The main purpose of a chart is to give an intuitive and easily understandable visual form for the information originally expressed as a set of numerical values. The information we seek, the form of the available numerical data, and various personal visual preferences all affect on the choice of what kind of chart should be used in any given situation. Davisor Chart offers a wide range of different chart types to choose from, and even wider range of visual detail attributes to affect the final outcome. Together, the possibilities to visualize any given data set are limitless.
To give you quickly a feeling of what to expect from Davisor Chart, the following cavalcade of chart examples shows what kind of different principal visualization styles are at you disposal. You will later learn how each detail in each example can be customized to meet the exact visual style of your own choosing.
2D charts are your basic bread-and-butter visualization tools: they produce compact, small, efficient, and clear graphs from various simple and complex data sets. With tasteful choices of colors and other visualization attributes, these basic charts may also be made to look very good, worthy for any presentation or publication.
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3D charts are your heavy duty, impress everybody visualization tools: they produce larger, heavier, and more impressive graphs from exactly the same data sets as their 2D counterparts. Finetuning the appearance of 3D charts will however often require somewhat more work than correspondinging 2D charts, and the resulting images tend to need to be bigger, too, but the final results are then all the more impressive. Please note that 3D Chart extension must be purchased separately.
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Although the freedom to choose is often a good thing in general, there are circumtances where infinite visualization alternatives may also become a burden. In particular, even without making any choices between small visual details like color hues and font faces, there are still literally thousands of ways to visualize a given data set. Fortunately, the nature and organization of available data often suggests what kind of chart type and visualization strategy should be used to visualize it.
The principal characteristics of any data set are:
Simple data series with little interaction across the series are often best visualized with simple line charts, while groups of similar series are well expressed with column and bar charts. More complex data series that depend on each other may be visualized with cumulative versions of the above chart types, while for really complex cases there are combo and bubble charts. For two-dimensional coordinate data there is then the scatter chart, and for the relative distribution of limited resources the pie chart. Finally, candle and OHLC charts provide popular specialized methods to visualize stock market data.
The task of choosing between different chart types is made significantly easier by the fact that all Davisor Chart types are very flexible on what kind of data they can accept, and very consistent on how they use their various visualization attributes. In particular, when choosing between different chart types, it is often possible to alternate between different chart types without the need to readjust any previously chosen visualization parameters, and still continue to get visually consistent and meaninfull results.
While most chart and data set types are mutually compatible, it is however still important to remember that the chart types that can be meaninfully applied for any particular visualization task depend primarily on the nature of the data set to be visualized. In particular, some data and visualization method combinations will always remain unusefull or impossible. For example, a scatter chart really does require coordinate pairs as input, while a pie chart can't visualize the same data in any meaninfull way. The old saying that tells to use the right tool to match the task is therefore still very relevant here.
The fewer there are data series, elements, and relationships between the elements, the more understandable the visualization outcome will often be. In particular, the more data is forced inside a single visualization, the less information can usually be understood from it. A good general principle is therefore to always keep your charts as simple and clear as possible. However, sometimes the whole point of the visualization task is to make some sense of a very complex data set. In these cases distributing the data among several distinct charts or preprosessing the data into some simpler form may help to get more information out of it.
Davisor Chart data sources and data processing framework provide some basic tools to divide and conquer your data. For more information, please see Davisor Chart Data Sources documentation.
