Title:	Automatic Plotting of Many Graphs
Version:	0.4.0
Date:	2025-09-09
Description:	Visual exploration and presentation of networks should not be difficult. This package includes functions for plotting networks and network-related metrics with sensible and pretty defaults. It includes 'ggplot2'-based plot methods for many popular network package classes. It also includes some novel layout algorithms, and options for straightforward, consistent themes.
URL:	https://stocnet.github.io/autograph/
BugReports:	https://github.com/stocnet/autograph/issues
License:	MIT + file LICENSE
Language:	en-GB
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Depends:	R (≥ 3.6.0), manynet
Imports:	dplyr (≥ 1.1.0), ggdendro, ggraph, ggplot2, igraph, patchwork, tidygraph
Suggests:	BiocManager, gganimate, ggforce (≥ 0.5.0), gifski, graphlayouts, methods, testthat (≥ 3.0.0)
Enhances:	ergm, Rgraphviz, RSiena
Config/Needs/build:	roxygen2, devtools
Config/Needs/check:	covr, lintr, spelling
Config/Needs/website:	pkgdown
Config/testthat/parallel:	true
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-09-09 16:22:47 UTC; hollway
Author:	James Hollway [cre, aut, ctb] (IHEID), Henrique Sposito [ctb]
Maintainer:	James Hollway <james.hollway@graduateinstitute.ch>
Repository:	CRAN
Date/Publication:	2025-09-10 06:40:53 UTC

autograph: Automatic Plotting of Many Graphs

Description

Visual exploration and presentation of networks should not be difficult. This package includes functions for plotting networks and network-related metrics with sensible and pretty defaults. It includes 'ggplot2'-based plot methods for many popular network package classes. It also includes some novel layout algorithms, and options for straightforward, consistent themes.

Details

For more details, please consult the README and work through the tutorial. The tutorial can be run locally using run_tute("visualisation").

Author(s)

James Hollway

See Also

Useful links:

• https://stocnet.github.io/autograph/

• Report bugs at https://github.com/stocnet/autograph/issues

Consistent palette calls

Description

These functions assist in calling particular parts of a theme's palette. For example, ag_base() will return the current theme's base or background color, and ag_highlight() will return the color used in that theme to highlight one or more nodes, lines, or such.

Usage

ag_base()

ag_highlight()

ag_positive()

ag_negative()

ag_qualitative(number)

ag_sequential(number)

ag_divergent(number)

ag_font()

Arguments

Value

One or more hexcodes as strings.

Layouts for snapping layouts to a grid

Description

The function uses approximate pattern matching to redistribute coarse layouts on square grid points, while preserving the topological relationships among the nodes (see Inoue et al. 2012).

Usage

depth_first_recursive_search(layout)

References

Inoue, Kentaro, Shinichi Shimozono, Hideaki Yoshida, and Hiroyuki Kurata. 2012. “Application of Approximate Pattern Matching in Two Dimensional Spaces to Grid Layout for Biochemical Network Maps” edited by J. Bourdon. PLoS ONE 7(6):e37739. doi:10.1371/journal.pone.0037739.

Layout algorithms based on configurational positions

Description

Configurational layouts locate nodes at symmetric coordinates to help illustrate particular configurations. Currently configurational layouts are available for 2-6 nodes. The "configuration" layout will choose the appropriate configurational layout automatically.

Usage

layout_configuration(.data, circular = TRUE, times = 1)

layout_tbl_graph_configuration(.data, circular = TRUE, times = 1)

layout_dyad(.data, circular = TRUE, times = 1)

layout_tbl_graph_dyad(.data, circular = TRUE, times = 1)

layout_triad(.data, circular = TRUE, times = 1)

layout_tbl_graph_triad(.data, circular = TRUE, times = 1)

layout_tetrad(.data, circular = TRUE, times = 1)

layout_tbl_graph_tetrad(.data, circular = TRUE, times = 1)

layout_pentad(.data, circular = TRUE, times = 1)

layout_tbl_graph_pentad(.data, circular = TRUE, times = 1)

layout_hexad(.data, circular = TRUE, times = 1)

layout_tbl_graph_hexad(.data, circular = TRUE, times = 1)

Arguments

See Also

Other mapping: layout_partition, plot_graphr, plot_graphs, plot_grapht

Layered layout

Description

Layered layout

Usage

layout_tbl_graph_layered(.data, center = NULL, circular = FALSE, times = 4)

Arguments

Value

Returns a table of coordinates.

