Package index • ggdibbler

Geoms

The uncertainty equivalent of the ggplot geom functions. This is how you will typically interact with ggdibbler, as it is how you usually interact with ggplot2.

geom_bar_sample() geom_col_sample(): Uncertain Bar Charts

geom_count_sample() stat_count() stat_sum_sample(): Uncertain Count overlapping points

geom_density_sample() stat_density_sample(): Visualise Densities with Uncertainty

geom_jitter_sample(): Uncertain Jittered Points

geom_path_sample() geom_line_sample() geom_step_sample(): Uncertain Connected observations

geom_point_sample(): Visualise Uncertain Points

geom_polygon_sample(): Uncertain Polygons

geom_rug_sample(): Uncertain Rug plots in the margins

geom_sf_sample(): Visualise Sf Objectjects with Uncertainty

geom_label_sample() geom_text_sample(): Uncertain Text

Stats

This is where most of the methods used to make uncertainty visualisation in ggdibbler work. These stat functions convert a distribution to a sample before running through their non-uncertainty counterpart.

stat_sample(): Generates a sample from a distribution

Scale

These scales support allow distributions to be passed to ggplot with ease. They identify the scale type needed, and allow you to personalise the limits/labels of continuous x and y axis.

scale_x_continuous_distribution() scale_y_continuous_distribution(): Position scales for continuous distributions

scale_x_discrete_distribution() scale_y_discrete_distribution(): Position scales for discrete distributions

scale_type(<distribution>): Sets scale for distributions

Data

ggdibbler comes with a handful of data sets with uncertain values to show the use of the package. Most of these datasets are ggplot2 datasets with uncertainty added in.

toy_temp_dist: A toy data set that provides data for a map with the temperature of each area represented by a random variable.

toy_temp: A toy data set that has the ambient temperature as measured by a collection of citizen scientists for each Iowa county

smaller_diamonds: An uncertain (and shrunk down) version of the diamonds data from`ggplot2`

uncertain_mpg: An uncertain version of the MPG data from `ggplot2`

uncertain_mtcars: An uncertain version of the mtcars data from base R `datasets`

uncertain_economics_long: An uncertain version of the economics data from`ggplot2`