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# System prepended metadata

title: 3.2 Rules of the data vizualisation game

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# 3.2 Rules of the data vizualisation game

[issue with outline](https://github.com/alan-turing-institute/rds-course/issues/21)


## What is a figure? Mapping data into aesthetics

> FIGURE: I envisage something like a big table of data, with arrows (with aesthetic mappings) showing the layers of a plot.

What is a figure? Mapping data onto aesthetics.

[our world in data datasets](https://github.com/owid/covid-19-data/tree/master/public/data/)

[all their vizualisations](https://ourworldindata.org/coronavirus-data)

[OWID data stories](https://ourworldindata.org/identify-covid-exemplars)


We will use data from [our world in data datasets](https://github.com/owid/covid-19-data/tree/master/public/data/)

## Coordinate systems and axes.

- The combination of a set of position scales and their relative geometric arrangement is called a **coordinate system** ([source](https://clauswilke.com/dataviz/coordinate-systems-axes.html)).



Cartesian:

- Total cases vs GDP:
    - stretch or compress axes
- Total cases vs total dates for a given date:
    - stretch or compress axes



non-linear:

- Plot of a linear function, and same plot with log scale
- Time series cases in the first wave (before April 2020)
    - Linear vs log

-polar coordinate:

-cyclical story of covid May to May.
    - Time series of 3 Uk countries (from https://coronavirus.data.gov.uk/details/cases)
    - Polar plot May to may
    - Binned polar plot of all Uk data
    
    

## Scaling data


Most plots do not show raw data, rather you would have transformed the data to bring clarity to your message (e.g. binning, averaging, rescaling).

When we altered the axes in the previous 


- Deaths vs cases:
    - First raw numbers, same scale (number of people)
    - Raw  numbers, adjusted scale,
    - Normalised numbers, adjusted scale.

- Agregation
    - Moving average vs raw cases data
    - Variable size bins: Europe life expectancy 
    
## Color scales.


Color as a tool to distinguish:

- US  vaccination rate and party politics in state
 
Color to represent data values: Do highlight what’s important, tell one story:
- US  vaccination rate and party politics in state



Color or opacity as a tool to highlight:


Where to get colors


## Additional dimension
- Size, Alpha


## Annotations: labels, legends, titles.

THIS SHOULD BE IN TELLING A STORY. 

- Do tell the ‘why’ and ‘how’: annotations. Good material in here: https://www.eea.europa.eu/data-and-maps/daviz/learn-more/chart-dos-and-donts
    - Use a descriptive chart title and other annotations that not only describe what is being measured, but also why the reader should care and how to read the chart. This will avoid misinterpretation and save time for the chart reader.


It is your responsibility to tell a good story. 



- Hierarchy of information

