This tutorial is a joint product of the Statnet Development Team:
Pavel N. Krivitsky (University of New South Wales)
Martina Morris (University of Washington)
Mark S. Handcock (University of California, Los Angeles)
Carter T. Butts (University of California, Irvine)
David R. Hunter (Penn State University)
Steven M. Goodreau (University of Washington)
Chad Klumb (University of Washington)
Skye Bender de-Moll (Oakland, CA)
The software demonstrated in this tutorial is authored by Emily Beylerian and Martina Morris.
statnet ProjectAll statnet packages are open-source, written for the
R computing environment, and published on CRAN. The
source repositories are hosted on GitHub. Our website is statnet.org
Need help? For general questions and comments, please email the statnet users group at statnet_help@uw.edu. You’ll need to join the listserv if you’re not already a member. You can do that here: statnet_help listserve.
Found a bug in our software? Please let us know by filing an issue in the appropriate package GitHub repository, with a reproducible example.
Want to request new functionality? We welcome suggestions – you can make a request by filing an issue on the appropriate package GitHub repository. The chances that this functionality will be developed are substantially improved if the requests are accompanied by some proposed code (we are happy to review pull requests).
For all other issues, please email us at contact@statnet.org.
This workshop and tutorial provide an introduction to using the
statnetWeb Shiny app for network analysis with
statnet. These online materials have been designed for both
formal workshops and self-study, with many examples to provide hands-on
experience with the software, and self-contained data.
With statnetWeb you can:
R (R Core Team 2021) is a free software environment for statistical computing and graphics. It runs on a wide variety of UNIX platforms, Windows and MacOS. It is designed to provide a platform for others to contribute “packages” – software that performs specific types of statistical analysis, visualization, and application building.
Shiny
is an R package that allows developers to create applications for
users that run in a web browser and provide a simple point-and-click
interface to the functionality of other R packages. For examples, you
can take a look at the apps hosted on the Shiny Gallery. A really
simple example is this K-means
clustering app. Instead of the user having to code up the R script
to run and plot the k-mean clustering of the data, they just need to
point and click the controls on the left side of the screen. Another
Shiny app that has some nice network vizualizations is the CRAN
explorer (you’ll need to page down to the interactive part about 3
pages down).
Shiny apps provide a simple way for non-technical users to interact
with data, using R packages in the background without having to learn
coding. While most Shiny apps are designed to be used to explore a
particular dataset on a website, we designed statnetWeb
instead as a pedagogical tool for learning how to analyze network
data.
statnet?statnet (Krivitsky et al.
2003-2020; Handcock et al. 2008) is a suite of R packages for the
management, exploration, statistical analysis, simulation and
visualization of network data. The statistical modeling framework relies
on Exponential-family Random Graph Models (ERGMs). All of the software
is open source (GPL-3), with development on GitHub, and packages published to the Comprehensive R Archive Network
(CRAN).
statnetWeb?statnetWeb
is an R Shiny app for network data analysis that gives a user access to
the functionality of three statnet packages:
network (Butts 2008, 2021) – storage and manipulation of network data
sna (Butts 2020) – descriptive statistics and graphics for exploratory network analysis`
ergm (Handcock et al. 2022) – statistical modeling of networks using Exponential-family Random Graph Models
This set of packages from the statnet suite is designed
for static (or “cross-sectional”) network analysis. While the models
that can be specified in ergm include some types of
continuously valued ties, this workshop focuses on simple binary tie
networks (0,1).
None. This is a very basic introductory workshop.
There are two ways to run statnetWeb, both of which are
covered in the tutorial:
via an online portal, in which case no software installation of any kind is required, one only needs an internet connection.
by launching the package statnetWeb from the R
command line. This will pop up a browser window on your local machine,
giving you the same experience as the online portal, but the session
will be running on your local machine rather than the cloud. In this
case, it is necessary to install:
the latest version of R (available here).
the statnetWeb package, which can be installed from
the R command line with the following expression:
install.packages("statnetWeb")Installing statnetWeb will automatically install the
three required packages from the statnet suite that will be
running in the background while you are using the app. Information about
installing additional packages from the Statnet suite can be found on
the statnet
workshop wiki.
Finally, our workshops are generally conducted using the free version of RStudio (available here). Since this particular workshop runs in a browser window, you won’t interact much with the R program directly, so it will have little impact on your experience here. But we recommend it as a very useful environment for running R.
Since this workshop is entirely interactive, there is no code to run, and the instructions for the guided interactive session are included in pdf format
You can save the tutorial from the browser window.
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.2.1 (2022-06-23 ucrt)
os Windows 10 x64 (build 19044)
system x86_64, mingw32
ui RTerm
language (EN)
collate English_United States.utf8
ctype English_United States.utf8
tz America/Los_Angeles
date 2022-06-27
pandoc 2.17.1.1 @ C:/Program Files/RStudio/bin/quarto/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
bslib 0.3.1 2021-10-06 [1] CRAN (R 4.2.0)
cli 3.3.0 2022-04-25 [1] CRAN (R 4.2.0)
digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0)
evaluate 0.15 2022-02-18 [1] CRAN (R 4.2.0)
fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0)
htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.2.0)
jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.2.0)
jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0)
knitr * 1.39 2022-04-26 [1] CRAN (R 4.2.0)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
rlang 1.0.2 2022-03-04 [1] CRAN (R 4.2.0)
rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.2.0)
rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.2.0)
sass 0.4.1 2022-03-23 [1] CRAN (R 4.2.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
stringi 1.7.6 2021-11-29 [1] CRAN (R 4.2.0)
stringr 1.4.0 2019-02-10 [1] CRAN (R 4.2.0)
xfun 0.31 2022-05-10 [1] CRAN (R 4.2.0)
yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0)
[1] C:/Users/Martina Morris/AppData/Local/R/win-library/4.2
[2] C:/Program Files/R/R-4.2.1/library
──────────────────────────────────────────────────────────────────────────────