# Get started with ggdibbler

## TLDR - What is `ggdibbler`?

You can pass random variables to ggplot now. Any geom, any aesthetic (except groups and facets), they all accept random variables. we make no judgements on your plot, we am not your rulers. Whatever your plot is, `ggdibbler` will allow you to pass a random variable to it. The rest of the vignette will explain how this works and the philosophy behind it. I know you are itching to get to the package, so I will quickly answer the main question I know you are dying to ask: Yes, I DO accept cash gifts as expressions of gratitude, cheers.

# The unfamiliar stuff

This is a brief explanation of the things you might not already be familiar with when learning about `ggdibbler`.

## Using distributional

`ggdibbler` is entirely built upon the R package `distributional` which allows you to store distributions in a data frame as distribution objects. The package is the foundation of `ggdibbler` because it perfectly embodies the concept of a random variable as a unit. The package is also shockingly flexible and I am yet to come across a case that cannot be represented by `distributional`. If you have uncertainty that can quantified then it can be represented in a vector and therefore can be visualised by `ggdibbler`.

- Bounded values? use `dist_uniform(lowerbound, upperbound)`
- Uncertain predicted classes? use `dist_categorical`
- Want to sample values from a specific vector of values (such as a posterior)? use `dist_sample`
- Classic empirical distribution? Go buck wild with `dist_normal`, `dist_poission`, and the like

There are also flexibilities that go beyond just the distribution you choose.

- You can transform distributions using standard arithmetic (in sensible situations) or with `dist_transformed`
- You can make mixed distributions using `dist_mixture` and truncate distributions with `dist_truncated`
- You can make your own bespoke distributions (although I am not sure how difficult that is, I have never had to do it).
- You can even have deterministic (there is a deterministic distribution) and distributional objects in the same column.

To give an example of the transformations, I just ripped the code below from one of Mitch’s talks on the package:

    dist_normal(1,3)
    #> <distribution[1]>
    #> [1] N(1, 9)

    # if transformation exists
    exp(3 * (2 + dist_normal(1,3)))
    #> <distribution[1]>
    #> [1] lN(9, 81)

    # if transformation doesn't exist uses dist_transformed
    (3 * (2 + dist_normal(1,3)))^2
    #> <distribution[1]>
    #> [1] t(N(9, 81))

All of the data sets in `ggdibbler` already have the distributional component added, but making a distribution vector is shockingly easy. Below is an example where we make normally distributed estimates by wrapping our typical estimate and standard error calculations in a `dist_normal` function.

    toy_temp_eg <- toy_temp |>
      group_by(county_name) |>
      summarise(temp_dist = dist_normal(mu = mean(recorded_temp),
                                        sigma = sd(recorded_temp)/sqrt(n())))

    print(head(toy_temp_eg))
    #> Simple feature collection with 6 features and 2 fields
    #> Geometry type: MULTIPOLYGON
    #> Dimension:     XY
    #> Bounding box:  xmin: 406217.2 ymin: -466551.1 xmax: 722461.8 ymax: -128604.1
    #> Projected CRS: NAD27 / US National Atlas Equal Area
    #> # A tibble: 6 × 3
    #>   county_name    temp_dist
    #>   <chr>             <dist>
    #> 1 Adair County N(30, 0.82)
    #> 2 Adams County    N(30, 1)
    #> 3 Allamakee C…  N(26, 0.3)
    #> 4 Appanoose C… N(23, 0.69)
    #> 5 Audubon Cou…  N(28, 0.8)
    #> 6 Benton Coun…    N(29, 2)
    #> # ℹ 1 more variable: county_geometry <MULTIPOLYGON [m]>

Now, I could spend all day on this, but ultimately, this is not a vignette for distributional (although they really should make one). It is definitely worth having a play around in `distributional` to get a feel for the package if you have never used it before.

## Using nested positions

Another component of `ggdibbler` that is not always immediately clear to users is the nested position system. This system is required to maintain the continuous mapping property that makes `ggdibbler` such a useful uncertainty visualisation system.

The syntax of the nested positions is `mainposition_nestedposition`, where the main position is what was in the limiting ggplot, and the nested position manages the over plotting caused by sampling from the distribution.

To get an idea of how the nested positions work, we have included a few examples using the identity, dodge and stack positions and am showing them alongside their `ggplot2` deterministic counterpart. The first column is the original ggplot we are going to add uncertainty to, the second and third column is the effect of the over plotting being managed with an identity (with decreased alpha) and dodge positions respectively.

