Manual Plotting with ggplot2

tl;dr

plot.netify() handles 90% of cases out of the box. This vignette is for the other 10% — when you want full control over geometry, layout, or combinations of color scales that the built-in path doesn’t expose. We cover extracting node and edge data, layering them with ggplot2, highlighting a single actor, layering multiple color scales, applying a ggplot theme, and exporting with ggsave(). Have fun!

This vignette provides an overview of how to create customizable plots using ggplot2 while still using netify to prepare the data.

Let’s load the necessary libraries.

library(netify)
library(ggplot2)

The advanced sections also use dplyr (for data manipulation) and ggnewscale (for multiple color legends). If dplyr is not installed, the core plotting workflow still runs; only the advanced edge-information section will be skipped.

library(ggnewscale)

Preparing data

First let’s create a netlet object from some dyadic data (ICEWS data) using the netify package.

# load icews data
data(icews)

# choose attributes
nvars <- c("i_polity2", "i_log_gdp", "i_log_pop")
dvars <- c("matlCoop", "verbConf", "matlConf")

# create a netify object
netlet <- netify(
    icews,
    actor1 = "i", actor2 = "j",
    time = "year",
    symmetric = FALSE, weight = "verbCoop",
    mode = "unipartite", sum_dyads = FALSE,
    actor_time_uniform = TRUE, actor_pds = NULL,
    diag_to_NA = TRUE, missing_to_zero = TRUE,
    nodal_vars = nvars,
    dyad_vars = dvars
)

# subset to a few actors
actors_to_keep <- c(
    "Australia", "Brazil",
    "Canada", "Chile", "China",
    "Colombia", "Egypt", "Ethiopia",
    "France", "Germany", "Ghana",
    "Hungary", "India", "Indonesia",
    "Iran, Islamic Republic Of",
    "Israel", "Italy", "Japan", "Kenya",
    "Korea, Democratic People's Republic Of",
    "Korea, Republic Of", "Nigeria", "Pakistan",
    "Qatar", "Russian Federation", "Saudi Arabia",
    "South Africa", "Spain", "Sudan",
    "Syrian Arab Republic", "Thailand",
    "United Kingdom", "United States",
    "Zimbabwe"
)
netlet <- subset_netify(
    netlet,
    actors = actors_to_keep
)

# print
netlet

## ✔ Hello, you have created network data, yay!
## • Unipartite
## • Asymmetric
## • Weights from `verbCoop`
## • Longitudinal: 13 Periods
## • # Unique Actors: 34
## Network Summary Statistics (averaged across time):
##           dens miss    mean recip trans
## verbCoop 0.887    0 179.484 0.978 0.928
## • Nodal Features: i_polity2, i_log_gdp, i_log_pop
## • Dyad Features: matlCoop, verbConf, matlConf

This is a longitudinal, weighted network with nodal and dyadic attributes. In a few more steps we will show how to highlight these attributes in the plot.

Quick start: just `plot(net)`

Before reaching for ggplot2 directly, remember that the fastest path to a network plot is the built-in plot() method. It picks a layout, draws edges, points, and (for small networks) auto-repelling labels, and returns a ggplot object you can keep customizing with + layers. Add theme_publication_netify() to apply the netify theme:

# one-liner -- works for cross-sectional or longitudinal netify objects
set.seed(6886)
plot(netlet) + theme_publication_netify()

That single call is usually enough for a first look. The rest of this vignette shows what to do when you want finer control over the underlying data and layers — for example, swapping the layout algorithm, adding edge-level information that the default plot doesn’t expose, or combining multiple color scales with ggnewscale.

Going manual: extract the plot data frames

When you need that finer control, use net_plot_data to create a data frame for ggplot2. net_plot_data extracts and sets up node and edge data from a netify object according to specified plotting arguments. It returns a list of different components but the most important one for users is the net_dfs element. This element contains two objects: edge_data and nodal_data. These are data frames that can be passed to ggplot2.

