Top 7 Business Strategy Models

UPDATED POST- Some Models I use for Business Strategy- to analyze the huge reams of qualitative and uncertain data that business generates. I have added a bonus the Business canvas Model (number 2)

  1. Porters 5 forces Model-To analyze industries
  2. Business Canvas
  3. BCG Matrix- To analyze Product Portfolios
  4. Porters Diamond Model- To analyze locations
  5. McKinsey 7 S Model-To analyze teams
  6. Gernier Theory- To analyze growth of organization
  7. Herzberg Hygiene Theory- To analyze soft aspects of individuals
  8. Marketing Mix Model- To analyze marketing mix.

Continue reading “Top 7 Business Strategy Models”

Data Frame in Python

Exploring some Python Packages and R packages to move /work with both Python and R without melting your brain or exceeding your project deadline


If you liked the data.frame structure in R, you have some way to work with them at a faster processing speed in Python.

Here are three packages that enable you to do so-

(1) pydataframe

An implemention of an almost R like DataFrame object. (install via Pypi/Pip: “pip install pydataframe”)


        u = DataFrame( { "Field1": [1, 2, 3],
                        "Field2": ['abc', 'def', 'hgi']},
                         ['Field1', 'Field2']
                         ["rowOne", "rowTwo", "thirdRow"])

A DataFrame is basically a table with rows and columns.

Columns are named, rows are numbered (but can be named) and can be easily selected and calculated upon. Internally, columns are stored as 1d numpy arrays. If you set row names, they’re converted into a dictionary for fast access. There is a rich subselection/slicing API, see help(DataFrame.get_item) (it also works for setting values). Please note that any slice get’s you another DataFrame, to access individual entries use get_row(), get_column(), get_value().

DataFrames also understand basic arithmetic and you can either add (multiply,…) a constant value, or another DataFrame of the same size / with the same column names, like this:

#multiply every value in ColumnA that is smaller than 5 by 6.
my_df[my_df[:,'ColumnA'] < 5, 'ColumnA'] *= 6

#you always need to specify both row and column selectors, use : to mean everything
my_df[:, 'ColumnB'] = my_df[:,'ColumnA'] + my_df[:, 'ColumnC']

#let's take every row that starts with Shu in ColumnA and replace it with a new list (comprehension)
select = my_df.where(lambda row: row['ColumnA'].startswith('Shu'))
my_df[select, 'ColumnA'] = [row['ColumnA'].replace('Shu', 'Sha') for row in my_df[select,:].iter_rows()]

Dataframes talk directly to R via rpy2 (rpy2 is not a prerequiste for the library!)


(2) pandas

Library Highlights

  • A fast and efficient DataFrame object for data manipulation with integrated indexing;
  • Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;
  • Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;
  • Flexible reshaping and pivoting of data sets;
  • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
  • Columns can be inserted and deleted from data structures for size mutability;
  • Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;
  • High performance merging and joining of data sets;
  • Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;
  • Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;
  • The library has been ruthlessly optimized for performance, with critical code paths compiled to C;
  • Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.

Why not R?

First of all, we love open source R! It is the most widely-used open source environment for statistical modeling and graphics, and it provided some early inspiration for pandas features. R users will be pleased to find this library adopts some of the best concepts of R, like the foundational DataFrame (one user familiar with R has described pandas as “R data.frame on steroids”). But pandas also seeks to solve some frustrations common to R users:

  • R has barebones data alignment and indexing functionality, leaving much work to the user. pandas makes it easy and intuitive to work with messy, irregularly indexed data, like time series data. pandas also provides rich tools, like hierarchical indexing, not found in R;
  • R is not well-suited to general purpose programming and system development. pandas enables you to do large-scale data processing seamlessly when developing your production applications;
  • Hybrid systems connecting R to a low-productivity systems language like Java, C++, or C# suffer from significantly reduced agility and maintainability, and you’re still stuck developing the system components in a low-productivity language;
  • The “copyleft” GPL license of R can create concerns for commercial software vendors who want to distribute R with their software under another license. Python and pandas use more permissive licenses.

(3) datamatrix

datamatrix 0.8

A Pythonic implementation of R’s data.frame structure.

Latest Version: 0.9

This module allows access to comma- or other delimiter separated files as if they were tables, using a dictionary-like syntax. DataMatrix objects can be manipulated, rows and columns added and removed, or even transposed


Modeling in Python

Continue reading “Data Frame in Python”

New Free Online Book by Rob Hyndman on Forecasting using #Rstats

From the creator of some of the most widely used packages for time series in the R programming language comes a brand new book, and its online!

This time the book is free, will be updated and 7 chapters are ready (to read!)

. If you do forecasting professionally, now is the time to suggest your own use cases to be featured as the book gets ready by end- 2012. The book is intended as a replace­ment for Makri­dakis, Wheel­wright and Hyn­d­man (Wiley 1998).

