code – Page 4 – DECISION STATS

Using Google Analytics API with R:dimensions and metrics

I modified the query I wrote earlier at http://www.decisionstats.com/using-google-analytics-with-r/to get multiple dimensions and metrics from the Google Analytics API, like hour of day,day of week to get cyclical parameters.We are adding the dimensions, and metrics to bring more depth in our analysis.Basically we are trying to do a time series analysis for forecasting web analytics data( which is basically time -stamped and rich in details ).

Basically I am modifying the dimensions and metrics parameters of the query code using the list at

http://code.google.com/apis/analytics/docs/gdata/dimsmets/dimsmets.html

query <- QueryBuilder()
query$Init(start.date = "2011-08-20",
                   end.date = "2012-08-25",
                   dimensions = c("ga:date","ga:hour","ga:dayOfWeek"),
                   metrics = c("ga:visitors","ga:visits","ga:pageviews","ga:timeOnSite"),
                   sort = c("ga:date","ga:hour","ga:dayOfWeek"),
                   table.id = paste(profiles$profile[3,3]))

#5. Make a request to get the data from the API 

ga.data <- ga$GetReportData(query)

#6. Look at the returned data 

str(ga.data)

head(ga.data$data)

and we need the lubridate package to create a ymd:hour (time stamp) since GA gives data aggregated at a hourly level at most. Also we need to smoothen the effect of weekend on web analytics data.

#Using package lubridate to convert character dates into time

library(lubridate)
ga.data$data[,1]=ymd(ga.data$data[,1])
ls()
dataset1=ga.data$data
names(dataset1) <- make.names(names(dataset1))
str(dataset1)
head(dataset1)

To be continued-

Web Analytics using R , Google Analytics and TS Forecasting

This is a continuation of the previous post on using Google Analytics .

Now that we have downloaded and plotted the data- we try and fit time series to the website data to forecast future traffic.

Some observations-

1) Google Analytics has 0 predictive analytics, it is just descriptive analytics and data visualization models (including the recent social analytics). However you can very well add in basic TS function using R to the GA API.

Why do people look at Website Analytics? To know today’s traffic and derive insights for the Future

2) Web Data clearly follows a 7 day peak and trough for weekly effects (weekdays and weekends), this is also true for hourly data …and this can be used for smoothing historic web data for future forecast.

3) On an advanced level, any hugely popular viral posts can be called a level shift (not drift) and accoringly dampened.

Test and Control!

Similarly using ARIMAX, we can factor in quantity and tag of posts as X regressor variables.

and now the code-( dont laugh at the simplicity please, I am just tinkering and playing with data here!)

You need to copy and paste the code at the bottom of this post http://www.decisionstats.com/using-google-analytics-with-r/ if you want to download your GA data down first.

Note I am using lubridate ,forecast and timeSeries packages in this section.

#Plotting the Traffic  plot(ga.data$data[,2],type="l")

library(timeSeries)
library(forecast)

#Using package lubridate to convert character dates into time
library(lubridate)
ga.data$data[,1]=ymd(ga.data$data[,1])
ls()
dataset1=ga.data$data
names(dataset1) <- make.names(names(dataset1))
str(dataset1)
head(dataset1)
dataset2 <- ts(dataset1$ga.visitors,start=0,frequency = frequency(dataset1$ga.visitors), names=dataset1$ga.date)
str(dataset2)
head(dataset2)
ts.test=dataset2[1:200]
ts.control=dataset2[201:275]

 #Note I am splitting the data into test and control here

fitets=ets(ts.test)
plot(fitets)
testets=ets(ts.control,model=fitets)
accuracy(testets)
plot(testets)
spectrum(ts.test,method='ar')
decompose(ts.test)

library("TTR")
bb=SMA(dataset2,n=7)#We are doing a simple moving average for every 7 days. Note this can be 24 hrs for hourly data, or 30 days for daily data for month # 

to month comparison or 12 months for annual
#We notice that Web Analytics needs sommethening for every 7 thday as there is some relation to traffic on weekedays /weekends /same time last week
head(dataset2,40)
head(bb,40)

par(mfrow=c(2,1))
plot(bb,type="l",main="Using Seven Day Moving Average for Web Visitors")
plot(dataset2,main="Original Data")

Created by Pretty R at inside-R.org

Though I still wonder why the R query, gA R code /package could not be on the cloud (why it needs to be downloaded)– cloud computing Gs?

