This project (see [`violinplot`](https://github.com/jkriege2/JKQtPlotter/tree/master/examples/violinplot) demonstrates how to use JKQTPlotter to draw <ahref="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].
First we generate some random numbers from a bimodal distribution (and as a by-product also from two single-distributions that form the bimodal):
```.cpp
size_t randomdatacol1=datastore1->addColumn("random data N(1,1)+N(6,2)");
size_t randomdatacol2=datastore1->addColumn("random data N(1,1)");
size_t randomdatacol3=datastore1->addColumn("random data N(6,2)");
std::random_device rd; // random number generators:
std::mt19937 gen{rd()};
std::uniform_int_distribution<> ddecide(0,1);
std::normal_distribution<> d1{1,1};
std::normal_distribution<> d2{6,2};
for (size_t i=0; i<50;i++){
double v=0;
if (i%2==0) {
v=d1(gen);
datastore1->appendToColumn(randomdatacol2, v);
} else {
v=d2(gen);
datastore1->appendToColumn(randomdatacol3, v);
}
datastore1->appendToColumn(randomdatacol1, v);
}
```
# Visualizing data as a Rug Plot
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.
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):
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`:
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:
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.
The result looks like this, if we use the same method as above to calculate also the violin plots for `randomdatacol2` and `randomdatacol3`:
Finally note that if you use `JKQTPViolinplotHorizontalElement` instead of the `JKQTPViolinplotVerticalElement` used above, you can also draw horizontal violin plots:
