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Something like this?

While my solution isn't exactly what you describe under Option 2, it is close. We use combineWidgets() to create a grid with a single column and a row height where one graph covers most of the screen height. We squeeze in a link between each widget instance that scrolls the browser window down to show the following graph when clicked.

Let me know if this is working for you. It should be possible to automatically adjust the row size according to the browser window size. Currently, this depends on the browser window height being around 1000px.

I modified your code for the graph creation slightly and wrapped it in a function. This allows us to create 25 different-looking graphs easily. This way testing the resulting HTML file is more fun! What follows the function definition is the code to create a list of HTML objects that we then feed into combineWidgets().

I think you might be slightly overcomplicating things. Ideally, you'd just want a single key drawing method for the whole layer. However, because you're using a Stat to do the majority of calculations, this becomes hairy to implement. In my answer, I'm avoiding this.

Let's say I'd want to use a geom-only implementation of such a layer. I can make the following (simplified) class/constructor pair. Below, I haven't bothered width_scale or height_scale parameters, just for simplicity.

We can use split

I suggest you first replace every NA values in b with 0 and then use inner_join to merge the result with the corresponding Site values in a. You could then replace non-NA values of a with their corresponding values in b leaving NA values in a intact. In the end we bind the modified data frame with a subset of a whose Site values are not present in b.

Maybe this is not so elegant but here is a way:

It may be faster with a join

You can do calculations within a function for the x and y values to construct the ggplot which extends the circle all the way round and gives labels correct heights.

I've adapted a function to work with other datasets. This takes a dataset in a tidy format, with:

• a 'year' column
• one row per 'event'
• a grouping variable (such as country)

I've used Nobel laurate data from here as an example dataset to show the function in practice. Data setup:

We could use match with sort