# A guide on how to use the package gglyph

#### Valentin Velev (University of Konstanz)

#### 2025-09-19

## The Package

`gglyph` is a package for creating directed network-style graphs for statistical and non-statistical data with custom edges. It builds on `ggplot2` and includes four functions:

- `geom_glyph()`: Create a network-based graph that illustrates pairwise relationships (statistical and non-statistical) using custom edges
- `process_data_statistical()`: Process statistical data (e.g., pairwise t-tests) for plotting
- `process_data_general()`: Process general / non-statistical data (any data with directional relationships) for plotting
- `generate_mock_data()`: Create mock data for experimenting with `geom_glyph()`

The pipeline is as follows:

1. Obtain a dataset with directed pairwise relationships. This can be done using the function `generate_mock_data()` or by using your own dataset (e.g., running pairwise t-tests on your data).
2. Process the dataset using either `process_data_statistical()`or `process_data_general()`.
3. Create a glyph plot with `geom_glyph()`.

The package also includes two datasets:

- PISA 2022: The Programme for International Student Assessment (PISA) is a global evaluation study conducted by the Organisation for Economic Co-operation and Development (OECD) that assesses the scholastic performance of 15-year-old students in reading, math, and science. It is held every three years and provides comparative data for countries to understand and improve their education systems.
- SIPRI Military Expenditure Database: The Stockholm International Peace Research Institute (SIPRI) Military Expenditure Database comprises panel data on the amount of financial resources dedicated by a state to raising and maintaining the state’s armed forces. The database includes data in local currencies, constant (2022) and current US dollars, as a share of gross domestic product (GDP), and per capita.

In the following chapter, I will illustrate how the main function `geom_glyph()` works and how its arguments are related to common `ggplot2` arguments.

## The Plotting Function

### Basics

To begin with, I have created a table table showing the equivalence of `geom_glyph()` arguments and common `ggplot2` arguments.

Table 1: Equivalence of geom_glyph and ggplot2 arguments
geom_glyph Argument | ggplot2 Equivalent | Explanation
--- | --- | ---
`edge_colour`, `node_colour` | `color` | Controls the outline color of the nodes/edges.
`edge_fill`, `node_fill` | `fill` | Controls the fill color of the nodes/edges.
`edge_alpha`, `node_alpha` | `alpha` | Controls the transparency of the nodes/edges.
`edge_size`, `node_size` | `size` | Controls the size of the nodes/edges.
`node_spacing` | N/A | Controls the space between the nodes; not a standard `ggplot2` argument.
`node_shape` | `shape` | Controls the shape of the nodes.
`label_size` | `fontsize` in `grid::gpar()` | Controls the font size of the node labels.
`group_label_size` | `theme(strip.text)` | Controls the font size of the facet labels (group titles).
`legend_title` | `title` in `guides()` | Sets the main title text within the legend.
`legend_subtitle` | `title` in `guides()` | Sets an additional subtitle.

### Some Examples

Now I will set up the vignette:

    # Load packages
    library(gglyph)
    library(tidyverse)
    library(readr)
    library(haven)
    library(purrr)
    library(viridisLite)
    library(kableExtra)
    library(patchwork)
    library(ggthemes)

    # Remove scientific notation
    options(scipen = 999, digits = 3)

    # Set seed for reproducibility
    set.seed(42)

And create mock data using the custom function `generate_mock_data()`, which comprises several arguments listed in Table 2:

Table 2: Arguments in `generate_mock_data`
Argument | Explanation
--- | ---
`n_nodes` | Number of nodes. Default is 5.
`n_edges` | Number of edges. Default is 7.
`n_groups` | Number of groups. Default is 1 (ungrouped).
`statistical` | Boolean indicator for whether to generate statistical data. Default is FALSE.
`p_threshold` | Statistical significance threshold. Default is 0.05.

This function can be used if you want to just play around with `geom_glyph()`. Here is how it can be used:

    mock_data <- generate_mock_data(n_nodes = 5, n_edges = 10, statistical = TRUE)
    mock_data_grouped <- generate_mock_data(n_nodes = 5, n_edges = 10, n_groups = 3, statistical = TRUE)

This is what data that can be directly passed to `geom_glyph()` must look like (more on this in the chapter on the data wrangling functions):

Table 3: Ungrouped data for `geom_glyph`
to | from | significance | threshold | angle.from | x.from | y.from | angle.to | x.to | y.to | type | label | angle | x | y
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
B | A | 0.046 | 0.05 | 1.571 | 0.000 | 1.000 | 0.314 | 0.951 | 0.309 | edge | NA | NA | NA | NA
C | A | 0.046 | 0.05 | 1.571 | 0.000 | 1.000 | -0.942 | 0.588 | -0.809 | edge | NA | NA | NA | NA
D | C | 0.026 | 0.05 | -0.942 | 0.588 | -0.809 | -2.199 | -0.588 | -0.809 | edge | NA | NA | NA | NA
E | B | 0.047 | 0.05 | 0.314 | 0.951 | 0.309 | -3.456 | -0.951 | 0.309 | edge | NA | NA | NA | NA
E | C | 0.012 | 0.05 | -0.942 | 0.588 | -0.809 | -3.456 | -0.951 | 0.309 | edge | NA | NA | NA | NA
NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | A | 1.571 | 0.000 | 1.000
NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | B | 0.314 | 0.951 | 0.309
NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | C | -0.942 | 0.588 | -0.809
NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | D | -2.199 | -0.588 | -0.809
NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | E | -3.456 | -0.951 | 0.309

Table 4: Grouped data for `geom_glyph`
to | group | from | significance | threshold | angle.from | x.from | y.from | angle.to | x.to | y.to | type | label | angle | x | y
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
C | Group 1 | A | 0.038 | 0.05 | 1.571 | 0.000 | 1.000 | -0.942 | 0.588 | -0.809 | edge | NA | NA | NA | NA
C | Group 1 | B | 0.001 | 0.05 | 0.314 | 0.951 | 0.309 | -0.942 | 0.588 | -0.809 | edge | NA | NA | NA | NA
E | Group 1 | B | 0.021 | 0.05 | 0.314 | 0.951 | 0.309 | -3.456 | -0.951 | 0.309 | edge | NA | NA | NA | NA
E | Group 1 | C | 0.039 | 0.05 | -0.942 | 0.588 | -0.809 | -3.456 | -0.951 | 0.309 | edge | NA | NA | NA | NA
E | Group 1 | D | 0.000 | 0.05 | -2.199 | -0.588 | -0.809 | -3.456 | -0.951 | 0.309 | edge | NA | NA | NA | NA
NA | Group 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | A | 1.571 | 0.000 | 1.000
NA | Group 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | B | 0.314 | 0.951 | 0.309
NA | Group 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | C | -0.942 | 0.588 | -0.809
NA | Group 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | D | -2.199 | -0.588 | -0.809
NA | Group 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | E | -3.456 | -0.951 | 0.309
D | Group 2 | B | 0.004 | 0.05 | 0.314 | 0.951 | 0.309 | -2.199 | -0.588 | -0.809 | edge | NA | NA | NA | NA
E | Group 2 | C | 0.017 | 0.05 | -0.942 | 0.588 | -0.809 | -3.456 | -0.951 | 0.309 | edge | NA | NA | NA | NA
E | Group 2 | D | 0.026 | 0.05 | -2.199 | -0.588 | -0.809 | -3.456 | -0.951 | 0.309 | edge | NA | NA | NA | NA
NA | Group 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | A | 1.571 | 0.000 | 1.000
NA | Group 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | B | 0.314 | 0.951 | 0.309
NA | Group 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | C | -0.942 | 0.588 | -0.809
NA | Group 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | D | -2.199 | -0.588 | -0.809
NA | Group 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | E | -3.456 | -0.951 | 0.309
C | Group 3 | B | 0.000 | 0.05 | 0.314 | 0.951 | 0.309 | -0.942 | 0.588 | -0.809 | edge | NA | NA | NA | NA
D | Group 3 | A | 0.038 | 0.05 | 1.571 | 0.000 | 1.000 | -2.199 | -0.588 | -0.809 | edge | NA | NA | NA | NA
D | Group 3 | B | 0.020 | 0.05 | 0.314 | 0.951 | 0.309 | -2.199 | -0.588 | -0.809 | edge | NA | NA | NA | NA
D | Group 3 | C | 0.036 | 0.05 | -0.942 | 0.588 | -0.809 | -2.199 | -0.588 | -0.809 | edge | NA | NA | NA | NA
E | Group 3 | A | 0.001 | 0.05 | 1.571 | 0.000 | 1.000 | -3.456 | -0.951 | 0.309 | edge | NA | NA | NA | NA
E | Group 3 | B | 0.016 | 0.05 | 0.314 | 0.951 | 0.309 | -3.456 | -0.951 | 0.309 | edge | NA | NA | NA | NA
NA | Group 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | A | 1.571 | 0.000 | 1.000
NA | Group 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | B | 0.314 | 0.951 | 0.309
NA | Group 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | C | -0.942 | 0.588 | -0.809
NA | Group 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | D | -2.199 | -0.588 | -0.809
NA | Group 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | node | E | -3.456 | -0.951 | 0.309

