(.), sep = ""),
.before = institutionCode) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Gaiarsa et al. 2021",
datasetID = "Gai"
)
#### 6.3 Out ####
# Save the dataset
readr::write_excel_csv(Gai_data, file = paste(path, outFile, sep = "/"))
# Return data
return(Gai_data)
} # END readr_Gai
#### 7.0 CAES ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_CAES <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = "Sheet1"){
# locally bind variables to the function
Tribe <- Morphospecies <- Successional_Stage <- genus <- specificEpithet <- NULL
eventDate <- catalogNumber <- . <- NULL
.<-PBIUSI<-Family<-Subfamily<-Genus<-species<-Country<-State_Prov<-Sec_Subdiv<-Locality<-Lat<-
Lon<-Start_Date<-Collector<-Sex<-Inst_Code<-Det_By<-Det_Date<-Coll_Method<-Spec_Count<-
Elev_m<-Trip_Code<-Project<-Det_History<-Tribe<-Host_Genus<-Host_Common_Name<-
Host_Relation<-Host_Location<-Loc_Notes<-Lat_Lon_Method<-End_Date<-eventDate<-
Elev_Det<-Macro_Habitat<-Micro_Habitat<-Pres_Method<-Spec_Notes<-genus<-specificEpithet<-
Lat_Lon_Accuracy<-Host_species<-Host_Family<-catalogNumber <- NULL
#### 7.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 7.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
CAES_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"), sheet = sheet) %>%
# Turn spaces into "_" in column names
dplyr::rename_with(., ~ gsub(" ", "_", .x, fixed = TRUE)) %>%
# Make columns DarwinCore-compatible
dplyr::rename(
catalogNumber = PBIUSI,
family = Family,
subfamily = Subfamily,
genus = Genus,
specificEpithet = species,
country = Country,
stateProvince = State_Prov,
county = Sec_Subdiv,
locality = Locality,
decimalLatitude = Lat,
decimalLongitude = Lon,
eventDate = Start_Date,
recordedBy = Collector,
sex = Sex,
institutionCode = Inst_Code,
identifiedBy = Det_By,
dateIdentified = Det_Date,
samplingProtocol = Coll_Method,
individualCount = Spec_Count,
elevation = Elev_m,
otherCatalogNumbers = Trip_Code,
bibliographicCitation = Project,
identificationRemarks = Det_History) %>%
# Add a bunch of columns from other columns
dplyr::mutate(
# Add previousIdentifications
previousIdentifications = paste0(
# ONLY do this IF there is something in the cell - is.na() finds "NA" values. the "!" reverses this to find complete.cases only.
dplyr::if_else(!is.na(Tribe), paste0("Tribe: ", Tribe),"")),
# Add associatedTaxa
# This will ONLY concatenate columns where they have a value.
associatedTaxa = stringr::str_c(
dplyr::if_else(!is.na(Host_Genus), paste0("Host_id: ", Host_Genus), ""),
dplyr::if_else(!is.na(Host_Common_Name),
paste0("Host_commonName: ", Host_Common_Name), ""),
dplyr::if_else(!is.na(Host_Relation),
paste0("Host_relation: ", Host_Relation), ""),
dplyr::if_else(!is.na(Host_Location),
paste0("Host_location: ", Host_Location), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Do the same as the last mutate, but for fieldNotes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(Loc_Notes),
paste0("fieldNotes: ", Loc_Notes), ""),
dplyr::if_else(!is.na(Lat_Lon_Method),
paste0("GPS_device: ", Lat_Lon_Method), ""),
dplyr::if_else(!is.na(End_Date),
paste0("Sampling period: ", eventDate, " to ", End_Date), ""),
dplyr::if_else(!is.na(Elev_Det),
paste0("Elevation_by: ", Elev_Det), ""),
dplyr::if_else(!is.na(Macro_Habitat),
paste0("Macro_Habitat: ", Macro_Habitat), ""),
dplyr::if_else(!is.na(Micro_Habitat),
paste0("Micro_Habitat: ", Micro_Habitat), ""),
dplyr::if_else(!is.na(Pres_Method),
paste0("Preservation_method: ", Pres_Method), ""),
dplyr::if_else(!is.na(Spec_Notes),
paste0("Specimen_notes: ", Spec_Notes), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Add scientificName
dplyr::mutate(
scientificName = stringr::str_c(
dplyr::if_else(!is.na(genus),
genus, ""),
dplyr::if_else(!is.na(specificEpithet),
specificEpithet, ""),
sep = " ")) %>%
# Get the coordinateUncertaintyInMeters by taking the UPPER uncertainty limit from the provided
# ranges. I.e., "100-1000" becomes "1000"
dplyr::mutate(
coordinateUncertaintyInMeters = stringr::str_extract(
Lat_Lon_Accuracy, pattern = "-.*|[0-9]+" ),
# Format eventDate
eventDate = lubridate::ymd(
# First convert from silly excel numeric format to real dates...
eventDate %>%
as.numeric() %>%
as.Date(., origin = "1899-12-30"),
truncated = 2, quiet = FALSE), # 215 failed to parse.
dataSource = "CAES_Anthophila") %>%
# Add the scientificNameAuthorship column using the specificEpithet column
# dplyr::mutate(scientificNameAuthorship = stringr::str_replace(
# string = verbatimScientificName,
# pattern = paste0(".*", specificEpithet, " "),
# replacement = ""
# )) %>%
# Remove those now redundant columns
dplyr::select(!c(Tribe, Host_Genus, Host_species, Host_Family, Host_Common_Name,
Host_Relation, Host_Location, Loc_Notes, Lat_Lon_Method,
End_Date, Elev_Det, Macro_Habitat, Micro_Habitat, Pres_Method, Spec_Notes,
Lat_Lon_Accuracy)) %>%
# Remove double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("CAES_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Connecticut Agricultural Experiment Station",
datasetID = "CAES"
)
#### 7.3 Out ####
# Save the dataset
readr::write_excel_csv(CAES_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(CAES_data)
} # END readr_CAES
#### 9.0 KP ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_KP <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
.<-ID<-Family<-Subfamily<-Genus<-Det<-Number<-Collection_method<-Collector<-Order<-Suborder<-
subgenus<-species<-subspecies<-author<-whole_sci_name<-Country<-State<-County_Parish<-
Location<-Lat<-Long<-decimalLatitude<-decimalLongitude<-Tribe<-sp_group<-Male<-Female<-
genus<-specificEpithet<-infraspecificEpithet<-Collection_date<-catalogNumber <- NULL
#### 9.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 9.2 Read+ ####
# Reads in the .xlsx file, trims the white spaces, and formats the columns to the correct type
KP_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/")) %>%
# Turn spaces into "_" in column names
dplyr::rename_with(., ~ gsub(" ", "_", .x, fixed = TRUE)) %>%
# Make columns DarwinCore-compatible
dplyr::rename(
catalogNumber = ID,
family = Family,
subfamily = Subfamily,
genus = Genus,
identifiedBy = Det,
individualCount = Number,
samplingProtocol = Collection_method,
recordedBy = Collector,
order = Order,
suborder = Suborder,
subgenus = subgenus,
specificEpithet = species,
infraspecificEpithet = subspecies,
scientificNameAuthorship = author,
verbatimScientificName = whole_sci_name,
country = Country,
stateProvince = State,
county = County_Parish,
locality = Location,
decimalLatitude = Lat,
decimalLongitude = Long) %>%
# round the coordinates to six decimal places
dplyr::mutate(
decimalLatitude = decimalLatitude %>% as.numeric() %>% base::round(digits = 6) %>%
suppressWarnings(classes = "warning"),
decimalLongitude = decimalLongitude %>% as.numeric() %>% base::round(digits = 6) %>%
suppressWarnings(classes = "warning")) %>%
# Add a bunch of columns from other columns
dplyr::mutate(
previousIdentifications = stringr::str_c(
dplyr::if_else(!is.na(Tribe),
paste0("Tribe: ", Tribe), ""),
dplyr::if_else(!is.na(sp_group),
paste0("sp_group: ", sp_group), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Do the same as the last mutate, but for fieldNotes
dplyr::mutate(
sex = stringr::str_c(
dplyr::if_else(!is.na(Male) & Male != 0,
paste0(Male, " M"), ""),
dplyr::if_else(!is.na(Female) & Female != 0,
paste0(Female, " F"), ""),
sep = "|") %>%
# Remove extra bars "|".
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Format eventDate and add dataSource
dplyr::mutate(
# Create scientificName
scientificName = stringr::str_c(
dplyr::if_else(!is.na(genus),
paste0(genus), ""),
dplyr::if_else(!is.na(specificEpithet),
paste0(specificEpithet), ""),
dplyr::if_else(!is.na(infraspecificEpithet),
paste0(infraspecificEpithet), ""),
sep = " ") %>% stringr::str_squish() %>% stringr::str_trim(side = "both"),
# Format eventDate
eventDate = lubridate::ymd_hms(Collection_date,
truncated = 5, quiet = FALSE, tz = "UTC"), # 215 failed to parse.
dataSource = "KP_Anthophila") %>%
# Remove those now redundant columns
dplyr::select(!c(Male, Female, sp_group, Tribe, Collection_date)) %>%
# Remove double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("KP_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "USDA ARS Southeastern USA",
datasetID = "KP"
)
#### 9.3 Out ####
# Save the dataset
readr::write_excel_csv(KP_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(KP_data)
} # END readr_KP
#### 11.0 EcoS ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_EcoS <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
Species <- . <- scientificName <- Latitude <- Longitude <- Year <- catalogNumber <- NULL
Collection <- ID_project <- NULL
#### 11.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 11.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
EcoS_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE, guess_max = 33000) %>%
# Add institution information
dplyr::mutate(
institutionCode = Collection,
datasetName = Collection,
catalogNumber = ID_project,
otherCatalogNumbers = stringr::str_c(Collection, ID_project, sep = "_")) %>%
# Add taxonomic information
dplyr::mutate(
scientificName = Species) %>%
# Split genus and species names
tidyr::separate(
data = ., col = scientificName,
into = c("genus", "specificEpithet"),
sep = "_", remove = FALSE) %>%
# Lat Lon
dplyr::mutate(
decimalLatitude = Latitude,
decimalLongitude = Longitude) %>%
# Year
dplyr::mutate(
year = Year) %>%
# Add dataset information
dplyr::mutate(dataSource = "EcoS_Anthophila") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("EcoS_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# add the database_id column
dplyr::mutate(
datasetName = "EcoSur",
datasetID = "EcoS"
)
#### 11.3 Out ####
# Save the dataset
readr::write_excel_csv(EcoS_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(EcoS_data)
} # END readr_EcoS
#### 12.0 GeoL ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_GeoL <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
geolocate_Latitude<-geolocate_Longitude<-geolocate_UncertaintyRadiusMeters<-database_id<-
bels_decimallatitude<-bels_decimallongitude<-bels_coordinateuncertaintyinmeters<-datasource<-
scientificname<-infraspecificepithet<-specificepithet<-acceptednameusage<-taxonrank<-
scientificnameauthorship<-countrycode<-stateprovince<-eventdate<-basisofrecord<-
occurrencestatus<-recordnumber<-recordedby<-eventid<-samplingprotocol<-samplingeffort<-
individualcount<-catalognumber<-rightsholder<-institutioncode<-datasetname<-
othercatalognumbers<-occurrenceid<-coreid<-recordid<-collectionid<-
verbatimscientificname<-verbatimeventdate<-id <- . <- NULL
rightsHolder <- continent <- type <- samplingProtocol <- NULL
island <- municipality <- verbatimEventDate <- catalogNumber <- NULL
#### 12.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 12.2 Read+ ####
###### a. GeoL_high ####
# Reads in the .xlsx file, trims the white spaces, and formats the columns to the correct type
GeoL_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = "GEOLOCATE HIGH") %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Convert GeoLocate columns into dwc columns
dplyr::rename(
decimalLatitude = geolocate_Latitude, decimalLongitude = geolocate_Longitude,
coordinateUncertaintyInMeters = geolocate_UncertaintyRadiusMeters) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# Remove blanks
tidyr::drop_na(database_id) %>%
# Temporarily add an identifier column
dplyr::mutate(
tempSource = "GeoL",
rightsHolder = rightsHolder %>% as.character(),
island = island %>% as.character(),
municipality = municipality %>% as.character(),
verbatimEventDate = verbatimEventDate %>% as.character()
)
# User output
writeLines(paste0(
" - We have read in ",
format(nrow(GeoL_data), big.mark = ","), " occurrence records from the 'GEOLOCATE HIGH' sheet."
))
###### b. BELS_high ####
# Reads in the .xlsx file, trims the white spaces, and formats the columns to the correct type
BELS_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = "BELS High") %>%
# Convert GeoLocate columns into dwc columns
dplyr::rename(
decimalLatitude = bels_decimallatitude,
decimalLongitude = bels_decimallongitude,
coordinateUncertaintyInMeters = bels_coordinateuncertaintyinmeters,
dataSource = datasource,
scientificName = scientificname,
infraspecificEpithet = infraspecificepithet,
specificEpithet = "_specificepithet",
species = specificepithet,
acceptedNameUsage = acceptednameusage,
taxonRank = taxonrank,
scientificNameAuthorship = scientificnameauthorship,
countryCode = countrycode,
stateProvince = stateprovince,
eventDate = eventdate,
basisOfRecord = basisofrecord,
occurrenceStatus = occurrencestatus,
recordNumber = recordnumber,
recordedBy = recordedby,
eventID = eventid,
samplingProtocol = samplingprotocol,
samplingEffort = samplingeffort,
individualCount = individualcount,
catalogNumber = catalognumber,
rightsHolder = rightsholder,
institutionCode = institutioncode,
datasetName = datasetname,
otherCatalogNumbers = othercatalognumbers,
occurrenceID = occurrenceid,
coreid = coreid,
recordId = recordid,
collectionID = collectionid,
verbatimScientificName = verbatimscientificname,
verbatimEventDate = verbatimeventdate,
id = id) %>%
# Correct some formatting
dplyr::mutate(continent = continent %>% as.character(),
type = type %>% as.character(),
id = id %>% as.character(),
samplingProtocol = samplingProtocol %>% as.character(),
island = island %>% as.character(),
municipality = municipality %>% as.character(),
verbatimEventDate = verbatimEventDate %>% as.character()) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# Remove blanks
tidyr::drop_na(database_id) %>%
# Temporarily add an identifier column
dplyr::mutate(
tempSource = "Bels"
)
# User output
writeLines(paste0(
" - We have read in ",
format(nrow(BELS_data), big.mark = ","), " occurrence records from the 'BELS High' sheet."