Examples

ties <- data.frame(
  from = c("A", "A", "B", "C", "D", "F", "F", "E"),
  to   = c("B", "C", "D", "E", "E", "E", "G", "G"),
  stringsAsFactors = FALSE)

coords <- layout_tbl_graph_layered(ties, times = 6)
coords

Matching layout

Description

This layout works to position nodes opposite their matching nodes. See manynet::to_matching() for more details on the matching procedure.

Usage

layout_tbl_graph_matching(.data, center = NULL, circular = FALSE, times = 1)

Arguments

Value

Returns a table of nodes' x and y coordinates.

Layout algorithms based on bi- or other partitions

Description

These algorithms layout networks based on two or more partitions, and are recommended for use with graphr() or {ggraph}. Note that these layout algorithms use {Rgraphviz}, a package that is only available on Bioconductor. It will first need to be downloaded using BiocManager::install("Rgraphviz"). If it has not already been installed, there is a prompt the first time these functions are used though.

The "hierarchy" layout layers the first node set along the bottom, and the second node set along the top, sequenced and spaced as necessary to minimise edge overlap. The "alluvial" layout is similar to "hierarchy", but places successive layers horizontally rather than vertically. The "railway" layout is similar to "hierarchy", but nodes are aligned across the layers. The "ladder" layout is similar to "railway", but places successive layers horizontally rather than vertically. The "concentric" layout places a "hierarchy" layout around a circle, with successive layers appearing as concentric circles. The "multilevel" layout places successive layers as multiple levels. The "lineage" layout ranks nodes in Y axis according to values.

Usage

layout_concentric(
  .data,
  membership,
  radius = NULL,
  order.by = NULL,
  circular = FALSE,
  times = 1000
)

layout_tbl_graph_concentric(
  .data,
  membership,
  radius = NULL,
  order.by = NULL,
  circular = FALSE,
  times = 1000
)

layout_multilevel(.data, level, circular = FALSE)

layout_tbl_graph_multilevel(.data, level, circular = FALSE)

layout_lineage(.data, rank, circular = FALSE)

layout_tbl_graph_lineage(.data, rank, circular = FALSE)

layout_hierarchy(.data, center = NULL, circular = FALSE, times = 1000)

layout_tbl_graph_hierarchy(
  .data,
  center = NULL,
  circular = FALSE,
  times = 1000
)

layout_alluvial(.data, circular = FALSE, times = 1000)

layout_tbl_graph_alluvial(.data, circular = FALSE, times = 1000)

layout_railway(.data, circular = FALSE, times = 1000)

layout_tbl_graph_railway(.data, circular = FALSE, times = 1000)

layout_ladder(.data, circular = FALSE, times = 1000)

layout_tbl_graph_ladder(.data, circular = FALSE, times = 1000)

Arguments

Source

Diego Diez, Andrew P. Hutchins and Diego Miranda-Saavedra. 2014. "Systematic identification of transcriptional regulatory modules from protein-protein interaction networks". Nucleic Acids Research, 42 (1) e6.

See Also

Other mapping: layout_configuration(), plot_graphr, plot_graphs, plot_grapht

Examples

#graphr(ison_southern_women, layout = "concentric", membership = "type",
#           node_color = "type", node_size = 3)
#graphr(ison_lotr, layout = "multilevel",
#           node_color = "Race", level = "Race", node_size = 3)
# ison_adolescents %>%
#   mutate(year = rep(c(1985, 1990, 1995, 2000), times = 2),
#          cut = node_is_cutpoint(ison_adolescents)) %>%
#   graphr(layout = "lineage", rank = "year", node_color = "cut",
#              node_size = migraph::node_degree(ison_adolescents)*10)
#graphr(ison_southern_women, layout = "hierarchy", center = "events",
#           node_color = "type", node_size = 3)
#graphr(ison_southern_women, layout = "alluvial")

Valence-based layout

Description

Valence-based layout

Usage

layout_valence(
  .data,
  times = 500,
  center = NULL,
  circular = FALSE,
  repulsion_coef = 1,
  attraction_coef = 0.05
)

layout_tbl_graph_valence(
  .data,
  times = 500,
  center = NULL,
  circular = FALSE,
  repulsion_coef = 1,
  attraction_coef = 0.05
)

Arguments

Examples

edges <- data.frame(
  from = c("A", "B", "C", "D"),
  to   = c("B", "C", "D", "A"),
  weight = c(2, 3, 1, 4),
  sign = c(1, -1, 1, -1)  # 1 = positive, -1 = negative
  )
graphr(as_igraph(edges), layout="valence")

Precooked results for demonstrating plotting

Description

These are all pre-cooked results objects, saved here to save time in testing and demonstrating how autograph plots look.

Usage

data(res_migraph_reg)

data(res_migraph_test)

data(res_migraph_diff)

data(res_manynet_diff)

data(siena_gof)

data(siena_influence)

data(siena_selection)

data(monan_conv)

data(monan_gof)

data(ergm_res)

data(ergm_gof)

Format

An object of class netlm of length 15.

An object of class network_test of length 9.

An object of class diffs_model (inherits from data.frame) with 20 rows and 11 columns.

An object of class diff_model (inherits from tbl_df, tbl, data.frame) with 4 rows and 10 columns.

An object of class sienaGOF of length 1.

An object of class influenceTable (inherits from data.frame) with 25 rows and 4 columns.

An object of class selectionTable (inherits from data.frame) with 25 rows and 4 columns.

An object of class traces.monan of length 3.

An object of class gof.stats.monan of length 2.

An object of class ergm of length 35.

An object of class gof.ergm (inherits from gof) of length 30.

Plotting logical marks Plotting numeric measures

Description

These functions plot distributions for node, tie, and network measures, as defined in the {manynet} package.

Usage

## S3 method for class 'node_measure'
plot(x, type = c("h", "d"), ...)

## S3 method for class 'tie_measure'
plot(x, type = c("h", "d"), ...)

## S3 method for class 'network_measures'
plot(x, ...)

Arguments

Value

plot.node_measure() and plot.tie_measure() returns a histogram and/or density plot of the distribution of the measure.

plot.network_measures() returns a plot of the measure traced over time.

Examples

plot(manynet::node_deg(ison_karateka))
plot(manynet::tie_betweenness(ison_karateka))

Plotting categorical memberships

Description

This plotting method operates on "node_member" class objects from the {manynet} package, plotting the dendrogram of their membership.

Usage

## S3 method for class 'node_member'
plot(x, ...)

## S3 method for class 'matrix'
plot(x, ..., membership = NULL)

Arguments

Value

plot.node_member() returns a dendrogram, with labels colored to indicate the different clusters, and with the optimal cutpoint shown by a dashed highlight line.

plot.matrix() returns a plot of an adjacency or incidency matrix, potentially with the rows and columns reordered to illustrate an additional membership vector.

Examples

plot(manynet::node_in_walktrap(ison_southern_women, "e"))
plot(as_matrix(ison_adolescents),
  membership = node_in_walktrap(ison_adolescents, "e"))
plot(as_matrix(ison_southern_women),
  membership = node_in_walktrap(ison_southern_women, "e"))

Plotting tabular motifs

Description

These functions will plot graphs of the motifs used in a vector of results of e.g. a triad census.

Usage

## S3 method for class 'node_motif'
plot(x, ...)

## S3 method for class 'network_motif'
plot(x, ...)

Arguments

Value

plot.node_motif() returns a set of graphs that illustrate the motifs mentioned in the results from a node_motif function in {manynet}.

plot.network_motif() returns a set of graphs that illustrate the motifs mentioned in the results from a net_motif function in {manynet}.

Plotting methods for MRQAP models

Description

These plotting methods are for results obtained by fitting an MRQAP model. The S3 classes are "netlm" or "netlogit", and so are compatible with the results from either the {sna} or {migraph} packages.

Usage

## S3 method for class 'netlm'
plot(x, ...)

## S3 method for class 'netlogit'
plot(x, ...)

Arguments

Value

A plot showing the location of observed statistics compared to the distribution of statistics from permuted networks.

Examples

# Here's something I cooked up with migraph earlier:
plot(res_migraph_reg)

Plotting diffusion models

Description

Plotting diffusion models

Usage

## S3 method for class 'diff_model'
plot(x, ..., all_steps = TRUE)

## S3 method for class 'diffs_model'
plot(x, ...)

## S3 method for class 'learn_model'
plot(x, ...)

Arguments

Value

plot.diff_model() returns a bar chart of the number of new infected nodes at each time point, as well as an overlay line plot of the total of infected

Examples

plot(res_manynet_diff)
plot(res_migraph_diff)
plot(play_learning(ison_networkers, beliefs = runif(net_nodes(ison_networkers))))

Plotting methods for CUG and QAP tests

Description

These plotting methods are for results obtained by testing some statistic against those produced in a reference distribution of conditional uniform graphs or as a quadratic assignment procedure. The S3 class is "network_test".

Usage

## S3 method for class 'network_test'
plot(x, ..., threshold = 0.95, tails = c("two", "one"))

Arguments

Value

A distribution of the simulated or permuted statistics, with 2.5% shaded at each end, and a line highlighting where the observed statistic lies on this distribution.

Examples

# Here's something I cooked up with migraph earlier:
plot(res_migraph_test)

Plotting convergence diagnostics

Description

These plotting methods are for diagnosing the convergence of simulation-based estimation procedures, such as those used in MoNAn and ergm. These plots are useful for identifying whether the estimation procedure has adequately explored the state space and converged to a stable distribution.

Usage

## S3 method for class 'ag_conv'
plot(x, ...)

## S3 method for class 'traces.monan'
plot(x, ...)

## S3 method for class 'mcmc.list'
plot(x, ...)

Arguments

Value

The function shows a line plot tracing the statistics obtained at each simulation step, as well as a density plot showing the distribution of the statistics over the entire simulation.

See Also

Other MoNAn: plot_gof

Other ergm: plot_gof

Examples

plot(monan_conv)
plot(ergm_res$sample)

Plotting goodness-of-fit results

Description

These plot methods plot goodness of fit objects created using RSiena::sienaGOF(), MoNAn::monanGOF(), or the 'ergm' package's gof() function. Internally, the GOF object is translated into a common class (ag_gof), which has its own plot method to ensure a consistent look and feel. It is not expected that users will create ag_gof class objects themselves.

The plot shows a violin plot of the distribution of statistics from the simulations, with a boxplot inside the violin to show the interquartile range, and dashed lines connecting the 5th and 95th percentiles. The boxplot also shows outliers as crosses. The observed statistics are shown as points and connected by a line. The observed statistics are also labelled with their value. If a p-value is available (as in the case of RSiena::sienaGOF()), it is shown beneath the x-axis.

Usage

## S3 method for class 'ag_gof'
plot(x, ...)

## S3 method for class 'gof.stats.monan'
plot(x, cumulative = FALSE, ...)

## S3 method for class 'sienaGOF'
plot(x, cumulative = FALSE, ...)

## S3 method for class 'gof.ergm'
plot(x, cumulative = FALSE, statistic = c("deg", "espart", "dist"), ...)

Arguments

Details

Since these plots methods are in {autograph}, the plots are automatically themed according to the current theme set using stocnet_theme(). The function uses the highlight colour defined in the current theme to highlight the observed statistics. The function also uses the base colour defined in the current theme to draw the violin and box plots.

It is however completely customisable. While a title is automatically generated so that the graph is informative, this can be customised by specifying the main argument in the plotting function, or added after the fact using {ggplot2} functions such as ggtitle() or labs().

The user can choose whether to plot the statistics cumulatively or not. This is typically handled within RSiena::sienaGOF(), but for MoNAn::monanGOF() and the 'ergm' package's gof() function the cumulative option is handled here. The default is to plot the non-cumulative statistics. This is because the non-cumulative statistics are often more interpretable, and the cumulative statistics can be obtained by setting cumulative = TRUE.