    # ggplot IDENTITY
    p1 <- ggplot(mpg, aes(class)) +
      geom_bar_sample(aes(fill = drv),
                      position = "identity", alpha=0.7)+
      theme_few() +
      theme(legend.position="none", aspect.ratio = 1) +
      ggtitle("ggplot2: identity")

    # ggdibbler identity
    p2 <- ggplot(uncertain_mpg, aes(class)) +
      geom_bar_sample(aes(fill = drv), alpha=0.1,
                      position = "identity_identity")+
      theme_few() +
      theme(legend.position="none", aspect.ratio = 1)+
      ggtitle("identity_identity")

    p3 <- ggplot(uncertain_mpg, aes(class)) +
      geom_bar_sample(aes(fill = drv), alpha=0.7,
                      position = "identity_dodge")+
      theme_few() +
      theme(legend.position="none", aspect.ratio = 1)+
      ggtitle("identity_dodge")

    # ggplot dodge
    p5 <- ggplot(mpg, aes(class)) +
      geom_bar_sample(aes(fill = drv),
                      position = position_dodge(preserve="single"))+
      theme_few() +
      theme(legend.position="none", aspect.ratio = 1)+
      ggtitle("ggplot: dodge")

    p6 <- ggplot(uncertain_mpg, aes(class)) +
      geom_bar_sample(aes(fill = drv), alpha=0.1,
                      position = "dodge_identity")+
      theme_few() +
      theme(legend.position="none", aspect.ratio = 1)+
      ggtitle("dodge_identity")

    p7 <- ggplot(uncertain_mpg, aes(class)) +
      geom_bar_sample(aes(fill = drv),
                      position = "dodge_dodge")+
      theme_few() +
      theme(legend.position="none", aspect.ratio = 1)+
      ggtitle("dodge_dodge")

    # ggplot stack
    p9 <- ggplot(mpg, aes(class)) +
      geom_bar_sample(aes(fill = drv),
                      position = "stack")+
      theme_few() +
      theme(legend.position="none", aspect.ratio = 1)+
      ggtitle("ggplot2: stack")

    p10 <- ggplot(uncertain_mpg, aes(class)) +
      geom_bar_sample(aes(fill = drv), alpha=0.1,
                      position = "stack_identity")+
      theme_few() +
      theme(legend.position="none", aspect.ratio = 1)+
      ggtitle("stack_identity")

    p11 <- ggplot(uncertain_mpg, aes(class)) +
      geom_bar_sample(aes(fill = drv),
                      position = "stack_dodge")+
      theme_few() +
      theme(legend.position="none", aspect.ratio = 1)+
      ggtitle("stack_dodge")

    (p1 | p2 | p3 ) / (p5 | p6 | p7) / (p9 | p10 | p11)

# Examples

## Working directly with model output

Sometimes you will get lucky and the modelling package you are using will directly spit out a `distributional` objects. This is the case in a package such as `fable`. In this case, you can literally just directly visualise the distributions with zero pre-processing

    forecast <- as_tsibble(sunspot.year) |>
      model(ARIMA(value)) |>
      forecast(h = "10 years")

    ggplot(forecast) +
      geom_line_sample(aes(x = index, y = value),
                           times=100, alpha=0.5) +
      theme_few()

This is not a common occurrence, but when it occurs the workflow is so seamless that uncertainty visualisation becomes as easy as visualising regular data. The more R programs that spit out a distribution directly, the easier it will be for you to visualise uncertainty. I cannot control this, but hopefully being able to seamlessly visualise the distributions will make transparency in predictions and model outputs seamless to implement into any data analysis workflow.

## A basic `geom_abline`

The most basic estimate we can have, is the estimate for a simple linear regression. A simple linear regression is actually used in the `ggplot2` documentation as the example for `geom_abline`.

    # plot data
    p <- ggplot(mtcars, aes(wt, mpg)) + geom_point() + theme_few()
    p

    # Calculate slope and intercept of line of best fit
    # get coef and standard error
    summary(lm(mpg ~ wt, data = mtcars))
    #>
    #> Call:
    #> lm(formula = mpg ~ wt, data = mtcars)
    #>
    #> Residuals:
    #>     Min      1Q  Median      3Q     Max
    #> -4.5432 -2.3647 -0.1252  1.4096  6.8727
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)  37.2851     1.8776  19.858  < 2e-16 ***
    #> wt           -5.3445     0.5591  -9.559 1.29e-10 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 3.046 on 30 degrees of freedom
    #> Multiple R-squared:  0.7528, Adjusted R-squared:  0.7446
    #> F-statistic: 91.38 on 1 and 30 DF,  p-value: 1.294e-10

    # ggplot, just error estimate
    p1 <- p + geom_abline(intercept = 37, slope = -5)
    # ggdibbler for coef AND standard error
    p2 <- p + geom_abline_sample(intercept = dist_normal(37, 1.8), slope = dist_normal(-5, 0.56),
                           times=30, alpha=0.3)
    p1 + p2

A fun aspect of this approach, is that we can simultaneously see the impact of the variance in the intercept and the slope.

