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Mosaic plot showing cross-sectional distribution through time of different musical themes in the Guardian's list of "1000 songs to hear before you die". A mosaic plot, Marimekko chart, Mekko chart, or sometimes percent stacked bar plot, is a graphical visualization of data from two or more qualitative variables. [1]
Alternatively, Stacked bar charts (also known as Composite bar charts) stack bars on top of each other so that the height of the resulting stack shows the combined result. Unlike a grouped bar chart where each factor is displayed next to another, each with their own bar, the stacked bar chart displays multiple data points stacked in a single ...
Makes a horizontal stacked chart of up to 12 counts (plus a gray bar if the total is greater than the sum of the 12). If no total is supplied, defaults to 100 (for percentages). By default, uses nice rainbow of colors that don't correspond to reserved article class or importance colors, but colors can be customized.
The radar chart is a chart and/or plot that consists of a sequence of equi-angular spokes, called radii, with each spoke representing one of the variables. The data length of a spoke is proportional to the magnitude of the variable for the data point relative to the maximum magnitude of the variable across all data points.
UpSet plots are a data visualization method for showing set data with more than three intersecting sets. UpSet shows intersections in a matrix, with the rows of the matrix corresponding to the sets, and the columns to the intersections between these sets (or vice versa).
The bars can be plotted vertically or horizontally. A bar graph shows comparisons among discrete categories. One axis of the chart shows the specific categories being compared, and the other axis represents a measured value. Some bar graphs present bars clustered in groups of more than one, showing the values of more than one measured variable.
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Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.