The function also checks whether any of the statistics have zero variance across the simulations, and if so, these statistics are not plotted, with a message to the user indicating which statistics were omitted.

Note that these methods overwrite any plot methods for these classes that may be provided by the original packages. You may receive such a warning in the console when loading the package. Please load {autograph} after these other packages to ensure the plotting methods included in this package are used, or specify the package when calling the plotting method directly, e.g., autograph:::plot.sienaGOF(res_siena_gof).

Value

A violin plot showing the distribution of statistics from the simulations and a line joining points showing the observed statistics.

References

Hintze, J. L. and Nelson, R. D. 1998. "Violin plots: A box plot-density trace synergism". The American Statistician, 52:181–184. doi:10.1080/00031305.1998.10480559

See Also

Other MoNAn: plot_convergence

Other RSiena: plot_interp

Other ergm: plot_convergence

Examples

plot(monan_gof)
plot(siena_gof, cumulative = TRUE)
plot(ergm_gof, statistic = "espart")

Easily graph networks with sensible defaults

Description

This function provides users with an easy way to graph (m)any network data for exploration, investigation, inspiration, and communication.

It builds upon {ggplot2} and {ggraph} to offer pretty and extensible graphing solutions. However, compared to those solutions, graphr() contains various algorithms to provide better looking graphs by default. This means that just passing the function some network data will often be sufficient to return a reasonable-looking graph.

The function also makes it easy to modify many of the most commonly adapted aspects of a graph, including node and edge size, colour, and shape, as arguments rather than additional functions that you need to remember. These can be defined outright, e.g. node_size = 8, or in reference to an attribute of the network, e.g. node_size = "wealth".

Lastly, graphr() uses {ggplot2}-related theme information, so it is easy to make colour palette and fonts institution-specific and consistent. See e.g. theme_iheid() for more.

To learn more about what can be done visually, try run_tute("Visualisation").

Usage

graphr(
  .data,
  layout,
  labels = TRUE,
  node_color,
  node_shape,
  node_size,
  node_group,
  edge_color,
  edge_size,
  snap = FALSE,
  ...,
  node_colour,
  edge_colour
)

Arguments

Value

A ggplot2::ggplot() object. The last plot can be saved to the file system using ggplot2::ggsave().

See Also

Other mapping: layout_configuration(), layout_partition, plot_graphs, plot_grapht

Examples

graphr(ison_adolescents)
ison_adolescents %>%
  mutate(color = rep(c("introvert","extrovert"), times = 4),
         size = ifelse(node_is_cutpoint(ison_adolescents), 6, 3)) %>%
  mutate_ties(ecolor = rep(c("friends", "acquaintances"), times = 5)) %>%
  graphr(node_color = "color", node_size = "size",
         edge_size = 1.5, edge_color = "ecolor")

Easily graph a set of networks with sensible defaults

Description

This function provides users with an easy way to graph lists of network data for comparison.

It builds upon this package's graphr() function, and inherits all the same features and arguments. See graphr() for more. However, it uses the {patchwork} package to plot the graphs side by side and, if necessary, in successive rows. This is useful for lists of networks that represent, for example, ego or component subgraphs of a network, or a list of a network's different types of tie or across time. By default just the first and last network will be plotted, but this can be overridden by the "waves" parameter.

Where the graphs are of the same network (same nodes), the graphs may share a layout to facilitate comparison. By default, successive graphs will use the layout calculated for the "first" network, but other options include the "last" layout, or a mix, "both", of them.

Usage

graphs(netlist, waves, based_on = c("first", "last", "both"), ...)

Arguments

Value

Multiple ggplot2::ggplot() objects displayed side-by-side.

See Also

Other mapping: layout_configuration(), layout_partition, plot_graphr, plot_grapht

Examples

#graphs(to_egos(ison_adolescents))
#graphs(to_egos(ison_adolescents), waves = 8)
#graphs(to_egos(ison_adolescents), waves = c(2, 4, 6))
#graphs(play_diffusion(ison_adolescents))

Easily animate dynamic networks with sensible defaults

Description

This function provides users with an easy way to graph dynamic network data for exploration and presentation.