The book is writ­ten for three audi­ences:

(1) people find­ing them­selves doing fore­cast­ing in busi­ness when they may not have had any for­mal train­ing in the area;

(2) undergraduate stu­dents study­ing busi­ness;

(3) MBA stu­dents doing a fore­cast­ing elec­tive.

The book is dif­fer­ent from other fore­cast­ing text­books in sev­eral ways.

  • It is free and online, mak­ing it acces­si­ble to a wide audience.
  • It is con­tin­u­ously updated. You don’t have to wait until the next edi­tion for errors to be removed or new meth­ods to be dis­cussed. We will update the book frequently.
  • There are dozens of real data exam­ples taken from our own con­sult­ing prac­tice. We have worked with hun­dreds of busi­nesses and orga­ni­za­tions help­ing them with fore­cast­ing issues, and this expe­ri­ence has con­tributed directly to many of the exam­ples given here, as well as guid­ing our gen­eral phi­los­o­phy of forecasting.
  • We empha­sise graph­i­cal meth­ods more than most fore­cast­ers. We use graphs to explore the data, analyse the valid­ity of the mod­els fit­ted and present the fore­cast­ing results.

A print ver­sion and a down­load­able e-version of the book will be avail­able to pur­chase on Ama­zon, but not until a few more chap­ters are written.


(Ajay-Support the open textbook movement!)

If you’ve found this book helpful, please consider helping to fund free, open and online textbooks. (Donations via PayPal.)

Look for yourself at


Who made Who in #Rstats

While Bob M, my old mentor and fellow TN man maintains the website how popular R is across various forums, I am interested in who within R community of 3 million (give or take a few) is contributing more. I am very sure by 2014, we can have a new fork of R called Hadley R, in which all packages would be made by Hadley Wickham and you wont need anything else.

But jokes apart, since I didnt have the time to

1) scrape CRAN for all package authors

2) scrape for lines of code across all packages

3) allocate lines of code (itself a dubious software productivity metric) to various authors of R packages-


1) scraping the entire and 2011’s R help list

2) determine who is the most frequent r question and answer user (ala SAS-L’s annual MVP and rookie of the year awards)

I did the following to atleast who is talking about R across easily scrapable Q and A websites

Stack Overflow still rules over all. shows the statistics on who made whom in R on Stack Overflow

All in all, initial ardour seems to have slowed for #Rstats on Stack Overflow ? or is it just summer?

No the answer- credit to Rob J Hyndman is most(?) activity is shifting to Stats Exchange

You could also paste this in Notepad and some graphs on Average Score / Answer or even make a social network graph if you had the time.

Do NOT (Go/Bi) search for Stack Overflow API or web scraping stack overflow- it gives you all the answers on the website but 0 answers on how to scrape these websites.

I have added a new website called Meta Optimize to this list based on Tal G’s interview of Joseph Turian,  at

There are only 17 questions tagged R but it seems a lot of views is being generated.

I also decided to add views from Quora since it is Q and A site (and one which I really like)

Again very few questions but lot many followers

Google Visualization Tools Can Help You Build a Personal Dashboard

The Google Visualization API is a great way for people to make dashboards with slick graphics based  on data without getting into the fine print of the scripting language  itself.  It utilizes the same tools as Google itself does, and makes visualizing data using API calls to the Visualization API. Thus a real-time customizable dashboard that is publishable to the internet can be created within minutes, and more importantly insights can be much more easily drawn from graphs than from looking at rows of tables and numbers.

  1. There are 41 gadgets (including made by both Google and third-party developers ) available in the Gadget  Gallery (
  2. There are 12 kinds of charts available in the Chart Gallery ( .
  3. However there 26 additional charts in the charts page at )

Building and embedding charts is simplified to a few steps

  • Load the AJAX API
  • Load the Visualization API and the appropriate package (like piechart or barchart from the kinds of chart)
  • Set a callback to run when the Google Visualization API is loaded
    • Within the Callback – It creates and populates a data table, instantiates the particular chart type chosen, passes in the data and draws it.
    • Create the data table with appropriately named columns and data rows.
    • Set chart options with Title, Width and Height
  • Instantiate and draw the chart, passing in some options including the name and id
  • Finally write the HTML/ Div that will hold the chart

You can simply copy and paste the code directly from without getting into any details, and tweak them according to your data, chart preference and voila your web dashboard is ready!
That is the beauty of working with API- you can create and display genius ideas without messing with the scripting languages and code (too much). If you like to dive deeper into the API, you can look at the various objects at

First launched in Mar 2008, Google Visualization API has indeed come a long way in making dashboards easier to build for people wanting to utilize advanced data visualization . It came about directly as a result of Google’s 2007 acquisition of GapMinder (of Hans Rosling fame).
As invariably and inevitably computing shifts to the cloud, visualization APIs will be very useful. Tableau Software has been a pioneer in selling data visualizing to the lucrative business intelligence and business dashboards community (you can see the Tableau Software API at ), and Google Visualization can do the same and capture business dashboard and visualization market , if there is more focus on integrating it from Google in it’s multiple and often confusing API offerings.
However as of now, this is quite simply the easiest way to create a web dashboard for your personal needs. Google guarantees 3 years of backward compatibility with this API and it is completely free.