))
###### c. merge ####
GeoL_data <- GeoL_data %>%
# Remove data that occurs in BELS_data
dplyr::filter(!database_id %in% BELS_data$database_id) %>%
# Combine datasets
dplyr::bind_rows(BELS_data) %>%
dplyr::select(!database_id) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("GEOL_data_", 1:nrow(.), sep = ""),
.before = catalogNumber)
# User output
writeLines(paste0(
" - We have kept ",
format(sum(GeoL_data$tempSource == "GeoL", na.rm = FALSE), big.mark = ","),
" occurrences from GeoLocate, and ",
format(sum(GeoL_data$tempSource == "Bels", na.rm = FALSE), big.mark = ","),
" records from BELS (",
format(nrow(GeoL_data), big.mark = ","),
" in total). BELS was given preference over GeoLocate"
))
#### 12.3 Out ####
# Save the dataset
readr::write_excel_csv(GeoL_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(GeoL_data)
} # END readr_GeoL
#### 13.0 EaCO ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_EaCO <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
County<-State<-Genus<-genus<-species<-dateRange<-dateSet<-dateCollected<-treatment<-
trapNumber<-samplingRound<-coordinates<-decimalLatitude<-decimalLongitude<-recordNumber<-
.<-catalogNumber <- NULL
#### 13.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 13.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
EaCO_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/")) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Rename columns
dplyr::rename(
recordNumber = 'Specimen Number',
county = County,
stateProvince = State,
genus = Genus,
species = 'Species (if available)',
dateRange = 'Date Range for collection',
treatment = 'Treatment type',
trapNumber = 'Trap number',
samplingRound = 'Sampling Round',
coordinates = 'GPS Coordinates of Traps') %>%
# Drop rows without species names or that aren't bees or aren't useable names
dplyr::filter(!is.na(genus), !genus == "",
!is.na(species), !species == "",
!stringr::str_detect(species, pattern = "[0-9]"),
!stringr::str_detect(species, pattern = "/"),
!stringr::str_detect(tolower(species), pattern = "sp.")) %>%
# Split dates
tidyr::separate(
col = dateRange, sep = "-",
into = c("dateSet", "dateCollected")) %>%
# Add year and convert to ymd format
dplyr::mutate(
dateSet = stringr::str_c(dateSet, "/2017") %>% lubridate::mdy(truncated = 2),
dateCollected = stringr::str_c(dateCollected, "/2017") %>% lubridate::mdy(truncated = 2),
year = 2017) %>%
# Add scientificName
dplyr::mutate(scientificName = stringr::str_c(genus, species, sep = " ")) %>%
# Add field notes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(dateSet), paste0("dateSet: ", dateSet), ""),
dplyr::if_else(!is.na(dateCollected),
paste0("dateCollected: ", dateCollected), ""),
dplyr::if_else(!is.na(treatment),
paste0("treatmentType: ", treatment), ""),
dplyr::if_else(!is.na(trapNumber),
paste0("trapNumber: ", trapNumber), ""),
dplyr::if_else(!is.na(samplingRound),
paste0("samplingRound: ", samplingRound), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Add samplingEffort
dplyr::mutate(
samplingEffort = dateCollected - dateSet
) %>%
# rescue coordinates
tidyr::separate(
col = coordinates,
into = c("decimalLatitude", "decimalLongitude"),
sep = ",") %>%
dplyr::mutate(
decimalLatitude = decimalLatitude %>% stringr::str_remove(pattern = "[a-zA-Z]"),
decimalLongitude = decimalLongitude %>% stringr::str_remove(pattern = "[a-zA-Z]")) %>%
dplyr::mutate(
catalogNumber = stringr::str_c("EastColarado_",recordNumber, sep = "")) %>%
# Add dataset information
dplyr::mutate(dataSource = "EaCO_Anthophila") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("EaCO_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Eastern Colorado (Arathi Seshadri)",
datasetID = "EaCo",
institutionCode = "USDA ARS"
)
#### 13.3 Out ####
# Save the dataset
readr::write_excel_csv(EaCO_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(EaCO_data)
} # END readr_EaCO
#### 14.0 MABC ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_MABC <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = "Hoja1"){
# locally bind variables to the function
genus <- specificEpithet <- collectionSite <- siteCode <- hour <- tribe <- eventDate <- NULL
. <- catalogNumber <- NULL
#### 14.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 14.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
MABC_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Rename columns
dplyr::rename(
catalogNumber = 'Ejemplar',
eventDate = 'Fecha colecta',
country = paste0('Pa\u00eds'),
stateProvince = 'Estado/Provincia',
municipality = 'Municipio',
locality = 'Localidad',
samplingProtocol = 'Metodo colecta',
decimalLatitude = 'Coordenadas Lat',
decimalLongitude = 'Coordenadas Long',
verbatimElevation = 'Altitud',
georeferenceVerificationStatus = paste0('Datos georeferenciaci\u00f3n'),
recordedBy = 'Colector',
identifiedBy = 'Identificador',
family = 'Familia',
subfamily = 'Subfamilia',
genus = 'Genero',
subgenus = 'Subgenero',
specificEpithet = 'Especie',
infraspecificEpithet = 'Subespecie',
species = 'Nombre especie',
taxonID = paste0('C\u00f3digo especie'),
sex = 'Sexo',
# Non-standard fields
tribe = 'Tribu',
collectionSite = 'Sitio Colecta',
siteCode = paste0('C\u00f3digo sitio'),
hour = 'Hora') %>%
# Add scientificName
dplyr::mutate(scientificName = stringr::str_c(genus, specificEpithet, sep = " ")) %>%
# Add field notes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(collectionSite), paste0("collectionSite: ", collectionSite), ""),
dplyr::if_else(!is.na(siteCode),
paste0("siteCode: ", siteCode), ""),
dplyr::if_else(!is.na(hour),
paste0("time: ", hour), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Add identificationRemarks
dplyr::mutate(
identificationRemarks = stringr::str_c(
dplyr::if_else(!is.na(tribe), paste0("tribe: ", tribe), ""))) %>%
# Add year and ensure ymd format
dplyr::mutate(eventDate = lubridate::dmy(eventDate)) %>%
# Add dataset information
dplyr::mutate(dataSource = "MABC_Anthophila") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("MABC_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# add the database_id column
dplyr::mutate(
datasetName = "MABC",
datasetID = "MABC"
)
#### 14.3 Out ####
# Save the dataset
readr::write_excel_csv(MABC_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(MABC_data)
} # END readr_MABC
#### 15.0 Col ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Col <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = sheet){
# locally bind variables to the function
day <- year <- eventDateInitial <- eventDate <- month2 <- day2 <- eventDate2 <- NULL
scientificName <- . <- catalogNumber <- NULL
#### 15.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 15.2 Read+ ####
###### a. Col_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
Col_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Fix some special cases that already involve "." in the column names
setNames(., stringr::str_replace_all(colnames(.), " ", ". ")) %>%
setNames(., stringr::str_replace_all(colnames(.), " $", ".")) %>%
# Rename columns
dplyr::rename(
catalogNumber = paste0('C\u00f3digo de Barras'),
recordedBy = 'Colectores [Aggregated]',
recordedByID = 'Colectores asociados',
eventDateInitial = paste0('Fecha colecci\u00f3n inicial'),
order = 'Orden',
family = 'Familia',
genus = paste0('G\u00e9nero'),
specificEpithet = 'Especie',
scientificNameAuthorship = 'Especie Author',
typeStatus = 'Tipo',
identifiedBy = 'Determinador [Formatted]',
dateIdentified = paste0('Fecha determinaci\u00f3n'),
country = paste0('Pa\u00eds'),
stateProvince = 'Departamento',
municipality = 'Municipio',
locationRemarks = 'Corregimiento Departamental',
locality = 'Localidad',
decimalLatitude = 'Latitud georref. dec.',
decimalLongitude = 'Longitud georref. dec.',
scientificName = 'Nombre Completo',
day = "dia",
month = "mes",
year = "Ano"
# Previous column names:
# collectionID = 'Colecci\\u00f3n/Guid',
# collectionCode = 'C\\u00f3digo',
# occurrenceID = 'Collection Object/GUID',
# identificationRemarks = 'Observaciones generales',
# eventID = 'Evento de Recolecci\\u00f3n/Guid',
# recordedByID = 'Numero de colector',
# eventDateFinal = 'Fecha colecci\\u00f3n final',
# class = 'Clase',
# infraspecificEpithet = 'Subespecie',
# namePublishedInID = 'Referencia original',
# identificationID = 'Determinaciones/Guid',
# identificationQualifier = 'Obs. Determinaci\\u00f3n',
# locationID = 'LocalityID',
# geodeticDatum = 'Datum geod\\u00e9sico',
# coordinateUncertaintyInMeters = 'Precisi\\u00f3n coord. georref.',
# minimumElevationInMeters = 'Elevaci\\u00f3n m\\u00ednima',
# maximumElevationInMeters = 'Elevaci\\u00f3n m\\u00e1xima',
# georeferenceProtocol = 'Protocolo de georreferenciaci\\u00f3n',
# verbatimLatitude = 'Informaci\\u00f3n geogr\\u00e1fica/Latitude1 literal',
# verbatimLongitude = 'Longitude1 literal',
# locationRemarks = 'Observaciones ninf Geogr\\u00e1fica',
# lifeStage = 'Estado de desarrollo',
# habitat = 'H\\u00e1bitat',
# continent = 'Continente',
# tribe = 'Tribu',
# subSpeciesAuthor = 'Subespecie Author'
) %>%
# Fix some date issues:
# a. replace 00 dates with "" to be picked u pby truncated
dplyr::mutate(
day = dplyr::if_else(day == "0" | day == "00",
"", day) %>%
stringr::str_remove("to.*|^0") %>%
as.numeric(na.rm = TRUE),
month = dplyr::if_else(month == "0" | month == "00",
"", month) %>%
stringr::str_remove("^0") %>%
as.numeric(na.rm = TRUE),
year = year %>% as.numeric()
) %>%
dplyr::mutate(
eventDate = lubridate::ymd(paste(year, month, day, sep = "/"),
truncated = 2),
.after = eventDateInitial) %>%
# b. Fix inverted day-month
dplyr::mutate(
day2 = dplyr::if_else(is.na(eventDate),
month,
1),
month2 = dplyr::if_else(is.na(eventDate),
day,
1),
eventDate2 = dplyr::if_else(is.na(eventDate),
lubridate::ymd(paste(year, month2, day2, sep = "/"),
truncated = 2),
eventDate),
.after = year
) %>%
# Combine into final date values
dplyr::mutate(
# Take eventDate if it's not empty, and eventDate2 if it is empty
eventDate = dplyr::if_else(is.na(eventDate),
eventDate2,
eventDate),
day = dplyr::if_else(is.na(day),
day2,
day),
month = dplyr::if_else(is.na(month),
month2,
month)) %>%
dplyr::select(!c(eventDate2, day2, month2)) %>%
# Remvoe extra spaces in subgenus
dplyr::mutate(scientificName = scientificName %>%
stringr::str_replace(pattern = "\\( ",
replacement = "\\(")) %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Col_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "Col_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Colombia - Diego Alexander Guevara Farias",
datasetID = "Col"
)
#### 15.3 Out ####
# Save the dataset
readr::write_excel_csv(Col_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(Col_data)
} # END readr_Col
#### 16.0 FSCA ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_FSCA <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
. <- catalogNumber <- recordID <- NULL
#### 16.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 16.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
FSCA_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
# Add dataset information
dplyr::mutate(dataSource = "FSCA_Anthophila") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("FSCA_data_", 1:nrow(.), sep = ""),
.before = 1) %>%
dplyr::mutate(license = dataLicense) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Florida State Collection of Arthropods",
datasetID = "FSCA",
institutionCode = "FSCA"
)
#### 16.3 Out ####
# Save the dataset
readr::write_excel_csv(FSCA_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(FSCA_data)
} # END readr_FSCA
#### 17.0 SMC ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_SMC <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
collectionMethod <- locale <- latitude <- longitude <- organismName <- scientificName <- NULL
observationDate <- eventDate <- . <- NULL
#### 17.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 17.2 Read+ ####
SMC_Data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE, guess_max = 33000) %>%
# Rename some columns to make format consistent with DarwinCore
dplyr::rename(
samplingProtocol = collectionMethod,
locality = locale,
decimalLatitude = latitude,
decimalLongitude = longitude,
scientificName = organismName) %>%
# Make new columns
dplyr::mutate(
# Remove underscore from scientificName strings
scientificName = gsub("_", " ", scientificName),
# Add basis of record
basisOfRecord = "specimen",
# Add eventDate
eventDate = lubridate::mdy(observationDate),
# Parse eventDate into day, month, and year
month = lubridate::month(eventDate),
day = lubridate::day(eventDate),
year = lubridate::year(eventDate)) %>%
# Add dataSource information
dplyr::mutate(dataSource = "SMC_Anthophila") %>%
# add the database_id column
dplyr::mutate(
database_id = paste("SMC_data_", 1:nrow(.), sep = "")) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Texas SMC literature data",
datasetID = "SMC"
)
# Save the dataset
readr::write_excel_csv(SMC_Data, file = paste(path, outFile, sep = "/"))
# Return data
return(SMC_Data)
} # END readr_SMC
#### 18.0 Bal ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Bal <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = "animal_data"){
# locally bind variables to the function
siteID<-year<-animalID<-abundance<-samplingMethod<-censusType<-decimalLatitude<-
decimalLongitude<-studyLocation<-siteDescription<-studyID<-locationID<-.<-
samplingIntensity<-eventDate<-catalogNumber <- NULL
#### 18.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 18.2 Read+ ####
# Reads in the .xlsx file, trims the white spaces, and formats the columns to the correct type
Bal_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet, startRow = 2) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Make columns DarwinCore-compatible
dplyr::rename(
locationID = siteID,
year = year,
eventDate = date,
scientificName = animalID,
individualCount = abundance,
samplingProtocol = samplingMethod,
fieldNotes = censusType,
decimalLatitude = decimalLatitude,
decimalLongitude = decimalLongitude,
locality = studyLocation,
locationRemarks = siteDescription) %>%
# Add some columns
dplyr::mutate(
catalogNumber = stringr::str_c(studyID, "_", locationID,"_", 1:nrow(.), sep = ""),
samplingEffort = stringr::str_c(samplingIntensity, " hours")
) %>%
# round the coordinates to six decimal places
dplyr::mutate(
decimalLatitude = decimalLatitude %>% as.numeric() %>% base::round(digits = 6) %>%
suppressWarnings(classes = "warning"),
decimalLongitude = decimalLongitude %>% as.numeric() %>% base::round(digits = 6) %>%
suppressWarnings(classes = "warning")) %>%
# Format eventDate and add dataSource
# Format eventDate
dplyr::mutate(
eventDate = lubridate::dmy(eventDate,
truncated = 2, quiet = FALSE),
# Parse eventDate into day, month, and year
month = lubridate::month(eventDate),
day = lubridate::day(eventDate),
year = lubridate::year(eventDate),
dataSource = "Bal_Anthophila") %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Bal_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Ballare et al. 2019",
datasetID = "Bal"
)
#### 18.3 Out ####
# Save the dataset
readr::write_excel_csv(Bal_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(Bal_data)
} # END readr_Bal
#### 19.0 Lic ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Lic <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
Kingdom<-Order<-Family_or_grp<-Genus<-Species<-sex<-Collector<-Determiner<-genus<-species<-
occurrenceID<-eventID<-eventDate<-.<-catalogNumber<-family <- NULL
#### 19.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 19.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
Lic_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE, guess_max = 33000) %>%
dplyr::rename(
kingdom = Kingdom,
order = Order,
family = Family_or_grp,
genus = Genus,
species = Species,
sex = sex,
recordedBy = Collector,
identifiedBy = Determiner) %>%
# Add taxonomic information
dplyr::mutate(
scientificName = stringr::str_c(
dplyr::if_else(!is.na(genus),
paste0(genus), ""),
dplyr::if_else(!is.na(species),
paste0(species), ""),
sep = " ")) %>%
# Make a catalogue number
dplyr::mutate(
catalogNumber = stringr::str_c(
dplyr::if_else(!is.na(occurrenceID),
paste0(occurrenceID), ""),
dplyr::if_else(!is.na(eventID),
paste0(eventID), ""),
sep = "_")) %>%
# Format eventDate
dplyr::mutate(
eventDate = lubridate::dmy(eventDate,
truncated = 2, quiet = FALSE),
# Parse eventDate into day, month, and year
month = lubridate::month(eventDate),
day = lubridate::day(eventDate),
year = lubridate::year(eventDate)) %>%
# Add dataset information
dplyr::mutate(dataSource = "Lic_Anthophila") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Lic_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Elinor Lichtenberg Canola Data",
datasetID = "Lic"
)
# filter to bee families only
Lic_data <- Lic_data %>%
dplyr::filter(tolower(family) %in%
tolower(c("Andrenidae","Apidae","Colletidae","Halictidae","Megachilidae",
"Melittidae","Stenotritidae")))
#### 19.3 Out ####
# Save the dataset
readr::write_excel_csv(Lic_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(Lic_data)
} # END readr_Lic
#### 20.0 Arm ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Arm <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = "Sheet1"){
# locally bind variables to the function
fam<-genus<-sp<-species<-sex<-locality<-munic<-state<-y<-x<-elev<-specificEpithet<-ecoregion<-
veget<-g<-m<-s<-G<-M<-S<-day<-year<-.<-family <- NULL
#### 20.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 20.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
Arm_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"), sheet = sheet) %>%
# Make columns DarwinCore-compatible
dplyr::rename(
family = fam,
genus = genus,
specificEpithet = sp,
species = species,
sex = sex,
locality = locality,
municipality = munic,
stateProvince = state,
decimalLatitude = y,
decimalLongitude = x,
verbatimElevation = elev) %>%
# Add a bunch of columns from other columns
dplyr::mutate(
# Add scientificName
# This will ONLY concatenate columns where they have a value.
scientificName = stringr::str_c(
dplyr::if_else(!is.na(genus), genus, ""),
dplyr::if_else(!is.na(specificEpithet),
specificEpithet, ""),
sep = " ") ) %>%
# Do the same as the last mutate, but for fieldNotes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(ecoregion),
paste0("ecoregion: ", ecoregion), ""),
dplyr::if_else(!is.na(veget),
paste0("vegetation: ", veget), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Add scientificName
dplyr::mutate(
verbatimLatitude = stringr::str_c(
dplyr::if_else(!is.na(g),
as.character(g), ""),
dplyr::if_else(!is.na(m),
as.character(m), ""),
dplyr::if_else(!is.na(s),
as.character(s), ""),
sep = " "),
verbatimLongitude = stringr::str_c(
dplyr::if_else(!is.na(G),
as.character(G), ""),
dplyr::if_else(!is.na(M),
as.character(M), ""),
dplyr::if_else(!is.na(S),
as.character(S), ""),
sep = " ")) %>%
# Get the coordinateUncertaintyInMeters by taking the UPPER uncertainty limit from the provided
# ranges. I.e., "100-1000" becomes "1000"
dplyr::mutate(
# Format eventDate
eventDate = lubridate::dmy(paste(
day, month, year, sep = "-"
)), # 215 failed to parse.