## A more complicated case with geom_sf

Lets work through a longer and more difficult case of uncertainty visualisation, that involves some actual data manipulation, statistical thinking, and a somewhat complicated visualisation; a spatial visualisation with an uncertain fill. To look at this, we are going to use one of the example data sets that comes with `ggdibbler`,`toy_temp`.

The `toy_temp` data set is a simulated data set that represents observations collected from citizen scientists in several counties in Iowa. Each county has several measurements made by individual scientists at the same time on the same day, but their exact location is not provided to preserve anonymity. Different counties can have different numbers of citizen scientists and the temperature measurements can have a significant amount of variance due to the recordings being made by different people in slightly different locations within the county. Each recorded temperature comes with the county the citizen scientist belongs to, the temperature recording the made, and the scientist’s ID number. There are also variables to define spatial elements of the county, such as it’s geometry, and the county centroid’s longitude and latitude.

    glimpse(toy_temp)
    #> Rows: 990
    #> Columns: 6
    #> $ county_name      <chr> "Lyon County", "Dubuque County", "Crawford County", "…
    #> $ county_geometry  <MULTIPOLYGON [m]> MULTIPOLYGON (((274155.2 -1..., MULTIPOL…
    #> $ county_longitude <dbl> 306173.3, 746092.2, 381255.2, 696287.1, 729905.9, 306…
    #> $ county_latitude  <dbl> -172880.7, -239861.5, -318675.9, -153979.0, -280551.9…
    #> $ recorded_temp    <dbl> 21.08486, 28.94271, 26.39905, 27.10343, 34.20208, 20.…
    #> $ scientistID      <chr> "#74991", "#22780", "#55325", "#46379", "#84259", "#9…

While it is slightly difficult, we can view the individual observations by plotting them to the centroid longitude and latitude (with a little jitter) and drawing the counties in the background for referee.

    # Plot Raw Data
    ggplot(toy_temp) +
      geom_sf(aes(geometry=county_geometry), fill="white") +
      ggtitle("ggdibbler some error") +
      geom_jitter(aes(x=county_longitude, y=county_latitude, colour=recorded_temp),
                  width=5000, height =5000, alpha=0.7) +
      scale_colour_distiller(palette = "OrRd") +
      theme_map()

Typically, we would not visualise the data this way. A much more common approach would be to take the average of each county and display that in a choropleth map, displayed below.

    # Mean data
    toy_temp_mean <- toy_temp |>
      group_by(county_name) |>
      summarise(temp_mean = mean(recorded_temp))

    # Plot Mean Data
    ggplot(toy_temp_mean) +
      ggtitle("ggdibbler some error")+
      scale_fill_distiller(palette = "OrRd") +
      geom_sf(aes(geometry=county_geometry, fill=temp_mean), colour="white") +
      theme_map()

This plot is fine, but it does loose a key piece of information, specifically the understanding that this mean is an estimate. That means that this estimate has a sampling distribution that is invisible to us when we make this visualisation.

We can see that there is a wave like pattern in the data, but sometimes spatial patterns are not the result of significant differences in population, and may disappear if we were to include the variance of the estimates. We can calculate the estimated standard error alongside the mean.

    # Mean and variance data
    toy_temp_est <- toy_temp |>
      group_by(county_name) |>
      summarise(temp_mean = mean(recorded_temp),
                temp_se = sd(recorded_temp)/sqrt(n()))

Getting an estimate along with its variance is also a common format governments supply data. Just like in our citizen scientist case, this if often done to preserve anonymity.

The problem with this format of data, is that there is no way for us to include the variance information in the visualisation. We can only visualise the estimate and its variance separately. So, instead of trying to use the estimate and its variance as different values, we combine them as a single distribution variable.

    # Distribution
    toy_temp_dist <- toy_temp_est |>
      mutate(temp_dist = dist_normal(temp_mean, temp_se)) |>
      select(county_name, temp_dist)

    # Plot Distribution Data
    ggplot(toy_temp_dist) +
      geom_sf_sample(aes(geometry=county_geometry, fill=temp_dist),
                     times=50, linewidth=0) +
      scale_fill_distiller(palette = "OrRd")  +
      theme_map()

To maintain flexibility, the `geom_sf_sample` does not highlight the original boundary lines, but that can be easily added just by adding another layer.

    ggplot(toy_temp_dist) +
      geom_sf_sample(aes(geometry = county_geometry, fill=temp_dist),
                     linewidth=0, times=50) +
      scale_fill_distiller(palette = "OrRd") +
      geom_sf(aes(geometry = county_geometry),
              fill=NA, linewidth=1, colour="white") +
      theme_map()

By default, `geom_sf_sample` will subdivide the data. It is unlikely that you will ever notice this default, as the random part in a geom_sf is almost always the fill, but in the unlikely event the random object is the SF part, you will need to change the position to `"identity"` and use `alpha` to manage over plotting. Alternatively, making an animated plot (detailed below) also requires the position to be changed to `"identity"`. Note that unlike `geom_sf_sample`, `geom_polygon_sample` does not have subdivide turned on by default.

