R/ClimateMethods/Graphics/plot_compare_longterm_tendaily_method.R

In StatisticalServicesCentre/ClimateObject: What the package does (short line)

#=============================================================================
#' Compare yearly long term mean with 10-daily totals
#' @title Compare yearly long term mean with ten-daily totals
#' @name plot_compare_longterm_tendaiily_totals
#' @author Abib Duut 2015 

#' @description \code{plot_compare_longterm_tendaiily_totals} 
#' this plots the long term mean against ten daily totals
#' 
#' @param data_list list. 
#' 
#' @param time_period This is can be 
#' "10-daily", "season", "year". Default value: "10-daily" 
#  
#' @examples
#' climObj <- climate(data_tables=list(data),date_format="%Y-%m-%d")
#' # where "data" is a data.frame containing the desired data to be plotted.
#' climObj$plot_compare_longterm_tendaiily_totals()
#' @return  a comparison plot
#' 
climate$methods(plot_compare_longterm_tendaily_totals = function(data_list = list(),var_label=rain_label,date_format="%Y-%m-%d" ,time_period="10-daily", year=1960,type=type, lty=lty, lwd=lwd,
                                                                 xlim = c( min(ndte, na.rm=TRUE),col=col, max( curr_data[[year_col]], na.rm=TRUE)), xlab = "Year", ylab = "Rain Total", main =c(data_name, title),
                                        rain_threshold=0.85){  
  #data required 
  data_list=add_to_data_info_required_variable_list(data_list, 
                                                    	list(var_label, date_label))
  var_threshold<-rain_threshold
  #date time period is "daily" todo check it works for all time periods
  data_list=add_to_data_info_time_period(data_list, daily_label)
  climate_data_objs = get_climate_data_objects(data_list)
  
  	for(data_obj in climate_data_objs) {
	    data_name = data_obj$get_meta(data_name_label)    
	    date_col = data_obj$getvname(date_label)
	    year_col = data_obj$getvname(year_label)
	    month_col= data_obj$getvname(month_label)
    	#curr_threshold = data_obj$get_meta(var_threshold_label)
    	var_col  = data_obj$getvname(var_label)
      station_id_col = data_obj$getvname(station_label)
      
      
#Get the data frames for analysis
    curr_data_list = data_obj$get_data_for_analysis(data_list)
    # loop for computing 
    for( curr_data in curr_data_list ) {
      curr_data[[year_col]] = year(curr_data[[date_col]])
      curr_data[[month_col]]= month(curr_data[[date_col]])
      #longterm data.
      curr_data1<-curr_data
      View(curr_data)
      print("1i")
      
      #subset by year.
      curr_data<-subset(curr_data, curr_data[[year_col]]==year)
      print("1ii")
      #split rain values in to groups of 10 for ten daily totals.
	    dat1<-split(curr_data[[var_col]], ceiling(seq_along(curr_data[[var_col]])/10))
	    print("1iii")
	    dat2<-lapply(dat1, sum)
	    print("1iv")
	    #split date values in to groups of 10 for the axis.
	    deta<-split(curr_data[[date_col]], ceiling(seq_along(curr_data[[date_col]])/10))
	    print("2")
	    View(deta)
	      ndte<-NULL
		      for(i in 1:length(deta)){
			    dte<-as.Date(deta[[i]][10], format=date_format)
			    ndte<-as.Date(rbind(ndte,dte),format=date_format)
		  }
	      print("3")
	#compute monthly mean for each year.	
	curr_data1[[year_col]]<-year(curr_data1[[date_col]])
	curr_data1[[month_col]]<-month(curr_data1[[date_col]])
	m_tot<-ddply(curr_data1,.(curryear_col,month_col),summarise,sum(as.numeric(Rain),na.rm=T))
	names(m_tot)<-c("Year","Month","M_Total")
	m_stat<-ddply(m_tot,.(Month),summarise,mean(M_Total,na.rm=T))
	names(m_stat)<-c("Month" ,"Mean")
	print("4")
	#plotting
	plot(ndte, dat2,pch = pch, ylim= c(ylim, max(dat2, na.rm=TRUE)),type=type, lty=lty, lwd=lwd,
	     xlim = c( min(ndte, na.rm=TRUE), max( curr_data[[year_col]],col=col, na.rm=TRUE)), xlab = "Year", ylab = "Rain Total", main =c(data_name, title))
  lines(m_stat, col=col)
  print("5")
				}
			}
		}
)