dataSource = "Arm_Anthophila") %>%
# Remove double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Arm_data_", 1:nrow(.), sep = ""),
.before = family) %>%
dplyr::mutate(license = dataLicense) %>%
# Remove spent columns
dplyr::select(!tidyselect::any_of(c("veget", "ecoregion", "g", "m", "s", "G", "M", "S"))) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Armando Falcon-Brindis",
datasetID = "Arm"
)
#### 20.3 Out ####
# Save the dataset
readr::write_excel_csv(Arm_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(Arm_data)
} # END readr_Arm
#### 21.0 Dorey ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Dor <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
. <- catalogNumber <- eventDate <- stateOrProvince <- NULL
#### 21.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 21.2 Read+ ####
Dor_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::rename(stateProvince = stateOrProvince) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Dor_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "Dor_Anthophila") %>%
# Format date
dplyr::mutate(eventDate = eventDate %>% lubridate::dmy(., truncated = 2),
year = lubridate::year(eventDate),
month = lubridate::month(eventDate),
day = lubridate::day(eventDate)) %>%
# Pick up dates of different formats and format together.
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "James B Dorey Bee Data",
datasetID = "Dorey"
)
#### 21.3 Out ####
# Save the dataset
readr::write_excel_csv(Dor_data, file = paste(path, outFile, sep = "/"))
# Return data
return(Dor_data)
} # END readr_Dor
#### 22.0 MEPB ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_MEPB <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = NULL){
# locally bind variables to the function
catalog_number<-pollinator_family<-pollinator_genus<-pollinator_species<-collection_method<-
day_collected<-month_collected<-year_collected<-location_description<-latitude<-longitude<-
basis_of_record<-genus<-specificEpithet<-year<-day<-eventDate<-collector_number<-
location_name<-habitat<-.<-catalogNumber <- NULL
#### 22.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 22.2 Read+ ####
MEPB_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"), sheet = sheet) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Fix broken encodings
dplyr:: mutate(
dplyr::across(
.cols = dplyr::everything(),
.fns = ~ stringr::str_replace_all(.,
pattern = c("\\u00c3\\u00b3"="\\u00f3",
"\\u00c3\\u00a9"="\\u00e9",
"\\u00c3\\u00b1"="\\u00f1",
"\\u00c2\\u00b0"="\\u00b0",
"\\u00c3"="\\u00e1")
))) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("MEPB_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "MEPB_Anthophila") %>%
# Format date
dplyr::mutate(eventDate = lubridate::ymd(stringr::str_c(year %>% as.numeric(),
month %>% as.numeric(),
day %>% as.numeric(),
sep = "/"),
truncated = 2)) %>%
# Pick up dates of different formats and format together.
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetID = "MEPB"
)
#### 22.3 Out ####
# Save the dataset
readr::write_excel_csv(MEPB_data, file = paste(path, outFile, sep = "/"))
# Return data
return(MEPB_data)
} # END readr_MEPB
#### 23.0 Brazil ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_BBD <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
. <- catalogNumber <- year <- day <- dateLastModified <- dateLastModified2 <- NULL
identifiedBy <- Spcslink.identifiedby <- NULL
#### 23.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 23.2 Read+ ####
lookupCols <- c(verbatimScientificName = "Scientificname_ORIGINAL",
verbatimLatitude = "Lat_original",
verbatimLongitude = "Long_original")
BBD_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
# Rename columns
dplyr::rename(
tidyselect::any_of(lookupCols),
id = "CodeBBdatabase_curated",
scientificName = "Scientific name corrected",
family = "Family",
institutionCode = "institutioncode",
# infraspecificEpithet = "Spcslink.subspecies",
# scientificNameAuthorship = "Spcslink.scientificnameauthor",
day = "Day",
month = "Month",
year = "Year",
country = "Country",
stateProvince = "State",
decimalLatitude = "Latitude_dec.degrees",
decimalLongitude = "Longitude_dec.degrees",
coordinateUncertaintyInMeters = "Precision.of.coord.meters",
verbatimLocality = "Locality.original",
georeferenceRemarks = "NotasLatLong",
county = "Spcslink.county",
recordedBy = "Collector",
collectionCode = "Collection",
references = "Source",
sex = "Sex",
identifiedBy = "Det_By",
catalogNumber = "Codigo",
otherCatalogNumbers = "Spcslink.collectioncode",
dateIdentified = "Spcslink.yearidentified",
dateLastModified = "Spcslink.datelastmodified",
basisOfRecord = "Spcslink.basisofrecord",
# occurrenceID = "Spcslink.collectioncode",
eventRemarks = "NotesOnLocality") %>%
#dplyr::mutate(
# decimalLatitude = decimalLatitude %>% stringr::str_replace("\\,", "\\."),
# decimalLongitude = decimalLongitude %>% stringr::str_replace("\\,", "\\."))
# add the database_id column
dplyr::mutate(
database_id = paste("BBD_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Edit catalogNumber for AMNH
dplyr::mutate(catalogNumber = stringr::str_replace(
catalogNumber, pattern = "AMNHBEE", replacement = "AMNH_BEE"),
catalogNumber = stringr::str_replace(
catalogNumber, pattern = "^0\\.", replacement = ".")) %>%
# Add dataset information
dplyr::mutate(dataSource = "BBD_Anthophila") %>%
# Format date
dplyr::mutate(eventDate = lubridate::ymd(paste0(
year, month, day, sep = "/"), truncated = 2, quiet = TRUE)) %>%
dplyr::mutate(dateLastModified2 =
lubridate::dmy_hms(dateLastModified, truncated = 5, quiet = TRUE),
.after = dateLastModified) %>%
dplyr::mutate(dateLastModified2 = dplyr::if_else(is.na(dateLastModified2),
lubridate::ymd_hms(dateLastModified, truncated = 5, quiet = TRUE),
dateLastModified2)) %>%
dplyr::mutate(dateLastModified2 = dplyr::if_else(is.na(dateLastModified2),
lubridate::mdy_hms(dateLastModified, truncated = 5, quiet = TRUE),
dateLastModified2)) %>%
dplyr::mutate(dateLastModified2 = dplyr::if_else(is.na(dateLastModified2),
lubridate::ymd(dateLastModified, truncated = 2, quiet = TRUE),
dateLastModified2)) %>%
dplyr::mutate(dateLastModified2 = dplyr::if_else(is.na(dateLastModified2),
as.Date(dateLastModified %>% as.numeric(na.rm = TRUE),
origin = "1899-12-30") %>%
lubridate::ymd_hms(., truncated = 5, quiet = TRUE),
dateLastModified2),
.after = dateLastModified) %>%
dplyr::mutate(dateLastModified = dateLastModified2) %>%
dplyr::select(!dateLastModified2) %>%
# Pick up dates of different formats and format together.
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "BBD_Brazil",
datasetID = "BBD"
) %>%
# If identifiedBy is not filled where it should be, try the other column rpovided
dplyr::mutate(identifiedBy = dplyr::if_else(is.na(identifiedBy),
Spcslink.identifiedby,
identifiedBy))
#### 23.3 Out ####
# Save the dataset
readr::write_excel_csv(BBD_data, file = paste(path, outFile, sep = "/"))
# Return data
return(BBD_data)
} # END readr_BBD
#### 24.0 MPUJ ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_MPUJ <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = sheet){
# locally bind variables to the function
collector1stName <- collectorsLastName <- determined1stName <- determinedLastName <- NULL
day <- year <- eventDate <- endDate <- . <- catalogNumber <- fieldNotes <- NULL
`Start Date (Year)` <- `Start Date (Month)` <- `Start Date (Day)` <- NULL
#### 24.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 24.2 Read+ ####
###### a. MPUJ_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
MPUJ_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Rename columns
dplyr::rename(
catalogNumber = 'Catalog Number',
individualCount = "Count",
otherCatalogNumbers = "Alt Cat Number",
typeStatus = "Type Status",
identificationQualifier = "Qualifier",
sex = "Sex",
samplingProtocol = "Method",
habitat = "Habitat",
continent = "Continent",
country = "Country",
stateProvince = "State",
county = "County",
locality = "Locality Name",
minimumElevationInMeters = "Min Elevation",
maximumElevationInMeters = "Max Elevation",
fieldNotes = "Locality and Habitat Notes",
decimalLongitude = "Latitude1",
decimalLatitude = "Longitude1",
verbatimLatitude = "Lat1text",
verbatimLongitude = "Long1text",
scientificName = "Full Name",
kingdom = "Kingdom",
order = "Order",
family = "Family",
subfamily = "Subfamily",
genus = "Genus",
specificEpithet = "Species",
infraspecificEpithet = "Subspecies",
scientificNameAuthorship = "Species Author",
day = `Start Date (Day)`,
month = `Start Date (Month)`,
year = `Start Date (Year)`,
associatedTaxa = "Associated Taxa",
associatedOccurrences = "Associated Ocurrence",
lifeStage = "Stage",
collector1stName = 'Collectors/First Name',
collectorsLastName = 'Collectors/Last Name',
determined1stName = 'Determiner/First Name',
determinedLastName = 'Determiner/Last Name',
endDate = "End Date",
verbatimEventDate = "Verbatim Date"
) %>%
dplyr::mutate(year = year %>% as.numeric(),
month = month %>% as.numeric(),
day = day %>% as.numeric()) %>%
dplyr::mutate(recordedBy = stringr::str_c(collector1stName, collectorsLastName,
sep = " "),
identifiedBy = stringr::str_c(determined1stName, determinedLastName,
sep = " ")) %>%
dplyr::mutate(eventDate = lubridate::dmy(stringr::str_c(day, month, year, sep = "/"),
truncated = 2)) %>%
dplyr::mutate(
fieldNotes =
stringr::str_c(
dplyr::if_else(!is.na(fieldNotes),
paste0("fieldNotes: ", fieldNotes), ""),
dplyr::if_else(!is.na(eventDate),
paste0("startDate: ", eventDate), ""),
dplyr::if_else(!is.na(endDate),
paste0("endDate: ", endDate), ""),
sep = "|")
) %>%
dplyr::mutate(basisOfRecord = "Preserved specimen") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("MPUJ_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "MPUJ_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Colombia MPUJ - Diego Alexander Guevara Farias",
datasetID = "MPUJ"
)
#### 24.3 Out ####
# Save the dataset
readr::write_excel_csv(MPUJ_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(MPUJ_data)
} # END readr_MPUJ
#### 25.0 STRI ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
#'
readr_STRI <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
fieldNotes <- Catalognumber <- . <- day <- year <- catalogNumber <- recordId <- NULL
#### 25.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 25.2 Read+ ####
###### a. STRI_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
STRI_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::rename(recordId = "recordID") %>%
# Rename columns
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(Catalognumber),
paste0("secondary catalog #: ", Catalognumber), ""),
sep = "|"),
species = "scientificName") %>%
# Format dates
dplyr::mutate(
# If day is not recorded, set to first day of month
day = dplyr::if_else(day == 0, 1, day),
eventDate = lubridate::dmy(stringr::str_c(day, month, year, sep = "/"),
truncated = 2)) %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("STRI_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "STRI_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "STRI",
datasetID = "STRI"
)
#### 25.3 Out ####
# Save the dataset
readr::write_excel_csv(STRI_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(STRI_data)
} # END readr_STRI
#### 26.0 PALA ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
#'
readr_PALA <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
romanNumerals <- numeralConversion <- catalogNumber <- type <- country <- municipality <- NULL
locality <- decimalLatitude <- decimalLongitude <- verbatimElevation <- verbatimEventDate <- NULL
recordedBy <- collectionCode <- otherCatalogNumbers <- associatedTaxa <- taxonRemarks <- NULL
family <- genus <- references <- specificEpithet <- scientificName <- . <- eventDate <- NULL
#### 26.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
requireNamespace("mgsub")
#### 26.2 Read+ ####
###### a. month strings ####
# Prepare month strings to convert from roman numerals
romanNumerals <- c("i","ii","iii","iv","v","vi","vii","viii","ix","x","xi","xii",
"I","II","III","IV","V","VI","VII","VIII","IX","X","XI","XII")
numeralConversion <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct","Nov", "Dec",
"Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct","Nov", "Dec")
###### b. PALA_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
PALA_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
# Rename columns
dplyr::rename(
catalogNumber = "catalogNumber",
type = "Type",
country = "Country",
municipality = "Muninciplaity",
locality = "Site",
decimalLatitude = "Latitud",
decimalLongitude = "Longitude",
verbatimElevation = "elevation",
verbatimEventDate = "date",
recordedBy = "recordedby",
collectionCode = "Collection",
otherCatalogNumbers = "othercatalognumber",
associatedTaxa = "AssociatedTaxa",
taxonRemarks = "taxonremarks",
family = "Family",
genus = "Genus",
specificEpithet = "species",
references = "Citation"
) %>%
# Add in sciName
dplyr::mutate(scientificName = stringr::str_c(genus, specificEpithet, sep = " ")) %>%
# Format date
dplyr::mutate(eventDate = verbatimEventDate %>%
mgsub::mgsub(
pattern = paste("[-/ \\.]", romanNumerals, "[ -/\\.]", sep = ""),
replacement = numeralConversion) %>%
lubridate::dmy(truncated = 2, quiet = TRUE),
.after = verbatimEventDate) %>%
# Add day, month, year
dplyr::mutate(
day = lubridate::day(eventDate),
month = lubridate::month(eventDate),
year = lubridate::year(eventDate),
.after = eventDate
) %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("PALA_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "PALA_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "PALA",
datasetID = "PALA"
)
#### 26.3 Out ####
# Save the dataset
readr::write_excel_csv(PALA_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(PALA_data)
} # END readr_PALA
#### 27.0 JoLa ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
#'
readr_JoLa <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = c("pre-1950", "post-1950")){
# locally bind variables to the function
fieldNotes <- Catalognumber <-`Start Date (Year)` <- genus <- specificEpithet <- NULL
year <- . <- NULL
#### 27.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 27.2 Read+ ####
###### a. JoLa_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
# Read in both sheets and bind them together
JoLa_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet[1]) %>%
dplyr::bind_rows(openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet[2])) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Rename the columns
dplyr::rename(
specificEpithet = "Species",
decimalLatitude = "Latitude1",
decimalLongitude = "Longitude1",
year = `Start Date (Year)`
) %>%
# Add in higher taxonomic information
dplyr::mutate(
genus = "Lasioglossum",
family = "Halictidae",
order = "Hymenoptera",
scientificName = stringr::str_c(genus, specificEpithet, sep = " ")
) %>%
dplyr::mutate(
eventDate = lubridate::ymd(year, truncated = 2)
) %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("JoLa_data_", 1:nrow(.), sep = ""),
.before = 1) %>%
# Add dataset information
dplyr::mutate(dataSource = "JoLa_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "JoLa",
datasetID = "JoLa"
)
#### 27.3 Out ####
# Save the dataset
readr::write_excel_csv(JoLa_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(JoLa_data)
} # END readr_JoLa
#### 28.0 VicWam ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
#'
readr_VicWam <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = "Combined"){
# locally bind variables to the function
fieldNotes <- Catalognumber <- recordNumber <- genus <- specificEpithet <- NULL
year <- . <- VicWam_data <- otherCatalogNumbers <- institutionCode <- class <- order <- NULL
infraspecificEpithet <- NULL
NEAREST <- DISTANCE <- DIST_UNIT <- DIRECTION <- locality <- locality2 <- PLACEACCURACY <-
coordinateUncertaintyInMeters <- DTFR <- day <- month <- year <- eventDate <- eventDate2 <-
DTTO <- dayTO <- monthTO <- yearTO <- eventDateTO <- eventDateTO2 <- LABELFAMILY <-
LABELGENUS <- LABELSPECIES <- associatedTaxa <- stateProvince <- country <- dataSource <-
license <- decimalLatitude <- decimalLongitude <- lengthTest <- decimalLatitude2 <-
decimalLongitude2 <- NULL
#### 28.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 28.2 Read+ ####
###### a. JoLa_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
# Read in both sheets and bind them together
VicWam_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet[1]) %>%
# Start by renaming columns
dplyr::rename(
recordNumber = "Reg_DoreyExtension",
otherCatalogNumbers = "COLLNUM",
institutionCode = "INSTITUTE",
class = "CLASS",
order = "ORDER",
"superfamily" = "SUPERFAMILY",
"family" = "FAMILY",
"subfamily" = "SUBFAMILY",
"tribe" = "TRIBE",
"genus" = "GENUS",
"subgenus" = "SUBGENUS",
"specificEpithet" = "SPECIES",
"infraspecificEpithet" = "SUBSPECIES",
"identifiedBy" = "DTMNDBY",
"dateIdentified" = "DTMNDDT",
"lifeStage" = "LIFEHISTORY",
"sex" = "SEX",
"identificationQualifier" = "NAMEQUALIFIER",
"typeStatus" = "SPCMTYPE",
"individualCount" = "SPECNUM",
"stateProvince" = "STATE",
"locality" = "SITE",
"decimalLatitude" = "LATDEC",
"decimalLongitude" = "LONGDEC",
"recordedBy" = "COLLTOR",
"basisOfRecord" = "STORAGE",
"verbatimLatitude" = "LATITUDE",
"verbatimLongitude" = "LONGITUDE",
"occurrenceRemarks" = "NOTES"
) %>%
# Create the scientificName
dplyr::mutate(scientificName = stringr::str_c(
dplyr::if_else(!is.na(genus),
paste0(genus), ""),
dplyr::if_else(!is.na(specificEpithet),
paste0(specificEpithet), ""),
dplyr::if_else(!is.na(infraspecificEpithet),
paste0(infraspecificEpithet), ""),
sep = " "
) %>% stringr::str_squish()) %>%
# modify locality
dplyr::mutate(locality2 = stringr::str_c(dplyr::if_else(complete.cases(NEAREST),
NEAREST, ""),
dplyr::if_else(complete.cases(DISTANCE),
as.character(DISTANCE), ""),
dplyr::if_else(complete.cases(DIST_UNIT),
DIST_UNIT, ""),
dplyr::if_else(complete.cases(DIRECTION),
paste0(DIRECTION, "\u00b0"),
""),
sep = " ") %>% as.character() %>%
stringr::str_squish(),
.after = locality) %>%
dplyr::mutate(locality = dplyr::if_else(is.na(locality),
locality2, locality)) %>%
dplyr::select(!locality2) %>%
# Extract coordinateUncertaintyInMeters
dplyr::mutate(
coordinateUncertaintyInMeters = PLACEACCURACY %>% stringr::str_extract(
"[0-9]+\\s[m(km)(mi)]+") %>% stringr::str_replace_all(c("km" = "000",
"m" = "")) %>%
stringr::str_replace(" ","") %>% as.numeric(),
.after = PLACEACCURACY
) %>%
# Create eventDate
dplyr::mutate(
day = DTFR %>% stringr::str_extract("^[0-9]+/") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "00", NA_character_, .),
month = DTFR %>% stringr::str_extract("/[0-9]+/") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "00", NA_character_, .),
year = DTFR %>% stringr::str_extract("/[0-9]+$") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "0000", NA_character_, .),
eventDate = lubridate::dmy(stringr::str_c(day, month, year, sep = "/"), truncated = 2),
eventDate2 = dplyr::if_else(!stringr::str_detect(DTFR, "[a-zA-Z]"), # convert from silly excel numeric format to real dates...
as.Date(as.numeric(DTFR), origin = "1899-12-30") %>% as.character(),
DTFR) %>% lubridate::ymd(truncated = 2),
# if eventDate is empty, use eventDate2
eventDate = dplyr::if_else(is.na(eventDate),
eventDate2, eventDate),
.after = DTFR
) %>% dplyr::select(!eventDate2) %>%
# Create the date to
dplyr::mutate(
dayTO = DTTO %>% stringr::str_extract("^[0-9]+/") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "00", NA_character_, .),
monthTO = DTTO %>% stringr::str_extract("/[0-9]+/") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "00", NA_character_, .),
yearTO = DTTO %>% stringr::str_extract("/[0-9]+$") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "0000", NA_character_, .),
eventDateTO = lubridate::dmy(stringr::str_c(dayTO, monthTO, yearTO, sep = "/"), truncated = 2),
eventDateTO2 = dplyr::if_else(!stringr::str_detect(DTTO, "[a-zA-Z]"), # convert from silly excel numeric format to real dates...