With this data we can plot some basic glyphs using the previously generated mock data:

    # Non-grouped
    ggplot(data = mock_data) +
      geom_glyph()

    # Grouped
    ggplot(data = mock_data_grouped) +
      geom_glyph() +
      facet_wrap(~ group)

Note that the function works well with up to 9 nodes:

    plot_list <- list()

    for (num_nodes in 3:9) {
      data <- generate_mock_data(n_nodes = num_nodes, n_edges = num_nodes * 5, statistical = TRUE)
      p <- ggplot(data = data) +
        geom_glyph(label_size = 9, node_size = 0.5)
      plot_list[[length(plot_list) + 1]] <- p
    }

    final_grid <- wrap_plots(plot_list, ncol = 2)
    final_grid

This style of plots was first used in this paper, where the authors investigated the relationship between spokesperson and the likelihood of message resharing during the COVID-19 pandemic using pairwise statistical tests. In that paper, the plots were painstakingly created manually in Photoshop. Now we have a package for that ;).

### Some Prettier Examples… Well, depends on the eye of the beholder

These plots can also be improved aesthetically using the arguments in Table 1. To illustrate, I will use the mock data created earlier.

First, you can change the fill color of the nodes and edges.

Note that if an edge or a node outline colour is provided but not a fill colour, the outline colour is used for both. This also applies if a fill colour is provided but no outline colour.

Furthermore, if you use a colour function such as `viridis` and you do not manually set a `scale_*_manual()` (more on this below), you will always get the default legend (black nodes and grey edge).

    # Non-grouped
    ggplot(data = mock_data) +
      geom_glyph(node_fill = "purple", edge_fill = "purple")

    # Grouped
    ggplot(data = mock_data_grouped) +
      geom_glyph(node_fill = viridis, edge_fill = viridis) +
      facet_wrap(~ group)

Next, you can change the outline color of the nodes and edges:

    # Non-grouped
    ggplot(data = mock_data) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        edge_colour = "black",
        edge_fill = "purple"
      )

    # Grouped
    ggplot(data = mock_data_grouped) +
      geom_glyph(
        node_colour = "black",
        node_fill = viridis,
        edge_colour = "black",
        edge_fill = viridis
      ) +
      facet_wrap(~ group)

Further, you can change the size of both the nodes and the edges:

    # Non-grouped
    ggplot(data = mock_data) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        edge_colour = "black",
        edge_fill = "purple",
        edge_size = 0.75
      )

    # Grouped
    ggplot(data = mock_data_grouped) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        edge_colour = "black",
        edge_fill = "purple",
        edge_size = 0.75
      ) +
      facet_wrap(~ group)

Then, you can change the transparency of the nodes and the edges as well as the spacing between the nodes:

    # Non-grouped
    ggplot(data = mock_data) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        node_alpha = 0.5,
        node_spacing = 0.5,
        edge_colour = "black",
        edge_fill = "purple",
        edge_size = 0.75,
        edge_alpha = 0.5
      )

    # Grouped
    ggplot(data = mock_data_grouped) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        node_alpha = 0.5,
        node_spacing = 0.5,
        edge_colour = "black",
        edge_fill = "purple",
        edge_size = 0.75,
        edge_alpha = 0.5
      ) +
      facet_wrap(~ group)

The shape of the nodes can also be changed. Click here for a list of all `ggplot2` shapes.