Also how about adding some MORE predictive analytics to Google Analytics, chaps!

To be continued-

auto.arima() and forecasts!!!

cross validations!!!

and adapting the idiosyncratic periods and cycles of web analytics to time series !!

Using Google Analytics with R

Some code to read in data from Google Analytics data. Some modifications include adding the SSL authentication code and modifying (in bold) the table.id parameter to choose correct website from a GA profile with many websites

The Google Analytics Package files can be downloaded from http://code.google.com/p/r-google-analytics/downloads/list

It provides access to Google Analytics data natively from the R Statistical Computing programming language. You can use this library to retrieve an R data.frame with Google Analytics data. Then perform advanced statistical analysis, like time series analysis and regressions.

Supported Features

Access to v2 of the Google Analytics Data Export API Data Feed
A QueryBuilder class to simplify creating API queries
API response is converted directly into R as a data.frame
Library returns the aggregates, and confidence intervals of the metrics, dynamically if they exist
Auto-pagination to return more than 10,000 rows of information by combining multiple data requests. (Upper Limit 1M rows)
Authorization through the ClientLogin routine
Access to all the profiles ids for the authorized user
Full documentation and unit tests

Code-

> library(XML)

> library(RCurl)

Loading required package: bitops

> #Change path name in the following to the folder you downloaded the Google Analytics Package

> source(“C:/Users/KUs/Desktop/CANADA/R/RGoogleAnalytics/R/RGoogleAnalytics.R”)

> source(“C:/Users/KUs/Desktop/CANADA/R/RGoogleAnalytics/R/QueryBuilder.R”)

> # download the file needed for authentication

> download.file(url=”http://curl.haxx.se/ca/cacert.pem”, destfile=”cacert.pem”)

trying URL ‘http://curl.haxx.se/ca/cacert.pem’ Content type ‘text/plain’ length 215993 bytes (210 Kb) opened

URL downloaded 210 Kb

> # set the curl options

> curl <- getCurlHandle()

> options(RCurlOptions = list(capath = system.file(“CurlSSL”, “cacert.pem”,

+ package = “RCurl”),

+ ssl.verifypeer = FALSE))

> curlSetOpt(.opts = list(proxy = ‘proxyserver:port’), curl = curl)

An object of class “CURLHandle” Slot “ref”: <pointer: 0000000006AA2B70>

> # 1. Create a new Google Analytics API object

> ga <- RGoogleAnalytics()

> # 2. Authorize the object with your Google Analytics Account Credentials

> ga$SetCredentials(“USERNAME”, “PASSWORD”)

> # 3. Get the list of different profiles, to help build the query

> profiles <- ga$GetProfileData()

> profiles #Error Check to See if we get the right website

$profile AccountName ProfileName TableId

1 dudeofdata.com dudeofdata.com ga:44926237

2 knol.google.com knol.google.com ga:45564890

3 decisionstats.com decisionstats.com ga:46751946

$total.results

total.results

1 3

> # 4. Build the Data Export API query

> #Modify the start.date and end.date parameters based on data requirements

> #Modify the table.id at table.id = paste(profiles$profile[X,3]) to get the X th website in your profile

> # 4. Build the Data Export API query

> query <- QueryBuilder() > query$Init(start.date = “2012-01-09”, + end.date = “2012-03-20”, + dimensions = “ga:date”,

+ metrics = “ga:visitors”,

+ sort = “ga:date”,

+ table.id = paste(profiles$profile[3,3]))

> #5. Make a request to get the data from the API

> ga.data <- ga$GetReportData(query)

[1] “Executing query: https://www.google.com/analytics/feeds/data?start-date=2012%2D01%2D09&end-date=2012%2D03%2D20&dimensions=ga%3Adate&metrics=ga%3Avisitors&sort=ga%3Adate&ids=ga%3A46751946”

> #6. Look at the returned data

> str(ga.data)

List of 3

$ data :’data.frame’: 72 obs. of 2 variables: ..

$ ga:date : chr [1:72] “20120109” “20120110” “20120111” “20120112” … ..

$ ga:visitors: num [1:72] 394 405 381 390 323 47 169 67 94 89 …

$ aggr.totals :’data.frame’: 1 obs. of 1 variable: ..