Subdivide is the only position that is specifically created for `ggdibbler`. It was inspired by the pixel map used in `Vizumap`. All the other position systems used are nested versions of the existing `ggplot2` positions.

## What about animated plots?

Yup, you can do that too. It’s actually very simple. Hypothetical outcome plots have been suggested as an uncertainty visualisation technique, and they are also easily implemented in `ggdibbler`.

In a HOPs plot, it seems that animations replace our distribution specific position adjustment and the continuous mapping theorem still holds. As the variance of each random variable approaches 0, the difference between the frames approaches zero until the animation looks like a single static plot.

Implementing the code is trivially easy when we just have the animation run through every draw using the `drawID` variable (which is exactly what we do in the other position adjustments).

    # bar chart
    hops <- ggplot(uncertain_mpg, aes(class)) +
      geom_bar_sample(aes(fill = drv),
                      position = "stack_identity", times=30) +
      theme_few() +
      transition_states(after_stat(drawID))

    animate(hops, renderer =  gifski_renderer())

## What if my plot uses ggplot2 extensions?

If you represent uncertainty as a distributional object, use the `sample_expand` function, and group by `drawID`, you can apply the signal suppression methods to literally any graphic. It won’t always work as nicely as `ggdibbler` (please download the package, it looks good on my CV and I need a job) and you can’t use the nested position system, but it works well enough. Writing extensions that use `ggdibbler` is covered in a separate vignette.

This section will go through an example with `ggraph` and uncertain edges. So you have this random data set.

    set.seed(10)
    uncertain_edges <- tibble::tibble(from = sample(5, 20, TRUE),
                        to = sample(5, 20, TRUE),
                        weight = runif(20)) |>
      dplyr::group_by(from, to) |>
      dplyr::mutate(to = distributional::dist_sample(list(sample(seq(from=max(1,to-1),
                                                     to = min(to+1, 5)),
                                          replace = TRUE)))) |>
      ungroup()

    head(uncertain_edges)
    #> # A tibble: 6 × 3
    #>    from        to weight
    #>   <int>    <dist>  <dbl>
    #> 1     3 sample[3] 0.761
    #> 2     1 sample[2] 0.573
    #> 3     2 sample[3] 0.448
    #> 4     4 sample[3] 0.0838
    #> 5     3 sample[2] 0.219
    #> 6     2 sample[3] 0.0756

You need to apply `sample_expand` before you convert the data to a graph, because `ggraph` is all totalitarian about what it will keep in its graph data set, and it doesn’t allow distributions.

    graph_sample <- uncertain_edges |>
      sample_expand(times=50) |>
      as_tbl_graph()

Now, the ideal visualisation would allow us to add transparency with a small amount of jitter to straight lines. That doesn’t seem to be possible in `ggraph` (as far as I can tell). It seems that, similar to `ggplot2`, the lines are actually made up of many individual points, and the line geometry simply interpolates between them. When it applies the jitter, it does it to each point, rather than the line as a whole. So, adding the jitter does work, it just doesn’t work exactly as you might expect. It does produce an uncertainty visualisation, though.

    jitter = position_jitter(width=0.01, height=0.01)
    ggraph(graph_sample, layout = 'fr', weights = weight) +
      geom_edge_link(aes(group=drawID), position=jitter, alpha=0.1,
                     linewidth=0.05) +
      geom_node_point(size=5)

You can mess around with alpha, linewidth, and the times argument in the `sample_expand` function to get something you are happy with. There are some other weird over plotting edge things you can utilise to make the uncertainty implicitly represented by some other aesthetic (like thickness), but in general the approaches all look similar (and convey similar information)

    # uncertainty indicated by transparency
    ggraph(graph_sample, layout = 'fr', weights = weight) +
      geom_edge_link(aes(group=drawID), alpha=0.005,
                     linewidth=2) +
      geom_node_point()

    # Thickness = probability of an edge (thicker = more probable)
    ggraph(graph_sample, layout = 'fr', weights = weight) +
      geom_edge_parallel0(aes(group=drawID),
                          sep = unit(0.05, "mm"), alpha=0.3, linewidth=0.1) +
      geom_node_point(size=15)