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GitHub - rrrlw/ICON: R package that provides complex systems datasets from the Colorado Index of Complex Networks (ICON) at https://icon.colorado.edu/.
R package that provides complex systems datasets from the Colorado Index of Complex Networks (ICON) at https://icon.colorado.edu/. - rrrlw/ICON
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GitHub - rrrlw/ICON: R package that provides complex systems datasets from the Colorado Index of Complex Networks (ICON) at https://icon.colorado.edu/.

GitHub - rrrlw/ICON: R package that provides complex systems datasets from the Colorado Index of Complex Networks (ICON) at https://icon.colorado.edu/.

ICON: easy access to complex systems datasets

Travis-CI Build Status AppVeyor Build Status Codecov test coverage

License: CC0-1.0 CRAN version CRAN Downloads

Preprint

Overview

The ICON R package provides easy-to-use and easy-to-access datasets from the Index of COmplex Networks (ICON) database available at the University of Colorado website. All datasets can be loaded with a single function call and new datasets are being slowly added from ICON at https://icon.colorado.edu. Currently, the ICON R package includes 1,075 complex networks.

Installation

To install the ICON package, run the following R code:

# install from CRAN (older, fewer networks)
install.packages("ICON")

# install development version from GitHub (updated, more networks)
devtools::install_github("rrrlw/ICON")

Sample code

The sample code below demonstrates network visualization using the igraph R package. For a more detailed look at network analysis (using the network R package) and visualization (using the ggnetwork R package), please take a look at the package vignette.

# load ICON package and data frame of available datasets
library("ICON")
data(ICON_data)

# vector of names of available datasets
print(ICON_data$Var_name)

# look at entire data frame in Rstudio
View(ICON_data)

# load the chess dataset for use and look at the first few lines
get_data("chess")
head(chess)

# load another dataset for use
get_data("seed_disperse_beehler")

# plot interaction network using igraph
library("igraph")
my_graph <- graph_from_edgelist(as.matrix(seed_disperse_beehler[, 1:2]), directed = FALSE)
plot(my_graph, vertex.label = NA, vertex.size = 5)

# following plot is generated (exact vertex positioning varies each time code is run)

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