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_abline_sample() geom_hline_sample() geom_vline_sample(): Reference lines with uncertainty: horizontal, vertical, and diagonal
geom_bar_sample() geom_col_sample() stat_count_sample(): Uncertain Bar Charts
geom_bin_2d_sample() stat_bin_2d_sample(): Uncertain heatmap of 2d bin counts
geom_boxplot_sample() stat_boxplot_sample(): An uncertain box and whiskers plot (in the style of Tukey)
geom_contour_sample() geom_contour_filled_sample() stat_contour_sample() stat_contour_filled_sample(): Uncertain 2D contours of a 3D surface
geom_count_sample() stat_sum_sample(): Uncertain Count overlapping points
geom_density_2d_sample() geom_density_2d_filled_sample() stat_density_2d_sample() stat_density_2d_filled_sample(): Uncertain contours of a 2D density estimate
geom_density_sample() stat_density_sample(): Visualise densities with Uncertainty
geom_dotplot_sample(): Dot plot with uncertainty
geom_hex_sample() stat_bin_hex_sample(): Uncertain hexagonal heatmap of 2d bin counts
geom_freqpoly_sample() geom_histogram_sample() stat_bin_sample(): Histograms and frequency polygons with uncertainty
geom_jitter_sample(): Uncertain Jittered Points
geom_crossbar_sample() geom_errorbar_sample() geom_linerange_sample() geom_pointrange_sample(): Vertical intervals: lines, crossbars & errorbars with uncertainty
geom_path_sample() geom_line_sample() geom_step_sample(): Uncertain Connected observations
geom_point_sample(): Visualise Uncertain Points
geom_polygon_sample(): Uncertain Polygons
geom_qq_line_sample() stat_qq_line_sample() geom_qq_sample() stat_qq_sample(): A quantile-quantile plot with uncertainty
geom_quantile_sample() stat_quantile_sample(): Quantile regression with uncertainty
geom_ribbon_sample() geom_area_sample() stat_align_sample(): Ribbons and area plots with uncertainty
geom_rug_sample(): Uncertain Rug plots in the margins
geom_curve_sample() geom_segment_sample(): Line segments and curves with uncertainty
geom_sf_sample(): Visualise Sf Objects with Uncertainty
geom_smooth_sample() stat_smooth_sample(): Uncertain Smooth
geom_spoke_sample(): Line segments parameterised by location, direction and distance, with uncertainty
geom_label_sample() geom_text_sample(): Uncertain Text
geom_raster_sample() geom_rect_sample() geom_tile_sample(): Plot rectangles with uncertainty
geom_violin_sample() stat_ydensity_sample(): Violin plots with uncertainty

Stats

These are the ggdibbler versions of the layer better specified by the stats in the ggplot documentation.

sample_expand() dibble_to_tibble(): Simulate outcomes from dibble to make a tibble
stat_identity_sample(): Generates a sample from a distribution
stat_ecdf_sample(): Compute uncertain empirical cumulative distributions
stat_ellipse_sample(): Compute normal data ellipses with uncertainty
stat_connect_sample(): Connect uncertain observations
stat_manual_sample(): Manually compute transformations with uncertainty
stat_summary_2d_sample() stat_summary_hex_sample(): Bin and summarise in 2d (rectangle & hexagons) with uncertain inputs
stat_summary_bin_sample() stat_summary_sample(): Summarise y values at unique/binned x with uncertainty
stat_unique_sample(): Remove duplicates (with uncertainty?)

Position

These positions allow you to nest the distribution overplotting within a different ggplot2 position adjustment

position_dodge_dodge() position_dodge_identity() position_identity_dodge(): Nested dodge positions
position_identity_identity(): Nested identity positions
position_nest(): Any combination of nested positions
position_stack_identity() position_stack_dodge(): Nested stack positions
position_subdivide(): Subdivide position aesthetic in a geometry

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 uncertain_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`
uncertain_faithfuld: 2d density estimate of Old Faithful data with uncertainty
uncertain_faithful: Old Faithful data with uncertainty
walktober: Step Counts from Walktober 2025 Challenge