$ aggregate.totals: num 28348

$ total.results: num 72

> head(ga.data$data)

ga:date ga:visitors

1 20120109 394

2 20120110 405

3 20120111 381

4 20120112 390

5 20120113 323

6 20120114 47 >

> #Plotting the Traffic >

> plot(ga.data$data[,2],type=”l”)

Update- Some errors come from pasting Latex directly to WordPress. Here is some code , made pretty-r in case you want to play with the GA api

library(XML)

library(RCurl)

#Change path name in the following to the folder you downloaded the Google Analytics Package 

source("C:/Users/KUs/Desktop/CANADA/R/RGoogleAnalytics/R/RGoogleAnalytics.R")

source("C:/Users/KUs/Desktop/CANADA/R/RGoogleAnalytics/R/QueryBuilder.R")
# download the file needed for authentication
download.file(url="http://curl.haxx.se/ca/cacert.pem", destfile="cacert.pem")

# set the curl options
curl <- getCurlHandle()
options(RCurlOptions = list(capath = system.file("CurlSSL", "cacert.pem",
package = "RCurl"),
ssl.verifypeer = FALSE))
curlSetOpt(.opts = list(proxy = 'proxyserver:port'), curl = curl)

# 1. Create a new Google Analytics API object 

ga <- RGoogleAnalytics()

# 2. Authorize the object with your Google Analytics Account Credentials 

ga$SetCredentials("ohri2007@gmail.com", "XXXXXXX")

# 3. Get the list of different profiles, to help build the query

profiles <- ga$GetProfileData()

profiles #Error Check to See if we get the right website

# 4. Build the Data Export API query 

#Modify the start.date and end.date parameters based on data requirements 

#Modify the table.id at table.id = paste(profiles$profile[X,3]) to get the X th website in your profile 
# 4. Build the Data Export API query
query <- QueryBuilder()
query$Init(start.date = "2012-01-09",
                   end.date = "2012-03-20",
                   dimensions = "ga:date",
                   metrics = "ga:visitors",
                   sort = "ga:date",
                   table.id = paste(profiles$profile[3,3]))

#5. Make a request to get the data from the API 

ga.data <- ga$GetReportData(query)

#6. Look at the returned data 

str(ga.data)

head(ga.data$data)

#Plotting the Traffic 

plot(ga.data$data[,2],type="l")

Created by Pretty R at inside-R.org

Text Mining Barack Obama using R #rstats

We copy and paste President Barack Obama’s “Yes We Can” speech in a text document and read it in. For a word cloud we need a dataframe with two columns, one with words and the the other with frequency.We read in the transcript from http://www.nytimes.com/2008/01/08/us/politics/08text-obama.html?pagewanted=all&_r=0 and paste in the file located in the local directory- /home/ajay/Desktop/new. Note tm is a powerful package and will read ALL the text documents within the particular folder

library(tm)

library(wordcloud)

txt2=”/home/ajay/Desktop/new”

b=Corpus(DirSource(txt2), readerControl = list(language = “eng”))

> b b b tdm m1 v1 d1 wordcloud(d1$word,d1$freq)

Now it seems we need to remove some of the very commonly occuring words like “the” and “and”. We are not using the standard stopwords in english (the tm package provides that see Chapter 13 Text Mining case studies), as the words “we” and “can” are also included .

> b tdm m1 v1 d1 wordcloud(d1$word,d1$freq)

But let’s see how the wordcloud changes if we remove all English Stopwords.

> b tdm m1 v1 d1 wordcloud(d1$word,d1$freq)

and you can draw your own conclusions from the content of this famous speech based on your political preferences.

Politicians can give interesting speeches but they may be full of simple sounding words…..

Citation-

1. Ingo Feinerer (2012). tm: Text Mining Package. R package version0.5-7.1.

Ingo Feinerer, Kurt Hornik, and David Meyer (2008). Text Mining
Infrastructure in R. Journal of Statistical Software 25/5. URL:
http://www.jstatsoft.org/v25/i05/

2. Ian Fellows (2012). wordcloud: Word Clouds. R package version 2.0.

http://CRAN.R-project.org/package=wordcloud

3. You can see more than 100 of Obama’s speeches at http://obamaspeeches.com/

Quote- numbers dont lie, people do.

