# Run some checks and basic formatting on the data
#' @importFrom dplyr distinct
frescalo_checks <- function(site_col, sp_col, year_col, start_col, end_col,
Data, time_periods, ignore.ireland, ignore.channelislands,
species_to_include){
# Check column names are in the data
new.colnames <- c(site_col,sp_col,year_col,start_col,end_col)
missingColNames <- new.colnames[!new.colnames %in% names(Data)]
if(length(missingColNames) > 0) stop(paste(unlist(missingColNames),'is not the name of a column in data\n\n'))
# Remove columns that are not needed
Data <- Data[,names(Data) %in% new.colnames]
# Ensure date columns are dates
if(!is.null(start_col)){
Data <- colToDate(Data, start_col)
}
if(!is.null(end_col)){
Data <- colToDate(Data, end_col)
}
if(!is.null(year_col)){
if(!is.numeric(Data[year_col][,1])){
stop('column specified by year_col must be numeric')
}
}
if(!is.null(start_col) & !is.null(end_col)){
for( i in c(start_col,end_col)){
if(!'POSIXct' %in% class(Data[[i]]) & !'Date' %in% class(Data[[i]])){
warning(paste('column',i,'Date is not in a date format. This should be of class "Date" or "POSIXct", conversion attempted'))
Data[[i]]<-as.Date(Data[[i]])
}
}
}
#If the data has a startdate and enddate ensure the dates are within one
#of the time periods, else if it just has a year, ensure this is in the
#right time period
if(!is.null(start_col) & !is.null(end_col)){
for(ii in 1:length(time_periods[,1])){
Data$yearnew[as.numeric(format(Data[start_col][[1]],'%Y'))>=time_periods[ii,1][[1]] &
as.numeric(format(Data[end_col][[1]],'%Y'))<=time_periods[ii,2][[1]]]<-rowMeans(time_periods[ii,])[[1]]
}
} else {
for(ii in 1:length(time_periods[,1])){
Data$yearnew[Data[year_col]>=time_periods[ii,1][[1]] &
Data[year_col]<=time_periods[ii,2][[1]]]<-rowMeans(time_periods[ii,])[[1]]
}
}
# Those that are not in these time periods are removed
Data <- Data[!is.na(Data$yearnew),]
#rename columns
newnames <- c('site','CONCEPT')
oldnames <- c(site_col,sp_col)
Data <- change_colnames(Data,newnames,oldnames)
# Ensure CONCEPT is a factor
if(!is.na(sp_col))Data$CONCEPT <- as.factor(Data$CONCEPT)
# Include only desired species (note this removes data for unwanted species, this data is not used
# in the analysis)
if(!is.null(species_to_include)) Data <- Data[Data$CONCEPT %in% species_to_include,]
# remove irish and/or channel islands data if desired
if(ignore.ireland) Data <- subset(Data, regexpr('^[A-Z]{2}', Data$site)==1)
if(ignore.channelislands) Data <- subset(Data, grepl('^[Ww][[:alpha:]]{1}', Data$site)==FALSE)
# make sure there are no empty site names
if('' %in% unique(Data$site)) stop('Site names cannot be empty (i.e. "")')
# retain only the columns we need (site, concept, timeperiod)
# at the same time keep only unique rows
before <- nrow(Data)
Data <- na.omit(distinct(Data[c('site','CONCEPT','yearnew')]))
after <- nrow(Data)
if(before < after) warning(paste(before-after, 'rows of data are removed as they are duplicates within a time period'))
return(Data)
}
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