Babyplots is an easy to use library for creating interactive 3d graphs for exploring and presenting data.

Babyplots is available as a JavaScript library, as an R package, as a Python package, and as an add-in for Microsoft PowerPoint. While the R package, Python package and JavaScript library allow the creation of new plots, the PowerPoint add-in can only be used to display exported plots. This website also provides an interactive node-based editor for creating babyplots visualizations called NPC (node plot creator) or simply Creator.

Find the individual documentation pages through the links below:

There are currently seven types of visualizations that can be created with babyplots.

Point clouds are 3-dimensional scatter plots, where three variables (x, y and z) define the positions of points in the coordinate system. One additional variable can be visualized by the color of the points. Point clouds are optimized for very large data sets.

Shape clouds are 3-dimensional scatter plots, similar to point clouds, where three variables (x, y and z) define the positions of 3-dimensional shapes in the coordinate system. One additional variable can be visualized by the color of the shapes. Each data point of a shape cloud has the same shape, but you can combine multiple shape clouds with different shapes.

Additionally, each data point can have an information text assigned to it that is displayed when the corresponding shape in the plot is clicked.

The surface plot creates a 3-dimensional surface that is defined by a height variable for each point on a x-y plane given by the rows and columns of a 2d matrix. An additional variable can be visualized by the color of the surface points. The color is interpolated between the defined points of the surface.

The heat map is a 3-dimensional bar chart defined similarly to the surface plot, where the height of each bar on a x-y plane is given by the value in a row-column pair of a 2d matrix. An additional variable can be visualized by the color of the bar.

Lines are 3-dimensional continuous lines that are described by the x, y and z coordinates of the points along the line. Each point can also have a color variable which is interpolated along the line connecting the points. Additionally, labels can be added alongside the points of the line.

The image stack visualizes a set of 2-dimensional slices with pixel values. The input is a tiff stack with rgb channels. It was created and optimized for fluorescent microscopy z-stack data and therefore works best with black background and bright signal.

The mesh object visualization adds a 3d model, or a scene of multiple 3d models from a glTF object.