For information to be passed into ggplot2, or any visualisation software, it usually needs to be expressed as data. This restriction prevents us from visualising inputs that are too uncertain to be expressed as single data values. This can include things like estimates, model predictions, bounded values or observations with large measurement error.
Now, users can simply replace a vector of data with a vector of random variables created using distributional and visualise these types of inputs using ggdibbler. Any quantifiable uncertainty can be expressed as a distribution, and any distribution can be passed to an existing ggplot2 geom with ggdibbler. Users are not limited by data type, the data can be continuous or discrete, sf objects, character or factors, or even other distributions. You are also not limited to any type of distribution, theoretical, empirical, truncated, and mixed distributions are all allowed by distributional and therefore accepted by ggdibbler. No matter what you pass, ggdibbler will incorporate the uncertainty into your graphic as noise and allows you to see how the variation may change the conclusions you take away from your graphics.
The power of ggdibbler is in its simplicity and flexibility. There is no need to learn specific package syntax or new functions and you can pass a distribution to ANY combination of aesthetics in ggplot2. It is a universal, exploratory tool for uncertainty visualisation.
Installation
You can install the stable version of ggdibbler from CRAN with:
# install.packages("pak")
install.packages("ggdibbler")or you can install the development version of ggdibbler from GitHub with:
# install.packages("pak")
pak::pak("harriet-mason/ggdibbler")Examples
In essence, ggdibbler makes visualising uncertainty easy, as you just replace the geom_* with geom_*_sample and replace your deterministic variable with a distributional variable, and you get an uncertainty visualisation.
You will often run into an overplotting problem when visualising uncertainty, so using a jitter or adjusting the alpha value will allow you to see all outcomes of the distribution. Otherwise you will only see the final draw.
Below are some examples of ggdibbler code alongside their uncertain ggplot2 counterpart. The only parameters added by ggdibbler are the times argument, which decides how many samples to draw from the distribution, and the seed argument which allows you to keep the same random draw across multiple layers.
library(distributional)
library(dplyr)
library(sf)
library(ggplot2)
library(ggdibbler)
library(patchwork)
set.seed(1343)
p1 <- ggplot(faithfuld, aes(waiting, eruptions, z = density)) +
ggtitle("ggplot2") +
geom_contour()
p2 <- ggplot(uncertain_faithfuld, aes(waiting, eruptions, z = density0))+
ggtitle("ggdibbler") +
geom_contour_sample(alpha=0.2)
p1 + p2
Overlapping positions in other geom can be fixed with standard position adjustments, such as position = "identity_dodge" where the position of the original plot is an identity, and the position adjustment specific to the draws is a dodge.
p1 <- ggplot(faithfuld, aes(waiting, eruptions)) +
geom_raster(aes(fill = density)) +
ggtitle("ggplot2")+
theme(legend.position = "bottom")
p2 <- ggplot(uncertain_faithfuld, aes(waiting, eruptions)) +
geom_raster_sample(aes(fill = density)) +
ggtitle("ggdibbler some error")+
theme(legend.position = "bottom")
p3 <- ggplot(uncertain_faithfuld, aes(waiting, eruptions)) +
geom_raster_sample(aes(fill = density2)) +
ggtitle("ggdibbler more error")+
theme(legend.position = "bottom")
p1 + p2 + p3
You can mix and match positions to get some interesting looking graphics that all converge to the same ggplot2 plot.
p1 <- ggplot(mpg, aes(class)) +
geom_bar_sample(aes(fill = drv),
position = "stack")+
theme(legend.position="none")+
ggtitle("stack")
p2 <- ggplot(uncertain_mpg, aes(class)) +
geom_bar_sample(aes(fill = drv), alpha=0.15,
position = "stack_identity")+
theme(legend.position="none")+
ggtitle("stack_identity")
p3 <- ggplot(uncertain_mpg, aes(class)) +
geom_bar_sample(aes(fill = drv),
position = "stack_dodge")+
theme(legend.position="none")+
ggtitle("stack_dodge")
p1 | p2 | p3
The only geoms that implement new positioning (i.e. not nested versions of existing ggplot2 positions) are geom_sf_sample, and geom_pollygon_sample. These geometries use the subdivide position which is inspired by the pixel map implemented in Vizumap.
# Make average summary of data
toy_temp_mean <- toy_temp |>
dplyr::group_by(county_name) |>
summarise(temp_mean = mean(recorded_temp))
# plot it
p1 <- ggplot(toy_temp_mean) +
geom_sf(aes(geometry=county_geometry, fill=temp_mean), linewidth=0.7) +
scale_fill_distiller(palette = "OrRd") +
labs(fill="temp")+
ggtitle("ggplot2")+
theme(legend.position = "bottom")
# sample map
p2 <- toy_temp_dist |>
ggplot() +
geom_sf_sample(aes(geometry = county_geometry, fill=temp_dist), linewidth=0, times=50) +
geom_sf(aes(geometry = county_geometry), fill=NA, linewidth=0.7) +
scale_fill_distiller(palette = "OrRd") +
labs(fill="temp")+
ggtitle("ggdibbler")+
theme(legend.position = "bottom")
p1+p2
Limitations of the Package
There are three primary limitations of the software to keep in mind. First, it only allows you to visualise the distribution as a sample. We are planning to expand this to quantiles in later versions but it is not currently an option. Second, if multiple distributions are passed, they are assumed to be independent. This is actually not as much of a limitation as people think, there are often a simple work around in most cases, but it isn’t as frictionless as we would like. Finally, all desired scale and position functions are not always available as we built the minimum required level of nesting before this release. This is another feature we hope to build up in the future.
Future additions to the package
Future plans for ggdibbler are tracked in the Github issues. If you have a suggestion or bug, please let us know!