as.Date(as.numeric(DTTO), origin = "1899-12-30") %>% as.character(),
DTTO %>% as.character()) %>% lubridate::ymd(truncated = 2),
# if eventDateTO is empty, use eventDateTO2
eventDateTO = dplyr::if_else(is.na(eventDateTO),
eventDateTO2, eventDateTO) %>% as.character(),
eventDateTO = dplyr::if_else(is.na(eventDateTO),
stringr::str_c(
dplyr::if_else(complete.cases(yearTO),
yearTO,""),
dplyr::if_else(complete.cases(monthTO),
monthTO,""),
dplyr::if_else(complete.cases(dayTO),
dayTO,""),
sep = " ") %>% stringr::str_squish() %>%
stringr::str_replace(" ", "-"),
eventDateTO),
.after = DTTO
) %>%
dplyr::select(!tidyselect::any_of(c("eventDateTO2", "dayTO", "monthTO", "yearTO"))) %>%
# Add field notes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(eventDate),
paste0("startDate: ", eventDate), ""),
dplyr::if_else(!is.na(eventDateTO),
paste0("endDate: ", eventDateTO), ""),
dplyr::if_else(!is.na(LABELFAMILY),
paste0("associatedFamily: ", LABELFAMILY), ""),
dplyr::if_else(!is.na(LABELGENUS),
paste0("associatedGenus: ", LABELGENUS), ""),
dplyr::if_else(!is.na(LABELSPECIES),
paste0("associatedSpecies: ", LABELSPECIES), ""),
sep = "|")) %>%
# Add associatedTaxa
dplyr::mutate(associatedTaxa = stringr::str_c(
dplyr::if_else(!is.na(LABELGENUS),
paste0(LABELGENUS), ""),
dplyr::if_else(!is.na(LABELSPECIES),
paste0(LABELSPECIES), ""),
sep = " ") %>% stringr::str_squish()) %>%
# Worst case, use family
dplyr::mutate(associatedTaxa = stringr::str_c(
dplyr::if_else(is.na(associatedTaxa),
paste0(LABELFAMILY), associatedTaxa)
)) %>%
# Format country name
dplyr::mutate(country = stringr::str_to_sentence(country)) %>%
dplyr::mutate(stateProvince = stringr::str_replace_all(stateProvince,
c("^ACT$" = "Australian Capital Territory",
"New South wales" = "New South Wales",
"^NSW" = "New South Wales",
"^NT$" = "Northern Territory",
"^Qld$" = "Queensland",
"^SA$" = "South Australia",
"^Tas$" = "Tasmania",
"^Vic$" = "Victoria",
"^WA$" = "Western Australia",
"Western australia" = "Western Australia"))) %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("VicWam_data_", 1:nrow(.), sep = ""),
.before = 1) %>%
# Add dataset information
dplyr::mutate(dataSource = "VicWam_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "VicWam",
datasetID = "VicWam"
) %>%
# Format some lats and lons to decimal degrees
# first, squish out extra spaces
dplyr::mutate(
decimalLatitude = decimalLatitude %>% stringr::str_squish(),
decimalLongitude = decimalLongitude %>% stringr::str_squish(),
) %>%
# make a column to test for the lat/lon length
dplyr::mutate(lengthTest = dplyr::if_else(decimalLatitude %>% stringr::str_detect("S"),
dplyr::if_else(stringr::str_count(decimalLatitude,
"\\s") > 2,
"Long", "Short"),
"NA"),
.before = decimalLatitude) %>%
# Convert to DD
# Long coordinates
dplyr::mutate(decimalLatitude2 = dplyr::if_else(decimalLatitude %>% stringr::str_detect("S") &
lengthTest == "Long",
stringr::str_c(stringr::str_extract(decimalLatitude, "^[0-9]{2}") %>%
as.numeric() +
(stringr::str_extract(decimalLatitude,
"\\s[0-9]{2}\\s") %>%
as.numeric() / 60) +
(stringr::str_extract(decimalLatitude,
"[0-9]{2}\\sS") %>%
stringr::str_remove("S") %>%
as.numeric() / 3600)) %>%
as.character(),
decimalLatitude),
decimalLongitude2 = dplyr::if_else(decimalLongitude %>% stringr::str_detect("E") &
lengthTest == "Long",
stringr::str_c(stringr::str_extract(decimalLongitude, "^[0-9]+") %>%
as.numeric() +
(stringr::str_extract(decimalLongitude,
"\\s[0-9]{2}\\s") %>%
as.numeric() / 60) +
(stringr::str_extract(decimalLongitude,
"\\s[0-9]+\\sE") %>%
stringr::str_remove("E") %>%
as.numeric() / 3600)) %>%
as.character(),
decimalLongitude),
.after = decimalLatitude) %>%
# Short coordinates
dplyr::mutate(decimalLatitude2 = dplyr::if_else(decimalLatitude %>% stringr::str_detect("S") &
lengthTest == "Short",
stringr::str_c(stringr::str_extract(decimalLatitude, "^[0-9]{2}") %>%
as.numeric() +
(stringr::str_extract(decimalLatitude,
"\\s[0-9]{2}\\sS") %>%
stringr::str_remove("S") %>%
as.numeric() / 60) ) %>%
as.character(),
decimalLatitude2) %>% as.numeric(),
decimalLongitude2 = dplyr::if_else(decimalLongitude %>% stringr::str_detect("E") &
lengthTest == "Short",
stringr::str_c(stringr::str_extract(decimalLongitude, "^[0-9]+") %>%
as.numeric() +
(stringr::str_extract(decimalLongitude,
"\\s[0-9]{2}+\\sE") %>%
stringr::str_remove("E") %>%
as.numeric() / 60) ) %>%
as.character(),
decimalLongitude2) %>% as.numeric(),
.after = decimalLatitude) %>%
# Change lat/lon to correct accuracy
dplyr::mutate(
decimalLatitude2 = dplyr::if_else(lengthTest == "Long",
round(decimalLatitude2, digits = 2),
dplyr::if_else(lengthTest == "Short",
round(decimalLatitude2, digits = 1),
decimalLatitude2)),
decimalLongitude2 = dplyr::if_else(lengthTest == "Long",
round(decimalLongitude2, digits = 2),
dplyr::if_else(lengthTest == "Short",
round(decimalLongitude2, digits = 1),
decimalLongitude2))
) %>%
# Add coordinateUncertaintyInMeters for these as well based on the length of coordinates
dplyr::mutate(
coordinateUncertaintyInMeters = coordinateUncertaintyInMeters %>% as.numeric(),
coordinateUncertaintyInMeters = dplyr::if_else(
is.na(coordinateUncertaintyInMeters),
dplyr::if_else(lengthTest == "Long", 1000, 10000),
coordinateUncertaintyInMeters
)) %>%
# Replace the lat/lon columns with the working columns
dplyr::select(!c("decimalLongitude", "decimalLatitude")) %>%
dplyr::rename(decimalLongitude = "decimalLongitude2",
decimalLatitude = "decimalLatitude2") %>%
# All of Australia is in the southern hemisphere, correct these latitudes to negative
dplyr::mutate(decimalLatitude = dplyr::if_else(
stateProvince %in% c("Australian Capital Territory", "New South Wales",
"Northern Territory", "Queensland", "South Australia",
"Tasmania", "Victoria", "Western Australia", ""),
abs(decimalLatitude) * - 1,
decimalLatitude)) %>%
# Do the same for longitude but hte entire dataset is in the eastern hemisphere
dplyr::mutate(decimalLongitude = abs(decimalLongitude))
#### 28.3 Out ####
# Save the dataset
readr::write_excel_csv(VicWam_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(VicWam_data)
} # END readr_VicWam
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Defines functions readr_EPEL readr_BeeBDC
Documented in readr_BeeBDC readr_EPEL
# These functions were written by James B Dorey beginning on the 17th of June 2022 to read in,
# Format and save various datasets.
# For questions, please email jbdorey[at]me.com
#### x.0 readr_BeeBDC function ####
#' A wrapper for all of the data readr_functions
#'
#' 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.
#'
#' This function wraps several internal readr functions. Users may call
#' readr_BeeBDC and select the dataset name to import a certain dataset. These datasets include:
#'
#' Excel (.xlsx) formatted datasets: CAES, MABC, Col, Bal, MEPB, MUPJ, Arm, JoLa, and VicWam.
#'
#' CSV (.csv) formatted datasets: EPEL, ASP, BMin, BMont, Ecd, Gai, KP, EcoS, GeoL, EaCo, FSCA, SMC,
#' Lic, Dor, BBD, STRI, and PALA
#'
#' See Dorey et al. 2023 BeeBDC... for further details.
#'
#' @param dataset Character. The name of the dataset to be read in. For example readr_CAES can
#' be called using "readr_CAES" or "CAES". This is not caps sensitive.
#' @param path A character path. The path to the directory containing the data.
#' @param inFile Character or character path. The name of the file itself (can also be the
#' remainder of a path including the file name).
#' @param outFile Character or character path. The name of the Darwin Core format file to be saved.
#' @param dataLicense Character. The license to accompany each record in the Darwin Core 'license'
#' column.
#' @param sheet A character String. For those datasets read from an .xlsx format, provide the
#' sheet name.
#' NOTE: This will be ignored for .csv readr_ functions and required for .xlsx readr_ functions.
#'
#' @return A data frame that is in Darwin Core format.
#' @export
#'
#' @importFrom readr read_csv write_excel_csv
#' @importFrom dplyr rename mutate select if_else %>%
#' @importFrom lubridate ymd month
#' @importFrom stringr str_c
#'
#'
#' @examples
#' \dontrun{
#' # An example using a .xlsx file
#'Arm_Data <- readr_BeeBDC(
#' dataset = "Arm",
#' path = paste0(tempdir(), "/Additional_Datasets"),
#' inFile = "/InputDatasets/Bee database Armando_Final.xlsx",
#' outFile = "jbd_Arm_Data.csv",
#' sheet = "Sheet1",
#' dataLicense = "https://creativecommons.org/licenses/by-nc-sa/4.0/")
#'
#'
#' # An example using a .csv file
#'EPEL_Data <- readr_BeeBDC(
#' dataset = "readr_EPEL",
#' path = paste0(tempdir(), "/Additional_Datasets"),
#' inFile = "/InputDatasets/bee_data_canada.csv",
#' outFile = "jbd_EPEL_data.csv",
#' dataLicense = "https://creativecommons.org/licenses/by-nc-sa/4.0/")
#' }
readr_BeeBDC <- function(
dataset = NULL,
path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = NULL){
#### x.0 Prep ####
##### x.1 Errors ####
###### a. FATAL errors ####
if(is.null(dataset)){
stop(" - Please provide an argument for dataset. This can be from any of the readr_ functions")
}
if(is.null(path)){
stop(" - Please provide an argument for the path to a folder containing your file.")
}
if(is.null(inFile)){
stop(" - Please provide an argument for the inFile name to find.")
}
if(is.null(outFile)){
stop(" - Please provide an argument for outFile to save as.")
}
if(is.null(dataLicense)){
stop(" - Please provide an argument for dataLicense.")
}
##### x.2 Data types ####
# Create the lists of potential data types for .xlsx or .csv inputs
excelTypes <- c("CAES", "MABC", "Col", "Bal", "MEPB", "MPUJ", "Arm", "JoLa", "VicWam")
csvTypes <- c("EPEL", "ASP", "BMin", "BMont", "Ecd", "Gai", "KP", "EcoS", "GeoL",
"EaCo", "FSCA", "SMC", "Lic", "Dor", "BBD", "PALA", "STRI")
# Wrong entry test
if(!tolower(dataset) %in% tolower(c(paste0("readr_",csvTypes), csvTypes,
paste0("readr_",excelTypes), excelTypes)) ){
stop("The dataset does not match a readr_BeeBDC function")
}
#### x.0 Choose function ####
##### x.1 Excel functions ####
# EXCEL test
if(tolower(dataset) %in% tolower(c(paste0("readr_",excelTypes), excelTypes)) ){
message("A .xlsx data type was chosen...")
# If no sheet is provided
if(is.null(sheet)){
stop(" - No sheet argument was provided. Please check for the sheet name to import.")
}
###### a. CAES ####
if(tolower(dataset) %in% tolower(c("readr_CAES", "CAES")) ){
dataOut <- readr_CAES(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### b. MABC ####
if(tolower(dataset) %in% tolower(c("readr_MABC", "MABC")) ){
dataOut <- readr_MABC(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### c. Col ####
if(tolower(dataset) %in% tolower(c("readr_Col", "Col")) ){
dataOut <- readr_Col(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### d. Bal ####
if(tolower(dataset) %in% tolower(c("readr_Bal", "Bal")) ){
dataOut <- readr_Bal(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### e. MEPB ####
if(tolower(dataset) %in% tolower(c("readr_MEPB", "MEPB")) ){
dataOut <- readr_MEPB(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### f. MPUJ ####
if(tolower(dataset) %in% tolower(c("readr_MPUJ", "MPUJ")) ){
dataOut <- readr_MPUJ(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### f. Arm ####
if(tolower(dataset) %in% tolower(c("readr_Arm", "Arm")) ){
dataOut <- readr_Arm(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### g. JoLa ####
if(tolower(dataset) %in% tolower(c("readr_JoLa", "JoLa")) ){
dataOut <- readr_JoLa(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
##### h. VicWam ####
if(tolower(dataset) %in% tolower(c("readr_VicWam", "VicWam")) ){
dataOut <- readr_VicWam(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense,
sheet = sheet)}
}# END Excel functions
##### x.2 CSV functions ####
# CSV test
if(tolower(dataset) %in% tolower(c(paste0("readr_",csvTypes), csvTypes)) ){
message("A .csv data type was chosen...")
