Add Uncertainty to Data Visualisations
Reference lines with uncertainty: horizontal, vertical, and diagonal
Uncertain Bar Charts
Uncertain heatmap of 2d bin counts
An uncertain box and whiskers plot (in the style of Tukey)
Uncertain 2D contours of a 3D surface
Uncertain Count overlapping points
Uncertain contours of a 2D density estimate
Visualise densities with Uncertainty
Dot plot with uncertainty
Uncertain hexagonal heatmap of 2d bin counts
Histograms and frequency polygons with uncertainty
Uncertain Jittered Points
Vertical intervals: lines, crossbars & errorbars with uncertainty
Uncertain Connected observations
Visualise Uncertain Points
Uncertain Polygons
A quantile-quantile plot with uncertainty
Quantile regression with uncertainty
Ribbons and area plots with uncertainty
Uncertain Rug plots in the margins
Line segments and curves with uncertainty
Visualise Sf Objects with Uncertainty
Uncertain Smooth
Line segments parameterised by location, direction and distance, with ...
Uncertain Text
Plot rectangles with uncertainty
Violin plots with uncertainty
Nested dodge positions
Nested identity positions
Any combination of nested positions
Nested stack positions
Subdivide position aesthetic in a geometry
Simulate outcomes from dibble to make a tibble
Position scales for continuous distributions
Position scales for discrete distributions
Sets scale for distributions
Connect uncertain observations
Compute uncertain empirical cumulative distributions
Compute normal data ellipses with uncertainty
Generates a sample from a distribution
Manually compute transformations with uncertainty
Bin and summarise in 2d (rectangle & hexagons) with uncertain inputs
Summarise y values at unique/binned x with uncertainty
Remove duplicates (with uncertainty?)
A 'ggplot2' extension for visualising uncertainty with the goal of signal suppression. Usually, uncertainty visualisation focuses on expressing uncertainty as a distribution or probability, whereas 'ggdibbler' differentiates itself by viewing an uncertainty visualisation as an adjustment to an existing graphic that incorporates the inherent uncertainty in the estimates. You provide the code for an existing plot, but replace any of the variables with a vector of distributions, and it will convert the visualisation into it's signal suppression counterpart.
Useful links