wernerdaehn edited a comment on issue #12724: URL: https://github.com/apache/superset/issues/12724#issuecomment-824606806
@rusackas By all means, Evan! More than happy to contribute. As a preliminary start, here is my thinking: According to explanatory statistics there are four types of scales, ordered by capabilities: - Nominal: Only useful calculation is around counting. Example: Color. - Ordinal: has in addition an order. Example: User satisfaction 1-10. It is clear that 1 is better than 2 but a difference between 1-and-3 does not have the same meaning as 8-and-10. - Interval: has in addition a useful meaning of distance between two values. Example: Today it is 5°C warmer than yesterday. - Ratio: in addition it has a value of 0 and hence absolute comparisons do make sense. Example: Revenue was 10% higher. If somebody wants to visualize a nominal value and a ratio value, e.g. Revenue per Color, a Bar chart is one of the few that makes sense. For two ratio values, e.g. revenue per customer-age a scatter plot is suited. The next type of decision is the number of axis. - If there is a single nominal axis, e.g. gender, the pie chart might be interesting to show the number of customers per gender. - If I want to visualize the revenue compared to the previous year revenue per country and time, I need a chart type that can show a ratio scale, a list of regions and the development over time. A geomap colored as a heatmap and a time animation would do the trick. The type of axis can further be refined: - time: year, month, day, timestamp, week, weekday - geo - hierarchy One side effect of these types is how to render missing values. A country without revenue should still be present (geomap) or not (bar chart). A month without revenue should still be shown, you do not want to see just 11 months. The number of distinct values of nominal and ordinal scales is an important decision point as well. A Pie chart with 5000 categories might not be the best suited chart type. Showing above revenue per country over time could be shown as line chart with one line per country. Excellent for comparisons between countries unless you have 100 countries and 100 lines hence. The final decision type is the purpose of the visualization: - Comparison - Relationship - Proportion - Percent of the whole - Location - Distribution... The nice thing is that we can start small and grow the solution. Initially we just categorize each column of the result set into the scale type and each chart has the information which scale type it allows for what axis. That by itself would reduce the list of charts to offer by a lot. And from that we can grow and grow with the available metadata on the data and the chart info. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
