introducing vizscorer: a bot advisor to improve your ggplot plots How to measure a good plot? Preparing a training dataset of plots How to train Machine Learning to recognize a good plot? Can Machine learning talk back to humans? Putting all together: vizscorer and the scorer_bot Where to go from here and how to help introducing vizscorer: a bot advisor to improve your ggplot plots One of the most frustrating issues I face in my professional life is the plentitude of ineffective reports generated within my company.
theory of data graphics maximise data-ink ratio, within reason maximise data density and the size of the data matrix, within reason treat graphics as paragraphs and shape them appropriately integrity of data graphics always show data in their context try to produce a small lie factor show data variation, not design variation use as many dimensions as the number of dimensions in your data how to apply Tufte’s principles in R I have recently completed a great reading: Edward Tufte’s The visual display of quantitative information.
this short post is exactly what it seems: a showcase of all ggplot2 themes available within the ggplot2 package. I was doing such a list for myself ( you know that feeling …“how would it look like with this theme? let’s try this one…”) and at the end I thought it could have be useful for my readers. At least this post will save you the time of trying all differents themes just to have a sense of how they look like.