###### a. EPEL ####
if(tolower(dataset) %in% tolower(c("readr_EPEL", "EPEL")) ){
dataOut <- readr_EPEL(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### b. ASP ####
if(tolower(dataset) %in% tolower(c("readr_ASP", "ASP")) ){
dataOut <- readr_ASP(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### c. BMin ####
if(tolower(dataset) %in% tolower(c("readr_BMin", "BMin")) ){
dataOut <- readr_BMin(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### d. BMont ####
if(tolower(dataset) %in% tolower(c("readr_BMont", "BMont")) ){
dataOut <- readr_BMont(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### e. Ecd ####
if(tolower(dataset) %in% tolower(c("readr_Ecd", "Ecd")) ){
dataOut <- readr_Ecd(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### f. Gai ####
if(tolower(dataset) %in% tolower(c("readr_Gai", "Gai")) ){
dataOut <- readr_Gai(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### g. KP ####
if(tolower(dataset) %in% tolower(c("readr_KP", "KP")) ){
dataOut <- readr_KP(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### h. EcoS ####
if(tolower(dataset) %in% tolower(c("readr_EcoS", "EcoS")) ){
dataOut <- readr_EcoS(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### i. GeoL ####
if(tolower(dataset) %in% tolower(c("readr_GeoL", "GeoL")) ){
dataOut <- readr_GeoL(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### j. EaCO ####
if(tolower(dataset) %in% tolower(c("readr_EaCO", "EaCo")) ){
dataOut <- readr_EaCO(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### k. FSCA ####
if(tolower(dataset) %in% tolower(c("readr_FSCA", "FSCA")) ){
dataOut <- readr_FSCA(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### l. SMC ####
if(tolower(dataset) %in% tolower(c("readr_SMC", "SMC")) ){
dataOut <- readr_SMC(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### m. Lic ####
if(tolower(dataset) %in% tolower(c("readr_Lic", "Lic")) ){
dataOut <- readr_Lic(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### n. Dor ####
if(tolower(dataset) %in% tolower(c("readr_Dor", "Dor")) ){
dataOut <- readr_Dor(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### o. BBD ####
if(tolower(dataset) %in% tolower(c("readr_BBD", "BBD")) ){
dataOut <- readr_BBD(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### p. STRI ####
if(tolower(dataset) %in% tolower(c("readr_STRI", "STRI")) ){
dataOut <- readr_STRI(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
##### g. PALA ####
if(tolower(dataset) %in% tolower(c("readr_PALA", "PALA")) ){
dataOut <- readr_PALA(path = path,
inFile = inFile,
outFile = outFile,
dataLicense = dataLicense)}
} #END csv
return(dataOut)
} # END readr_BeeBDC
#### 1.0 EPEL ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_EPEL <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
catalog_number<-pollinator_family<-pollinator_genus<-pollinator_species<-collection_method<-
day_collected<-month_collected<-year_collected<-location_description<-latitude<-longitude<-
basis_of_record<-genus<-specificEpithet<-year<-day<-eventDate<-collector_number<-
location_name<-habitat<-.<-catalogNumber <- NULL
#### 1.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 1.2 Read+ ####
EPEL_Data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
# Rename some columns to make the consistent with DarwinCore
dplyr::rename(
catalogNumber = catalog_number,
family = pollinator_family,
genus = pollinator_genus,
specificEpithet = pollinator_species,
samplingProtocol = collection_method,
day = day_collected,
month = month_collected,
year = year_collected,
locality = location_description,
decimalLatitude = latitude,
decimalLongitude = longitude,
basisOfRecord = basis_of_record) %>%
# Make new columns
dplyr::mutate(
# Merge to scientific name
scientificName = paste(genus, specificEpithet),
# Add data source
dataSource = "EPEL_Anthophila",
# Add eventDate
eventDate = lubridate::ymd(paste(year, month, day, sep = "-"),
truncated = 2),
month = lubridate::month(eventDate)) %>%
# Add fieldNotes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(collector_number),
paste0("Collector_number: ", collector_number), ""),
dplyr::if_else(!is.na(location_name),
paste0("location_name: ", location_name), ""),
dplyr::if_else(!is.na(habitat),
paste0("habitat: ", habitat), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Remove these input columns
dplyr::select(!c(collector_number, location_name, habitat)) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("EPEL_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Elle Pollination Ecology Lab",
datasetID = "EPEL",
institutionCode = "SFU"
)
#### 1.3 Out ####
# Save the dataset
readr::write_excel_csv(EPEL_Data, file = paste(path, outFile, sep = "/"))
# Return data
return(EPEL_Data)
} # END readr_EPEL
#### 2.0 ASP ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_ASP <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
Tribe <- Morphospecies <- Successional_Stage <- genus <- specificEpithet <- NULL
eventDate <- catalogNumber <- . <- elevation <- NULL
#### 2.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 2.2 Read+ ####
ASP_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::mutate(verbatimElevation = elevation) %>%
dplyr::mutate(
# Add previousIdentifications
previousIdentifications = stringr::str_c(
dplyr::if_else(!is.na(Tribe),
paste0("Tribe: ", Tribe), ""),
dplyr::if_else(!is.na(Morphospecies),
paste0("Morphospecies: ", Morphospecies), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
dplyr::mutate(
# Add locationRemarks
locationRemarks = paste0("Successional Stage:", Successional_Stage),
# Add scientificName
scientificName = paste(genus, specificEpithet),
# Add dataSource
dataSource = "ASP_Anthophila",
# Add eventDate
eventDate = lubridate::dmy(eventDate,
truncated = 2),
# I'm sorry but this catalogNumber is useless. I'm going to edit it more-unique
catalogNumber = dplyr::if_else(!is.na(catalogNumber),
paste0("ASP_", catalogNumber), "")) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("ASP_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Allan Smith-Pardo",
datasetID = "ASP"
)
#### 2.3 Out ####
# Save the dataset
readr::write_excel_csv(ASP_data, file = paste(path, outFile, sep = "/"))
# Return data
return(ASP_data)
} # END readr_ASP
#### 3.0 BMin ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_BMin <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
eventDate <- . <- catalogNumber <- NULL
catalog_number <- pollinator_family <- pollinator_genus <- pollinator_species <- NULL
collection_method <- day_collected <- month_collected <- year_collected <- NULL
location_description <- latitude <- longitude <- basis_of_record <- genus <- NULL
specificEpithet <- year <- day <- eventDate <- collector_number <- location_name <- NULL
habitat <- . <- catalogNumber <- NULL
#### 3.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 3.2 Read+ ####
BMin_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::mutate(
# Format eventDate
eventDate = lubridate::dmy(eventDate,
truncated = 2, quiet = TRUE),
dataSource = "BMin_Anthophila") %>%
# add the database_id column
dplyr::mutate(
database_id = paste("BMin_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Robert Minckley",
datasetID = "BMin",
institutionCode = "University of Rochester"
)
#### 3.3 Out ####
# Save the dataset
readr::write_excel_csv(BMin_data, file = paste(path, outFile, sep = "/"))
# Return data
return(BMin_data)
} # END readr_BMin
#### 4.0 BMont ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_BMont <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
occurence_lsid <- fieldNotes <- GPS_device <- . <- catalogNumber <- eventDate <- dateTest <- NULL
tempDate <- mdy <- VerbatimEventDate <- NULL
#### 4.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 4.2 Read+ ####
BMont_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::rename(
occurrenceID = occurence_lsid,
verbatimEventDate = VerbatimEventDate) %>%
dplyr::mutate(
# Add fieldNotes
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(fieldNotes),
paste0("fieldNotes: ", fieldNotes), ""),
dplyr::if_else(!is.na(GPS_device),
paste0("GPS_device: ", GPS_device), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
dplyr::mutate(dataSource = "BMont_Anthophila") %>%
# add the database_id column
dplyr::mutate(
database_id = paste("BMont_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Find the date ranges
dplyr::mutate(dateTest = dplyr::if_else(stringr::str_count(eventDate) > 11,
stringr::str_replace(string = eventDate,
pattern = "/", replacement = ","),
"FALSE")) %>%
# Append these to fieldNotes
dplyr::mutate(fieldNotes = dplyr::if_else(dateTest != FALSE & !is.na(dateTest),
paste0(fieldNotes,
"|dateRange: ", eventDate), fieldNotes)) %>%
# Now remove those eventDates with a range and instead insert the start date
dplyr::mutate(tempDate = dplyr::if_else(dateTest != FALSE & !is.na(dateTest),
paste0(stringr::str_replace(eventDate,
pattern = ".*/",
replacement = "")),
eventDate)) %>%
# Pick up dates of different formats and format together.
dplyr::mutate(mdy = lubridate::mdy(tempDate, quiet = TRUE)) %>%
dplyr::mutate(ymd = lubridate::ymd(tempDate, quiet = TRUE)) %>%
tidyr::unite(col = eventDate,
mdy, ymd, sep = "", na.rm = TRUE,
remove = FALSE) %>%
# Remove working columns
dplyr::select(!c(dateTest, tempDate, mdy, ymd)) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Bombus Montana",
datasetID = "BMon"
)
#### 4.3 Out ####
# Save the dataset
readr::write_excel_csv(BMont_data, file = paste(path, outFile, sep = "/"))
# Return data
return(BMont_data)
} # END readr_BMont
#### 5.0 Ecd ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Ecd <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
year <- day <- institutionCode <- id <- . <- catalogNumber <- recordID <- NULL
#### 5.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 5.2 Read+ ####
Ecd_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::rename(recordId = recordID) %>%
dplyr::mutate(
# Format eventDate
eventDate = lubridate::ymd(paste(year, month, day, sep = "-"),
truncated = 2, quiet = TRUE),
dataSource = "Ecd_Anthophila",
# I want to make sure id is unique...
id = paste(institutionCode, id, sep = "_")) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Ecd_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Ecdysis",
datasetID = "Ecd"
)
#### 5.3 Out ####
# Save the dataset
readr::write_excel_csv(Ecd_data, file = paste(path, outFile, sep = "/"))
# Return data
return(Ecd_data)
} # END readr_Ecd
###### 6.0 Gai ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Gai <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
species<-subspecies<-SpecimenLocation<-eventTime<-EndTime<-TempStart<-TempEnd<-WindStart<-
WindEnd<-SkyStart<-SkyEnd<-Site<-siteLocality<-syd<-eventDate<-.<-institutionCode <- NULL
#### 6.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 6.2 Read+ ####
Gai_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
# Make columns DarwinCore-compatible
dplyr::rename(
collectionCode = 'Collection Code',
otherCatalogNumbers = 'Other Catalog Number',
specificEpithet = species,
infraspecificEpithet = subspecies) %>%
# Add locationRemarks from a bunch of other columns
dplyr::mutate(
locationRemarks = stringr::str_c(
dplyr::if_else(!is.na(SpecimenLocation), paste0("SpecimenLocation: ", SpecimenLocation), ""),
dplyr::if_else(!is.na(eventTime),
paste0("StartTime: ", eventTime), ""),
dplyr::if_else(!is.na(EndTime),
paste0("EndTime: ", EndTime), ""),
dplyr::if_else(!is.na(TempStart),
paste0("TempStart: ", TempStart), ""),
dplyr::if_else(!is.na(TempEnd),
paste0("TempEnd: ", TempEnd), ""),
dplyr::if_else(!is.na(WindStart),
paste0("WindStart: ", WindStart), ""),
dplyr::if_else(!is.na(WindEnd),
paste0("WindEnd: ", WindEnd), ""),
dplyr::if_else(!is.na(SkyStart),
paste0("SkyStart: ", SkyStart), ""),
dplyr::if_else(!is.na(SkyEnd),
paste0("SkyEnd: ", SkyEnd), ""),
dplyr::if_else(!is.na(Site),
paste0("Site: ", Site), ""),
dplyr::if_else(!is.na(siteLocality),
paste0("siteLocality: ", siteLocality), ""),
dplyr::if_else(!is.na(syd),
paste0("syd: ", syd), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
dplyr::mutate(
# Format eventDate
eventDate = lubridate::mdy(eventDate,
truncated = 2, quiet = FALSE),
dataSource = "Gai_Anthophila") %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Gai_data_", 1:nrow(.), sep = ""),
.before = institutionCode) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Gaiarsa et al. 2021",
datasetID = "Gai"
)
#### 6.3 Out ####
# Save the dataset
readr::write_excel_csv(Gai_data, file = paste(path, outFile, sep = "/"))
# Return data
return(Gai_data)
} # END readr_Gai
#### 7.0 CAES ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_CAES <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = "Sheet1"){
# locally bind variables to the function
Tribe <- Morphospecies <- Successional_Stage <- genus <- specificEpithet <- NULL
eventDate <- catalogNumber <- . <- NULL
.<-PBIUSI<-Family<-Subfamily<-Genus<-species<-Country<-State_Prov<-Sec_Subdiv<-Locality<-Lat<-
Lon<-Start_Date<-Collector<-Sex<-Inst_Code<-Det_By<-Det_Date<-Coll_Method<-Spec_Count<-
Elev_m<-Trip_Code<-Project<-Det_History<-Tribe<-Host_Genus<-Host_Common_Name<-
Host_Relation<-Host_Location<-Loc_Notes<-Lat_Lon_Method<-End_Date<-eventDate<-
Elev_Det<-Macro_Habitat<-Micro_Habitat<-Pres_Method<-Spec_Notes<-genus<-specificEpithet<-
Lat_Lon_Accuracy<-Host_species<-Host_Family<-catalogNumber <- NULL
#### 7.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 7.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
CAES_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"), sheet = sheet) %>%
# Turn spaces into "_" in column names
dplyr::rename_with(., ~ gsub(" ", "_", .x, fixed = TRUE)) %>%
# Make columns DarwinCore-compatible
dplyr::rename(
catalogNumber = PBIUSI,
family = Family,
subfamily = Subfamily,
genus = Genus,
specificEpithet = species,
country = Country,
stateProvince = State_Prov,
county = Sec_Subdiv,
locality = Locality,
decimalLatitude = Lat,
decimalLongitude = Lon,
eventDate = Start_Date,
recordedBy = Collector,
sex = Sex,
institutionCode = Inst_Code,
identifiedBy = Det_By,
dateIdentified = Det_Date,
samplingProtocol = Coll_Method,
individualCount = Spec_Count,
elevation = Elev_m,
otherCatalogNumbers = Trip_Code,
bibliographicCitation = Project,
identificationRemarks = Det_History) %>%
# Add a bunch of columns from other columns
dplyr::mutate(
# Add previousIdentifications
previousIdentifications = paste0(
# ONLY do this IF there is something in the cell - is.na() finds "NA" values. the "!" reverses this to find complete.cases only.
dplyr::if_else(!is.na(Tribe), paste0("Tribe: ", Tribe),"")),
# Add associatedTaxa
# This will ONLY concatenate columns where they have a value.
associatedTaxa = stringr::str_c(
dplyr::if_else(!is.na(Host_Genus), paste0("Host_id: ", Host_Genus), ""),
dplyr::if_else(!is.na(Host_Common_Name),
paste0("Host_commonName: ", Host_Common_Name), ""),
dplyr::if_else(!is.na(Host_Relation),
paste0("Host_relation: ", Host_Relation), ""),
dplyr::if_else(!is.na(Host_Location),
paste0("Host_location: ", Host_Location), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Do the same as the last mutate, but for fieldNotes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(Loc_Notes),
paste0("fieldNotes: ", Loc_Notes), ""),
dplyr::if_else(!is.na(Lat_Lon_Method),
paste0("GPS_device: ", Lat_Lon_Method), ""),
dplyr::if_else(!is.na(End_Date),
paste0("Sampling period: ", eventDate, " to ", End_Date), ""),
dplyr::if_else(!is.na(Elev_Det),
paste0("Elevation_by: ", Elev_Det), ""),
dplyr::if_else(!is.na(Macro_Habitat),
paste0("Macro_Habitat: ", Macro_Habitat), ""),
dplyr::if_else(!is.na(Micro_Habitat),
paste0("Micro_Habitat: ", Micro_Habitat), ""),
dplyr::if_else(!is.na(Pres_Method),
paste0("Preservation_method: ", Pres_Method), ""),
dplyr::if_else(!is.na(Spec_Notes),
paste0("Specimen_notes: ", Spec_Notes), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Add scientificName
dplyr::mutate(
scientificName = stringr::str_c(
dplyr::if_else(!is.na(genus),
genus, ""),
dplyr::if_else(!is.na(specificEpithet),
specificEpithet, ""),
sep = " ")) %>%
# Get the coordinateUncertaintyInMeters by taking the UPPER uncertainty limit from the provided
# ranges. I.e., "100-1000" becomes "1000"
dplyr::mutate(
coordinateUncertaintyInMeters = stringr::str_extract(
Lat_Lon_Accuracy, pattern = "-.*|[0-9]+" ),
# Format eventDate
eventDate = lubridate::ymd(
# First convert from silly excel numeric format to real dates...
eventDate %>%
as.numeric() %>%
as.Date(., origin = "1899-12-30"),
truncated = 2, quiet = FALSE), # 215 failed to parse.
dataSource = "CAES_Anthophila") %>%
# Add the scientificNameAuthorship column using the specificEpithet column
# dplyr::mutate(scientificNameAuthorship = stringr::str_replace(
# string = verbatimScientificName,
# pattern = paste0(".*", specificEpithet, " "),
# replacement = ""
# )) %>%
# Remove those now redundant columns
dplyr::select(!c(Tribe, Host_Genus, Host_species, Host_Family, Host_Common_Name,
Host_Relation, Host_Location, Loc_Notes, Lat_Lon_Method,
End_Date, Elev_Det, Macro_Habitat, Micro_Habitat, Pres_Method, Spec_Notes,
Lat_Lon_Accuracy)) %>%
# Remove double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("CAES_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Connecticut Agricultural Experiment Station",
datasetID = "CAES"
)
#### 7.3 Out ####
# Save the dataset
readr::write_excel_csv(CAES_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(CAES_data)
} # END readr_CAES
#### 9.0 KP ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_KP <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
.<-ID<-Family<-Subfamily<-Genus<-Det<-Number<-Collection_method<-Collector<-Order<-Suborder<-
subgenus<-species<-subspecies<-author<-whole_sci_name<-Country<-State<-County_Parish<-
Location<-Lat<-Long<-decimalLatitude<-decimalLongitude<-Tribe<-sp_group<-Male<-Female<-
genus<-specificEpithet<-infraspecificEpithet<-Collection_date<-catalogNumber <- NULL
#### 9.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 9.2 Read+ ####
# Reads in the .xlsx file, trims the white spaces, and formats the columns to the correct type
KP_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/")) %>%
# Turn spaces into "_" in column names
dplyr::rename_with(., ~ gsub(" ", "_", .x, fixed = TRUE)) %>%
# Make columns DarwinCore-compatible
dplyr::rename(
catalogNumber = ID,
family = Family,
subfamily = Subfamily,
genus = Genus,
identifiedBy = Det,
individualCount = Number,
samplingProtocol = Collection_method,
recordedBy = Collector,
order = Order,
suborder = Suborder,
subgenus = subgenus,
specificEpithet = species,
infraspecificEpithet = subspecies,
scientificNameAuthorship = author,
verbatimScientificName = whole_sci_name,
country = Country,
stateProvince = State,
county = County_Parish,
locality = Location,
decimalLatitude = Lat,
decimalLongitude = Long) %>%
# round the coordinates to six decimal places
dplyr::mutate(
decimalLatitude = decimalLatitude %>% as.numeric() %>% base::round(digits = 6) %>%
suppressWarnings(classes = "warning"),
decimalLongitude = decimalLongitude %>% as.numeric() %>% base::round(digits = 6) %>%
suppressWarnings(classes = "warning")) %>%
# Add a bunch of columns from other columns
dplyr::mutate(
previousIdentifications = stringr::str_c(
dplyr::if_else(!is.na(Tribe),
paste0("Tribe: ", Tribe), ""),
dplyr::if_else(!is.na(sp_group),
paste0("sp_group: ", sp_group), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Do the same as the last mutate, but for fieldNotes
dplyr::mutate(
sex = stringr::str_c(
dplyr::if_else(!is.na(Male) & Male != 0,
paste0(Male, " M"), ""),
dplyr::if_else(!is.na(Female) & Female != 0,
paste0(Female, " F"), ""),
sep = "|") %>%
# Remove extra bars "|".
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Format eventDate and add dataSource
dplyr::mutate(
# Create scientificName
scientificName = stringr::str_c(
dplyr::if_else(!is.na(genus),
paste0(genus), ""),
dplyr::if_else(!is.na(specificEpithet),
paste0(specificEpithet), ""),
dplyr::if_else(!is.na(infraspecificEpithet),
paste0(infraspecificEpithet), ""),
sep = " ") %>% stringr::str_squish() %>% stringr::str_trim(side = "both"),
# Format eventDate
eventDate = lubridate::ymd_hms(Collection_date,
truncated = 5, quiet = FALSE, tz = "UTC"), # 215 failed to parse.
dataSource = "KP_Anthophila") %>%
# Remove those now redundant columns
dplyr::select(!c(Male, Female, sp_group, Tribe, Collection_date)) %>%
# Remove double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("KP_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "USDA ARS Southeastern USA",
datasetID = "KP"
)
#### 9.3 Out ####
# Save the dataset
readr::write_excel_csv(KP_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(KP_data)
} # END readr_KP
#### 11.0 EcoS ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_EcoS <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
Species <- . <- scientificName <- Latitude <- Longitude <- Year <- catalogNumber <- NULL
Collection <- ID_project <- NULL
#### 11.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 11.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
EcoS_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE, guess_max = 33000) %>%
# Add institution information
dplyr::mutate(
institutionCode = Collection,
datasetName = Collection,
catalogNumber = ID_project,
otherCatalogNumbers = stringr::str_c(Collection, ID_project, sep = "_")) %>%
# Add taxonomic information
dplyr::mutate(
scientificName = Species) %>%
# Split genus and species names
tidyr::separate(
data = ., col = scientificName,
into = c("genus", "specificEpithet"),
sep = "_", remove = FALSE) %>%
# Lat Lon
dplyr::mutate(
decimalLatitude = Latitude,
decimalLongitude = Longitude) %>%
# Year
dplyr::mutate(
year = Year) %>%
# Add dataset information
dplyr::mutate(dataSource = "EcoS_Anthophila") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("EcoS_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# add the database_id column
dplyr::mutate(
datasetName = "EcoSur",
datasetID = "EcoS"
)
#### 11.3 Out ####
# Save the dataset
readr::write_excel_csv(EcoS_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(EcoS_data)
} # END readr_EcoS
#### 12.0 GeoL ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_GeoL <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
geolocate_Latitude<-geolocate_Longitude<-geolocate_UncertaintyRadiusMeters<-database_id<-
bels_decimallatitude<-bels_decimallongitude<-bels_coordinateuncertaintyinmeters<-datasource<-
scientificname<-infraspecificepithet<-specificepithet<-acceptednameusage<-taxonrank<-
scientificnameauthorship<-countrycode<-stateprovince<-eventdate<-basisofrecord<-
occurrencestatus<-recordnumber<-recordedby<-eventid<-samplingprotocol<-samplingeffort<-
individualcount<-catalognumber<-rightsholder<-institutioncode<-datasetname<-
othercatalognumbers<-occurrenceid<-coreid<-recordid<-collectionid<-
verbatimscientificname<-verbatimeventdate<-id <- . <- NULL
rightsHolder <- continent <- type <- samplingProtocol <- NULL
island <- municipality <- verbatimEventDate <- catalogNumber <- NULL
#### 12.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 12.2 Read+ ####
###### a. GeoL_high ####
# Reads in the .xlsx file, trims the white spaces, and formats the columns to the correct type
GeoL_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = "GEOLOCATE HIGH") %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Convert GeoLocate columns into dwc columns
dplyr::rename(
decimalLatitude = geolocate_Latitude, decimalLongitude = geolocate_Longitude,
coordinateUncertaintyInMeters = geolocate_UncertaintyRadiusMeters) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# Remove blanks
tidyr::drop_na(database_id) %>%
# Temporarily add an identifier column
dplyr::mutate(
tempSource = "GeoL",
rightsHolder = rightsHolder %>% as.character(),
island = island %>% as.character(),
municipality = municipality %>% as.character(),
verbatimEventDate = verbatimEventDate %>% as.character()
)
# User output
writeLines(paste0(
" - We have read in ",
format(nrow(GeoL_data), big.mark = ","), " occurrence records from the 'GEOLOCATE HIGH' sheet."