    # Non-grouped
    ggplot(data = mock_data) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        node_alpha = 0.5,
        node_spacing = 0.5,
        node_shape = 24,
        edge_colour = "black",
        edge_fill = "purple",
        edge_size = 0.75,
        edge_alpha = 0.5
      )

    # Grouped
    ggplot(data = mock_data_grouped) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        node_alpha = 0.5,
        node_spacing = 0.5,
        node_shape = 24,
        edge_colour = "black",
        edge_fill = "purple",
        edge_size = 0.75,
        edge_alpha = 0.5
      ) +
      facet_wrap(~ group)

In addition, the size of the labels can be changed:

    # Non-grouped
    ggplot(data = mock_data) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        node_alpha = 0.5,
        node_spacing = 0.5,
        node_shape = 24,
        edge_colour = "black",
        edge_fill = "purple",
        edge_size = 0.75,
        edge_alpha = 0.5,
        label_size = 14
      )

    # Grouped
    ggplot(data = mock_data_grouped) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        node_alpha = 0.5,
        node_spacing = 0.5,
        node_shape = 24,
        edge_colour = "black",
        edge_fill = "purple",
        edge_size = 0.75,
        edge_alpha = 0.5,
        label_size = 10,
        group_label_size = 15
      ) +
      facet_wrap(~ group)

Similarly, the legend title and subtitle can be changed:

    # Non-grouped
    ggplot(data = mock_data) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        node_alpha = 0.5,
        node_spacing = 0.5,
        node_shape = 24,
        edge_colour = "black",
        edge_fill = "purple",
        edge_size = 0.75,
        edge_alpha = 0.5,
        label_size = 14,
        legend_title = "Legend Title",
        legend_subtitle = "Legend Subtitle"
      )

    # Grouped
    ggplot(data = mock_data_grouped) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        node_alpha = 0.5,
        node_spacing = 0.5,
        node_shape = 24,
        edge_colour = "black",
        edge_fill = "purple",
        edge_size = 0.75,
        edge_alpha = 0.5,
        label_size = 10,
        group_label_size = 15,
        legend_title = "Legend Title",
        legend_subtitle = "Legend Subtitle"
      ) +
      facet_wrap(~ group)

Finally, you can use the standard `ggplot2` functions with `+` to change certain aspects of the appearance.

Note that if you would like to use `ggplot2`’s `scale_*_manual()` for a faceted plot, you need specify a grouping variable in the `mapping` argument in `ggplot()`. Further, `scale_colour_manual()` and `scale_fill_manual()` will apply to the edges and `scale_shape_manual()` to the nodes.

Furthermore, if you have data with more than 6 groups and you manually specify different shapes for each using `scale_shape_manual()` the warning:

    Warning message:
    The shape palette can deal with a maximum of 6 discrete values because more than 6 becomes difficult to
    discriminate
    ℹ you have requested 9 values. Consider specifying shapes manually if you need that many have them.

will appear. This can safely be ignored.

    # Non-grouped
    ggplot(data = mock_data) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        node_alpha = 0.5,
        node_spacing = 0.5,
        node_shape = 24,
        edge_colour = "black",
        edge_fill = "purple",
        edge_size = 0.75,
        edge_alpha = 0.5,
        label_size = 14,
        legend_title = "Legend Title",
        legend_subtitle = "Legend Subtitle"
      ) +
      labs(title = "Very Creative Title") +
      theme(
        legend.box.margin = margin(l = 20, r = 20),
        strip.background = element_rect(fill = "white", color = "black", linewidth = 0.5)
      )

    # Grouped
    ggplot(data = mock_data_grouped, aes(colour = group, fill = group, shape = group)) +
      geom_glyph(
        node_colour = "black",
        node_fill = "purple",
        node_size = 0.5,
        node_alpha = 0.5,
        node_spacing = 0.5,
        edge_size = 0.75,
        edge_alpha = 0.5,
        label_size = 10,
        group_label_size = 15,
        legend_title = "Legend Title",
        legend_subtitle = "Legend Subtitle"
      ) +
      facet_wrap(~ group) +
      labs(title = "Very Creative Title") +
      scale_color_manual(values = c("Group 1" = "black", "Group 2" = "green", "Group 3" = "blue")) +
      scale_fill_manual(values = c("Group 1" = "red", "Group 2" = "black", "Group 3" = "yellow")) +
      scale_shape_manual(values = c("Group 1" = 22, "Group 2" = 23, "Group 3" = 24)) +
      theme(
        legend.box.margin = margin(l = 20, r = 20),
        strip.background = element_rect(fill = "white", color = "black", linewidth = 0.5)
      )

Please note again that if you manually set the colour, fill, or shape, you should not use the corresponding `geom_glyph()` argument.

