
This is a link to the Bad Visualisation Shiny App source code. A live version of the app is hosted here: https://qutcds.shinyapps.io/How_to_Make_a_Bad_Visualisation/ This interactive Shiny app is intended to demonstrate some of the ways that visualisations can be created in a less than effective way, whether by accident or design. The issues covered include the selection of colours and scales, the type of chart used, and the quantity of data displayed at once. This Shiny app was originally created to accompany the talk How to Make a Really Bad Visualisation , given by Jim Hogan at the 2018 Winter School in Mathematical and Computational Biology . The source code for the app is freely available on Bitbucket . Data used in these examples is adapted from the published sources cited or randomly generated using the sampling functions provided by R. Suggestions for additional topics or offers to contribute are more than welcome. Please email us or raise an issue on the repo.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
