1975 | 1975 - Modelando Domínios Ricos | Architecture library

I think below function will solve your requirement:

IIUC:

You could multiply the proportions by a fixed amount (say 8, though you can use whatever number suits you to make your data clear), and use scale_size_identity. This ensures the size of the dots is always proportional to their value.

If you want to round your data before graphing, round the data in the data frame before making the ggplot object, as @benson23 advised.

Like @Bas advised, you can modify the hovertemplate. You will need to do this for each trace (one for each geom). It doesn't have to be exactly as he has written this, you could write "Year: %{x}
Count of Issues: %{y}" for p$x$data[[2]], which is the geom_point layer (or trace when it is plotly).

I benefit from knowing that you wanted custom labels, either way. If you're going to round the labels, especially custom labels, round them when you create the labels.

I've added text to each geom_. (I used round, but format would have worked there, as well.)

I also added tooltip = "text" to the ggplotly() call (easy to forget that part!).

Use cummean. Since you're first value is NA, you can group_by NA values and country, and apply the cummean.

ggplotly is an amazing tool, but it doesn't cover everything.

If you want a secondary axis on your plot, you have to add it. (At least as far as I know.)

I used plotly_json(ggplotly(p)) to capture content like ticklen and tickcolor so the two axes would match.

I started with adding a trace and making it transparent., then I added a new margin call and yaxis2.

Here's a way to do it using data.table::rleid and dplyr grammar:

There seems to be something wrong with the formulation. Just looking at the formulas in the image, the last constraint for i=I-1=39-1=38 sums z elements (38-1)*4 + 1 + 6, (38-1)*4 + 2 + 6, (38-1)*4 + 3 + 6 and (38-1)*4 + 4 + 6 which is elements 155 156 157 158 but z goes from 1 to 4*39 and so has only 156 elements. Furthermore not all the z values participate in a constraint.

Given the problems cited let us change the problem to o ne that makes sense and assume we want to minimize the sum of the squares of the successive differences of the I*J elements of z subject to the first 4 elements of z summing to Cal[1, 2], the next 4 summing to Cal[2, 2], and so on up to the last 4 elements of z summing to Cal[39, 2]. In that case we can write the Aeq constraint matrix as a block diagonal matrix using the kronecker product shown. We ignore K. (Cal is shown reproducibly in the Note at the end.)

Use DataFrame.explode for column instead lists, so possible subtract years by Series.dt.year, last aggregate lists: