Barplots should use percentages instead of counts for their measure on the Y-axis
• Plots of factor data should not display arbitrary measures such as counts
• Fix: Scale counts with against their total count • Time: Within a day
Barplots do not acknowledge data missing from a simulation
• Plots of factor data tend to not include potential entries that are missing from a simualtion. Consider the case where possible values are a range from 1 to 3, however the simulation only produces the results 1, 3. 2 will be absent from the barplot
Fix: If appropriate labels are included in the “qi” function of a model, this isn’t a problem. Otherwise, we can perhaps look at the passed data.frame
3.Time: Within a day Sort integer factor data (particularly that which is categorical) properly
Sort integer factor data (particularly that which is categorical) properly
In short, ensure that 1 < 10 < 0100, even if data is character strings
Fix: Test the data.frame and categorize it by ”numeric” ”integer” ”factor” in advance. This may require us to cast the plotted data as a different data-type than is returned from the simulations
Time: Needs some research. 1-2 days (guess-timate).
4.Note: This is tricky. And requires use to know something about what
kind of data the user expects.
Barplots should use percentages instead of counts for their measure on the Y-axis • Plots of factor data should not display arbitrary measures such as counts • Fix: Scale counts with against their total count • Time: Within a day
Barplots do not acknowledge data missing from a simulation • Plots of factor data tend to not include potential entries that are missing from a simualtion. Consider the case where possible values are a range from 1 to 3, however the simulation only produces the results 1, 3. 2 will be absent from the barplot
Sort integer factor data (particularly that which is categorical) properly