Facebook and R

Part 1 How do people at Facebook use R?

tamar Rosenn, Facebook

Itamar conveyed how Facebook’s Data Team used R in 2007 to answer two questions about new users: (i) which data points predict whether a user will stay? and (ii) if they stay, which data points predict how active they’ll be after three months?

For the first question, Itamar’s team used recursive partitioning (via the rpartpackage) to infer that just two data points are significantly predictive of whether a user remains on Facebook: (i) having more than one session as a new user, and (ii) entering basic profile information.

For the second question, they fit the data to a logistic model using a least angle regression approach (via the lars package), and found that activity at three months was predicted by variables related to three classes of behavior: (i) how often a user was reached out to by others, (ii) frequency of third party application use, and (iii) what Itamar termed “receptiveness” — related to how forthcoming a user was on the site.

source-http://www.dataspora.com/2009/02/predictive-analytics-using-r/

and cute graphs like the famous

https://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919

and

studying baseball on facebook

https://www.facebook.com/notes/facebook-data-team/baseball-on-facebook/10150142265858859

by counting the number of posts that occurred the day after a team lost divided by the total number of wins, since losses for great teams are remarkable and since winning teams’ fans just post more.

But mostly at

https://www.facebook.com/data?sk=notes and https://www.facebook.com/data?v=app_4949752878

and creating new packages

1. jjplot (not much action here!)

https://r-forge.r-project.org/scm/viewvc.php/?root=jjplot

though

I liked the promise of JJplot at

http://pleasescoopme.com/2010/03/31/using-jjplot-to-explore-tipping-behavior/

2. ising models

https://github.com/slycoder/Rflim

https://www.facebook.com/note.php?note_id=10150359708746212

3. R pipe

https://github.com/slycoder/Rpipe

even the FB interns are cool

http://brenocon.com/blog/2009/02/comparison-of-data-analysis-packages-r-matlab-scipy-excel-sas-spss-stata/

Part 2 How do people with R use Facebook?

Using the API at https://developers.facebook.com/tools/explorer

and code mashes from

http://romainfrancois.blog.free.fr/index.php?post/2012/01/15/Crawling-facebook-with-R

http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html

but the wonderful troubleshooting code from http://www.brocktibert.com/blog/2012/01/19/358/

which needs to be added to the code first

and using network package

>access_token=”XXXXXXXXXXXX”

Annoyingly the Facebook token can expire after some time, this can lead to huge wait and NULL results with Oauth errors

If that happens you need to regenerate the token

What we need
> require(RCurl)
> require(rjson)
> download.file(url=”http://curl.haxx.se/ca/cacert.pem”, destfile=”cacert.pem”)

Roman’s Famous Facebook Function (altered)

> facebook <- function( path = “me”, access_token , options){
+ if( !missing(options) ){
+ options <- sprintf( “?%s”, paste( names(options), “=”, unlist(options), collapse = “&”, sep = “” ) )
+ } else {
+ options <- “”
+ }
+ data <- getURL( sprintf( “https://graph.facebook.com/%s%s&access_token=%s”, path, options, access_token ), cainfo=”cacert.pem” )
+ fromJSON( data )
+ }

Now getting the friends list
> friends <- facebook( path=”me/friends” , access_token=access_token)
> # extract Facebook IDs
> friends.id <- sapply(friends$data, function(x) x$id)
> # extract names
> friends.name <- sapply(friends$data, function(x) iconv(x$name,”UTF-8″,”ASCII//TRANSLIT”))
> # short names to initials
> initials <- function(x) paste(substr(x,1,1), collapse=””)
> friends.initial <- sapply(strsplit(friends.name,” “), initials)

This matrix can take a long time to build, so you can change the value of N to say 40 to test your network. I needed to press the escape button to cut short the plotting of all 400 friends of mine.
> # friendship relation matrix
> N <- length(friends.id)
> friendship.matrix <- matrix(0,N,N)
> for (i in 1:N) {
+ tmp <- facebook( path=paste(“me/mutualfriends”, friends.id[i], sep=”/”) , access_token=access_token)
+ mutualfriends <- sapply(tmp$data, function(x) x$id)
+ friendship.matrix[i,friends.id %in% mutualfriends] <- 1
+ }

Plotting using Network package in R (with help from the comments at http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html)

> require(network)

>net1<- as.network(friendship.matrix)

> plot(net1, label=friends.initial, arrowhead.cex=0)

(Rgraphviz is tough if you are on Windows 7 like me)

but there is an alternative igraph solution at https://github.com/sciruela/facebookFriends/blob/master/facebook.r

After all that-..talk.. a graph..of my Facebook Network with friends initials as labels..

