FiglinQ features 35 distinct trace types in 6 broad categories. The traces can be mixed with each other in a single chart and adjusted using a multitude of parameters, so in fact many more distinctive traces can be created.
A scatter plot is a type of plot or mathematical diagram using Cartesian coordinates to display values for (typically) two variables in a set of data. If the points are coded (color/shape/size), one additional variable can be displayed. The data are displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis.
See also Styling Scatter plots
A bar trace presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A bar trace shows comparisons among discrete categories. One axis of the chart shows the specific categories being compared, and the other axis represents a measured value.
See also Styling Bar plots
A line trace displays information as a series of data points called 'markers' connected by straight or curved line segments. It is a basic type of chart common in many fields. It is similar to a scatter plot except that the measurement points are ordered (typically by their x-axis value) and joined with line segments. A line chart is often used to visualize a trend in data over intervals of time – a time series – thus the line is often drawn chronologically.
See also Styling Line plots
An area trace displays graphically quantitative data. It is based on the line chart. The area between the axis and the line is emphasized with a colored surface. Commonly an area trace is used to compare two or more quantities with each other.
See also Styling Area plots
A heatmap trace shows a magnitude of a phenomenon as color, in two dimensions. The variation in color may be by hue or intensity, giving visual cues about how the phenomenon is clustered or varies over space.
See also Styling Heatmap plots
Contour traces are a way to show a three-dimensional surface on a two-dimensional plane. They plot two predictor variables (X and Y) on the y-axis and a response variable Z as contours. These contours are sometimes called z-slices or iso-response values. This type of chart is widely used in cartography, where contour lines on a topological map indicate elevations that are the same. Many other disciples use contour charts including: astrology, meteorology, and physics. Contour lines commonly show altitude (like height of a geographical features), but they can also be used to show density, brightness, or electric potential.
See also Styling Contour plots
A pie trace is a circular statistical graphic divided into slices to illustrate numerical proportions. In a pie chart, the arc length of each slice (and consequently its central angle and area), is proportional to the quantity it represents. Pie charts are very widely used in the business world and the mass media.
See also Styling Pie charts
Table trace is used to generate richly styled tables from data contained in the associated grids. Tables are generally used to present textual or other descriptive data that is difficult to visualize with other means.
See also Styling Tables
A box trace can graphically depict individual points of numerical datasets, as well as their basic statistical parameters (mean, meadian, quartiles, standard deviation, range). Box traces may have lines extending from the boxes (whiskers) indicating variability outside the upper and lower quartiles. Outliers may be plotted as individual points. Box plots are non-parametric: they display variation in samples of a statistical population without making any assumptions of the underlying statistical distribution. Boxplots are becoming increasingly popular and appreciated in scientific publishing because they allow visualization of the underlying individual data points, as well as basic statistical properties of datasets.
See also Styling Box plots
Violin traces are similar to box traces, except that they also show the probability density of the data at different values, usually smoothed by a kernel density estimator. Showing the full distribution of the data, a violin trace is generally more informative than a plain box trace. Violin traces are particularly useful when the data distribution is multimodal (i.e. contains more than one peak). In this case a violin plot shows the presence of different peaks, their position and relative amplitude, provided that there is a sufficient number of data points. Like box traces, violin traces are used to represent comparison of a variable distribution (or sample distribution) across different categories.
See also Styling Violin plots
A histogram trace is an approximate representation of the distribution of numerical data. To construct a histogram, FiglinQ bins the range of values (i.e. divides the entire range of values into a series of automatic or custom-defined intervals), then counts how many values fall into each interval, and then represents the counts as bars, similar to the bar trace. The bins are usually specified as consecutive, non-overlapping, adjacent intervals. A histogram trace can also be normalized to display relative frequencies values in each bin.
See also Styling Histogram plots
See this page for more information on 2D histogram traces.
See this page for more information on 2D contour histogram traces.