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ebdStrip.R
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ebdStrip.R
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library (plyr)
library (dplyr)
library (rgdal)
library (sp)
library (compare)
source("eBirdColumnStrip.R")
#################################################################
# eBird Record Stripper #
# #
# This script pre-processes eBird records file and india #
# shape files to create RDS files for uploading to shiny #
#################################################################
#######################Configurations###########################
#Name of the eBird quarterly archive WITHOUT .zip
ebd_file_name <- 'ebd_IN_relApr-2021'
#Name of the India region shape file archive WITHOUT .zip
india_shape_file <- 'indiama-editedSQ'
state <- 'IN-KL'
################################################################
#Unzip and read eBird records
#unzip(paste('..\\data\\',ebd_file_name,'.zip',sep=''))
#ebd <- read.delim(paste(ebd_file_name,'.txt',sep=''), na.strings = c("NA", "", "null"), as.is=TRUE, quote="")
ebd <- readEbdColumns (ebd_file_name, '..\\data\\')
#' Returns the number of days in a vector of month. Feb returns 29 by default
#'
#' @param m Index of the month 1..12.
#' @return Number of days in a particular month.
#' @examples
#' daysInMonth(5)
daysInMonth <- Vectorize (function(m=30){
# Return the number of days in the month
return(switch(m,
'01' = 31,
'02' = 29,
'03' = 31,
'04' = 30,
'05' = 31,
'06' = 30,
'07' = 31,
'08' = 31,
'09' = 30,
'10' = 31,
'11' = 30,
'12' = 31))
}
)
filterRecords <- function (state, dat)
{
print(nrow(dat))
print(state)
# Strip unwanted columns from eBird records
ebd_records <- subset(dat[dat$STATE.CODE == state,], select = c("TAXONOMIC.ORDER", "OBSERVATION.COUNT", "UNIQUE_SAMPLING_ID", "COMMON.NAME"))
#Remove entries from shared lists
ebd_records <- ebd_records[!duplicated(ebd_records[c("UNIQUE_SAMPLING_ID","COMMON.NAME")]),]
# Strip unwanted columns from eBird records
ebd_records <- subset(ebd_records, select = c("TAXONOMIC.ORDER", "OBSERVATION.COUNT", "UNIQUE_SAMPLING_ID"))
# Write to RDS file with compression
saveRDS(ebd_records, paste0('..\\data\\ebd_records_',state,'.rds'))
}
# Remove lists with NA duration (e.g. historical)
ebd <- ebd[!is.na(ebd$DURATION.MINUTES),]
#Add unique list identifier for removing duplicates
ebd <- within (ebd, UNIQUE_SAMPLING_ID <- ifelse(is.na(GROUP.IDENTIFIER),SAMPLING.EVENT.IDENTIFIER,GROUP.IDENTIFIER))
#If subspecies, copy subspecies common name
ebd <- within (ebd, COMMON.NAME <- ifelse(CATEGORY=='issf',SUBSPECIES.COMMON.NAME,COMMON.NAME))
#Create state list by removing duplicate state entries
ebd_states <- ebd[!duplicated(ebd$STATE.CODE),]
# Strip unwanted columns from eBird states
ebd_states <- subset(ebd_states, select = c("STATE.CODE", "STATE"))
#Splitting into state based records
sapply (ebd_states$STATE.CODE,filterRecords, dat <- ebd)
dat <- NULL #Release memory
#Create species list by removing duplicate species entries
ebd_species <- ebd[!duplicated(ebd$TAXONOMIC.ORDER),]
#Create district list by removing duplicate district entries
ebd_districts <- ebd[!duplicated(ebd$COUNTY.CODE),]
#Create unique lists by removing duplicate lists
ebd_lists <- ebd[!duplicated(ebd$UNIQUE_SAMPLING_ID),]
# At this point, the primary ebd data is no longer needed
ebd <-NULL #Release memory
# Strip unwanted columns from eBird species
ebd_species <- subset(ebd_species, select = c("TAXONOMIC.ORDER", "COMMON.NAME"))
# Strip unwanted columns from eBird districts
ebd_districts <- subset(ebd_districts, select = c("COUNTY.CODE", "COUNTY"))
# Add a fortnight field. There are 12 months a year, 24 fortnights. E.g. 5.5 is 11th fortnight in a year
# E.g. 14th of September = 0.5 * int (14/30 + 0.5) = 0. 15th of February = 0.5 * int (15/30 + 0.5) = 0.5
ebd_lists <- within (ebd_lists, Fortnight <- as.numeric(format(as.Date(OBSERVATION.DATE),"%m")) +
0.5 * as.integer(0.5 + as.numeric(format(as.Date(OBSERVATION.DATE),"%d"))/daysInMonth (as.integer(format(as.Date(OBSERVATION.DATE),"%m")))))
# Strip unwanted columns from eBird lists
ebd_lists <- subset(ebd_lists, select = c("STATE.CODE", "COUNTY.CODE", "Fortnight", "DURATION.MINUTES", "LONGITUDE", "LATITUDE", "UNIQUE_SAMPLING_ID", "ALL.SPECIES.REPORTED"))
#Unzip and open the shape file
unzip(paste('..\\data\\',india_shape_file,'.zip',sep=''))
indiamap <- rgdal::readOGR(paste(india_shape_file,'.shp', sep=''), india_shape_file)
sp::coordinates(ebd_lists) <- ~LONGITUDE+LATITUDE
# Map the CRS
sp::proj4string(ebd_lists) <- sp::proj4string(indiamap)
ebd_filters <- data.frame(FILTER=character(),
stringsAsFactors=FALSE)
ebd_lists_with_filter <- NULL
for (filterindex in 1:nrow(indiamap@data))
{
# Store filter metadata in another dataframe
ebd_filters [filterindex, ] <- as.character(indiamap$AREA_1[filterindex])
head(ebd_filters,20)
# Filter lists according to set filter polygons
india_selected <- indiamap[filterindex, ]
rgl_ebd_lists <- ebd_lists
rgl_ebd_lists$FILTER <- 0;
rgl_ebd_lists <- rgl_ebd_lists[india_selected, ]
# For all filtered lists, assign the filter_index
rgl_ebd_lists$FILTER <- filterindex;
if(!is.null(ebd_lists_with_filter))
{
ebd_lists_with_filter <- rbind (ebd_lists_with_filter, rgl_ebd_lists)
}
else
{
ebd_lists_with_filter <- rgl_ebd_lists
}
filterindex <- filterindex + 1
}
# Strip the list before joining
ebd_lists_with_filter <- subset(as.data.frame(ebd_lists_with_filter), select = c("UNIQUE_SAMPLING_ID", "FILTER"))
ebd_lists <- as.data.frame (ebd_lists)
# Join the filter assigned lists to the full lists. Remaining expected to be filter=0
ebd_lists <- join (ebd_lists, ebd_lists_with_filter, by = 'UNIQUE_SAMPLING_ID')
ebd_lists$FILTER[is.na(ebd_lists$FILTER)] <- 0
# Bug. Why join has 2 more than actual lists
saveRDS(ebd_species, '..\\data\\ebd_species.rds')
saveRDS(ebd_states, '..\\data\\ebd_states.rds')
saveRDS(ebd_districts, '..\\data\\ebd_districts.rds')
saveRDS(ebd_filters, '..\\data\\ebd_filters.rds')
saveRDS(ebd_lists, '..\\data\\ebd_lists.rds')
#Remove temp files
unlink ('*.txt')
unlink ('*.pdf')
unlink (paste(india_shape_file,'.*',sep=''))