R/ClimateMethods/Graphics/plot_annual_rain_total_method.R

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

#------------------------------------------------------------------------
# This function plots annual totals of rainfall amount, per year
# It is here to demonstrate how an output method 
#
# It has the following optional arguments:
# data_list: Specify the subset of the data to use. 
# threshold: threshold which determines if a day is dry if the rainfall amount is below it. This overrides the threshold stored in the metadata if provided.
# 
# ----------------------------------------------------------------------------


climate$methods(plot_annual_rain_total = function (data_list=list(), col1="blue",ylab,xlab="Year",na.rm=TRUE, pch=20,ylim=0,type="b",lty=2,col2="red",lwd = 2,lwd2 = 1.5,
                                                   var_label = rain_label,plot_line = FALSE,ygrid=0, graph_parameter = par(mfrow=c(2,2)),plot_window = FALSE,
                                                   main_title="Plot - Yearly Rain Count",grid=FALSE){
  # convert data 
  data_list = c(data_list, convert_data=TRUE)
  # time period
  data_list = add_to_data_info_time_period(data_list, yearly_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)
    
    # Must add these columns if not present 
    if( !(data_obj$is_present(year_label) ) ) { 
      data_obj$add_year_col() 
    }
    year_col = data_obj$getvname(year_label)
    
    interset_var_col = data_obj$getvname ("Total Rain") 
    
    if(missing(ylab)){
      ylab = interset_var_col
    }    
    curr_data_list = data_obj$get_data_for_analysis(data_list)
    if (plot_window){   
      par = graph_parameter 
    } 
    # loop for plotting 
    for( curr_data in curr_data_list ) { 
      plot_yearly_summary <- plot( curr_data[[year_col]], curr_data[[interset_var_col]],type=type,pch=pch,xlab=xlab, col=col1,ylim= c(ylim, max(curr_data[[interset_var_col]], na.rm=na.rm)),
                                   xlim = c( min(curr_data[[year_col]], na.rm=na.rm), max( curr_data[[year_col]], na.rm=na.rm)),
                                   ylab=ylab, main= c( data_name, main_title))
      if (grid){
        grid(length(curr_data[[year_col]]),ygrid, lwd = lwd)
      }      
      
      #if (!plot_line) {
      # reg=lm(curr_data[[interset_var_col]] ~ curr_data[[year_col]])
      #abline(reg,col=col2,lwd=lwd2 )
      #print(summary(reg))
      #}
      
    }
  }
  par(mfrow=c(1,1))

# 
# 
# climate$methods(plot_annual_rain_total = function(data_list=list(), threshold = 0.85)
#   {    
#   # get_climate_data_objects returns a list of the climate_data objects specified
#   # in the arguements.
#   # If no objects specified then all climate_data objects will be taken by default
#   # TO DO have options such as colours and the rest
#   data_list=add_to_data_info_required_variable_list(data_list, list(rain_label))
#   data_list=add_to_data_info_time_period(data_list, yearly_label)
#   climate_data_objs_list = get_climate_data_objects(data_list)
#   
#   for(data_obj in climate_data_objs_list) {
#     curr_threshold = data_obj$get_meta(threshold_label,threshold)
#     
#     rain_col  = data_obj$variables[[rain_label]]
#     # If doy or year column is not in the data frame, create it.
#     if ( !(data_obj$is_present(dos_label)&data_obj$is_present(season_label))) {
#       # add_doy_col function does not exist yet.
#       data_obj$add_doy_col()
#     }
#     dos_col = data_obj$variables[[dos_label]]
#     season_col = data_obj$variables[[season_label]]
#     curr_data_list=data_obj$get_data_for_analysis(data_list)
#   }
#     
#    
##############################################################################
#   data$year<-year(data$Date)
#   data$Rain<-as.numeric(data$Rain)
#   
#   #count NA's in data
#   mc<-ddply(data,.(year),summarize,sum(is.na(Rain)))
#   names(mc)<-c("year","count")
#   mc<-mc$year[mc$count>20]
#   
#   #find annual totals
#   an<-ddply(data,.(year),summarize,sum(na.omit(Rain)))
#   names(an)<-c("Year","tot")
#   
#   #exclude years in final cm
#   ned<-an$Year		
#   ned<-ned%in%mc
#   
#   an$ned<-ned	
#   nan<-subset(an,ned==F,select=c(Year,tot))
#   db<-nan
#   plot4<-plot(db$Year,db$tot,type="b",col="blue",pch=20,xlab="",ylim=c(0,max(db$tot)+5),ylab="Rain Total(mm)",main="Annual Rainfall Total")
#   abline(h=mean(db$tot[db$tot>0]),lty=2,col="red")
#   
  #summary of total	
  
  # n<-which.is.max(db$tot)
  # print(mean(db$tot))
  # print(paste(db$Year[n],db$tot[n]))
  # print(quantile(db$tot, 0.20))
  
  # m<-which.min(db$tot)
  # print(paste(db$Year[m],db$tot[m]))
  
  
  #Annual rainfall totals for across the years.  
  #Split data by year 
  #smb<-subset(data,!Rain==NA,select=c(Rain,Year))
  #smb<-as.data.frame(smb)
  #sdata<-split(smb$Rain,smb$Year)
  #stot<-sapply(sdata,sum,na.rm=TRUE)
  #stot<-as.data.frame(stot)
  #rw<-row.names(stot)
  #plot(rw,stot$stot,type="b",col="blue", pch=20,xlab="Year",ylab="Rain Total",main="Annual Rain Total")
  #abline(h=mean(stot$stot),lty=2,col="red")
  #grid(10,10,lwd=2)
  
  #This for plot summaries.
  #x<-summary(db$tot)
  
  #The value of summaries differ from that of instats, and add abline showing trend. Increase line width.
  
  #text(Average #annual# rainfall amount is "print("mincount")" mm. It ranges from "print("mincount")" mm to "print("mincount")"mm with 
  #"print("year")" and "print("year")" being the years with the least and most amount of rainfall recorded respectively. Also it can ve seen that 
  #"for" every "print("mincount")" out of "print("mincount")" years there is an expected annual rainfall of over "print("mincount")"mm 'in' this region and with 
  #	no visible trend.)
 # plot4 #;x
}
)