# create a data frame for plotting
set.seed(6886)
plot_data <- net_plot_data(netlet)

# get relevant dfs
net_dfs <- plot_data$net_dfs

# check structure of what's here
str(net_dfs)

## List of 2
##  $ edge_data :'data.frame':  12937 obs. of  11 variables:
##   ..$ from    : chr [1:12937] "Australia" "Australia" "Australia" "Australia" ...
##   ..$ to      : chr [1:12937] "Brazil" "Canada" "Chile" "China" ...
##   ..$ time    : chr [1:12937] "2002" "2002" "2002" "2002" ...
##   ..$ verbCoop: num [1:12937] 3 24 1 518 1 15 28 42 1 61 ...
##   ..$ matlCoop: num [1:12937] 0 1 0 15 0 0 3 2 0 0 ...
##   ..$ verbConf: num [1:12937] 0 3 0 43 6 2 11 1 0 3 ...
##   ..$ matlConf: num [1:12937] 0 3 1 3 0 4 0 6 0 3 ...
##   ..$ x1      : num [1:12937] -0.0576 -0.0576 -0.0576 -0.0576 -0.0576 ...
##   ..$ y1      : num [1:12937] 0.132 0.132 0.132 0.132 0.132 ...
##   ..$ x2      : num [1:12937] 0.25671 0.03958 0.22088 -0.00051 0.25814 ...
##   ..$ y2      : num [1:12937] 0.061 -0.1548 0.2778 0.0518 -0.0817 ...
##  $ nodal_data:'data.frame':  442 obs. of  10 variables:
##   ..$ name      : chr [1:442] "Australia" "Australia" "Australia" "Australia" ...
##   ..$ time      : chr [1:442] "2002" "2003" "2004" "2005" ...
##   ..$ i_polity2 : int [1:442] 10 10 10 10 10 10 10 10 10 10 ...
##   ..$ i_log_gdp : num [1:442] 27.6 27.6 27.6 27.7 27.7 ...
##   ..$ i_log_pop : num [1:442] 16.8 16.8 16.8 16.8 16.8 ...
##   ..$ x         : num [1:442] -0.0576 0.1062 -0.1676 0.0248 0.0547 ...
##   ..$ y         : num [1:442] 0.1316 -0.0741 0.0391 -0.1551 0.1232 ...
##   ..$ name_text : chr [1:442] "Australia" "Australia" "Australia" "Australia" ...
##   ..$ name_label: chr [1:442] "Australia" "Australia" "Australia" "Australia" ...
##   ..$ id        : chr [1:442] "Australia_2002" "Australia_2003" "Australia_2004" "Australia_2005" ...

# check the first few rows of the edge data
head(net_dfs$edge_data)

##        from       to time verbCoop matlCoop verbConf matlConf          x1
## 1 Australia   Brazil 2002        3        0        0        0 -0.05761221
## 2 Australia   Canada 2002       24        1        3        3 -0.05761221
## 3 Australia    Chile 2002        1        0        0        1 -0.05761221
## 4 Australia    China 2002      518       15       43        3 -0.05761221
## 5 Australia Colombia 2002        1        0        6        0 -0.05761221
## 6 Australia    Egypt 2002       15        0        2        4 -0.05761221
##          y1            x2          y2
## 1 0.1316246  0.2567066471  0.06102817
## 2 0.1316246  0.0395816516 -0.15478831
## 3 0.1316246  0.2208771149  0.27780000
## 4 0.1316246 -0.0005098899  0.05176884
## 5 0.1316246  0.2581434830 -0.08165964
## 6 0.1316246 -0.0063939248 -0.10525516

# check the first few rows of the nodal data
head(net_dfs$nodal_data)

##         name time i_polity2 i_log_gdp i_log_pop           x           y
## 79 Australia 2002        10  27.55492  16.78568 -0.05761221  0.13162457
## 80 Australia 2003        10  27.58556  16.79718  0.10621976 -0.07408432
## 81 Australia 2004        10  27.62686  16.80787 -0.16761260  0.03905379
## 82 Australia 2005        10  27.65791  16.82005  0.02483645 -0.15514885
## 83 Australia 2006        10  27.68495  16.83354  0.05468896  0.12317962
## 84 Australia 2007        10  27.72203  16.85179  0.03834328 -0.11702110
##    name_text name_label             id
## 79 Australia  Australia Australia_2002
## 80 Australia  Australia Australia_2003
## 81 Australia  Australia Australia_2004
## 82 Australia  Australia Australia_2005
## 83 Australia  Australia Australia_2006
## 84 Australia  Australia Australia_2007

The x and y in nodal_data and the x1, y1, x2, and y2 in edge_data are the coordinates of the nodes and edges, respectively. These are the coordinates that will be used to plot the network.

Creating a plot

Now that we have the data, we can create a plot using ggplot2. We’ll use geom_segment and geom_point (or geom_label, geom_text, and the ggrepel equivalents) to plot the edges and nodes, respectively.

ggplot() +
    geom_segment(
        data = net_dfs$edge_data,
        aes(
            x = x1,
            y = y1,
            xend = x2,
            yend = y2
        ),
        color = "lightgrey",
        alpha = .2
    ) +
    geom_point(
        data = net_dfs$nodal_data,
        aes(
            x = x,
            y = y,
            size = i_log_pop,
            color = i_polity2
        )
    ) +
    labs(
        color = "Polity",
        size = "Log(Pop.)"
    ) +
    scale_color_viridis_c(option = "cividis") +
    facet_wrap(~time, scales = "free") +
    theme_netify()

Note scale_color_viridis_c(option = "cividis") — a colorblind-safe perceptually-uniform palette. For continuous variables this is almost always a better default than a hand-picked two-color gradient.