))
###### b. BELS_high ####
# Reads in the .xlsx file, trims the white spaces, and formats the columns to the correct type
BELS_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = "BELS High") %>%
# Convert GeoLocate columns into dwc columns
dplyr::rename(
decimalLatitude = bels_decimallatitude,
decimalLongitude = bels_decimallongitude,
coordinateUncertaintyInMeters = bels_coordinateuncertaintyinmeters,
dataSource = datasource,
scientificName = scientificname,
infraspecificEpithet = infraspecificepithet,
specificEpithet = "_specificepithet",
species = specificepithet,
acceptedNameUsage = acceptednameusage,
taxonRank = taxonrank,
scientificNameAuthorship = scientificnameauthorship,
countryCode = countrycode,
stateProvince = stateprovince,
eventDate = eventdate,
basisOfRecord = basisofrecord,
occurrenceStatus = occurrencestatus,
recordNumber = recordnumber,
recordedBy = recordedby,
eventID = eventid,
samplingProtocol = samplingprotocol,
samplingEffort = samplingeffort,
individualCount = individualcount,
catalogNumber = catalognumber,
rightsHolder = rightsholder,
institutionCode = institutioncode,
datasetName = datasetname,
otherCatalogNumbers = othercatalognumbers,
occurrenceID = occurrenceid,
coreid = coreid,
recordId = recordid,
collectionID = collectionid,
verbatimScientificName = verbatimscientificname,
verbatimEventDate = verbatimeventdate,
id = id) %>%
# Correct some formatting
dplyr::mutate(continent = continent %>% as.character(),
type = type %>% as.character(),
id = id %>% as.character(),
samplingProtocol = samplingProtocol %>% as.character(),
island = island %>% as.character(),
municipality = municipality %>% as.character(),
verbatimEventDate = verbatimEventDate %>% as.character()) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# Remove blanks
tidyr::drop_na(database_id) %>%
# Temporarily add an identifier column
dplyr::mutate(
tempSource = "Bels"
)
# User output
writeLines(paste0(
" - We have read in ",
format(nrow(BELS_data), big.mark = ","), " occurrence records from the 'BELS High' sheet."
))
###### c. merge ####
GeoL_data <- GeoL_data %>%
# Remove data that occurs in BELS_data
dplyr::filter(!database_id %in% BELS_data$database_id) %>%
# Combine datasets
dplyr::bind_rows(BELS_data) %>%
dplyr::select(!database_id) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("GEOL_data_", 1:nrow(.), sep = ""),
.before = catalogNumber)
# User output
writeLines(paste0(
" - We have kept ",
format(sum(GeoL_data$tempSource == "GeoL", na.rm = FALSE), big.mark = ","),
" occurrences from GeoLocate, and ",
format(sum(GeoL_data$tempSource == "Bels", na.rm = FALSE), big.mark = ","),
" records from BELS (",
format(nrow(GeoL_data), big.mark = ","),
" in total). BELS was given preference over GeoLocate"
))
#### 12.3 Out ####
# Save the dataset
readr::write_excel_csv(GeoL_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(GeoL_data)
} # END readr_GeoL
#### 13.0 EaCO ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_EaCO <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
County<-State<-Genus<-genus<-species<-dateRange<-dateSet<-dateCollected<-treatment<-
trapNumber<-samplingRound<-coordinates<-decimalLatitude<-decimalLongitude<-recordNumber<-
.<-catalogNumber <- NULL
#### 13.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 13.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
EaCO_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/")) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Rename columns
dplyr::rename(
recordNumber = 'Specimen Number',
county = County,
stateProvince = State,
genus = Genus,
species = 'Species (if available)',
dateRange = 'Date Range for collection',
treatment = 'Treatment type',
trapNumber = 'Trap number',
samplingRound = 'Sampling Round',
coordinates = 'GPS Coordinates of Traps') %>%
# Drop rows without species names or that aren't bees or aren't useable names
dplyr::filter(!is.na(genus), !genus == "",
!is.na(species), !species == "",
!stringr::str_detect(species, pattern = "[0-9]"),
!stringr::str_detect(species, pattern = "/"),
!stringr::str_detect(tolower(species), pattern = "sp.")) %>%
# Split dates
tidyr::separate(
col = dateRange, sep = "-",
into = c("dateSet", "dateCollected")) %>%
# Add year and convert to ymd format
dplyr::mutate(
dateSet = stringr::str_c(dateSet, "/2017") %>% lubridate::mdy(truncated = 2),
dateCollected = stringr::str_c(dateCollected, "/2017") %>% lubridate::mdy(truncated = 2),
year = 2017) %>%
# Add scientificName
dplyr::mutate(scientificName = stringr::str_c(genus, species, sep = " ")) %>%
# Add field notes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(dateSet), paste0("dateSet: ", dateSet), ""),
dplyr::if_else(!is.na(dateCollected),
paste0("dateCollected: ", dateCollected), ""),
dplyr::if_else(!is.na(treatment),
paste0("treatmentType: ", treatment), ""),
dplyr::if_else(!is.na(trapNumber),
paste0("trapNumber: ", trapNumber), ""),
dplyr::if_else(!is.na(samplingRound),
paste0("samplingRound: ", samplingRound), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Add samplingEffort
dplyr::mutate(
samplingEffort = dateCollected - dateSet
) %>%
# rescue coordinates
tidyr::separate(
col = coordinates,
into = c("decimalLatitude", "decimalLongitude"),
sep = ",") %>%
dplyr::mutate(
decimalLatitude = decimalLatitude %>% stringr::str_remove(pattern = "[a-zA-Z]"),
decimalLongitude = decimalLongitude %>% stringr::str_remove(pattern = "[a-zA-Z]")) %>%
dplyr::mutate(
catalogNumber = stringr::str_c("EastColarado_",recordNumber, sep = "")) %>%
# Add dataset information
dplyr::mutate(dataSource = "EaCO_Anthophila") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("EaCO_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Eastern Colorado (Arathi Seshadri)",
datasetID = "EaCo",
institutionCode = "USDA ARS"
)
#### 13.3 Out ####
# Save the dataset
readr::write_excel_csv(EaCO_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(EaCO_data)
} # END readr_EaCO
#### 14.0 MABC ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_MABC <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = "Hoja1"){
# locally bind variables to the function
genus <- specificEpithet <- collectionSite <- siteCode <- hour <- tribe <- eventDate <- NULL
. <- catalogNumber <- NULL
#### 14.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 14.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
MABC_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Rename columns
dplyr::rename(
catalogNumber = 'Ejemplar',
eventDate = 'Fecha colecta',
country = paste0('Pa\u00eds'),
stateProvince = 'Estado/Provincia',
municipality = 'Municipio',
locality = 'Localidad',
samplingProtocol = 'Metodo colecta',
decimalLatitude = 'Coordenadas Lat',
decimalLongitude = 'Coordenadas Long',
verbatimElevation = 'Altitud',
georeferenceVerificationStatus = paste0('Datos georeferenciaci\u00f3n'),
recordedBy = 'Colector',
identifiedBy = 'Identificador',
family = 'Familia',
subfamily = 'Subfamilia',
genus = 'Genero',
subgenus = 'Subgenero',
specificEpithet = 'Especie',
infraspecificEpithet = 'Subespecie',
species = 'Nombre especie',
taxonID = paste0('C\u00f3digo especie'),
sex = 'Sexo',
# Non-standard fields
tribe = 'Tribu',
collectionSite = 'Sitio Colecta',
siteCode = paste0('C\u00f3digo sitio'),
hour = 'Hora') %>%
# Add scientificName
dplyr::mutate(scientificName = stringr::str_c(genus, specificEpithet, sep = " ")) %>%
# Add field notes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(collectionSite), paste0("collectionSite: ", collectionSite), ""),
dplyr::if_else(!is.na(siteCode),
paste0("siteCode: ", siteCode), ""),
dplyr::if_else(!is.na(hour),
paste0("time: ", hour), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Add identificationRemarks
dplyr::mutate(
identificationRemarks = stringr::str_c(
dplyr::if_else(!is.na(tribe), paste0("tribe: ", tribe), ""))) %>%
# Add year and ensure ymd format
dplyr::mutate(eventDate = lubridate::dmy(eventDate)) %>%
# Add dataset information
dplyr::mutate(dataSource = "MABC_Anthophila") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("MABC_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# add the database_id column
dplyr::mutate(
datasetName = "MABC",
datasetID = "MABC"
)
#### 14.3 Out ####
# Save the dataset
readr::write_excel_csv(MABC_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(MABC_data)
} # END readr_MABC
#### 15.0 Col ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Col <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = sheet){
# locally bind variables to the function
day <- year <- eventDateInitial <- eventDate <- month2 <- day2 <- eventDate2 <- NULL
scientificName <- . <- catalogNumber <- NULL
#### 15.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 15.2 Read+ ####
###### a. Col_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
Col_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Fix some special cases that already involve "." in the column names
setNames(., stringr::str_replace_all(colnames(.), " ", ". ")) %>%
setNames(., stringr::str_replace_all(colnames(.), " $", ".")) %>%
# Rename columns
dplyr::rename(
catalogNumber = paste0('C\u00f3digo de Barras'),
recordedBy = 'Colectores [Aggregated]',
recordedByID = 'Colectores asociados',
eventDateInitial = paste0('Fecha colecci\u00f3n inicial'),
order = 'Orden',
family = 'Familia',
genus = paste0('G\u00e9nero'),
specificEpithet = 'Especie',
scientificNameAuthorship = 'Especie Author',
typeStatus = 'Tipo',
identifiedBy = 'Determinador [Formatted]',
dateIdentified = paste0('Fecha determinaci\u00f3n'),
country = paste0('Pa\u00eds'),
stateProvince = 'Departamento',
municipality = 'Municipio',
locationRemarks = 'Corregimiento Departamental',
locality = 'Localidad',
decimalLatitude = 'Latitud georref. dec.',
decimalLongitude = 'Longitud georref. dec.',
scientificName = 'Nombre Completo',
day = "dia",
month = "mes",
year = "Ano"
# Previous column names:
# collectionID = 'Colecci\\u00f3n/Guid',
# collectionCode = 'C\\u00f3digo',
# occurrenceID = 'Collection Object/GUID',
# identificationRemarks = 'Observaciones generales',
# eventID = 'Evento de Recolecci\\u00f3n/Guid',
# recordedByID = 'Numero de colector',
# eventDateFinal = 'Fecha colecci\\u00f3n final',
# class = 'Clase',
# infraspecificEpithet = 'Subespecie',
# namePublishedInID = 'Referencia original',
# identificationID = 'Determinaciones/Guid',
# identificationQualifier = 'Obs. Determinaci\\u00f3n',
# locationID = 'LocalityID',
# geodeticDatum = 'Datum geod\\u00e9sico',
# coordinateUncertaintyInMeters = 'Precisi\\u00f3n coord. georref.',
# minimumElevationInMeters = 'Elevaci\\u00f3n m\\u00ednima',
# maximumElevationInMeters = 'Elevaci\\u00f3n m\\u00e1xima',
# georeferenceProtocol = 'Protocolo de georreferenciaci\\u00f3n',
# verbatimLatitude = 'Informaci\\u00f3n geogr\\u00e1fica/Latitude1 literal',
# verbatimLongitude = 'Longitude1 literal',
# locationRemarks = 'Observaciones ninf Geogr\\u00e1fica',
# lifeStage = 'Estado de desarrollo',
# habitat = 'H\\u00e1bitat',
# continent = 'Continente',
# tribe = 'Tribu',
# subSpeciesAuthor = 'Subespecie Author'
) %>%
# Fix some date issues:
# a. replace 00 dates with "" to be picked u pby truncated
dplyr::mutate(
day = dplyr::if_else(day == "0" | day == "00",
"", day) %>%
stringr::str_remove("to.*|^0") %>%
as.numeric(na.rm = TRUE),
month = dplyr::if_else(month == "0" | month == "00",
"", month) %>%
stringr::str_remove("^0") %>%
as.numeric(na.rm = TRUE),
year = year %>% as.numeric()
) %>%
dplyr::mutate(
eventDate = lubridate::ymd(paste(year, month, day, sep = "/"),
truncated = 2),
.after = eventDateInitial) %>%
# b. Fix inverted day-month
dplyr::mutate(
day2 = dplyr::if_else(is.na(eventDate),
month,
1),
month2 = dplyr::if_else(is.na(eventDate),
day,
1),
eventDate2 = dplyr::if_else(is.na(eventDate),
lubridate::ymd(paste(year, month2, day2, sep = "/"),
truncated = 2),
eventDate),
.after = year
) %>%
# Combine into final date values
dplyr::mutate(
# Take eventDate if it's not empty, and eventDate2 if it is empty
eventDate = dplyr::if_else(is.na(eventDate),
eventDate2,
eventDate),
day = dplyr::if_else(is.na(day),
day2,
day),
month = dplyr::if_else(is.na(month),
month2,
month)) %>%
dplyr::select(!c(eventDate2, day2, month2)) %>%
# Remvoe extra spaces in subgenus
dplyr::mutate(scientificName = scientificName %>%
stringr::str_replace(pattern = "\\( ",
replacement = "\\(")) %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Col_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "Col_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Colombia - Diego Alexander Guevara Farias",
datasetID = "Col"
)
#### 15.3 Out ####
# Save the dataset
readr::write_excel_csv(Col_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(Col_data)
} # END readr_Col
#### 16.0 FSCA ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_FSCA <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
. <- catalogNumber <- recordID <- NULL
#### 16.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 16.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
FSCA_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
# Add dataset information
dplyr::mutate(dataSource = "FSCA_Anthophila") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("FSCA_data_", 1:nrow(.), sep = ""),
.before = 1) %>%
dplyr::mutate(license = dataLicense) %>%
# keep only valid columns
dplyr::select( tidyselect::any_of(names(ColTypeR()[[1]]))) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Florida State Collection of Arthropods",
datasetID = "FSCA",
institutionCode = "FSCA"
)
#### 16.3 Out ####
# Save the dataset
readr::write_excel_csv(FSCA_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(FSCA_data)
} # END readr_FSCA
#### 17.0 SMC ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_SMC <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
collectionMethod <- locale <- latitude <- longitude <- organismName <- scientificName <- NULL
observationDate <- eventDate <- . <- NULL
#### 17.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 17.2 Read+ ####
SMC_Data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE, guess_max = 33000) %>%
# Rename some columns to make format consistent with DarwinCore
dplyr::rename(
samplingProtocol = collectionMethod,
locality = locale,
decimalLatitude = latitude,
decimalLongitude = longitude,
scientificName = organismName) %>%
# Make new columns
dplyr::mutate(
# Remove underscore from scientificName strings
scientificName = gsub("_", " ", scientificName),
# Add basis of record
basisOfRecord = "specimen",
# Add eventDate
eventDate = lubridate::mdy(observationDate),
# Parse eventDate into day, month, and year
month = lubridate::month(eventDate),
day = lubridate::day(eventDate),
year = lubridate::year(eventDate)) %>%
# Add dataSource information
dplyr::mutate(dataSource = "SMC_Anthophila") %>%
# add the database_id column
dplyr::mutate(
database_id = paste("SMC_data_", 1:nrow(.), sep = "")) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Texas SMC literature data",
datasetID = "SMC"
)
# Save the dataset
readr::write_excel_csv(SMC_Data, file = paste(path, outFile, sep = "/"))
# Return data
return(SMC_Data)
} # END readr_SMC
#### 18.0 Bal ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Bal <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = "animal_data"){
# locally bind variables to the function
siteID<-year<-animalID<-abundance<-samplingMethod<-censusType<-decimalLatitude<-
decimalLongitude<-studyLocation<-siteDescription<-studyID<-locationID<-.<-
samplingIntensity<-eventDate<-catalogNumber <- NULL
#### 18.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 18.2 Read+ ####
# Reads in the .xlsx file, trims the white spaces, and formats the columns to the correct type
Bal_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet, startRow = 2) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Make columns DarwinCore-compatible
dplyr::rename(
locationID = siteID,
year = year,
eventDate = date,
scientificName = animalID,
individualCount = abundance,
samplingProtocol = samplingMethod,
fieldNotes = censusType,
decimalLatitude = decimalLatitude,
decimalLongitude = decimalLongitude,
locality = studyLocation,
locationRemarks = siteDescription) %>%
# Add some columns
dplyr::mutate(
catalogNumber = stringr::str_c(studyID, "_", locationID,"_", 1:nrow(.), sep = ""),
samplingEffort = stringr::str_c(samplingIntensity, " hours")
) %>%
# round the coordinates to six decimal places
dplyr::mutate(
decimalLatitude = decimalLatitude %>% as.numeric() %>% base::round(digits = 6) %>%
suppressWarnings(classes = "warning"),
decimalLongitude = decimalLongitude %>% as.numeric() %>% base::round(digits = 6) %>%
suppressWarnings(classes = "warning")) %>%
# Format eventDate and add dataSource
# Format eventDate
dplyr::mutate(
eventDate = lubridate::dmy(eventDate,
truncated = 2, quiet = FALSE),
# Parse eventDate into day, month, and year
month = lubridate::month(eventDate),
day = lubridate::day(eventDate),
year = lubridate::year(eventDate),
dataSource = "Bal_Anthophila") %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Bal_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Ballare et al. 2019",
datasetID = "Bal"
)
#### 18.3 Out ####
# Save the dataset
readr::write_excel_csv(Bal_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(Bal_data)
} # END readr_Bal
#### 19.0 Lic ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Lic <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
Kingdom<-Order<-Family_or_grp<-Genus<-Species<-sex<-Collector<-Determiner<-genus<-species<-
occurrenceID<-eventID<-eventDate<-.<-catalogNumber<-family <- NULL
#### 19.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 19.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
Lic_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE, guess_max = 33000) %>%
dplyr::rename(
kingdom = Kingdom,
order = Order,
family = Family_or_grp,
genus = Genus,
species = Species,
sex = sex,
recordedBy = Collector,
identifiedBy = Determiner) %>%
# Add taxonomic information
dplyr::mutate(
scientificName = stringr::str_c(
dplyr::if_else(!is.na(genus),
paste0(genus), ""),
dplyr::if_else(!is.na(species),
paste0(species), ""),
sep = " ")) %>%
# Make a catalogue number
dplyr::mutate(
catalogNumber = stringr::str_c(
dplyr::if_else(!is.na(occurrenceID),
paste0(occurrenceID), ""),
dplyr::if_else(!is.na(eventID),
paste0(eventID), ""),
sep = "_")) %>%
# Format eventDate
dplyr::mutate(
eventDate = lubridate::dmy(eventDate,
truncated = 2, quiet = FALSE),
# Parse eventDate into day, month, and year
month = lubridate::month(eventDate),
day = lubridate::day(eventDate),
year = lubridate::year(eventDate)) %>%
# Add dataset information
dplyr::mutate(dataSource = "Lic_Anthophila") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Lic_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Elinor Lichtenberg Canola Data",
datasetID = "Lic"
)
# filter to bee families only
Lic_data <- Lic_data %>%
dplyr::filter(tolower(family) %in%
tolower(c("Andrenidae","Apidae","Colletidae","Halictidae","Megachilidae",
"Melittidae","Stenotritidae")))
#### 19.3 Out ####
# Save the dataset
readr::write_excel_csv(Lic_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(Lic_data)
} # END readr_Lic
#### 20.0 Arm ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Arm <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = "Sheet1"){
# locally bind variables to the function
fam<-genus<-sp<-species<-sex<-locality<-munic<-state<-y<-x<-elev<-specificEpithet<-ecoregion<-
veget<-g<-m<-s<-G<-M<-S<-day<-year<-.<-family <- NULL
#### 20.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 20.2 Read+ ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