In the following chapter, I will briefly go over the two functions for data wrangling and demonstrate how they together with the two datasets can be used to create glyphs.

## The Data Wrangling Functions

As mentioned above, `gglyph` includes two functions for data wrangling `process_data_statistical` and `process_data_general`. In the table below, I have listed the different arguments for each function.

Table 5: Arguments in `process_data_statistical` and `process_data_general`
Argument | Explanation
--- | ---
`data` | A DataFrame to be processed.
`from` | Column name for the start nodes.
`to` | Column name for the end nodes.
`group` | Column name for the grouping variable.
`sig`* | Column name for the significance level.
`tresh`* | Significance threshold. Default is 0.05.
* Argument is only available in `process_data_statistical`.

To illustrate how raw data is processed using `process_data_statistical` and `process_data_general`, I will use the two datasets in `gglyph` and show a “before and after”.

First, I will load and wrangle the datasets included in the package (see the first chapter).

For the PISA 2022 dataset, I used the country variable (CNT), the variable indicating the highest educational level attainment by either parent (HISCED), and an average score of the math comprehension items (PV*MATH) to conduct pairwise t-tests (with Bonferroni correction).

For the SIPRI dataset, I will use the absolute amount of military expenditures in current US dollars to create higher-lower pairwise relationships.

For both, I will use the ready-made datasets included in the package. For more information on how they were created, click here.

    data(pisa_2022)
    data(sipri_milex_1995_2023)

This is what the two datasets that I will henceforth work with look like:

Table 6: Raw statistical data (PISA)
from | to | group | sig
--- | --- | --- | ---
ISCED 2 | ISCED 0&1 | Austria | 1
ISCED 3 | ISCED 0&1 | Austria | 1
ISCED 4&5 | ISCED 0&1 | Austria | 1
ISCED 6+ | ISCED 0&1 | Austria | 1
ISCED 3 | ISCED 2 | Austria | 1
ISCED 4&5 | ISCED 2 | Austria | 1

Table 7: Raw non-statistical data (SIPRI MilEx)
from | to | group
--- | --- | ---
China | India | 1995
China | India | 1999
China | India | 2003
China | India | 2007
China | India | 2011
China | India | 2015

Compared with after using the the functions `process_data_statistical()` or `process_data_general()`:

    # Process the PISA data (statistical data)
    ## Grouped data
    processed_data_pisa_group <- process_data_statistical(
      data = pisa_2022,
      from = "from",
      to = "to",
      sig = "sig",
      group = "group",
      thresh = 0.05
    )

    ## Non-grouped data
    processed_data_pisa <- process_data_statistical(
      data = pisa_2022[pisa_2022$group == "Germany",],
      from = "from",
      to = "to",
      sig = "sig",
      thresh = 0.05
    )

    # Process the SIPRI MilEx data (non-statistical data)
    ## Grouped data
    processed_data_sipri_group <- process_data_general(
      data = sipri_milex_1995_2023,
      from = "from",
      to = "to",
      group = "group"
    )

    ## Non-grouped data
    processed_data_sipri <- process_data_general(
      data = sipri_milex_1995_2023[sipri_milex_1995_2023$group == "2023",],
      from = "from",
      to = "to"
    )

This is what the processed datasets look like:

(Note: I will only show the PISA dataset)

