Analysis and Visualization Tools

Point-and-click Tools:

Visualisation Libraries

If the point-and-click tools above are not suitable for your needs or if you prefer to customize your own visualization from scratch, you can code your own visualization with the help of the following libraries

R Libraries:

Python Libraries:

10 Principles of Effective Data Visualisation

1. Diagram First - Before you make a visual, prioritize the information you want to share, envision it, and design it. 
2. Use the Right Software - Recognize that you might need to learn a new software—or expand your knowledge of the software you already know.
3. Use an Effective Geometry and Show Data - Although seemingly straightforward, one geometry may work in more than one category, in addition to the fact that one dataset may be visualized with more than one geometry (sometimes even in the same figure).
4. Colours Always Mean Something - Color will often be the first visual information a reader gets, and with this knowledge, colour should be strategically used to amplify your visual message.
5. Include Uncertainty - Expressing uncertainty requires that readers be familiar with metrics of uncertainty and their interpretation; however, it is also the responsibility of the figure author to adopt the most appropriate measure of uncertainty.
6. Panel, when Possible (Small Multiples) - The strategy behind small multiples is that because many of the design elements are the same—for example, the axes, axes scales, and geometry are often the same—the differences in the data are easier to show.
7. Data and Models Are Different Things - Any visual of a model should be explained in the figure caption or referenced elsewhere in the document so that a reader can find the complete details on what the model visual is representing.
8. Simple Visuals, Detailed Captions - Captions should be standalone, which means that if the figure and caption were looked at independent from the rest of the study, the major point(s) could still be understood.
9. Consider an Infographic - An infographic of a study might be more effective outside of a peer-reviewed publication and in an oral or poster presentation where a visual needs to include more elements of the study but with less technical information.
10. Get an Opinion - Having one or more colleagues or people external to the study review figures will often provide useful feedback on what readers perceive, and therefore what is effective or ineffective in a visual.

Source: Midway, S. (2020). Principles of Effective Data Visualization. Patterns, 1(9), 100141. doi: 10.1016/j.patter.2020.100141