Pareto chart

Computes a table of statistics and plot a Pareto chart.

pareto.chart(data, plot = TRUE, …)

# S3 method for pareto.chart
plot(x, xlab = NULL, ylab = "Frequency", 
     ylab2 = "Cumulative Percentage", 
     cumperc = seq(0, 100, by = 25), 
     ylim = NULL, main = NULL,
     col = blues.colors(nlevels), 
     …)

Arguments

data	a vector of values. names(data) are used for labelling the bars.
plot	a logical specifying if the chart should be provided (TRUE, default).
x	the object of class 'pareto.chart' returned by a call to pareto.chart function.
xlab	a string specifying the label for the x-axis.
ylab	a string specifying the label for the y-axis.
ylab2	a string specifying the label for the second y-axis on the right side.
cumperc	a vector of percentage values to be used as tickmarks for the second y-axis on the right side.
ylim	a numeric vector specifying the limits for the y-axis.
main	a string specifying the main title to appear on the plot.
col	a value for the color, a vector of colors, or a palette for the bars. See the help for colors and palette.
…	other graphical arguments to be passed to the corresponding plot method, and eventually to the barplot function.

Value

Returns an object of class 'pareto.chart' containing the descriptive statistics used to draw the Pareto chart. This object has associated a print and plot mehod.

Details

A Pareto chart is a barplot where the categories are ordered in non increasing order, and a line is also added to show the cumulative sum.

References

Mason, R.L. and Young, J.C. (2002) Multivariate Statistical Process Control with Industrial Applications, SIAM.

Montgomery, D.C. (2005) Introduction to Statistical Quality Control, 5th ed. New York: John Wiley & Sons.

Ryan, T. P. (2011), Statistical Methods for Quality Improvement, 3rd ed. New York: John Wiley & Sons, Inc.

Scrucca, L. (2004). qcc: an R package for quality control charting and statistical process control. R News 4/1, 11-17.

Wetherill, G.B. and Brown, D.W. (1991) Statistical Process Control. New York: Chapman & Hall.

See also

barplot

Examples

defect <- c(80, 27, 66, 94, 33)
names(defect) <- c("price code", "schedule date", "supplier code", "contact num.", "part num.")
pareto.chart(defect, ylab = "Error frequency")
#>                
#> Pareto chart analysis for defect
#>                 Frequency Cum.Freq. Percentage Cum.Percent.
#>   contact num.   94.00000  94.00000   31.33333     31.33333
#>   price code     80.00000 174.00000   26.66667     58.00000
#>   supplier code  66.00000 240.00000   22.00000     80.00000
#>   part num.      33.00000 273.00000   11.00000     91.00000
#>   schedule date  27.00000 300.00000    9.00000    100.00000
pareto.chart(defect, ylab = "Error frequency", xlab = "Error causes", las=1)
#>                
#> Pareto chart analysis for defect
#>                 Frequency Cum.Freq. Percentage Cum.Percent.
#>   contact num.   94.00000  94.00000   31.33333     31.33333
#>   price code     80.00000 174.00000   26.66667     58.00000
#>   supplier code  66.00000 240.00000   22.00000     80.00000
#>   part num.      33.00000 273.00000   11.00000     91.00000
#>   schedule date  27.00000 300.00000    9.00000    100.00000
pareto.chart(defect, ylab = "Error frequency", col=rainbow(length(defect)))
#>                
#> Pareto chart analysis for defect
#>                 Frequency Cum.Freq. Percentage Cum.Percent.
#>   contact num.   94.00000  94.00000   31.33333     31.33333
#>   price code     80.00000 174.00000   26.66667     58.00000
#>   supplier code  66.00000 240.00000   22.00000     80.00000
#>   part num.      33.00000 273.00000   11.00000     91.00000
#>   schedule date  27.00000 300.00000    9.00000    100.00000
pareto.chart(defect, cumperc = seq(0, 100, by = 5), ylab2 = "A finer tickmarks grid")
#>                
#> Pareto chart analysis for defect
#>                 Frequency Cum.Freq. Percentage Cum.Percent.
#>   contact num.   94.00000  94.00000   31.33333     31.33333
#>   price code     80.00000 174.00000   26.66667     58.00000
#>   supplier code  66.00000 240.00000   22.00000     80.00000
#>   part num.      33.00000 273.00000   11.00000     91.00000
#>   schedule date  27.00000 300.00000    9.00000    100.00000