Changing the layout

By default layouts for node positions are drawn from the layout_nicely algorithm in the igraph package. Users can specify other layouts as, for example, say that you wanted to use the mds algorithm instead:

# create a df using mds instead
set.seed(6886)
plot_data_mds <- net_plot_data(
    netlet,
    list(
        layout = "mds"
    )
)

# see new x-y coordinates
lapply(plot_data_mds$net_dfs, head)

## $edge_data
##        from       to time verbCoop matlCoop verbConf matlConf         x1
## 1 Australia   Brazil 2002        3        0        0        0 -0.3548545
## 2 Australia   Canada 2002       24        1        3        3 -0.3548545
## 3 Australia    Chile 2002        1        0        0        1 -0.3548545
## 4 Australia    China 2002      518       15       43        3 -0.3548545
## 5 Australia Colombia 2002        1        0        6        0 -0.3548545
## 6 Australia    Egypt 2002       15        0        2        4 -0.3548545
##           y1          x2          y2
## 1 -0.1680305 -0.72811128  0.36196590
## 2 -0.1680305 -0.03316817 -0.05312512
## 3 -0.1680305 -0.65201941  0.27166650
## 4 -0.1680305 -0.03316817 -0.05312512
## 5 -0.1680305 -1.22997412 -0.06398966
## 6 -0.1680305  0.36325222  0.13977670
## 
## $nodal_data
##         name time i_polity2 i_log_gdp i_log_pop           x           y
## 79 Australia 2002        10  27.55492  16.78568 -0.35485450 -0.16803048
## 80 Australia 2003        10  27.58556  16.79718  0.20857397 -0.12705283
## 81 Australia 2004        10  27.62686  16.80787 -0.04224981  0.05123628
## 82 Australia 2005        10  27.65791  16.82005 -0.06014447 -0.02708750
## 83 Australia 2006        10  27.68495  16.83354  0.15073774  0.46114648
## 84 Australia 2007        10  27.72203  16.85179 -0.03161665 -0.03885021
##    name_text name_label             id
## 79 Australia  Australia Australia_2002
## 80 Australia  Australia Australia_2003
## 81 Australia  Australia Australia_2004
## 82 Australia  Australia Australia_2005
## 83 Australia  Australia Australia_2006
## 84 Australia  Australia Australia_2007

Highlighting a single actor

A common task: label or visually pop one specific actor in the plot. With the built-in plot.netify() you can do this directly via highlight = "United States" plus select_text / select_text_display:

set.seed(6886)
plot(netlet,
    highlight = "United States",
    highlight_color = c("United States" = "#0A3161", "Other" = "grey70"),
    select_text = "United States",
    select_text_display = "United States",
    time_filter = "2010"
) +
    theme_publication_netify()

## Warning: Non-positive edge weight found, ignoring all weights during
## graph layout.

## Warning in betweenness_cutoff_impl(graph = graph, vids = v, directed = directed, : Some weights are smaller than epsilon, calculations may suffer from numerical precision issues.
## Source: centrality/betweenness.c:441

If you are assembling the plot manually from net_plot_data(), the same effect is one extra layer — flag the focal actor in nodal_data and add a geom_text (or ggrepel::geom_text_repel) layer just for it. Note that net_plot_data() stores actor names in the column called name:

focal <- "United States"
nodal_focal <- subset(net_dfs$nodal_data, name == focal)

ggplot() +
    geom_segment(
        data = net_dfs$edge_data,
        aes(x = x1, y = y1, xend = x2, yend = y2),
        color = "lightgrey", alpha = .2
    ) +
    geom_point(
        data = net_dfs$nodal_data,
        aes(x = x, y = y),
        color = "grey60", size = 2
    ) +
    geom_point(
        data = nodal_focal,
        aes(x = x, y = y),
        color = "#0A3161", size = 4
    ) +
    geom_text(
        data = nodal_focal,
        aes(x = x, y = y, label = name),
        nudge_y = .05, fontface = "bold"
    ) +
    facet_wrap(~time, scales = "free") +
    theme_netify()

Add Edge Information

So far, we have focused on using color to convey information about nodal attributes in the network (population size and polity score). Now, let’s add more edge information to the plot. For example, we can include information about the matlConf dyadic attribute. Imagine we want to highlight edges of verbal cooperation that occur at the same time as when higher than average levels of material conflict occur in the network. First, let’s create the variable in the edge data.