Opinion piece-

I hope plans to make the Facebook R package get fulfilled (just as the twitteR package led to many interesting analysis)

and also Linkedin has an API at http://developer.linkedin.com/apis

I think it would be interesting to plot professional relationships across social networks as well. But I hope to see a LinkedIn package (or blog code) soon.

As for jjplot, I had hoped ggplot and jjplot merged or atleast had some kind of inclusion in the Deducer GUI. Maybe a Google Summer of Code project if people are busy!!

Also the geeks at Facebook.com can think of giving something back to the R community, as Google generously does with funding packages like RUnit, Deducer and Summer of Code, besides sponsoring meet ups etc.

(note – this is part of the research for the upcoming book ” R for Business Analytics”)

ps-

but didnt get time to download all my posts using R code at

https://gist.github.com/1634662#

or do specific Facebook Page analysis using R at

http://tonybreyal.wordpress.com/2012/01/06/r-web-scraping-r-bloggers-facebook-page-to-gain-further-information-about-an-authors-r-blog-posts-e-g-number-of-likes-comments-shares-etc/

Updated-

 #access token from https://developers.facebook.com/tools/explorer
access_token="AAuFgaOcVaUZAssCvL9dPbZCjghTEwwhNxZAwpLdZCbw6xw7gARYoWnPHxihO1DcJgSSahd67LgZDZD"
require(RCurl)
 require(rjson)
# download the file needed for authentication http://www.brocktibert.com/blog/2012/01/19/358/
download.file(url="http://curl.haxx.se/ca/cacert.pem", destfile="cacert.pem")
# http://romainfrancois.blog.free.fr/index.php?post/2012/01/15/Crawling-facebook-with-R
facebook <- function( path = "me", access_token = token, options){
if( !missing(options) ){
options <- sprintf( "?%s", paste( names(options), "=", unlist(options), collapse = "&", sep = "" ) )
} else {
options <- ""
}
data <- getURL( sprintf( "https://graph.facebook.com/%s%s&access_token=%s", path, options, access_token ), cainfo="cacert.pem" )
fromJSON( data )
}

 # see http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html

# scrape the list of friends
friends <- facebook( path="me/friends" , access_token=access_token)
# extract Facebook IDs
friends.id <- sapply(friends$data, function(x) x$id)
# extract names 
friends.name <- sapply(friends$data, function(x)  iconv(x$name,"UTF-8","ASCII//TRANSLIT"))
# short names to initials 
initials <- function(x) paste(substr(x,1,1), collapse="")
friends.initial <- sapply(strsplit(friends.name," "), initials)

# friendship relation matrix
#N <- length(friends.id)
N <- 200
friendship.matrix <- matrix(0,N,N)
for (i in 1:N) {
  tmp <- facebook( path=paste("me/mutualfriends", friends.id[i], sep="/") , access_token=access_token)
  mutualfriends <- sapply(tmp$data, function(x) x$id)
  friendship.matrix[i,friends.id %in% mutualfriends] <- 1
}
require(network)
net1<- as.network(friendship.matrix)
plot(net1, label=friends.initial, arrowhead.cex=0)

Created by Pretty R at inside-R.org

Interview: Hjálmar Gíslason, CEO of DataMarket.com

Here is an interview with Hjálmar Gíslason, CEO of Datamarket.com . DataMarket is an active marketplace for structured data and statistics. Through powerful search and visual data exploration, DataMarket connects data seekers with data providers.

Ajay- Describe your journey as an entrepreneur and techie in Iceland. What are the 10 things that surprised you most as a tech entrepreneur.

HG- DataMarket is my fourth tech start-up since at age 20 in 1996. The previous ones have been in gaming, mobile and web search. I come from a technical background but have been moving more and more to the business side over the years. I can still prototype, but I hope there isn’t a single line of my code in production!

