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124 lines
7.8 KiB
Markdown
124 lines
7.8 KiB
Markdown
# Example (JKQTPlotter): Violin Plots {#JKQTPlotterViolinplotGraphs}
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[TOC]
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[JKQTPlotterBasicJKQTPDatastoreStatistics]: @ref JKQTPlotterBasicJKQTPDatastoreStatistics "Advanced 1-Dimensional Statistics with JKQTPDatastore"
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[statisticslibrary]: @ref jkqtptools_math_statistics "JKQTPlotter Statistics Library"
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[JKQTPlotterBoxplotStyling]: @ref JKQTPlotterBoxplotStyling "Styling different aspects of boxplots"
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This project (see [`violinplot`](https://github.com/jkriege2/JKQtPlotter/tree/master/examples/violinplot) demonstrates how to use JKQTPlotter to draw <a href="https://en.wikipedia.org/wiki/Violin_plot">violin plots</a> using the classes `JKQTPViolinplotVerticalElement` and `JKQTPViolinplotHorizontalElement`. Violin plots can be thought of as an extension to box plots, as they are also used to represent the distribution of a random variable, but contain more info than the "simple" 5-number statistics used for boxplots: Violin Plots show an estimate of the desnsity distribution of the random vriable, e.g. calculated as a kernel density estimate, or as a simple histogram. The Plotting classes themselves do not calculate these estimates, but only draw them into the plot. The density estimates are calculated by functions from the [statisticslibrary].
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The source code of the main application is (see [`violinplot.cpp`](violinplot.cpp).
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# Generating a test Dataset
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First we generate some random numbers from a bimodal distribution (and as a by-product also from two single-distributions that form the bimodal):
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```.cpp
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size_t randomdatacol1=datastore1->addColumn("random data N(1,1)+N(6,2)");
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size_t randomdatacol2=datastore1->addColumn("random data N(1,1)");
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size_t randomdatacol3=datastore1->addColumn("random data N(6,2)");
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std::random_device rd; // random number generators:
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std::mt19937 gen{rd()};
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std::uniform_int_distribution<> ddecide(0,1);
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std::normal_distribution<> d1{1,1};
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std::normal_distribution<> d2{6,2};
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for (size_t i=0; i<50; i++) {
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double v=0;
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if (i%2==0) {
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v=d1(gen);
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datastore1->appendToColumn(randomdatacol2, v);
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} else {
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v=d2(gen);
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datastore1->appendToColumn(randomdatacol3, v);
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}
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datastore1->appendToColumn(randomdatacol1, v);
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}
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```
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# Visualizing data as a Rug Plot
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Samples from the bimodal (built from two gaussian distributions `d1` and `d2`) are collected in `randomdatacol1`, whereas `randomdatacol2` and `randomdatacol3` collect those numbers that were drawn from `d1` or `d2` respectively.