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

# create high_matlConf variable
net_dfs$edge_data <- net_dfs$edge_data |>
    group_by(time) |>
    mutate(
        high_matlConf = matlConf > mean(matlConf, na.rm = TRUE)
    ) |>
    ungroup() |>
    as.data.frame()

# check
head(net_dfs$edge_data)

##        from       to time verbCoop matlCoop verbConf matlConf          x1
## 1 Australia   Brazil 2002        3        0        0        0 -0.05761221
## 2 Australia   Canada 2002       24        1        3        3 -0.05761221
## 3 Australia    Chile 2002        1        0        0        1 -0.05761221
## 4 Australia    China 2002      518       15       43        3 -0.05761221
## 5 Australia Colombia 2002        1        0        6        0 -0.05761221
## 6 Australia    Egypt 2002       15        0        2        4 -0.05761221
##          y1            x2          y2 high_matlConf
## 1 0.1316246  0.2567066471  0.06102817         FALSE
## 2 0.1316246  0.0395816516 -0.15478831         FALSE
## 3 0.1316246  0.2208771149  0.27780000         FALSE
## 4 0.1316246 -0.0005098899  0.05176884         FALSE
## 5 0.1316246  0.2581434830 -0.08165964         FALSE
## 6 0.1316246 -0.0063939248 -0.10525516         FALSE

Now that we have the new variable in the data.frame, we can plot by it but note that we now need a color aesthetic for both points and segments, even though ggplot2 only supports one legend by aesthetic by default. We can get around this by using the new_scale_color function from the ggnewscale package.

# color line segments by this new variable
ggplot() +
    geom_segment(
        data = net_dfs$edge_data,
        aes(
            x = x1,
            y = y1,
            xend = x2,
            yend = y2,
            color = high_matlConf
        ),
        alpha = .2
    ) +
    scale_color_manual(
        name = "",
        values = c("grey", "red"),
        labels = c("Below Avg. Matl. Conf", "Above Avg.")
    ) +
    new_scale_color() +
    geom_point(
        data = net_dfs$nodal_data,
        aes(
            x = x,
            y = y,
            size = i_log_pop,
            color = i_polity2
        )
    ) +
    scale_color_viridis_c(name = "Polity", option = "cividis") +
    labs(
        size = "Log(Pop.)"
    ) +
    facet_wrap(~time, scales = "free") +
    theme_publication_netify() +
    theme(legend.position = "right")

Saving and exporting figures

Once a plot is laid out the way you want, export it with ggsave(). The function writes whatever ggplot was last printed (or whichever ggplot object you pass via plot=) to disk. A vector format (PDF or SVG) holds up at any size; for the web a PNG at 300 dpi is fine.

# last plot rendered
ggsave("icews_network.pdf", width = 9, height = 8)

# or pass an explicit plot object
p <- plot(netlet, time_filter = "2010") + theme_publication_netify()
ggsave("icews_2010.png", plot = p, width = 7, height = 6, dpi = 300)

Colorblind-safe palette guidance

A quick reference for picking colors that survive both colorblindness and grayscale printing:

Continuous variables — use scale_color_viridis_c() / scale_fill_viridis_c(). The viridis, magma, plasma, and cividis options are all perceptually uniform and colorblind-safe; cividis is the safest for the broadest set of color vision deficiencies and also prints well in black & white.
Categorical variables (≤8 levels) — use ColorBrewer’s Set2, Dark2, or Paired. Pass palette = "Set2" to scale_color_brewer(), or node_color_palette = "Set2" to plot.netify().
Two-class highlights — pair a strong color with neutral grey, as in the United States highlight example above (#0A3161 vs grey70).

References

Boschee, Elizabeth; Lautenschlager, Jennifer; O’Brien, Sean; Shellman, Steve; Starz, James; Ward, Michael, 2015, ``ICEWS Coded Event Data’’, doi:10.7910/DVN/28075 , Harvard Dataverse.
Pedersen, T. L. (2020). ggnewscale: Multiple Fill and Colour Scales in ‘ggplot2’. R package version 0.4.3. https://CRAN.R-project.org/package=ggnewscale
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.

Cassy Dorff and Shahryar Minhas

2026-05-29

tl;dr

Preparing data

Quick start: just `plot(net)`

Going manual: extract the plot data frames

Creating a plot

Changing the layout

Highlighting a single actor

Add Edge Information

Saving and exporting figures

Colorblind-safe palette guidance

References

Manual Plotting with ggplot2

Cassy Dorff and Shahryar Minhas

2026-05-29

tl;dr

Preparing data

Quick start: just plot(net)

Going manual: extract the plot data frames

Creating a plot

Changing the layout

Highlighting a single actor

Add Edge Information

Saving and exporting figures

Colorblind-safe palette guidance

References

Quick start: just `plot(net)`