It builds upon this package's graphr() function, and inherits all the same features and arguments. See graphr() for more. However, it uses the {gganimate} package to animate the changes between successive iterations of a network. This is useful for networks in which the ties and/or the node or tie attributes are changing.

A progress bar is shown if it takes some time to encoding all the .png files into a .gif.

Usage

grapht(
  tlist,
  keep_isolates = TRUE,
  layout,
  labels = TRUE,
  node_color,
  node_shape,
  node_size,
  edge_color,
  edge_size,
  ...,
  node_colour,
  edge_colour
)

Arguments

Value

Shows a .gif image. Assigning the result of the function saves the gif to a temporary folder and the object holds the path to this file.

Source

https://blog.schochastics.net/posts/2021-09-15_animating-network-evolutions-with-gganimate/

See Also

Other mapping: layout_configuration(), layout_partition, plot_graphr, plot_graphs

Examples

#ison_adolescents %>%
#  mutate_ties(year = sample(1995:1998, 10, replace = TRUE)) %>%
#  to_waves(attribute = "year", cumulative = TRUE) %>%
#  grapht()
#ison_adolescents %>% 
#  mutate(gender = rep(c("male", "female"), times = 4),
#         hair = rep(c("black", "brown"), times = 4),
#         age = sample(11:16, 8, replace = TRUE)) %>%
#  mutate_ties(year = sample(1995:1998, 10, replace = TRUE),
#              links = sample(c("friends", "not_friends"), 10, replace = TRUE),
#              weekly_meetings = sample(c(3, 5, 7), 10, replace = TRUE)) %>%
#  to_waves(attribute = "year") %>%
#  grapht(layout = "concentric", membership = "gender",
#             node_shape = "gender", node_color = "hair",
#             node_size =  "age", edge_color = "links",
#             edge_size = "weekly_meetings")
#grapht(play_diffusion(ison_adolescents, seeds = 5))

Plotting effects interpretation

Description

These functions support the interpretation of network and behavior effects found in stochastic actor-oriented models. They are S3 plotting methods for objects of class "selectionTable" or "influenceTable", created using RSiena::selectionTable() or RSiena::influenceTable(), respectively. They plot how the evaluation function for selection or influence changes based on ego's value and alter's value of some covariate. This helps to interpret the effect of that covariate on the network dynamics or behavior dynamics, respectively.

Usage

## S3 method for class 'selectionTable'
plot(x, quad = TRUE, separation = 0, ...)

## S3 method for class 'influenceTable'
plot(x, separation = 0, ...)

Arguments

Details

These functions were originally written by Tom Snijders, and adapted for use in the {autograph} package.

Value

A plot showing how the selection/influence evaluation function changes based on ego's value and alter's value of some covariate.

Author(s)

Tom Snijders

Thanks to Steffen Triebel and Rene Veenstra for corrections.

References

For plotting selection tables, please consult the RSiena manual, Sections 13.1 and 13.3.

For plotting selection tables, please consult the RSiena manual, Sections 13.2 and 13.4.

See Also

Other RSiena: plot_gof

Other RSiena: plot_gof

Examples

plot(siena_selection)
plot(siena_influence)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

dplyr

%>%

ggplot2

aes, element_blank, element_text, geom_point, geom_smooth, ggplot, ggsave, ggtitle, guides, labs, scale_colour_hue, scale_size, scale_x_continuous, theme, theme_grey, unit, xlab, ylab

Matching colors across palettes

Description

Sometimes a palette or particular colours are chosen to symbolise or represent a particular idea, such as red for "stop" or green for "go", or to convey some other interpretation. Yet institutional palettes do not necessarily include all colours, which can constrain how interpretable visualisations are under institutional branding requirements. match_color() helps to find the closest matching colours in a given palette to one or more input colours.

There is also a helper function, is_dark(), to determine whether a color is dark or light, which can be useful when deciding whether to use white or black text on top of a colored background.