Arm_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"), sheet = sheet) %>%
# Make columns DarwinCore-compatible
dplyr::rename(
family = fam,
genus = genus,
specificEpithet = sp,
species = species,
sex = sex,
locality = locality,
municipality = munic,
stateProvince = state,
decimalLatitude = y,
decimalLongitude = x,
verbatimElevation = elev) %>%
# Add a bunch of columns from other columns
dplyr::mutate(
# Add scientificName
# This will ONLY concatenate columns where they have a value.
scientificName = stringr::str_c(
dplyr::if_else(!is.na(genus), genus, ""),
dplyr::if_else(!is.na(specificEpithet),
specificEpithet, ""),
sep = " ") ) %>%
# Do the same as the last mutate, but for fieldNotes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(ecoregion),
paste0("ecoregion: ", ecoregion), ""),
dplyr::if_else(!is.na(veget),
paste0("vegetation: ", veget), ""),
sep = "|") %>%
# Remove extra bars "|".
stringr::str_replace_all(pattern = "(\\|){2,9}",
replacement = "\\|") %>%
stringr::str_replace_all(pattern = "(\\|$)+|(^\\|)+",
replacement = "")) %>%
# Add scientificName
dplyr::mutate(
verbatimLatitude = stringr::str_c(
dplyr::if_else(!is.na(g),
as.character(g), ""),
dplyr::if_else(!is.na(m),
as.character(m), ""),
dplyr::if_else(!is.na(s),
as.character(s), ""),
sep = " "),
verbatimLongitude = stringr::str_c(
dplyr::if_else(!is.na(G),
as.character(G), ""),
dplyr::if_else(!is.na(M),
as.character(M), ""),
dplyr::if_else(!is.na(S),
as.character(S), ""),
sep = " ")) %>%
# Get the coordinateUncertaintyInMeters by taking the UPPER uncertainty limit from the provided
# ranges. I.e., "100-1000" becomes "1000"
dplyr::mutate(
# Format eventDate
eventDate = lubridate::dmy(paste(
day, month, year, sep = "-"
)), # 215 failed to parse.
dataSource = "Arm_Anthophila") %>%
# Remove double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Arm_data_", 1:nrow(.), sep = ""),
.before = family) %>%
dplyr::mutate(license = dataLicense) %>%
# Remove spent columns
dplyr::select(!tidyselect::any_of(c("veget", "ecoregion", "g", "m", "s", "G", "M", "S"))) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Armando Falcon-Brindis",
datasetID = "Arm"
)
#### 20.3 Out ####
# Save the dataset
readr::write_excel_csv(Arm_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(Arm_data)
} # END readr_Arm
#### 21.0 Dorey ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_Dor <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
. <- catalogNumber <- eventDate <- stateOrProvince <- NULL
#### 21.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 21.2 Read+ ####
Dor_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::rename(stateProvince = stateOrProvince) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("Dor_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "Dor_Anthophila") %>%
# Format date
dplyr::mutate(eventDate = eventDate %>% lubridate::dmy(., truncated = 2),
year = lubridate::year(eventDate),
month = lubridate::month(eventDate),
day = lubridate::day(eventDate)) %>%
# Pick up dates of different formats and format together.
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "James B Dorey Bee Data",
datasetID = "Dorey"
)
#### 21.3 Out ####
# Save the dataset
readr::write_excel_csv(Dor_data, file = paste(path, outFile, sep = "/"))
# Return data
return(Dor_data)
} # END readr_Dor
#### 22.0 MEPB ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_MEPB <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = NULL){
# locally bind variables to the function
catalog_number<-pollinator_family<-pollinator_genus<-pollinator_species<-collection_method<-
day_collected<-month_collected<-year_collected<-location_description<-latitude<-longitude<-
basis_of_record<-genus<-specificEpithet<-year<-day<-eventDate<-collector_number<-
location_name<-habitat<-.<-catalogNumber <- NULL
#### 22.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 22.2 Read+ ####
MEPB_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"), sheet = sheet) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Fix broken encodings
dplyr:: mutate(
dplyr::across(
.cols = dplyr::everything(),
.fns = ~ stringr::str_replace_all(.,
pattern = c("\\u00c3\\u00b3"="\\u00f3",
"\\u00c3\\u00a9"="\\u00e9",
"\\u00c3\\u00b1"="\\u00f1",
"\\u00c2\\u00b0"="\\u00b0",
"\\u00c3"="\\u00e1")
))) %>%
# add the database_id column
dplyr::mutate(
database_id = paste("MEPB_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "MEPB_Anthophila") %>%
# Format date
dplyr::mutate(eventDate = lubridate::ymd(stringr::str_c(year %>% as.numeric(),
month %>% as.numeric(),
day %>% as.numeric(),
sep = "/"),
truncated = 2)) %>%
# Pick up dates of different formats and format together.
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetID = "MEPB"
)
#### 22.3 Out ####
# Save the dataset
readr::write_excel_csv(MEPB_data, file = paste(path, outFile, sep = "/"))
# Return data
return(MEPB_data)
} # END readr_MEPB
#### 23.0 Brazil ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_BBD <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
. <- catalogNumber <- year <- day <- dateLastModified <- dateLastModified2 <- NULL
identifiedBy <- Spcslink.identifiedby <- NULL
#### 23.1 Prep ####
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 23.2 Read+ ####
lookupCols <- c(verbatimScientificName = "Scientificname_ORIGINAL",
verbatimLatitude = "Lat_original",
verbatimLongitude = "Long_original")
BBD_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
# Rename columns
dplyr::rename(
tidyselect::any_of(lookupCols),
id = "CodeBBdatabase_curated",
scientificName = "Scientific name corrected",
family = "Family",
institutionCode = "institutioncode",
# infraspecificEpithet = "Spcslink.subspecies",
# scientificNameAuthorship = "Spcslink.scientificnameauthor",
day = "Day",
month = "Month",
year = "Year",
country = "Country",
stateProvince = "State",
decimalLatitude = "Latitude_dec.degrees",
decimalLongitude = "Longitude_dec.degrees",
coordinateUncertaintyInMeters = "Precision.of.coord.meters",
verbatimLocality = "Locality.original",
georeferenceRemarks = "NotasLatLong",
county = "Spcslink.county",
recordedBy = "Collector",
collectionCode = "Collection",
references = "Source",
sex = "Sex",
identifiedBy = "Det_By",
catalogNumber = "Codigo",
otherCatalogNumbers = "Spcslink.collectioncode",
dateIdentified = "Spcslink.yearidentified",
dateLastModified = "Spcslink.datelastmodified",
basisOfRecord = "Spcslink.basisofrecord",
# occurrenceID = "Spcslink.collectioncode",
eventRemarks = "NotesOnLocality") %>%
#dplyr::mutate(
# decimalLatitude = decimalLatitude %>% stringr::str_replace("\\,", "\\."),
# decimalLongitude = decimalLongitude %>% stringr::str_replace("\\,", "\\."))
# add the database_id column
dplyr::mutate(
database_id = paste("BBD_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Edit catalogNumber for AMNH
