R/ClimateMethods/Graphics/boxplot_new_method.R

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

#==================================================================================================
# BOXPLOT WITH METHOD
#' @title Plot boxplot with a specified method .
#' @name box_jitter
#' @author Frederic and Steve 2015 (AMI)

#' @description \code{Boxplot.jitter} 
#' Plot boxplot with jitter given climate object
#' 
#' @param data_list	A list containing stations for analysis, the years or
#' periods to be analyzed and the required variables from the data. 
#' If blank, the system will choose all data appropriate for the analysis.
#' @param var   This contains the variables list of interest in the plot.
#' @param Names	Character string for the variables plotted.
#' @param Method  The method to be used to separate coincident points.
#' @param Time_period 	Data time period. 
#' @param jitter This gives the mount of jittering applied.
#' @param Horizontal	Logical, If true, the boxplot is plotted horizontally.
#' @param Plot_jitter Logical, If true, the data points are added to the boxplot.
#' @param Ylab  	y axis label.
#' @param add  Logical, if TRUE, add the chart to the current 
#' @param Colpoints 	The color of the data points.
#' @param Na.rm   A logical indicating whether missing values should be removed.
#' @param Connect.median  Logical, if TRUE, the medians of the box are connected.
#' @param Plot.sd  Logical,if TRUE, the line indicating the standard deviation is added.
#' @param Lty   	The line type.
#' @param Main  	The main title.
#' @param varwidth  Logical, if TRUE, the boxes are drawn with widths proportional to 
#' the square-roots of the number of observations in the groups.
#' @param Outline   Logical, if not true, the outliers are not drawn.
#' @param Show.names  adding group labels which will be printed under each boxplot.
#'  
#' @examples
#' ClimateObj <- climate( data_tables = list( data ), data_time_periods = list("yearly") )
#' Default data_time_periods: "yearly"
#' # where "data" is a data.frame containing the desired data to be plotted.
#' climateObj$box_jitter()
#' @return return boxplot
#' 

climate$methods(box_jitter = function(data_list = list(), var = "Total Rain",  names=c(), method = "jitter",
                                      jitter = 0.1, time_period = yearly_label, connect.mean = FALSE,
                                       horizontal = FALSE, plot_jitter = TRUE, ylab = "Value", add = TRUE, 
                                      colpoints = "red", na.rm = TRUE,connect.median = FALSE, 
                                      plot.sd = FALSE, lty = 1, col.sd = "blue", main = c(data_name," Jitter Plot"),
                                      varwidth = FALSE, outline = TRUE, show.names = TRUE){
  
  #required variable
  data_list = add_to_data_info_required_variable_list(data_list, list(var))
  # data time period is daily
  data_list = add_to_data_info_time_period( data_list, time_period )
  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 )
    
    if (!is.list(var)){
      interest_var = list(var)
    }
    
    for(i in 1:length(var)){
      interest_var[[i]] <- data_obj$getvname(var[[i]]) 
    }
    
    if(missing(names)){
     names = var
    }
    
    # access data for analysis
    curr_data_list = data_obj$get_data_for_analysis( data_list )
    
    for( curr_data in curr_data_list ) {
      
     dat <- subset(curr_data, select = as.character(interest_var))
                    
      boxplot(dat, names = names, horizontal = horizontal, varwidth = varwidth, outline = outline, na.rm = na.rm,
              ylab =ylab,  main =  main, show.names = show.names )
      if( plot_jitter == TRUE){
      stripchart( dat , method = method, jitter = jitter, vertical = !(horizontal), add = add, col = colpoints )
      }
      if( connect.mean == TRUE ){
      lines( sapply(  dat, na.rm = na.rm, mean) )
      }      
      if( connect.median == TRUE) {
        lines(  sapply( dat, na.rm = na.rm, median) )
      }
      #if a line for the standard deviation is required 
      if( plot.sd == TRUE){
        #store the mean and standard values in different variables
        m =  sapply( dat, na.rm = na.rm, mean)
        s =  sapply( dat, na.rm = na.rm, sd)
        # counter of sd from the mean
        i = 1
        abline( h = m + (i * s), lty = lty, col = col.sd )
        
      }
    }
    
  }
}
)