Statistics

Plot and Highlight All Clique Triads in VISONE

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snaCliques

Description

This post describes how to identify group structures among a network of respondents in VISONE. For a network of selections we identify any cliques involving three or more members. A clique is defined as a group containing three or more members where everyone has chosen everyone else.

Identify all triads

A tried is a network structure containing exactly three members. There are many types of triads. A group of three members where everyone chooses everyone else is a triad (i.e., a clique). A group of three members where two people choose each other and nobody choose the third member is another type of triad. There are 16 unique ways three people can select each other.

Identify all triads. Click the ‘analysis’ tab. Next to ‘task’, select ‘grouping’ from the drop down list of available options. Select ‘cohesiveness’ from the drop down list next to ‘class’. Select the option ‘triad census’ next to ‘measure’. Click ‘analyze’.

Highlight all cliques

Highlight all cliques. Click an empty part of the graph. Press the keys ‘Ctrl’ and ‘a’. Open the attribute manager. Click the ‘link’ button. Click the ‘filter’ button. Select ‘default value’ from the first drop down list. Select ‘triadType300’ from the second drop down list. Select ‘has individual value’ from the third drop down list. Click the radial button ‘replace’. Click ‘select’. Click ‘close’.

From the main VISONE drop down bar, select ‘links’. Click ‘properties’. Click the given color next to ‘color:’. Select ‘rgb’ tab. Set the ‘red’, ‘green’, and ‘blue’ values to 0. Set the ‘alpha’ value to 255. Set ‘opacity’ to 50%. Click the ‘close’ button. Set the ‘width:’ value to 5.0. From the ‘edge properties’ dialogue box, click the ‘apply’ button. Click ‘close.

Reduce visibility of all non-clique selections. Select all nodes and links. Click an empty part of the graph. Press the keys ‘Ctrl’ and ‘a’. Open the attribute manager. Click the ‘link’ button. Click the ‘filter’ button. Select ‘default value’ from the first drop down list. Select ‘triadType300’ from the second drop down list. Select ‘has individual value’ from the third drop down list. Click the radial button ‘remove’. Click ‘select’. Click ‘close’.

From the main VISONE drop down bar, select ‘links’. Click ‘properties’. Click the given color next to ‘color:’. Set ‘opacity’ to 20%. Set the ‘width:’ value to 2.0. From the ‘edge properties’ dialogue box, click the ‘apply’ button. Click ‘close.

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Periodically Run an R Script as a Background Process using launchd under OSX

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launchdBeforelaunchdAfter

Computers are great at doing repetitive things a lot, so why deprive them of doing what they do best by manually re-running the same code every night? Here we create a simple bash script to execute an R script and define a *.plist so that launchd, under OSX, can run it periodically. The *.plist code given here is configured to run a shell script every day at 8:00 PM.

R script

setwd('~/')
xBar <- mean(c(1,2,1,21,2,3,2))
write.csv(xBar,'rOutput.csv')

Save code as rCode.R to the ~/ directory.

Bash shell script

/usr/bin/Rscript ~/rCode.R

Save code as rShellScript.sh file to the ~/ directory.

Execute chmod +x rShellScript.sh in the terminal to make this file runnable.

*.plist file

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>Label</key>
	<string>com.rTask</string>
	<key>ProgramArguments</key>
	<array>
		<string>/Path/to/shell/script/rShellScript.sh</string>
	</array>
	<key>StartCalendarInterval</key>
	<dict>
		<key>Hour</key>
		<integer>20</integer>
		<key>Minute</key>
		<integer>00</integer>
	</dict>
</dict>
</plist>

Save code as com.rTask.plist file under path ~/Library/LaunchAgents.

Run the command launchctl load ~/Library/LaunchAgents/com.rTask.plist.

In this way, we should now be able to periodically run an R script automatically. Fun, huh? For a more lengthy description of how to do this and how it all works see the Creating Launch Daemons and Agents section of the Daemons and Services Programming Guide under the Mac Developer Library at the developer.apple.com website.

Workflow with ESS, Knitr and R

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With literate programming we can now embed R code into our working LaTeX documents. Literate programming or reproducible research is often attributed to Donald Knuth, more information of which can be found on the literate programming website. This means that minor updates in code no longer require hours of copying and pasting output into our working document.

Of course Yi Hui’s knitr site gives you basically everything you need to get started. To setup knitr in R, install and load it as you would any other R package install.packages("knitr") and library("knitr").

To setup Emacs so that knitr can be used with ESS, follow the fine instructions provided by Simon Potter and the blog constantMindMapping.

The only real tip that I have to add is how to compile the *.Rnw file in Emacs. This can be done as shown on Simon Potter’s site by using the command M-n r followed by the command, as shown on Yi Hui’s github discussion, “knitr support in other editors (Emacs/ESS/Vim/Eclipse…),” M-n P. Note that this is a capital ‘P’.

The following documents were updated and compiled within seconds of each other! Given a more complicated set of statistical analyses this kind of work could have taken hours and resulted severe eyestrain or headache.

knitrExample