Reference

cxplot is a JavaScript wrapper for CanvasXpress, implementing the grammar of graphs found in the popular R ggplot2 package. It's worth noting that this library is entirely written in JavaScript and doesn't rely on R at all. While the accompanying documentation pages may not be frequently updated, the library itself is actively maintained and developed. The documentation provides only a limited number of usage examples. For reporting bugs or requesting features, please use the GitHub repository: https://github.com/neuhausi/cxplot

JavaScript Library Include the CanvasXpress JavaScript library in the `<head></head>` of your web page. `<link rel="stylesheet" href="path-to-canvasXpress.css" type="text/css"/>` `<script type="text/javascript" src="path-to-canvasXpress.min.js"></script>` Any code as decribed below should be placed in a `<script></script>` tag.
Plot basics All cxplot plots begin with a call to `var cxp = cxplot()`, supplying a DOM target id to place the visualization, a default data and aesthethic mappings, specified by `aes()`. You can also supply an additional parameter with custom events. You then add layers, scales, coords and facets with `cxp.`
`cxplot()` `aes()` Create a new cxplot Construct aesthetic mappings
Layers
Geoms A layer combines data, aesthetic mapping, a geom (geometric object), a stat (statistical transformation), and a position adjustment. Typically, you will create layers using a `geom_` function, overriding the default position and stat if needed.
`geom_abline()geom_vline()` `geom_hline()` Reference lines: horizontal, vertical, and diagonal
`geom_bar()` `geom_col()` `stat_count()` Bar charts
`geom_bin_2d()` `stat_bin_2d()` Heatmap of 2d bin counts
`geom_boxplot()` `stat_boxplot()` A box and whiskers plot (in the style of Tukey)
`geom_contour()` `geom_contour_filled()` `stat_contour()` `stat_contour_filled()` 2D contours of a 3D surface
`geom_density()` `stat_density()` Smoothed density estimates
`geom_density_2d()` `geom_density_2d_filled()` `stat_density_2d()` `stat_density_2d_filled()` Contours of a 2D density estimate
`geom_dotplot()` Dot plot
`geom_errorbarh()` Horizontal error bars
`geom_hex()` `stat_bin_hex()` Hexagonal heatmap of 2d bin counts
`geom_freqpoly()` `geom_histogram()` `stat_bin()` Histograms and frequency polygons
`geom_jitter()` Jittered points
`geom_path()` `geom_line()` `geom_step()` Connect observations
`geom_point()` Points
`geom_qq_line()` `stat_qq_line()` `geom_qq()` `stat_qq()` A quantile-quantile plot
`geom_quantile()` `stat_quantile()` Quantile regression
`geom_ribbon()` `geom_area()` Ribbons and area plots
`geom_rug()` Rug plots in the margins
`geom_smooth()` `stat_smooth()` Smoothed conditional means
`geom_text()` Text
`geom_raster()` Rectangles
`geom_violin()` `stat_ydensity()` Violin plot
Scales Scales control the details of how data values are translated to visual properties. Override the default scales to tweak details like the axis labels or legend keys, or to use a completely different translation from data to aesthetic. `labs()` and `lims()` are convenient helpers for the most common adjustments to the labels and limits.
`labs()` `xlab()` `ylab()` `ggtitle()` `xlim()` `ylim()` Set scale limits Modify axis, legend, and plot labels
Facetting Facetting generates small multiples, each displaying a different subset of the data. Facets are an alternative to aesthetics for displaying additional discrete variables.
`facet_wrap()` Wrap a 1d ribbon of panels into 2d
Themes Themes control the display of all non-data elements of the plot.
`theme()` Modify components of a theme

Reference

JavaScript Library

Plot basics

Layers

Geoms

Scales

Facetting

Themes