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Each bar can also have a comment, such as "comment7=xx" to show "(xx)" after the number in bar 7. For a 2-column bar chart, the 2nd column items have prefix "col2_" such as scale maximum, col2_data_max=110, and col2_data3=67 with col2_comment3=zz. See below: "Example with two data columns". Each bar chart can be formatted typically within 1/5 ...
In the mathematical discipline of graph theory, a graph labeling is the assignment of labels, traditionally represented by integers, to edges and/or vertices of a graph. [ 1 ] Formally, given a graph G = ( V , E ) , a vertex labeling is a function of V to a set of labels; a graph with such a function defined is called a vertex-labeled graph .
A GraphML file consists of an XML file containing a graph element, within which is an unordered sequence of node and edge elements. Each node element should have a distinct id attribute, and each edge element has source and target attributes that identify the endpoints of an edge by having the same value as the id attributes of those endpoints.
Module:Chart creates bar and pie charts on Wikipedia without need for external tools; Many spreadsheet, drawing, and desktop publishing programs allow you to create graphs and export them as images. gnuplot can produce a wide variety of charts and graphs; see samples with source code at Commons. In Python using matplotlib
A bar chart or bar graph is a chart or graph that 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 vertical bar chart is sometimes called a column chart and has been identified as the prototype of charts. [1]
Graph-tool, a free Python module for manipulation and statistical analysis of graphs. NetworkX, an open source Python library for studying complex graphs. Tulip (software) is a free software in the domain of information visualisation capable of manipulating huge graphs (with more than 1.000.000 elements).
Statistical graphics have been central to the development of science and date to the earliest attempts to analyse data. Many familiar forms, including bivariate plots, statistical maps, bar charts, and coordinate paper were used in the 18th century. Statistical graphics developed through attention to four problems: [3]
As far as I know graphs use javascript just for mouseover effects. So, as a short term solution I suggest to drop the mouse over effects and return graphs as server side generated, static PNG files. No javascript, no security issues. Then on long term, rewrite graphs to generate SVG with CSS for mouseover effects, still avoiding javascript.