ggdibbler

R package for implementing signal suppression in ggplot2

https://github.com/harriet-mason/ggdibbler

Repository

R package for implementing signal suppression in ggplot2

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# ggdibbler 


[![Codecov test coverage](https://codecov.io/gh/harriet-mason/ggdibbler/graph/badge.svg)](https://app.codecov.io/gh/harriet-mason/ggdibbler)


`ggdibbler` is an R package for implementing signal suppression in ggplot2. Usually, uncertainty visualisation focuses on expressing uncertainty as a distribution or probability, whereas ggdibble differentiates itself by viewing an uncertainty visualisation as a transformation of an existing graphic that incorperates uncertainty. The package allows you to replace any existing variable of observations in a graphic, with a variable of distributons. It is particularly useful for visualisations of estimates, such as a mean. You provide ggdibble with code for an existing plot, but repalace one of the variables with a distribution, and it will convert the visualisation into it's signal supression counterpart.

## Installation

You can install the development version of ggdibbler from [GitHub](https://github.com/) with:

``` r
# install.packages("pak")
pak::pak("harriet-mason/ggdibbler")
```

## Examples
Currently, the primary useage of ggdibbler is a variation on `geom_sf`, by having several alternatives to the standard fill.

```{r, message=FALSE, warning=FALSE}
library(ggdibbler)
library(ggplot2)
library(dplyr)
library(sf)
```

Typically, if we had an average estimate for a series of areas, we would simply display the average, or keep the average separate. Below is an example of this with a choropleth map.

```{r}
# Make average summary of data
toy_temp_mean <- toy_temp |> 
  dplyr::group_by(county_name) |>
  summarise(temp_mean = mean(recorded_temp))

# plot it
ggplot(toy_temp_mean) +
  geom_sf(aes(geometry=county_geometry, fill=temp_mean)) +
  scale_fill_distiller(palette = "OrRd")
```

We can use `geom_sf_sample` from the ggdibbler package to instead view each estimate as a sample of values from its sampling distribution.

```{r}
set.seed(1)
# sample map
toy_temp_dist |> 
  ggplot() + 
  geom_sf_sample(aes(geometry = county_geometry, fill=temp_dist), linewidth=0.1) + 
  geom_sf(aes(geometry = county_geometry), fill=NA, linewidth=1) +
  scale_fill_distiller(palette = "OrRd")
```


## Additions to the package
As `ggdibbler` is designed to alter existing graphic types to accept distributions as inputs there is a near infinite number of plots that could be changed with the package. At the moment the focus is on alterations to `geom_sf`, but we are happy to add any other functionality that users would like to have as a ggplot geom. If you have a suggestion, feel free to add it in the github issues.

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