rmultinom(1,1000,c(.1,.7,.2,.1))

gives me a vector of random numbers from a multinomial distribution with outcomes 1, 2, 3, and 4, where the probability of observing a ’1′ is 10 percent, the probability of observing a ’2′ is 70 per cent, and so on. But I could not find an equivalent function in Stata. Generating artificial data in R is not very elegant, so I kept digging and found a solution in section M-5 of the Mata handbook. Hidden in the entry on runiform is a reference to rdiscrete(r,c,p), a Mata function which generates a r*c matrix of draws from a multinomial distribution defined by a vector p of probabilities.

That leaves but one question: Is wrapping a handful of lines around a Mata call to replace a non-existent Stata function more elegant than calling an external program?

• The Data and Story Library – DASL (pronounced “dazzle”) is an online library of datafiles and stories that illustrate the use of basic statistics methods. We hope to provide data from a wide variety of topics so that statistics teachers can find real-world examples that will be interesting to their students. Use DASL’s powerful search engine to locate the story or datafile of interest.
• Drawing graphs using tikz/pgf & gnuplot | politicaldata.org -

• SNAP: Network datasets -
• Network data -
• CASOS Tools: Network Analysis Data | CASOS – Network Analysis Data Casos & INSA
• NodeXL Teaching – CASCI – datasets incl. senate
• Statistics and Data links roundup -

It’s surprisingly difficult to find suitable datasets for a sna workshop that are relevant for political scientists.

• Kai Arzheimer: Vorlesung Statistik II -
• Welcome | Teaching with Data (QSSDL) – TeachingWithData.org (TwD) is a repository of tools and educational materials designed to improve quantitative literacy skills in social science courses. Built especially for faculty teaching post-secondary courses in such areas as demography, economics, geography, political science, social psychology, and sociology, the materials include stand-alone learning activities, tools, and pedagogy services.The goal is to make it easier for faculty to bring real social science data into courses across the curriculum ranging from introductory classes to senior seminars.
• Social Science Statistics Blog: The changing nature of R resources -
• Gary King – What to do About Missing Values in Time Series Cross-Section Data – James Honaker and Gary King, “What to do About Missing Values in Time Series Cross-Section Data,” American Journal of Political Science, (April, 2010), forthcoming, copy at http://gking.harvard.edu/files/abs/pr-abs.shtml. (Paper: PDF)

Image via Wikipedia

These days, a bonanza of political information is freely available on the internet.  Sometimes this information comes in the guise of excel sheets, comma separated data or other formats which are more or less readily machine readable. But more often than not, information is presented as tables designed to be read by humans. This is where the gentle art of screen scraping, web scraping or spidering comes in. In the past, I have used kludgy Perl scripts to get electoral results at the district level off sites maintained by the French ministry of the interior or by universities (very interesting if you do not really speak/read French). A slightly more elegant approach might be to use R’s builtin Perl-like capabilities for doing the job, as demonstrated by Simon Jackman. Finally, Python is gaining ground in the political science community,  which has some very decent libraries for screen/web scraping – see this elaborate post on Drew Conway’s Zero Intelligence Agents blog. But, let’s face it: I am lazy. I want to spend time analysing the data, not scraping them. And so I was very pleased when I came across outwit, a massive plugin for the firefox browser (Linux, Mac and Windows versions available) that acts as a point-and-click scraper.

French Départements (from Wikipedia)

Say you need a dataset with the names and Insee numbers for all the French Départements. The (hopefully trustworthy) Wikipedia page has a neat table, complete with information on the Prefecture and many tiny coats of arms which are of absolutely no use at all. We could either key in the relevant data (doable, but a nuisance), or we could try to copy and paste the table into a word processor, hoping that we do not lose accents and other funny characters, and that WinWord or whatever we use converts the HTML table into something that we can edit to extract the information we really need.

Or you we could use outwit. One push of the button loads the page

Scraping a table with outwit

into a sub-window, a second push (data->tables) extracts the HTML tables on the page. Now, we can either mark the lines we are interested in by hand (often the quickest option) or use a filter to selfect them. One final click, and they are exported as a CSV file that can be read into R, OpenOffice, or Stata for post processing and analysis.

While I’m all in favour of scriptable and open-source tools like Perl, Python and R, outwit has a lot to go for it if all you need is a quick hack. Outwit also has functions to mass-download files (say PDFs) from a page and give the unique names. If the job is complex, there is even more functionality under the hood, and you can use the point-and-click interface to program you own scraper, though I would tend use a real programming language for these cases. At any rate, outwit is a useful and free tool for the lazy data analyst.

The Missing Manual

The only thing that is missing is a proper manual, but even this is not really a problem since Pajek’s creators have written a very accessible introduction to social network analysis that doubles up as documentation for the program (order from amazon.co.uk, amazon.com, amazon.de. However, Pajek has been under constant development since the 1990s (!) and has acquired a lot of new features since the book was published. Some of them are documented in an appendix, others are simply listed in the very short document that is the official manual for Pajek. You will want to go through the many presentations which are available via the Pajek wiki.

Of course, there is much more software available, often at no cost. If you do program Java or Python (I don’t), there are several libraries available that look very promising. Amongst the stand-alone programs, visone stands out because it can easily produce very attractive-looking graphs of small networks. Even more software has been developed in the context of other sciences that have an interest in networks (chemistry, biology, engineering etc.)
Here is a rather messy collection of links to sna software. Generally, you will want something that is more systematic and informative. Ines Mergel has recently launched a bid for creating a comprehensive software list on wikipedia. The resulting page on social network analysis software is obviously work in progress but provides very valuable guidance.

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