Usage

match_color(colors, pal)

is_dark(colors)

Arguments

Details

This function uses the Euclidean distance of colours in CIELAB space to those of a target palette to find the closes corresponding colours. It also ensures that each input color is matched to a unique color in the palette. If there are more input colors than unique colors in the palette, an error is returned.

By default, the current theme's qualitative palette is used, but any vector of hexcodes can be passed to the pal argument.

Value

A vector of hexcodes the length of the first argument.

Examples

match_color("#4575b4")
is_dark(c("#000","#FFF"))

Themed scales for further customization

Description

These functions enable to add color scales to be graphs.

Usage

scale_fill_iheid(direction = 1, ...)

scale_colour_iheid(direction = 1, ...)

scale_color_iheid(direction = 1, ...)

scale_edge_colour_iheid(direction = 1, ...)

scale_edge_color_iheid(direction = 1, ...)

scale_fill_centres(direction = 1, ...)

scale_colour_centres(direction = 1, ...)

scale_color_centres(direction = 1, ...)

scale_edge_colour_centres(direction = 1, ...)

scale_edge_color_centres(direction = 1, ...)

scale_fill_sdgs(direction = 1, ...)

scale_colour_sdgs(direction = 1, ...)

scale_color_sdgs(direction = 1, ...)

scale_edge_colour_sdgs(direction = 1, ...)

scale_edge_color_sdgs(direction = 1, ...)

scale_fill_ethz(direction = 1, ...)

scale_colour_ethz(direction = 1, ...)

scale_color_ethz(direction = 1, ...)

scale_edge_colour_ethz(direction = 1, ...)

scale_edge_color_ethz(direction = 1, ...)

scale_fill_uzh(direction = 1, ...)

scale_colour_uzh(direction = 1, ...)

scale_color_uzh(direction = 1, ...)

scale_edge_colour_uzh(direction = 1, ...)

scale_edge_color_uzh(direction = 1, ...)

scale_fill_rug(direction = 1, ...)

scale_colour_rug(direction = 1, ...)

scale_color_rug(direction = 1, ...)

scale_edge_colour_rug(direction = 1, ...)

scale_edge_color_rug(direction = 1, ...)

Arguments

Examples

#ison_brandes %>%
#mutate(core = migraph::node_is_core(ison_brandes)) %>%
#graphr(node_color = "core") +
#scale_color_iheid()
#graphr(ison_physicians[[1]], edge_color = "type") +
#scale_edge_color_ethz()

Setting a consistent theme for all plots

Description

This function enables plots to be quickly, easily and consistently themed. This is achieved by setting a theme option, usually at the start of an R session, that enables the palette to be used for all autograph-consistent plotting methods. This includes thematic colours for backgrounds, highlights, sequential, divergent and categorical colour schemes. The function sets these palettes to options that are then used by the various plotting functions.

If no theme is specified (i.e. the function is called without argument), the current theme is reported. The default theme is "default". This theme uses a white background, blue and red for highlighting, and a blue-white-red divergent palette. The themes can be changed at any time by calling stocnet_theme() or its alias set_stocnet_theme() with a different theme name.

Other themes include those based on the colour schemes of various universities, including ETH Zurich, UZH, UNIBE, RUG, and Oxford. Other themes include "bw" for black and white, "crisp" for a high-contrast black and white theme, "neon" for a dark theme with neon highlights, and "rainbow" for a colourful theme. Most themes are designed to be colour-blind safe.

Usage

stocnet_theme(theme = NULL)

set_stocnet_theme(theme = NULL)

Arguments

Value

This function sets the theme and palette(s) to be used across all stocnet packages. The palettes are written to options and held there.

Fonts

Some themes also set a preferred font for use in plots, if available on the system (a check is performed). In some cases, this includes a vector of options to try in sequence. If none of the preferred fonts are available, a sans-serif font is used. If you receive a warning about a missing font when setting a theme, try installing one of the preferred fonts or make sure that the font is available to R using extrafont::font_import() and extrafont::loadfont()

Examples

stocnet_theme("default")
plot(manynet::node_degree(ison_karateka))
stocnet_theme("rug")
plot(manynet::node_degree(ison_karateka))