How Search Engines Allows Copyright Infringement

Add the words- free download and search engines will show you all the links for the download of the copyrighted material .

Now why would anyone want to use the word “free download” . Unless search engines come up with a better filter to commonly used keywords for copyright infringement- this loop hole will stay.

Step 1

Use the chrome browser for convenient searching of  “name of copyrighted material” + “free download”

Screenshot 2014-05-20 18.56.31

Step 2

Scroll down page of Google Search Results


Screenshot 2014-05-20 18.56.45

Step 3

Download using whatever that particular website does

Screenshot 2014-05-20 18.56.02

Step 4

Dont be Evil. Yup. That’s my friends

Screenshot 2014-05-20 18.56.16




Step 5

Use Image Search to write blog post ruing your poverty


FOAS is now going to reduce your tax if you support open source

Announcement from the retiring founder of

FOAS is a nonprofit public benefit corporation registered in California. It has federal tax-exempt status under Internal Revenue Section 501(c)(3). FOAS has a worldwide mission to to promote free software, open access publishing, and reproducible research in statistics.

FOAS now has 501(c)(3) federal tax exempt status. All donations to FOAS, retroactively from September 2012, are now tax deductible.

What is FOAS-

The mission of the Foundation for Open Access Statistics (FOAS) is to promote free software, open access publishing, and reproducible research in statistics.

FOAS works to ensure the continued success of the Journal of Statistical Software (JSS), one of the few major open access journals that is free for both readers and authors. We also promote the use and development of free software for statistics, such as the R language and environment for computational statistics. We encourage members and the academic community at large to publish reproducible research that is publicly available online, e.g., in an open access journal or on an open access pre-print server.

You can join FOAS to show your support for free statistical software, open access publishing, and reproducible research in statistics. Membership is free and open to all. Members receive periodic email updates from FOAS, and may wish to join the FOAS-members list, join the Facebook FOAS Group, or follow FOAS on Diaspora.

Consider making a donation to FOAS. All donations are currently being used to support the operation of JSS.


eh, Google how about routing less money through the Irish and giving some to Californian open source…

Related- cultural icon to non open source companies.



This is why code written by scientists gets ugly

brilliant piece of why scientists write bad code

What You're Doing Is Rather Desperate

There’s a lot of discussion around why code written by self-taught “scientist programmers” rarely follows what a trained computer scientist would consider “best practice”. Here’s a recent post on the topic.

One answer: we begin with exploratory data analysis and never get around to cleaning it up.

An example. For some reason, a researcher (let’s call him “Bob”) becomes interested in a particular dataset in the GEO database. So Bob opens the R console and use the GEOquery package to grab the data:

Bob is interested in the covariates and metadata associated with the experiment, which he can access using pData().

Bob discovers that pd$characteristics_ch1.2 is “age at examination”, but it’s stored as a factor. He’d like to use it as a numeric variable. So he sets about figuring out how to do the conversion.

Three levels of nested methods. Ugly. However, it works, so Bob moves to…

View original post 34 more words

Dining in San Francisco – Let R Guide You

Rolling Your Rs

I’m frequently asked by newcomers to R to provide an easy to follow generic set of instructions on how to download data, transform it, aggregate it, make graphs, and write it all up for publication in a high impact journal – all by the end of the day ! While such a request is somewhat understandable coming from a student it’s somewhat awkward when coming from a seasoned research investigator, (i.e. someone who should know better). Obviously, no such set of instructions exists given the astonishing variety of available data stored in differing formats with varying degrees of adherence to a standard(s). Simply put, “real world” data wrangling and analysis can be a complex process independent of the language you choose to employ. Nonetheless, what I’ll attempt to do here is provide a “tour” involving some data I picked at random. I’ll be sticking to the problems of downloading, filtering…

View original post 1,413 more words

Analyzing Facebook Networks using #rstats

Analyzing Facebook Networks using #rstats is given below. Note this can be replicated by anyone- and you can theoretically be able to create adjacency matrix (friend of friends) of all people who have given permission into your app- thus maybe a network analysis of people to some group (like R Facebook group or even FOASStat is possible

While traditional marketing and targeting focusses on variables like demographics and psychographics, social network analysis lets you tap into influential people within communities with the right offer and then let the free retweet, reshare as proxy free ads. The hypothesis being that people in similar network clusters have similar consumer behaviours (not just in similar demographic or psychographics). Also this should ideally be able to capture changing consumer behaviour faster for better targeting.


1) Go to

2) Create a new app

Screenshot 2014-05-10 21.30.53

3) Change url in settings ( to http://localhost:1410 ). I wish we could do this in twitteR package and oAuth for TWitter but I have no clue . that. Also coming up post on Google Plus and LinkedIn (if I can)

Screenshot 2014-05-10 21.32.01

3) Copy the secret thingies from the dashboard. DO click the screenshots to enlarge (i.e to understand what I am trying to do here)

Screenshot 2014-05-10 21.25.00

4) Modify code below with the secret thingies above

#fbOAuth(app_id, app_secret, extended_permissions = TRUE)
fb_oauth=fbOAuth("241634542707615", "4f2e6273c08d1b56d1bf14626d9e19a6", extended_permissions = TRUE)
#fb_oauth <- fbOAuth(app_id="123456789", app_secret="1A2B3C4D")
save(fb_oauth, file="fb_oauth")

Created by Pretty R at

5) Hey now we have a fb ouath object we can just load it up. we the following code can then be modified at will to get your analysis

me <- getUsers("me", token=fb_oauth)
#getFQL(query, token)
my_friends <- getFriends(token=fb_oauth, simplify=F)
pie(table(my_friends$relationship_status),col=brewer.pal(5, "Set1"))
pie(table(my_friends$location),col=brewer.pal(20, "Greens"))
pie(table(my_friends$locale),col=brewer.pal(4, "Blues"))
pie(table(my_friends$gender),col=brewer.pal(3, "Oranges"))
mat <- getNetwork(token=fb_oauth, format="adj.matrix")
network <- graph.adjacency(mat, mode="undirected")
plot(network ,vertex.size=5, 

Created by Pretty R at

6) Voila my FB graph


and analyzed for how are my homies


Europeans into open relationships more


Places my friends are


Languages they speak

and all with R

Note- this is what an adjancency matrix is- just a fancy word for turning your Fb friends into a 1-0 matrix based on their relationships. The more friends you have the more interesting this analysis

Now lets do likes analysis

I use this code and my own username (since I am the only one who has allowed the app permissions!)


Boy I like a lot of websites huh- I wonder if I can cluster/ data reduce/association analysis  them (but that’s coming up  or is it?)


Screenshot 2014-05-23 01.31.42


Screenshot 2014-05-10 21.48.56

Hat tips-