Table 8: Processed ungrouped statistical data
to | from | significance | angle.from | x.from | y.from | angle.to | x.to | y.to | type | threshold | label | angle | x | y
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ISCED 2 | ISCED 0&1 | 0 | 1.571 | 0.000 | 1.000 | 0.314 | 0.951 | 0.309 | edge | 0.05 | NA | NA | NA | NA
ISCED 3 | ISCED 0&1 | 0 | 1.571 | 0.000 | 1.000 | -0.942 | 0.588 | -0.809 | edge | 0.05 | NA | NA | NA | NA
ISCED 3 | ISCED 2 | 0 | 0.314 | 0.951 | 0.309 | -0.942 | 0.588 | -0.809 | edge | 0.05 | NA | NA | NA | NA
ISCED 4&5 | ISCED 0&1 | 0 | 1.571 | 0.000 | 1.000 | -2.199 | -0.588 | -0.809 | edge | 0.05 | NA | NA | NA | NA
ISCED 4&5 | ISCED 2 | 0 | 0.314 | 0.951 | 0.309 | -2.199 | -0.588 | -0.809 | edge | 0.05 | NA | NA | NA | NA
ISCED 6+ | ISCED 2 | 0 | 0.314 | 0.951 | 0.309 | -3.456 | -0.951 | 0.309 | edge | 0.05 | NA | NA | NA | NA

Table 9: Processed grouped statistical data
to | group | from | significance | angle.from | x.from | y.from | angle.to | x.to | y.to | type | threshold | label | angle | x | y
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ISCED 0&1 | Czech Republic | ISCED 2 | 0.019 | 0.314 | 0.951 | 0.309 | 1.571 | 0.000 | 1.000 | edge | 0.05 | NA | NA | NA | NA
ISCED 2 | Austria | ISCED 0&1 | 0.000 | 1.571 | 0.000 | 1.000 | 0.314 | 0.951 | 0.309 | edge | 0.05 | NA | NA | NA | NA
ISCED 2 | Belgium | ISCED 0&1 | 0.000 | 1.571 | 0.000 | 1.000 | 0.314 | 0.951 | 0.309 | edge | 0.05 | NA | NA | NA | NA
ISCED 2 | France | ISCED 0&1 | 0.000 | 1.571 | 0.000 | 1.000 | 0.314 | 0.951 | 0.309 | edge | 0.05 | NA | NA | NA | NA
ISCED 2 | Germany | ISCED 0&1 | 0.000 | 1.571 | 0.000 | 1.000 | 0.314 | 0.951 | 0.309 | edge | 0.05 | NA | NA | NA | NA
ISCED 2 | Greece | ISCED 0&1 | 0.000 | 1.571 | 0.000 | 1.000 | 0.314 | 0.951 | 0.309 | edge | 0.05 | NA | NA | NA | NA

With this data the following plots can be created:

    ggplot(data = processed_data_pisa) +
      geom_glyph()

    ggplot(data = processed_data_pisa_group) +
      geom_glyph() +
      facet_wrap(~ group)

And for the SIPRI dataset:

    ggplot(data = processed_data_sipri) +
      geom_glyph()

    ggplot(data = processed_data_sipri_group) +
      geom_glyph() +
      facet_wrap(~ group)

After a bit of polishing, they can look like this:

    ggplot(data = processed_data_pisa) +
      geom_glyph(
        node_size = 1.175,
        node_colour = "black",
        edge_colour = "orange"
      ) +
      labs(title = "PISA 2022 Parental Education")

    ggplot(data = processed_data_pisa_group) +
      geom_glyph(
        node_size = 0.75,
        node_fill = rainbow,
        node_colour = "black",
        edge_fill = rainbow,
        label_size = 3.75,
        group_label_size = 6.75
      ) +
      facet_wrap(~ group) +
      labs(title = "PISA 2022 Parental Education")

And for the SIPRI dataset:

    ggplot(data = processed_data_sipri) +
      geom_glyph(
        node_size = 1.175,
        node_colour = "black",
        node_fill = "purple",
        edge_fill = "blue"
      ) +
      labs(title = "SIPRI Military Expenditures")

    ggplot(data = processed_data_sipri_group) +
      geom_glyph(
        node_fill = viridis,
        node_colour = "black",
        edge_fill = viridis
      ) +
      facet_wrap(~ group) +
      labs(title = "SIPRI Military Expenditures")

## Concluding Remarks

You can save the plot using `ggsave()` from `ggplot2`:

    ggsave(filename = "plot.pdf", plot = last_plot(), width = 8, height = 6, dpi = 300)

Finally, if you find any bugs or if you have any additional features that you would like me to add, please let me know at valentin.velev@uni-konstanz.de.