# Real life example

## Real Data

When dealing with new visualizations, it’s always important to assess their feasibility in real-life scenarios. Pie or donut charts can effectively display the structure of observable cases. A nested donut chart can handle a two-level hierarchy, displaying a higher-level overview as the internal donut and a more granular level as the external donut. The key idea is to arrange and align the internal and external levels.

To illustrate this concept, I will use GDP data obtained from The World Bank using the `wbstats::wb_data()` function. The indicator `NY.GDP.MKTP.PP.CD` represents Purchasing Power Parity GDP in current international dollars. Each country’s GDP is associated with its name and region, representing the aforementioned two-level hierarchy.

Additionally, I aim to showcase prominent ggplot2 features such as faceting and color palettes, seamlessly incorporated into the donutsk package.

## Data Preparation

For insightful charts, it’s essential to differentiate crucial data from less significant data. Using quantiles is a good approach to define thresholds for categorizing low GDP values into a special category, such as “Other”:

    library(donutsk)
    #> Loading required package: ggplot2
    library(dplyr)
    #>
    #> Attaching package: 'dplyr'
    #> The following objects are masked from 'package:stats':
    #>
    #>     filter, lag
    #> The following objects are masked from 'package:base':
    #>
    #>     intersect, setdiff, setequal, union

    GDP1 <- filter(GDP, date %in% c(2001, 2022)) |>
      group_by(date) |>
      mutate(country = if_else(GDP > quantile(GDP, .9), country, "Other")) |>
      group_by(date, region, region_ISO, country) |>
      summarise(GDP = sum(GDP), .groups = "drop")

    GDP1
    #> # A tibble: 54 × 5
    #>     date region                region_ISO country         GDP
    #>    <dbl> <chr>                 <chr>      <chr>         <dbl>
    #>  1  2001 East Asia & Pacific   EAS        Australia   5.33e11
    #>  2  2001 East Asia & Pacific   EAS        China       4.08e12
    #>  3  2001 East Asia & Pacific   EAS        Indonesia   1.06e12
    #>  4  2001 East Asia & Pacific   EAS        Japan       3.55e12
    #>  5  2001 East Asia & Pacific   EAS        Korea, Rep. 9.34e11
    #>  6  2001 East Asia & Pacific   EAS        Other       1.68e12
    #>  7  2001 Europe & Central Asia ECS        France      1.69e12
    #>  8  2001 Europe & Central Asia ECS        Germany     2.34e12
    #>  9  2001 Europe & Central Asia ECS        Italy       1.60e12
    #> 10  2001 Europe & Central Asia ECS        Other       4.37e12
    #> # ℹ 44 more rows

The donuts do not necessarily require eliminating duplication, but for the given case, I prefer to have one GDP value per country. The original dataset does not contain duplicates, but replacing country names with “Other” changes the situation. In general, I suggest considering aggregation to obtain a unique value per hierarchy for a clearer outcome.

The next step in data preparation is arranging the data. This can be done in two ways:

1. Utilize `dplyr::arrange()` to alphabetically order values within each region.
2. Utilize `donutsk::packing()` to distribute small values further apart from each other. While the second option simplifies the labeling task, the first option is preferable for comparing World GDP structures period versus period, since the order of countries will remain the same unless a country falls into the “Other” category. I would go with `donutsk::packing()` any time there are a lot of small values alongside with big ones.

The `donutsk::packing()` function requires a bit more complicated data preparation using `dplyr::nest_by()` and `tidyr::unnest()`. Once the preparation is ready, the data can be combined into one dataset for further visualization.

    GDP2_1 <- arrange(GDP1, date, region_ISO)

    GDP2_2 <- nest_by(GDP1, date) |>
      mutate(data = list(packing(data, GDP, region_ISO))) |>
      tidyr::unnest(cols = "data")

    GDP2 <- bind_rows(`arrange()`= GDP2_1, `packing()`= GDP2_2, .id = "Arrange type")

## Charting

Labeling the donut segments is not very straightforward since it’s highly possible to face label overlaps, which makes annotations unreadable. In order to overcome such obstacles, I would rather leverage `eye()` to accommodate labels onto the chart. Such a layout places labels in a controlled way, handling the label length pretty well.

To bring more clarity to the analysis, I would prefer to indicate the percentage for regions and countries using `glue::glue()` syntax and the internal pre-calculated variable `.prc`. It’s enough to pass expression like `{scales::percent(.prc)}` to get formatted percent.

    ggplot(GDP2, aes(value = GDP, fill = region)) +
      # Internal donat represents regions
      geom_donut_int(r_int = .25, col="white", linewidth=.1) +
      # External donat represents countries
      geom_donut_ext(aes(opacity = country), col="white", linewidth=.1, show.legend = F) +
      # Text annotations for internal donut
      geom_text_int(aes(label = "{scales::percent(.prc)}", col = region),
                    size=3, r = 1.25, show.legend = F) +
      # Label annotations for internal donut
      geom_label_ext(aes(col=region, label=paste0(country, "-{scales::percent(.prc, .01)}")),
                     size=3, col = "white", layout = eye(), show.legend = F,
                     label.padding=unit(0.1, "lines")) +
      # Link label annotations to specific country GDP segment
      geom_pin(aes(col = region),
               size=.5, linewidth=.1, show.legend = F, cut=0, layout = eye(), r = 2) +
      # Adjust colors schema with palette
      scale_fill_viridis_d(option = "mako", begin = .1, end = .8) +
      scale_color_viridis_d(option = "mako", begin = .1, end = .8) +
      coord_radial(theta = "y", expand = F) +
      # Splitting data to 4 subsets with different combinations Arrange type ~ Year
      facet_grid(`Arrange type`~date) +
      xlim(0, 5) +
      theme(legend.position = "inside", axis.text=element_blank(),
            axis.ticks=element_blank(), panel.grid=element_blank(),
            legend.position.inside=c(.5, .5), legend.direction = "horizontal") +
      labs(title = "GDP, PPP (current international $)")