Funny you should ask about the 10 things that have surprised me the most on this journey, as I gave a presentation – literally yesterday – titled: “9 things nobody told me about the start-up business”

They are:
* Do NOT generalize – especially not to begin with
* Prioritize – and ﬁnd a work-ﬂow that works for you
* Meet people – face to face
* You are a sales person – whether you like it or not
* Technology is not a product – it’s the entire experience
* Sell the current version – no matter how amazing the next one is
* Learn from mistakes – preferably others’
* Pick the right people – good people is not enough
* Tell a good story – but don’t make them up

I obviously elaborate on each of these points in the talk, but the points illustrate roughly some of the things I believe I’ve learned … so far 😉

9 things nobody told me about the start-up business

Ajay-

Both Amazon and Google have entered the public datasets space. Infochimps has 14,000+ public datasets. The US has http://www.data.gov/

So clearly the space is both competitive and yet the demand for public data repositories is clearly under served still.

How does DataMarket intend to address this market in a unique way to differentiate itself from others.

HG- DataMarket is about delivering business data to decision makers. We help data seekers find the data they need for planning and informed decision making, and data publishers reaching this audience. DataMarket.com is the meeting point, where data seekers can come to find the best available data, and data publishers can make their data available whether for free or for a fee. We’ve populated the site with a wealth of data from public sources such as the UN, Eurostat, World Bank, IMF and others, but there is also premium data that is only available to those that subscribe to and pay for the access. For example we resell the entire data offering from the EIU (Economist Intelligence Unit) (link: http://datamarket.com/data/list/?q=provider:eiu)

DataMarket.com allows all this data to be searched, visualized, compared and downloaded in a single place in a standard, unified manner.

We see many of these efforts not as competition, but as valuable potential sources of data for our offering, while others may be competing with parts of our proposition, such as easy access to the public data sets.

Ajay- What are your views on data confidentiality and access to data owned by Governments funded by tax payer money.

HG- My views are very simple: Any data that is gathered or created for taxpayers’ money should be open and free of charge unless higher priorities such as privacy or national security indicate otherwise.

Reflecting that, any data that is originally open and free of charge is still open and free of charge on DataMarket.com, just easier to find and work with.

Ajay- How is the technology entrepreneurship and venture capital scene in Iceland. What things work and what things can be improved?

HG- The scene is quite vibrant, given the small community. Good teams with promising concepts have been able to get the funding they need to get started and test their footing internationally. When the rapid growth phase is reached outside funding may still be needed.

There are positive and negative things about any location. Among the good things about Iceland from the stand point of a technology start-up are highly skilled tech people and a relatively simple corporate environment. Among the bad things are a tiny local market, lack of skills in international sales and marketing and capital controls that were put in place after the crash of the Icelandic economy in 2008.

I’ve jokingly said that if a company is hot in the eyes of VCs it would get funding even if it was located in the jungles of Congo, while if they’re only lukewarm towards you, they will be looking for any excuse not to invest. Location can certainly be one of them, and in that case being close to the investor communities – physically – can be very important.

We’re opening up our sales and marketing offices in Boston as we speak. Not to be close to investors though, but to be close to our market and current customers.

Ajay- Describe your hobbies when you are not founding amazing tech startups.

HG- Most of my time is spent working – which happens to by my number one hobby.

It is still important to step away from it all every now and then to see things in perspective and come back with a clear mind.

I *love* traveling to exotic places. Me and my wife have done quite a lot of traveling in Africa and S-America: safari, scuba diving, skiing, enjoying nature. When at home I try to do some sports activities 3-4 times a week at least, and – recently – play with my now 8 month old son as much as I can.

About-

http://datamarket.com/p/about/team/

Management

Hjálmar Gíslason, Founder and CEO: Hjalmar is a successful entrepreneur, founder of three startups in the gaming, mobile and web sectors since 1996. Prior to launching DataMarket, Hjalmar worked on new media and business development for companies in the Skipti Group (owners of Iceland Telecom) after their acquisition of his search startup – Spurl. Hjalmar offers a mix of business, strategy and technical expertise. DataMarket is based largely on his vision of the need for a global exchange for structured data.

hjalmar.gislason@datamarket.com

To know more, have a quick look at http://datamarket.com/

Interview Michal Kosinski , Concerto Web Based App using #Rstats

Here is an interview with Michal Kosinski , leader of the team that has created Concerto – a web based application using R. What is Concerto? As per http://www.psychometrics.cam.ac.uk/page/300/concerto-testing-platform.htm

Concerto is a web based, adaptive testing platform for creating and running rich, dynamic tests. It combines the flexibility of HTML presentation with the computing power of the R language, and the safety and performance of the MySQL database. It’s totally free for commercial and academic use, and it’s open source

Ajay- Describe your career in science from high school to this point. What are the various stats platforms you have trained on- and what do you think about their comparative advantages and disadvantages?

