| Title: | Automatic Plotting of Many Graphs |
| Version: | 0.1.2 |
| Date: | 2025-06-27 |
| 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: | cli, dplyr (≥ 1.1.0), ggdendro, ggplot2, tidyr |
| Suggests: | testthat (≥ 3.0.0), patchwork |
| Enhances: | 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: | 2025-06-27 20:37:39 UTC; hollway |
| Author: | James Hollway 
[cre, aut, ctb] (IHEID),
Henrique Sposito
[ctb] |
| Maintainer: | James Hollway <james.hollway@graduateinstitute.ch> |
| Repository: | CRAN |
| Date/Publication: | 2025-07-02 15:30:06 UTC |
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)
Arguments
number |
Integer of how many category colours to return. |
Value
One or more hexcodes as strings.
Layered layout
Description
Layered layout
Usage
layout_tbl_graph_layered(.data, center = NULL, circular = FALSE, times = 4)
Arguments
.data |
Some |
center, circular |
Extra parameters required for |
times |
Integer of sweeps that the algorithm will pass through. By default 4. |
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
.data |
Some |
center, circular, times |
Extra parameters required for |
Value
Returns a table of nodes' x and y coordinates.
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(res_siena_gof)
data(res_siena_influence)
data(res_siena_selection)
data(res_monan_traces)
data(res_monan_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.
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
x |
An object of "node_measure", "tie_measure", or "network_measures" class. |
type |
For node and tie measures, whether the plot should be "h" a histogram or "d" a density plot. By default "h". |
... |
Other arguments to be passed on. |
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
x |
An object of "node_member" class, for example as a result of
running |
... |
Other arguments to be passed on. |
membership |
A "node_member" membership vector. |
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
x |
An object of "node_motif" class, e.g. resulting from a call to
|
... |
Other arguments to be passed on. |
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}.
Many themes
Description
This function enables all plots to be quickly, easily and consistently themed. This is achieved by setting a theme option that enables the appropriate palette to be used for all autograph-consistent plotting methods.
The following themes are currently available: default, bw, iheid, ethz, uzh, rug, unibe, crisp, neon, rainbow.
Usage
stocnet_theme(theme = NULL)
Arguments
theme |
String naming a theme. By default "default". This string can be capitalised or not. |
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.
Examples
stocnet_theme("default")
plot(manynet::node_degree(ison_karateka))
stocnet_theme("rug")
plot(manynet::node_degree(ison_karateka))
Matching colors across palettes
Description
Sometimes particular colours are coded in certain ways to facilitate interpretation. For example, perhaps primary colours or traffic light colours are used to represent some discrete options. Yet institutional palettes vary in terms of which colours they have available. This function uses the Euclidean distance of colours in CIELAB space to those of a target palette to find the closes corresponding colours.
Usage
match_color(colors, pal)
Arguments
colors |
One or more hexcodes to match with colors from the palette. |
pal |
Optionally, a vector of hexcodes representing a palette in which to find matches. By default, the current theme's qualitative palette is used. |
Value
A vector of hexcodes the length of the first argument.
Examples
match_color("#4575b4")
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
x |
An object obtained by fitting an MRQAP model to some data.
For example, |
... |
Further arguments to be passed on to plot. |
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
x |
A "diff_model" of "diffs_model" class of object.
E.g. as a result from |
... |
Other arguments to be passed. |
all_steps |
Whether all steps should be plotted or just those where there is change in the distributions. |
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 influence tables
Description
These are functions for constructing and presenting influence tables for the interpretation of results for network and behavior dynamics obtained with the RSiena or multiSiena packages.
Usage
## S3 method for class 'influenceTable'
plot(x, separation = 0, ...)
Arguments
x |
An object of class "influenceTable",
created using |
separation |
This can be used to make the curves visually distinguishable if they overlap too much without it. An advisable value then is, e.g., 0.01. |
... |
Other arguments to be passed. |
Value
A plot showing how the influence evaluation function changes based on ego's value and alter's value of some covariate.
Author(s)
Tom Snijders
References
Consult also the RSiena manual, Sections 13.2 and 13.4. Gratitude to Steffen Triebel and Rene Veenstra for corrections.
Examples
plot(res_siena_influence)
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
x |
An object obtained from a conditional uniform graph or
quadratic assignment procedure test.
For example, |
... |
Other arguments to be passed on. |
threshold |
The empirical threshold to shade in the plot. |
tails |
By default "two" indicating a two-tailed test, but "one" for a one-tailed test is also available. |
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 selection tables
Description
These are functions for constructing and presenting selection tables for the interpretation of results for network dynamics obtained with the RSiena package.
Usage
## S3 method for class 'selectionTable'
plot(x, quad = TRUE, separation = 0, ...)
Arguments
x |
An object of class "selectionTable",
created using |
quad |
When TRUE (the default), a quadratic function
(average and total alter) is plotted.
Use |
separation |
This can be used to make the curves visually distinguishable if they overlap too much without it. An advisable value then is, e.g., 0.01. |
... |
Other arguments to be passed. |
Value
A plot showing how the selection evaluation function changes based on ego's value and alter's value of some covariate.
Author(s)
Tom Snijders
References
Consult also the RSiena manual, Sections 13.1 and 13.3.
Examples
plot(res_siena_selection)
SIENA Goodness of Fit
Description
This function plots goodness of fit objects created using RSiena.
Unlike the plot method included in the {RSiena} package,
this function utilises {ggplot2} and not {lattice},
which makes the output more compatible and themeable.
Usage
## S3 method for class 'sienaGOF'
plot(x, ...)
Arguments
x |
A sienaGOF object, as returned by |
... |
Other parameters to be passed to the plotting funciton,
for example |
Value
A violin plot showing the distribution of statistics from the simulations and a line highlighting the observed statistics.
Examples
plot(res_siena_gof)
plot.gof.stats.monan
Description
plot.gof.stats.monan
Usage
## S3 method for class 'gof.stats.monan'
plot(x, lvls, ...)
Arguments
x |
An object of class "gof.stats.monan". |
lvls |
The values for which the gofFunction should be calculated/plotted. |
... |
Additional plotting parameters, use discouraged. |
Value
The function plot.gof.stats.monan returns violin plots of the
gof tests with observed values superimposed in red.
Examples
plot(res_monan_gof, lvls = 1:15)
plot.traces.monan
Description
plot.traces.monan
Usage
## S3 method for class 'traces.monan'
plot(x, ...)
Arguments
x |
An object of class "traces.monan". |
... |
Additional plotting parameters, use not recommended. |
Value
The function plot.traces.monan shows a scatter plot of the
statistics of simulated networks from phase three of the esimtation.
Examples
plot(res_monan_traces)
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