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Such data can be visualized by `JKQTPSingleColumnSymbolsGraph`, here using a rug plot (using `gData1->setPositionScatterStyle(JKQTPSingleColumnSymbolsGraph::RugPlot);` ... but also e.g. a ee swarm plot would be possible):
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```.cpp
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JKQTPSingleColumnSymbolsGraph* gData1;
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plot->addGraph(gData1=new JKQTPSingleColumnSymbolsGraph(plot));
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gData1->setPosition(0);
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gData1->setPositionScatterStyle(JKQTPSingleColumnSymbolsGraph::RugPlot);
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gData1->setDataColumn(randomdatacol1);
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gData1->setDataDirection(JKQTPSingleColumnSymbolsGraph::DataDirection::Y);
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```
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# Drawing the (vertical) Violin Plot
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Now we need to calculate the kernel density estimate from the data in `randomdatacol1` and store the result in two new columns `cViol1Cat` and `cViol1Freq`:
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```.cpp
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size_t cViol1Cat=datastore1->addColumn("violin 1, cat");
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size_t cViol1Freq=datastore1->addColumn("violin 1, KDE");
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jkqtpstatKDE1DAutoranged(datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1),
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datastore1->backInserter(cViol1Cat), datastore1->backInserter(cViol1Freq),
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51, jkqtpstatKernel1DEpanechnikov,
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jkqtpstatEstimateKDEBandwidth(datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1)));
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```
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Finally we can add a `JKQTPViolinplotVerticalElement` to the plot and provide it with the kernel density estimate from above and with some additional statistical properties (minimum, maximum, average and median) of the dataset:
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```.cpp
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JKQTPViolinplotVerticalElement* gViol1;
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plot->addGraph(gViol1=new JKQTPViolinplotVerticalElement(plot));
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gViol1->setPos(2);
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gViol1->setMin(jkqtpstatMinimum(datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1)));
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gViol1->setMax(jkqtpstatMaximum(datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1)));
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gViol1->setMean(jkqtpstatAverage(datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1)));
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gViol1->setMedian(jkqtpstatMedian(datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1)));
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gViol1->setViolinPositionColumn(cViol1Cat);
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gViol1->setViolinFrequencyColumn(cViol1Freq);
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gViol1->setColor(gData1->getSymbolColor());
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gViol1->setViolinStyle(JKQTPGraphViolinplotStyleMixin::SmoothViolin);
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gViol1->setViolinPositionMode(JKQTPGraphViolinplotStyleMixin::ViolinBoth);
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```
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The center of the `gData1` was set to 0 and the center of the violin plot is set to `2`. With `JKQTPViolinplotVerticalElement::setViolinStyle()` you can choose the style of the violin plot and with `JKQTPViolinplotVerticalElement::setViolinPositionMode()` you can select whether the density estimate should be displayed on the left, the right or on both sides of the center-line.
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The result looks like this, if we use the same method as above to calculate also the violin plots for `randomdatacol2` and `randomdatacol3`:
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Note that we set different styles for the three plots with:
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```.cpp
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gViol2->setViolinStyle(JKQTPGraphViolinplotStyleMixin::StepViolin); // green plot
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gViol3->setViolinStyle(JKQTPGraphViolinplotStyleMixin::BoxViolin); // blue plot
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```
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Also for the green and blue plot, we did not calculate a kernel density estimate, but rather a simple histogram:
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```.cpp
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size_t cViol2Cat=datastore1->addColumn("violin 2, cat");
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size_t cViol2Freq=datastore1->addColumn("violin 2, Histogram");
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jkqtpstatHistogram1DAutoranged(datastore1->begin(randomdatacol2), datastore1->end(randomdatacol2),
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datastore1->backInserter(cViol2Cat), datastore1->backInserter(cViol2Freq),
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21, true, false, JKQTPStatHistogramBinXMode::XIsMid);
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```
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# Drawing a horizontal Violin Plot
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Finally note that if you use `JKQTPViolinplotHorizontalElement` instead of the `JKQTPViolinplotVerticalElement` used above, you can also draw horizontal violin plots:
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# Adapters as shortcuts to drawing Violin Plots
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Note that there also exist "adapters" that allow to draw violin plots in one line of code:
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```.cpp
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jkqtpstatAddVViolinplotHistogramAndOutliers(plot->getPlotter(), datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1), -5);
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jkqtpstatAddHViolinplotHistogramAndOutliers(plot->getPlotter(), datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1), -5);
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jkqtpstatAddVViolinplotHistogram(plot->getPlotter(), datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1), -10);
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jkqtpstatAddHViolinplotHistogram(plot->getPlotter(), datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1), -10);
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jkqtpstatAddVViolinplotKDEAndOutliers(plot->getPlotter(), datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1), -15);
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jkqtpstatAddHViolinplotKDEAndOutliers(plot->getPlotter(), datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1), -15);
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jkqtpstatAddVViolinplotKDE(plot->getPlotter(), datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1), -20);
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jkqtpstatAddHViolinplotKDE(plot->getPlotter(), datastore1->begin(randomdatacol1), datastore1->end(randomdatacol1), -20);
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```
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