Michal- I started with maths, but quickly realized that I prefer social sciences – thus after one year, I switched to a psychology major and obtained my MSc in Social Psychology with a specialization in Consumer Behaviour. At that time I was mostly using SPSS – as it was the only statistical package that was taught to students in my department. Also, it was not too bad for small samples and the rather basic analyses I was performing at that time.

My more recent research performed during my Mphil course in Psychometrics at Cambridge University followed by my current PhD project in social networks and research work at Microsoft Research, requires significantly more powerful tools. Initially, I tried to squeeze as much as possible from SPSS/PASW by mastering the syntax language. SPSS was all I knew, though I reached its limits pretty quickly and was forced to switch to R. It was a pretty dreary experience at the start, switching from an unwieldy but familiar environment into an unwelcoming command line interface, but I’ve quickly realized how empowering and convenient this tool was.

I believe that a course in R should be obligatory for all students that are likely to come close to any data analysis in their careers. It is really empowering – once you got the basics you have the potential to use virtually any method there is, and automate most tasks related to analysing and processing data. It is also free and open-source – so you can use it wherever you work. Finally, it enables you to quickly and seamlessly migrate to other powerful environments such as Matlab, C, or Python.

Ajay- What was the motivation behind building Concerto?

Michal- We deal with a lot of online projects at the Psychometrics Centre – one of them attracted more than 7 million unique participants. We needed a powerful tool that would allow researchers and practitioners to conveniently build and deliver online tests.

Also, our relationships with the website designers and software engineers that worked on developing our tests were rather difficult. We had trouble successfully explaining our needs, each little change was implemented with a delay and at significant cost. Not to mention the difficulties with embedding some more advanced methods (such as adaptive testing) in our tests.

So we created a tool allowing us, psychometricians, to easily develop psychometric tests from scratch an publish them online. And all this without having to hire software developers.

Ajay -Why did you choose R as the background for Concerto? What other languages and platforms did you consider. Apart from Concerto, how else do you utilize R in your center, department and University?

Michal- R was a natural choice as it is open-source, free, and nicely integrates with a server environment. Also, we believe that it is becoming a universal statistical and data processing language in science. We put increasing emphasis on teaching R to our students and we hope that it will replace SPSS/PASW as a default statistical tool for social scientists.

Ajay -What all can Concerto do besides a computer adaptive test?

Michal- We did not plan it initially, but Concerto turned out to be extremely flexible. In a nutshell, it is a web interface to R engine with a built-in MySQL database and easy-to-use developer panel. It can be installed on both Windows and Unix systems and used over the network or locally.

Effectively, it can be used to build any kind of web application that requires a powerful and quickly deployable statistical engine. For instance, I envision an easy to use website (that could look a bit like SPSS) allowing students to analyse their data using a web browser alone (learning the underlying R code simultaneously). Also, the authors of R libraries (or anyone else) could use Concerto to build user-friendly web interfaces to their methods.

Finally, Concerto can be conveniently used to build simple non-adaptive tests and questionnaires. It might seem to be slightly less intuitive at first than popular questionnaire services (such us my favourite Survey Monkey), but has virtually unlimited flexibility when it comes to item format, test flow, feedback options, etc. Also, it’s free.

Ajay- How do you see the cloud computing paradigm growing? Do you think browser based computation is here to stay?

Michal – I believe that cloud infrastructure is the future. Dynamically sharing computational and network resources between online service providers has a great competitive advantage over traditional strategies to deal with network infrastructure. I am sure the security concerns will be resolved soon, finishing the transformation of the network infrastructure as we know it. On the other hand, however, I do not see a reason why client-side (or browser) processing of the information should cease to exist – I rather think that the border between the cloud and personal or local computer will continually dissolve.

About

Michal Kosinski is Director of Operations for The Psychometrics Centre and Leader of the e-Psychometrics Unit. He is also a research advisor to the Online Services and Advertising group at the Microsoft Research Cambridge, and a visiting lecturer at the Department of Mathematics in the University of Namur, Belgium. You can read more about him at http://www.michalkosinski.com/

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