R/PlotWaitTimeByAgent.R

In stevenranney/ispiranteRanney: Functions to analyze Ispirante data

PlotWaitTimeByAgent <- structure(
function # Create a plot of the resolution time by date

  ##############################################################################
  # File:  PlotWaitTimeByAgent.R
  ##author<<  Steven H. Ranney
  ## Contact: \email{Steven.Ranney@gmail.com}
  # Created: 12/30/14  
  # Last Edited: 12/31/14 by SHR
  ##description<< This function plots the wait time (in hours) and produces a box 
  ## plot of those values for each \code{assignee_name} in the \code{dF}.  The 
  ## call volume of an agent is produced.
  #
  # TODO: add RData for example
  # TODO: add testing section
  ###############################################################################
  
  (dF, ##<< The dataFrame for which to plot resolution time by date level.
  ... ##<< Arguments to be passed to other functions.  Specifically, this can take 
      ## the form of identifying which \code{date} value is of interest.  For example
      ## the sample datasets provided all had either at least \code{assigned_at} 
      ## or \code{created_at} values.  The \code{\link{AssignDateAndDay}} can used
      ## either of these values as the date of interest.
  ){

  dF <- AssignDateAndDay(dF, ...)

  #Eliminate rows where there is no date assigned
  dF <- dF[which(!is.na(dF$assignedDate)), ]
  
  #Handling 'nan.0' values
  dF$requester_wait_time_in_minutes_within_business_hours[dF$requester_wait_time_in_minutes_within_business_hours == "nan.0"] <- NA
  dF$value <- as.numeric(dF$requester_wait_time_in_minutes_within_business_hours)
  
  #Count how many calls each assignee_name had
  nameVol <- aggregate(dF$dayOfWeek, by = list(dF$assignee_name), length)
  names(nameVol) <- c("assignee_name", "volume")
  nameVol$volume <- nameVol$volume/10
  
  
  if(length(which(is.na(dF$value))) == nrow(dF)){
    stop(print("Data frame has no data to display"))
    } else {
    
  ##details<< Wait time in all sample dataFrames is \code{factor}; this function
  ## converts apparently numeric values to \code{as.numeric}.
  
  ##details<< This functions converts values that are \code{nan.0} to \code{NA}.
  
  ##details<< The red dots on a plot indicate outliers in the data set.  In the 
  ## \code{\link{geom_boxplot}} documentation, outliers are defined as data points 
  ## fall outside of \emph{1.5 * IQR} where \emph{IQR} stands for the "Inter-Quartile
  ## Range" of the data.
 
  ylabel <- "Wait time (hours)" 
 
  xlabel <- "Assignee name"
  
  ##details<< Plots are generated by the function \code{\link{ggplot}}
  
  ##details<< The blue line in the top plot of the window is the number calls 
  ## received by each \code{assignee_name}.  Assignees with high volume and low 
  ## wait times are the most efficient agents.  The combination of these 
  ## plots can tell a manager which employees are most efficient at resolving 
  ## calls.
  
  ##details<< The red line in the bottom plot of the window produced from this function 
  ##is the mean wait-time value for the entire data frame.  Agents whose 
  ## medians are below this line may be more effective at researching and resolving customer 
  ## issues.  Further, agents that are more effective could 
  ## be considered as 'trainers' for new staff.
  
  #Boxplot of assignee_name and resolution time
  val <- data.frame(z = mean(dF$value/60, na.rm = T))
  
  g <- ggplot(dF, aes(x = assignee_name, y = (value/60), 
                       group = assignee_name))
  
  p1 <- g + geom_boxplot(mapping = NULL, data = dF, stat = "boxplot", 
                    outlier.colour = "red", outlier.shape = 16, 
                    outlier.size = 2) +
            labs(ylab(ylabel)) + labs(xlab(xlabel)) + # + labs(ggtitle("Resolution time")) +
            geom_hline(aes(yintercept = z), val, colour = "red")
  
  p1 <- p1 + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

  #Line plot of assignee_name and call volume
  volPlot <- ggplot(nameVol, aes(x = assignee_name, y = volume, 
                                  group = 1))
  p2 <- volPlot + geom_line(colour = "navyblue", size = 1.01) + 
        labs(ylab("Call volume (x10)"))# + labs(xlab(xlabel))# + labs(ggtitle("Resolution time"))

  p2 <- p2 + theme(axis.ticks.x = element_blank(), axis.text.x = element_blank(), 
                   axis.title.x = element_blank())  
  allPlots <- PlotMultipleObjects(p2, p1)
  
  return(allPlots)
  
      } #end else
})