The better alternative to the pie chart represents amount of values given in data.
Fanplot(Datavector,Names,Labels,MaxNumberOfSlices,main='',col,MaxPercentage=FALSE,ShrinkPies=0.05,Rline=1.1)
[1:n] a vector of n non unique values
Optional,
[1:k] names to search for in Datavector, if not set unique of Datavector is calculated.
Optional, [1:k] Labels if they are specially named, if not Names are used.
Default is k, integer value defining how many labels will be shown. Everything else will be summed up to Other.
Optional, title below the fan pie, see plot
Optional, default as other colors in this packages, else the same as in plot
default FALSE; if true the biggest slice is 100 percent instead of the biggest procentual count
Optional, distance between biggest and smallest slice of the pie
Optional, the distance between text and pie is defined here as the length of the line in numerical numbers
silent output by calling invisible of a list with
[1:k] percent values visualized in fanplot
[1:k] see input Labels, only relevant ones
A normal pie plot is dificult to interpret for a human observer, because humans are not trained well to observe angles [Gohil, 2015, p. 102]. Therefore, the fan plot is used. As proposed in [Gohil 2015] the fan.plot() of the plotrix package is used to solve this problem.
If Number of Slices is higher than MaxNumberOfSlices then ABCanalysis is applied (see [Ultsch/Lotsch, 2015]) and group A chosen.
If Number of Slices in group A is higher than MaxNumberOfSlices, then the most important ones out of group A are chosen.
If MaxNumberOfSlices is higher than Slices in group A, additional slices are shown depending on the percentage (from high to low).
[Gohil, 2015] Gohil, Atmajitsinh. R data Visualization cookbook. Packt Publishing Ltd, 2015.
[Ultsch/Lotsch, 2015] Ultsch. A ., Lotsch J.: Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data, PloS one, Vol. 10(6), pp. e0129767. doi 10.1371/journal.pone.0129767, 2015.
# NOT RUN {
data(categoricalVariable)
Fanplot(categoricalVariable)
# }
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