> For the complete documentation index, see [llms.txt](https://cleberjamaral.gitbook.io/mind/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cleberjamaral.gitbook.io/mind/knowledge/research/data-visualisation.md).

# data visualisation

Data visualization is a discipline created by [John Tukey](https://en.wikipedia.org/wiki/John_Tukey).

### Principles

* **Choose the right chart:** For instance, bar are good to show frequencies, absolute quantities. Lines are good to show variations over time, it can also show relative quantities, as a zoom to highlight some difference.
* **Avoid misunderstanding:** For instance, the bar must be proportional to what it represents.
* **Choose the right colour scheme:** if colours are showing important differences, so they must be easily differentiated. It is also good to avoid too much
* **Remove unnecessary information:** avoid any label and extra data. Less is more!
* **Have in mind the story the chart is supposed to tell:** at the end, it is expected to tell some story giving to the user some information. It is important to make sure the chart is delivering it.

### Navigating

* This topic is related to many disciplines among them: [machine learning](/mind/knowledge/research/machine-learning.md), [writing](/mind/knowledge/language/writing.md) and [math](/mind/knowledge/research/math.md).

### Sources

* [Drawing from data](https://www.drawingfromdata.com/) presents some challenges on data visualization and some possible solutions, for instance, how to filter and aggregate data and how to make a pairwise distance matrix.
* [The Beauty of Data Visualisation](https://medium.com/the-outlier/the-beauty-of-data-visualisation-976e2f96929c) briefly talks about the importance of this discipline giving a few examples.
* [Data is ugly](https://www.reddit.com/r/dataisugly/) and [data is beautiful](https://www.reddit.com/r/dataisbeautiful/) are reddit communities (subreddits) for discussing data visualisation
* Some beautiful customized palettes: [20 unique and memorable brand color palettes to inspire you](https://www.canva.com/learn/brand-color-palette/) and [branition](https://branition.com/colors)
* [Seaborn framework documentation](https://seaborn.pydata.org/tutorial/color_palettes.html) also brings useful information about palettes.
* [Covid-19 dashboards](https://dzone.com/articles/how-to-build-a-coronavirus-dashboard-in-java?edition=597295) in Java data visualization using [vaadin](https://vaadin.com/) an open-source framework for development of progressive web applications.
* [The 25 Best Data Visualizations of 2018](https://towardsdatascience.com/the-25-best-data-visualizations-of-2018-93643f0aad04)
* [WHAT CHARTS DO](https://medium.com/nightingale/what-charts-do-48ed96f70a74)
* [A diagram type for a niche data use case](https://medium.yaay.ch/a-diagram-type-for-a-niche-data-use-case-56114a5e8abf)