Data Visualization Examples - Maple Programming Help

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Data Visualization Examples

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Begin by loading the Statistics package.

with(Statistics):

Combining Visualizations

A := <5, 2, 5, 8, 4, 5, 10, 5>:

B := map(t -> t-.5, A):

  

Generate bar chart and line chart.

P := ColumnGraph(A, legend = ["Data Set A"]):

Q := LineChart(B, color = red, thickness = 3, symbol = circle, symbolsize = 10, legend = ["Data Set B"]):

  

Superimpose the two plots and display the result.

plots[display](P, Q, title = "Bar Chart and Line Chart", axis[2] = [gridlines = [7, thickness = 2, linestyle = dash, color = white]]);

Pie Chart

  

Generate a pie chart.

T := <a, b, c, a, a, b, a, a, a, b, b, c, c, 1, 2, 3, a, 1>:

PieChart(T, sector = 0..180, color = red..yellow);

Sampling from the Beta Distribution

  

Generate a random sample drawn from the non-central Beta distribution. Generate a box plot and a histogram and display them in a single plot.

X := RandomVariable(NonCentralBeta(1, 2, 3)):

S := Sample(X, 10^4):

P := BoxPlot(S, orientation = horizontal, offset = -.6, width = .1, deciles = false):

Q := Histogram(S):

R := DensityPlot(X, range = 0..1, thickness = 3):

plots[display](P, Q, R, gridlines = true);

  

Use a probability plot to compare a sample distribution and the original distribution.

T := Sample(X, 10^2):

ProbabilityPlot(T, X);

Exploring Data Sets

  

You can use the Explore command to interactively explore characteristics of datasets, such as point density using the ScatterPlot and SunflowerPlot commands:

Y := Vector( [Sample( RandomVariable(Normal(0, 1)), 200 ), Sample( RandomVariable(Normal(2.6, 1)), 200 )] ):

Z := Vector( [Sample( RandomVariable(Normal(0, 1)), 200 ), Sample( RandomVariable(Normal(2.6, 1)), 200 )] ):

ExcisePoints := proc( fractionexcised, dataset1, dataset2, plottype )
  uses Statistics;
   local plotopts;
   plotopts := view = [min(dataset1)..max(dataset1), min(dataset2)..max(dataset2)];

   `if`( plottype = "SunflowerPlot",
          SunflowerPlot( Excise(evalf[3](fractionexcised), dataset1, dataset2 ), plotopts ),
          ScatterPlot( Excise(evalf[3](fractionexcised), dataset1, dataset2 ), plotopts  ) );

end proc:

Explore( ExcisePoints( fractionexcised, Y, Z, plottype ),
        parameters = [[plottype = ["ScatterPlot","SunflowerPlot"], label = ` `, placement = bottom],
        [ fractionexcised = -1.0 .. 1.0, label = `Fraction Excised` ] ],
        initialvalues = [fractionexcised = 0],
        title = "Excising Data" );

Excising Data

 

 

 

  

Note: Before interacting with this interactive example, you need to execute the code. (Either execute the entire worksheet, or execute the first command to load the package and then execute all the commands in this Exploring Data Sets section.)

Return to Index for Example Worksheets

See Also

Statistics, Statistics Visualization Overview