As it was said, the `arrange()` method for the given case is preferable. It’s quite convenient to make structure comparisons for European countries, spotting Poland in the 2022 year as an additional one compared to 2001. It’s also easy to notice that Asia has captured the first GDP place from Europe. The aforementioned insights are way more difficult to identify with the `packing()` version.

This example can look a bit cumbersome because of additional artificial level of complexity - `Arrange type`. The Real life example will look as follows:

    ggplot(GDP2_1, aes(value = GDP, fill = region)) +
      # Internal donat represents regions
      geom_donut_int(r_int = .25, col="white", linewidth=.1) +
      # External donat represents countries
      geom_donut_ext(aes(opacity = country), col="white", linewidth=.1, show.legend = F) +
      # Text annotations for internal donut
      geom_text_int(aes(label = "{scales::percent(.prc)}", col = region),
                    size=3, r = 1.25, show.legend = F) +
      # Label annotations for internal donut
      geom_label_ext(aes(col=region, label=paste0(country, "-{scales::percent(.prc, .01)}")),
                     size=3, col = "white", layout = eye(), show.legend = F) +
      # Link label annotations to specific country GDP segment
      geom_pin(aes(col = region),
               size=.5, linewidth=.1, show.legend = F, cut=0, layout = eye(), r = 2) +
      # Adjust colors schema with palette
      scale_fill_viridis_d(option = "mako", begin = .1, end = .8) +
      scale_color_viridis_d(option = "mako", begin = .1, end = .8) +
      coord_radial(theta = "y", expand = F) +
      # Splitting data to 4 subsets with different combinations Arrange type ~ Year
      facet_grid(date~., switch = "x") +
      xlim(0, 4.5) +
      theme(legend.position = "inside", axis.text=element_blank(),
            axis.ticks=element_blank(), panel.grid=element_blank(),
            legend.position.inside=c(.5, .5), legend.direction = "horizontal") +
      labs(title = "GDP, PPP (current international $)", fill="")

There is another way to distribute labels without overlap, leveraging the `tv()` layout and the thinner parameter, which builds two levels to display labels. Since it’s another approach, I would relax the quantile threshold up to a value of 0.95.

    # Prepare data using more strict threshold
    GDP4 <- filter(GDP, date %in% c(2001, 2022)) |>
      group_by(date) |>
      mutate(country = if_else(GDP > quantile(GDP, .95), country, paste0("Other\n", region_ISO))) |>
      group_by(date, region, region_ISO, country) |>
      summarise(GDP = sum(GDP), .groups = "drop")

    # Prepare arranged data alphabethically
    GDP5_1 <- arrange(GDP4, date, region_ISO)

    # Utilize packing() for data ordering
    GDP5_2 <- nest_by(GDP4, date) |>
      mutate(data = list(packing(data, GDP, region_ISO))) |>
      tidyr::unnest(cols = "data")

    # Combine two arrange types together
    GDP5 <- bind_rows(`arrange()`= GDP5_1, `packing()`= GDP5_2, .id = "Arrange type")

    # Set layout parameters
    tv_lt <- tv(scale_x = 3, scale_y = 3, thinner = T, thinner_gap = .5)

    # Build donut chart
    ggplot(GDP5, aes(value = GDP, fill = region)) +
      geom_donut_int(r_int = .25, col="white", linewidth=.1) +
      geom_donut_ext(aes(opacity = country), col="white", linewidth=.1, show.legend = F) +
      geom_text_int(aes(label = "{scales::percent(.prc)}", col = region),
                    size=3, r = 1.25, show.legend = F) +
      geom_pin(aes(col = region), size=.5, linewidth=.1, show.legend = F, cut=.1, r = 1.9,
               layout =  tv_lt) +
      geom_label_ext(aes(col = region,
                         label = paste(stringr::str_wrap(country, 5),"\n{scales::percent(.prc, .01)}")),
                     size=3, col = "white", show.legend = F, label.padding=unit(0.1, "lines"),
                     lineheight = .8, layout = tv_lt) +
      scale_fill_viridis_d(option = "mako", begin = .1, end = .8) +
      scale_color_viridis_d(option = "mako", begin = .1, end = .8) +
      coord_radial(theta = "y", expand = F) +
      facet_grid(`Arrange type`~date) +
      theme(legend.position="inside", axis.text=element_blank(),
            axis.ticks=element_blank(), panel.grid=element_blank(),
            legend.position.inside=c(.5, .5), legend.direction = "horizontal") +
      labs(title = "GDP, PPP (current international $)")

These examples illustrate a trade-off between the `arrange()` and `packing()` functions. The `arrange()` function is better suited for year-over-year comparisons as it maintains the same order across the years. However, the `packing()` function allocates chart space more efficiently by distributing small values among larger values which leads to less label overlaps.