dplyr::mutate(catalogNumber = stringr::str_replace(
catalogNumber, pattern = "AMNHBEE", replacement = "AMNH_BEE"),
catalogNumber = stringr::str_replace(
catalogNumber, pattern = "^0\\.", replacement = ".")) %>%
# Add dataset information
dplyr::mutate(dataSource = "BBD_Anthophila") %>%
# Format date
dplyr::mutate(eventDate = lubridate::ymd(paste0(
year, month, day, sep = "/"), truncated = 2, quiet = TRUE)) %>%
dplyr::mutate(dateLastModified2 =
lubridate::dmy_hms(dateLastModified, truncated = 5, quiet = TRUE),
.after = dateLastModified) %>%
dplyr::mutate(dateLastModified2 = dplyr::if_else(is.na(dateLastModified2),
lubridate::ymd_hms(dateLastModified, truncated = 5, quiet = TRUE),
dateLastModified2)) %>%
dplyr::mutate(dateLastModified2 = dplyr::if_else(is.na(dateLastModified2),
lubridate::mdy_hms(dateLastModified, truncated = 5, quiet = TRUE),
dateLastModified2)) %>%
dplyr::mutate(dateLastModified2 = dplyr::if_else(is.na(dateLastModified2),
lubridate::ymd(dateLastModified, truncated = 2, quiet = TRUE),
dateLastModified2)) %>%
dplyr::mutate(dateLastModified2 = dplyr::if_else(is.na(dateLastModified2),
as.Date(dateLastModified %>% as.numeric(na.rm = TRUE),
origin = "1899-12-30") %>%
lubridate::ymd_hms(., truncated = 5, quiet = TRUE),
dateLastModified2),
.after = dateLastModified) %>%
dplyr::mutate(dateLastModified = dateLastModified2) %>%
dplyr::select(!dateLastModified2) %>%
# Pick up dates of different formats and format together.
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "BBD_Brazil",
datasetID = "BBD"
) %>%
# If identifiedBy is not filled where it should be, try the other column rpovided
dplyr::mutate(identifiedBy = dplyr::if_else(is.na(identifiedBy),
Spcslink.identifiedby,
identifiedBy))
#### 23.3 Out ####
# Save the dataset
readr::write_excel_csv(BBD_data, file = paste(path, outFile, sep = "/"))
# Return data
return(BBD_data)
} # END readr_BBD
#### 24.0 MPUJ ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
readr_MPUJ <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = sheet){
# locally bind variables to the function
collector1stName <- collectorsLastName <- determined1stName <- determinedLastName <- NULL
day <- year <- eventDate <- endDate <- . <- catalogNumber <- fieldNotes <- NULL
`Start Date (Year)` <- `Start Date (Month)` <- `Start Date (Day)` <- NULL
#### 24.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 24.2 Read+ ####
###### a. MPUJ_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
MPUJ_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Rename columns
dplyr::rename(
catalogNumber = 'Catalog Number',
individualCount = "Count",
otherCatalogNumbers = "Alt Cat Number",
typeStatus = "Type Status",
identificationQualifier = "Qualifier",
sex = "Sex",
samplingProtocol = "Method",
habitat = "Habitat",
continent = "Continent",
country = "Country",
stateProvince = "State",
county = "County",
locality = "Locality Name",
minimumElevationInMeters = "Min Elevation",
maximumElevationInMeters = "Max Elevation",
fieldNotes = "Locality and Habitat Notes",
decimalLongitude = "Latitude1",
decimalLatitude = "Longitude1",
verbatimLatitude = "Lat1text",
verbatimLongitude = "Long1text",
scientificName = "Full Name",
kingdom = "Kingdom",
order = "Order",
family = "Family",
subfamily = "Subfamily",
genus = "Genus",
specificEpithet = "Species",
infraspecificEpithet = "Subspecies",
scientificNameAuthorship = "Species Author",
day = `Start Date (Day)`,
month = `Start Date (Month)`,
year = `Start Date (Year)`,
associatedTaxa = "Associated Taxa",
associatedOccurrences = "Associated Ocurrence",
lifeStage = "Stage",
collector1stName = 'Collectors/First Name',
collectorsLastName = 'Collectors/Last Name',
determined1stName = 'Determiner/First Name',
determinedLastName = 'Determiner/Last Name',
endDate = "End Date",
verbatimEventDate = "Verbatim Date"
) %>%
dplyr::mutate(year = year %>% as.numeric(),
month = month %>% as.numeric(),
day = day %>% as.numeric()) %>%
dplyr::mutate(recordedBy = stringr::str_c(collector1stName, collectorsLastName,
sep = " "),
identifiedBy = stringr::str_c(determined1stName, determinedLastName,
sep = " ")) %>%
dplyr::mutate(eventDate = lubridate::dmy(stringr::str_c(day, month, year, sep = "/"),
truncated = 2)) %>%
dplyr::mutate(
fieldNotes =
stringr::str_c(
dplyr::if_else(!is.na(fieldNotes),
paste0("fieldNotes: ", fieldNotes), ""),
dplyr::if_else(!is.na(eventDate),
paste0("startDate: ", eventDate), ""),
dplyr::if_else(!is.na(endDate),
paste0("endDate: ", endDate), ""),
sep = "|")
) %>%
dplyr::mutate(basisOfRecord = "Preserved specimen") %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("MPUJ_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "MPUJ_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "Colombia MPUJ - Diego Alexander Guevara Farias",
datasetID = "MPUJ"
)
#### 24.3 Out ####
# Save the dataset
readr::write_excel_csv(MPUJ_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(MPUJ_data)
} # END readr_MPUJ
#### 25.0 STRI ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
#'
readr_STRI <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
fieldNotes <- Catalognumber <- . <- day <- year <- catalogNumber <- recordId <- NULL
#### 25.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 25.2 Read+ ####
###### a. STRI_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
STRI_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
dplyr::rename(recordId = "recordID") %>%
# Rename columns
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(Catalognumber),
paste0("secondary catalog #: ", Catalognumber), ""),
sep = "|"),
species = "scientificName") %>%
# Format dates
dplyr::mutate(
# If day is not recorded, set to first day of month
day = dplyr::if_else(day == 0, 1, day),
eventDate = lubridate::dmy(stringr::str_c(day, month, year, sep = "/"),
truncated = 2)) %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("STRI_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "STRI_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "STRI",
datasetID = "STRI"
)
#### 25.3 Out ####
# Save the dataset
readr::write_excel_csv(STRI_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(STRI_data)
} # END readr_STRI
#### 26.0 PALA ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
#'
readr_PALA <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL){
# locally bind variables to the function
romanNumerals <- numeralConversion <- catalogNumber <- type <- country <- municipality <- NULL
locality <- decimalLatitude <- decimalLongitude <- verbatimElevation <- verbatimEventDate <- NULL
recordedBy <- collectionCode <- otherCatalogNumbers <- associatedTaxa <- taxonRemarks <- NULL
family <- genus <- references <- specificEpithet <- scientificName <- . <- eventDate <- NULL
#### 26.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
requireNamespace("mgsub")
#### 26.2 Read+ ####
###### a. month strings ####
# Prepare month strings to convert from roman numerals
romanNumerals <- c("i","ii","iii","iv","v","vi","vii","viii","ix","x","xi","xii",
"I","II","III","IV","V","VI","VII","VIII","IX","X","XI","XII")
numeralConversion <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct","Nov", "Dec",
"Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct","Nov", "Dec")
###### b. PALA_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
PALA_data <- readr::read_csv(paste(path, inFile, sep = "/"),
trim_ws = TRUE) %>%
# Rename columns
dplyr::rename(
catalogNumber = "catalogNumber",
type = "Type",
country = "Country",
municipality = "Muninciplaity",
locality = "Site",
decimalLatitude = "Latitud",
decimalLongitude = "Longitude",
verbatimElevation = "elevation",
verbatimEventDate = "date",
recordedBy = "recordedby",
collectionCode = "Collection",
otherCatalogNumbers = "othercatalognumber",
associatedTaxa = "AssociatedTaxa",
taxonRemarks = "taxonremarks",
family = "Family",
genus = "Genus",
specificEpithet = "species",
references = "Citation"
) %>%
# Add in sciName
dplyr::mutate(scientificName = stringr::str_c(genus, specificEpithet, sep = " ")) %>%
# Format date
dplyr::mutate(eventDate = verbatimEventDate %>%
mgsub::mgsub(
pattern = paste("[-/ \\.]", romanNumerals, "[ -/\\.]", sep = ""),
replacement = numeralConversion) %>%
lubridate::dmy(truncated = 2, quiet = TRUE),
.after = verbatimEventDate) %>%
# Add day, month, year
dplyr::mutate(
day = lubridate::day(eventDate),
month = lubridate::month(eventDate),
year = lubridate::year(eventDate),
.after = eventDate
) %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("PALA_data_", 1:nrow(.), sep = ""),
.before = catalogNumber) %>%
# Add dataset information
dplyr::mutate(dataSource = "PALA_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "PALA",
datasetID = "PALA"
)
#### 26.3 Out ####
# Save the dataset
readr::write_excel_csv(PALA_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(PALA_data)
} # END readr_PALA
#### 27.0 JoLa ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
#'
readr_JoLa <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = c("pre-1950", "post-1950")){
# locally bind variables to the function
fieldNotes <- Catalognumber <-`Start Date (Year)` <- genus <- specificEpithet <- NULL
year <- . <- NULL
#### 27.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 27.2 Read+ ####
###### a. JoLa_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
# Read in both sheets and bind them together
JoLa_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet[1]) %>%
dplyr::bind_rows(openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet[2])) %>%
# Return spaces in column names to keep the consistent with file before renaming
setNames(., stringr::str_replace_all(colnames(.), "\\.", " ")) %>%
# Rename the columns
dplyr::rename(
specificEpithet = "Species",
decimalLatitude = "Latitude1",
decimalLongitude = "Longitude1",
year = `Start Date (Year)`
) %>%
# Add in higher taxonomic information
dplyr::mutate(
genus = "Lasioglossum",
family = "Halictidae",
order = "Hymenoptera",
scientificName = stringr::str_c(genus, specificEpithet, sep = " ")
) %>%
dplyr::mutate(
eventDate = lubridate::ymd(year, truncated = 2)
) %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("JoLa_data_", 1:nrow(.), sep = ""),
.before = 1) %>%
# Add dataset information
dplyr::mutate(dataSource = "JoLa_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "JoLa",
datasetID = "JoLa"
)
#### 27.3 Out ####
# Save the dataset
readr::write_excel_csv(JoLa_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(JoLa_data)
} # END readr_JoLa
#### 28.0 VicWam ####
#' @describeIn readr_BeeBDC
#'
#' Reads specific data files into Darwin Core format
#'
#'
#'
readr_VicWam <- function(path = NULL,
inFile = NULL,
outFile = NULL,
dataLicense = NULL,
sheet = "Combined"){
# locally bind variables to the function
fieldNotes <- Catalognumber <- recordNumber <- genus <- specificEpithet <- NULL
year <- . <- VicWam_data <- otherCatalogNumbers <- institutionCode <- class <- order <- NULL
infraspecificEpithet <- NULL
NEAREST <- DISTANCE <- DIST_UNIT <- DIRECTION <- locality <- locality2 <- PLACEACCURACY <-
coordinateUncertaintyInMeters <- DTFR <- day <- month <- year <- eventDate <- eventDate2 <-
DTTO <- dayTO <- monthTO <- yearTO <- eventDateTO <- eventDateTO2 <- LABELFAMILY <-
LABELGENUS <- LABELSPECIES <- associatedTaxa <- stateProvince <- country <- dataSource <-
license <- decimalLatitude <- decimalLongitude <- lengthTest <- decimalLatitude2 <-
decimalLongitude2 <- NULL
#### 28.1 Prep ####
# This will load the requireNamespaced packages. These packages may still need to be installed to
# R using install.packages("dplyr")... etc.
requireNamespace("dplyr")
requireNamespace("lubridate")
#### 28.2 Read+ ####
###### a. JoLa_data ####
# Reads in the .csv file, trims the white spaces, and formats the columns to the correct type
# Read in both sheets and bind them together
VicWam_data <- openxlsx::read.xlsx(paste(path, inFile, sep = "/"),
sheet = sheet[1]) %>%
# Start by renaming columns
dplyr::rename(
recordNumber = "Reg_DoreyExtension",
otherCatalogNumbers = "COLLNUM",
institutionCode = "INSTITUTE",
class = "CLASS",
order = "ORDER",
"superfamily" = "SUPERFAMILY",
"family" = "FAMILY",
"subfamily" = "SUBFAMILY",
"tribe" = "TRIBE",
"genus" = "GENUS",
"subgenus" = "SUBGENUS",
"specificEpithet" = "SPECIES",
"infraspecificEpithet" = "SUBSPECIES",
"identifiedBy" = "DTMNDBY",
"dateIdentified" = "DTMNDDT",
"lifeStage" = "LIFEHISTORY",
"sex" = "SEX",
"identificationQualifier" = "NAMEQUALIFIER",
"typeStatus" = "SPCMTYPE",
"individualCount" = "SPECNUM",
"stateProvince" = "STATE",
"locality" = "SITE",
"decimalLatitude" = "LATDEC",
"decimalLongitude" = "LONGDEC",
"recordedBy" = "COLLTOR",
"basisOfRecord" = "STORAGE",
"verbatimLatitude" = "LATITUDE",
"verbatimLongitude" = "LONGITUDE",
"occurrenceRemarks" = "NOTES"
) %>%
# Create the scientificName
dplyr::mutate(scientificName = stringr::str_c(
dplyr::if_else(!is.na(genus),
paste0(genus), ""),
dplyr::if_else(!is.na(specificEpithet),
paste0(specificEpithet), ""),
dplyr::if_else(!is.na(infraspecificEpithet),
paste0(infraspecificEpithet), ""),
sep = " "
) %>% stringr::str_squish()) %>%
# modify locality
dplyr::mutate(locality2 = stringr::str_c(dplyr::if_else(complete.cases(NEAREST),
NEAREST, ""),
dplyr::if_else(complete.cases(DISTANCE),
as.character(DISTANCE), ""),
dplyr::if_else(complete.cases(DIST_UNIT),
DIST_UNIT, ""),
dplyr::if_else(complete.cases(DIRECTION),
paste0(DIRECTION, "\u00b0"),
""),
sep = " ") %>% as.character() %>%
stringr::str_squish(),
.after = locality) %>%
dplyr::mutate(locality = dplyr::if_else(is.na(locality),
locality2, locality)) %>%
dplyr::select(!locality2) %>%
# Extract coordinateUncertaintyInMeters
dplyr::mutate(
coordinateUncertaintyInMeters = PLACEACCURACY %>% stringr::str_extract(
"[0-9]+\\s[m(km)(mi)]+") %>% stringr::str_replace_all(c("km" = "000",
"m" = "")) %>%
stringr::str_replace(" ","") %>% as.numeric(),
.after = PLACEACCURACY
) %>%
# Create eventDate
dplyr::mutate(
day = DTFR %>% stringr::str_extract("^[0-9]+/") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "00", NA_character_, .),
month = DTFR %>% stringr::str_extract("/[0-9]+/") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "00", NA_character_, .),
year = DTFR %>% stringr::str_extract("/[0-9]+$") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "0000", NA_character_, .),
eventDate = lubridate::dmy(stringr::str_c(day, month, year, sep = "/"), truncated = 2),
eventDate2 = dplyr::if_else(!stringr::str_detect(DTFR, "[a-zA-Z]"), # convert from silly excel numeric format to real dates...
as.Date(as.numeric(DTFR), origin = "1899-12-30") %>% as.character(),
DTFR) %>% lubridate::ymd(truncated = 2),
# if eventDate is empty, use eventDate2
eventDate = dplyr::if_else(is.na(eventDate),
eventDate2, eventDate),
.after = DTFR
) %>% dplyr::select(!eventDate2) %>%
# Create the date to
dplyr::mutate(
dayTO = DTTO %>% stringr::str_extract("^[0-9]+/") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "00", NA_character_, .),
monthTO = DTTO %>% stringr::str_extract("/[0-9]+/") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "00", NA_character_, .),
yearTO = DTTO %>% stringr::str_extract("/[0-9]+$") %>% stringr::str_remove_all("/") %>%
dplyr::if_else(. == "0000", NA_character_, .),
eventDateTO = lubridate::dmy(stringr::str_c(dayTO, monthTO, yearTO, sep = "/"), truncated = 2),
eventDateTO2 = dplyr::if_else(!stringr::str_detect(DTTO, "[a-zA-Z]"), # convert from silly excel numeric format to real dates...
as.Date(as.numeric(DTTO), origin = "1899-12-30") %>% as.character(),
DTTO %>% as.character()) %>% lubridate::ymd(truncated = 2),
# if eventDateTO is empty, use eventDateTO2
eventDateTO = dplyr::if_else(is.na(eventDateTO),
eventDateTO2, eventDateTO) %>% as.character(),
eventDateTO = dplyr::if_else(is.na(eventDateTO),
stringr::str_c(
dplyr::if_else(complete.cases(yearTO),
yearTO,""),
dplyr::if_else(complete.cases(monthTO),
monthTO,""),
dplyr::if_else(complete.cases(dayTO),
dayTO,""),
sep = " ") %>% stringr::str_squish() %>%
stringr::str_replace(" ", "-"),
eventDateTO),
.after = DTTO
) %>%
dplyr::select(!tidyselect::any_of(c("eventDateTO2", "dayTO", "monthTO", "yearTO"))) %>%
# Add field notes
dplyr::mutate(
fieldNotes = stringr::str_c(
dplyr::if_else(!is.na(eventDate),
paste0("startDate: ", eventDate), ""),
dplyr::if_else(!is.na(eventDateTO),
paste0("endDate: ", eventDateTO), ""),
dplyr::if_else(!is.na(LABELFAMILY),
paste0("associatedFamily: ", LABELFAMILY), ""),
dplyr::if_else(!is.na(LABELGENUS),
paste0("associatedGenus: ", LABELGENUS), ""),
dplyr::if_else(!is.na(LABELSPECIES),
paste0("associatedSpecies: ", LABELSPECIES), ""),
sep = "|")) %>%
# Add associatedTaxa
dplyr::mutate(associatedTaxa = stringr::str_c(
dplyr::if_else(!is.na(LABELGENUS),
paste0(LABELGENUS), ""),
dplyr::if_else(!is.na(LABELSPECIES),
paste0(LABELSPECIES), ""),
sep = " ") %>% stringr::str_squish()) %>%
# Worst case, use family
dplyr::mutate(associatedTaxa = stringr::str_c(
dplyr::if_else(is.na(associatedTaxa),
paste0(LABELFAMILY), associatedTaxa)
)) %>%
# Format country name
dplyr::mutate(country = stringr::str_to_sentence(country)) %>%
dplyr::mutate(stateProvince = stringr::str_replace_all(stateProvince,
c("^ACT$" = "Australian Capital Territory",
"New South wales" = "New South Wales",
"^NSW" = "New South Wales",
"^NT$" = "Northern Territory",
"^Qld$" = "Queensland",
"^SA$" = "South Australia",
"^Tas$" = "Tasmania",
"^Vic$" = "Victoria",
"^WA$" = "Western Australia",
"Western australia" = "Western Australia"))) %>%
# Remove any double white-spaces
apply(., 2, stringr::str_squish) %>% dplyr::as_tibble() %>%
# add the database_id column
dplyr::mutate(
database_id = paste("VicWam_data_", 1:nrow(.), sep = ""),
.before = 1) %>%
# Add dataset information
dplyr::mutate(dataSource = "VicWam_Anthophila") %>%
dplyr::mutate(license = dataLicense) %>%
# add the database_id column
dplyr::mutate(
datasetName = "VicWam",
datasetID = "VicWam"
) %>%
# Format some lats and lons to decimal degrees
# first, squish out extra spaces
dplyr::mutate(
decimalLatitude = decimalLatitude %>% stringr::str_squish(),
decimalLongitude = decimalLongitude %>% stringr::str_squish(),
) %>%
# make a column to test for the lat/lon length
dplyr::mutate(lengthTest = dplyr::if_else(decimalLatitude %>% stringr::str_detect("S"),
dplyr::if_else(stringr::str_count(decimalLatitude,
"\\s") > 2,
"Long", "Short"),
"NA"),
.before = decimalLatitude) %>%
# Convert to DD
# Long coordinates
dplyr::mutate(decimalLatitude2 = dplyr::if_else(decimalLatitude %>% stringr::str_detect("S") &
lengthTest == "Long",
stringr::str_c(stringr::str_extract(decimalLatitude, "^[0-9]{2}") %>%
as.numeric() +
(stringr::str_extract(decimalLatitude,
"\\s[0-9]{2}\\s") %>%
as.numeric() / 60) +
(stringr::str_extract(decimalLatitude,
"[0-9]{2}\\sS") %>%
stringr::str_remove("S") %>%
as.numeric() / 3600)) %>%
as.character(),
decimalLatitude),
decimalLongitude2 = dplyr::if_else(decimalLongitude %>% stringr::str_detect("E") &
lengthTest == "Long",
stringr::str_c(stringr::str_extract(decimalLongitude, "^[0-9]+") %>%
as.numeric() +
(stringr::str_extract(decimalLongitude,
"\\s[0-9]{2}\\s") %>%
as.numeric() / 60) +
(stringr::str_extract(decimalLongitude,
"\\s[0-9]+\\sE") %>%
stringr::str_remove("E") %>%
as.numeric() / 3600)) %>%
as.character(),
decimalLongitude),
.after = decimalLatitude) %>%
# Short coordinates
dplyr::mutate(decimalLatitude2 = dplyr::if_else(decimalLatitude %>% stringr::str_detect("S") &
lengthTest == "Short",
stringr::str_c(stringr::str_extract(decimalLatitude, "^[0-9]{2}") %>%
as.numeric() +
(stringr::str_extract(decimalLatitude,
"\\s[0-9]{2}\\sS") %>%
stringr::str_remove("S") %>%
as.numeric() / 60) ) %>%
as.character(),
decimalLatitude2) %>% as.numeric(),
decimalLongitude2 = dplyr::if_else(decimalLongitude %>% stringr::str_detect("E") &
lengthTest == "Short",
stringr::str_c(stringr::str_extract(decimalLongitude, "^[0-9]+") %>%
as.numeric() +
(stringr::str_extract(decimalLongitude,
"\\s[0-9]{2}+\\sE") %>%
stringr::str_remove("E") %>%
as.numeric() / 60) ) %>%
as.character(),
decimalLongitude2) %>% as.numeric(),
.after = decimalLatitude) %>%
# Change lat/lon to correct accuracy
dplyr::mutate(
decimalLatitude2 = dplyr::if_else(lengthTest == "Long",
round(decimalLatitude2, digits = 2),
dplyr::if_else(lengthTest == "Short",
round(decimalLatitude2, digits = 1),
decimalLatitude2)),
decimalLongitude2 = dplyr::if_else(lengthTest == "Long",
round(decimalLongitude2, digits = 2),
dplyr::if_else(lengthTest == "Short",
round(decimalLongitude2, digits = 1),
decimalLongitude2))
) %>%
# Add coordinateUncertaintyInMeters for these as well based on the length of coordinates
dplyr::mutate(
coordinateUncertaintyInMeters = coordinateUncertaintyInMeters %>% as.numeric(),
coordinateUncertaintyInMeters = dplyr::if_else(
is.na(coordinateUncertaintyInMeters),
dplyr::if_else(lengthTest == "Long", 1000, 10000),
coordinateUncertaintyInMeters
)) %>%
# Replace the lat/lon columns with the working columns
dplyr::select(!c("decimalLongitude", "decimalLatitude")) %>%
dplyr::rename(decimalLongitude = "decimalLongitude2",
decimalLatitude = "decimalLatitude2") %>%
# All of Australia is in the southern hemisphere, correct these latitudes to negative
dplyr::mutate(decimalLatitude = dplyr::if_else(
stateProvince %in% c("Australian Capital Territory", "New South Wales",
"Northern Territory", "Queensland", "South Australia",
"Tasmania", "Victoria", "Western Australia", ""),
abs(decimalLatitude) * - 1,
decimalLatitude)) %>%
# Do the same for longitude but hte entire dataset is in the eastern hemisphere
dplyr::mutate(decimalLongitude = abs(decimalLongitude))
#### 28.3 Out ####
# Save the dataset
readr::write_excel_csv(VicWam_data, file = paste(path, outFile, sep = "/"))
# Return the data from the function to the user
return(VicWam_data)
} # END readr_VicWam
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