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.


and cute graphs like the famous



studying baseball on facebook

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 and


and creating new packages

1. jjplot (not much action here!)


I liked the promise of JJplot at

2. ising models

3. R pipe


even the FB interns are cool


Part 2 How do people with R use Facebook?

Using the API at

and code mashes from

but the wonderful troubleshooting code from

which needs to be added to the code first


and using network package


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=”;, 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( “;, 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
> <- sapply(friends$data, function(x) x$id)
> # extract names
> <- 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(,” “), 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(
> friendship.matrix <- matrix(0,N,N)
> for (i in 1:N) {
+ tmp <- facebook( path=paste(“me/mutualfriends”,[i], sep=”/”) , access_token=access_token)
+ mutualfriends <- sapply(tmp$data, function(x) x$id)
+ friendship.matrix[i, %in% mutualfriends] <- 1
+ }


Plotting using Network package in R (with help from the  comments at

> require(network)


> 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


After all 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

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 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”)



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

or do specific Facebook Page analysis using R at


 #access token from
# download the file needed for authentication
download.file(url="", destfile="cacert.pem")
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( "", path, options, access_token ), cainfo="cacert.pem" )
fromJSON( data )

 # see

# scrape the list of friends
friends <- facebook( path="me/friends" , access_token=access_token)
# extract Facebook IDs <- sapply(friends$data, function(x) x$id)
# extract names <- 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(," "), initials)

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

Created by Pretty R at

Interview Kelci Miclaus, SAS Institute Using #rstats with JMP

Here is an interview with Kelci Miclaus, a researcher working with the JMP division of the SAS Institute, in which she demonstrates examples of how the R programming language is a great hit with JMP customers who like to be flexible.


Ajay- How has JMP been using integration with R? What has been the feedback from customers so far? Is there a single case study you can point out where the combination of JMP and R was better than any one of them alone?

Kelci- Feedback from customers has been very positive. Some customers are using JMP to foster collaboration between SAS and R modelers within their organizations. Many are using JMP’s interactive visualization to complement their use of R. Many SAS and JMP users are using JMP’s integration with R to experiment with more bleeding-edge methods not yet available in commercial software. It can be used simply to smooth the transition with regard to sending data between the two tools, or used to build complete custom applications that take advantage of both JMP and R.

One customer has been using JMP and R together for Bayesian analysis. He uses R to create MCMC chains and has found that JMP is a great tool for preparing the data for analysis, as well as displaying the results of the MCMC simulation. For example, the Control Chart platform and the Bubble Plot platform in JMP can be used to quickly verify convergence of the algorithm. The use of both tools together can increase productivity since the results of an analysis can be achieved faster than through scripting and static graphics alone.

I, along with a few other JMP developers, have written applications that use JMP scripting to call out to R packages and perform analyses like multidimensional scaling, bootstrapping, support vector machines, and modern variable selection methods. These really show the benefit of interactive visual analysis of coupled with modern statistical algorithms. We’ve packaged these scripts as JMP add-ins and made them freely available on our JMP User Community file exchange. Customers can download them and now employ these methods as they would a regular JMP platform. We hope that our customers familiar with scripting will also begin to contribute their own add-ins so a wider audience can take advantage of these new tools.


Ajay- Are there plans to extend JMP integration with other languages like Python?

Kelci- We do have plans to integrate with other languages and are considering integrating with more based on customer requests. Python has certainly come up and we are looking into possibilities there.

 Ajay- How is R a complimentary fit to JMP’s technical capabilities?

Kelci- R has an incredible breadth of capabilities. JMP has extensive interactive, dynamic visualization intrinsic to its largely visual analysis paradigm, in addition to a strong core of statistical platforms. Since our brains are designed to visually process pictures and animated graphs more efficiently than numbers and text, this environment is all about supporting faster discovery. Of course, JMP also has a scripting language (JSL) allowing you to incorporate SAS code, R code, build analytical applications for others to leverage SAS, R and other applications for users who don’t code or who don’t want to code.

JSL is a powerful scripting language on its own. It can be used for dialog creation, automation of JMP statistical platforms, and custom graphic scripting. In other ways, JSL is very similar to the R language. It can also be used for data and matrix manipulation and to create new analysis functions. With the scripting capabilities of JMP, you can create custom applications that provide both a user interface and an interactive visual back-end to R functionality. Alternatively, you could create a dashboard using statistical and/or graphical platforms in JMP to explore the data and with the click of a button, send a portion of the data to R for further analysis.

Another JMP feature that complements R is the add-in architecture, which is similar to how R packages work. If you’ve written a cool script or analysis workflow, you can package it into a JMP add-in file and send it to your colleagues so they can easily use it.

Ajay- What is the official view on R from your organization? Do you think it is a threat, or a complimentary product or another statistical platform that coexists with your offerings?

Kelci- Most definitely, we view R as complimentary. R contributors are providing a tremendous service to practitioners, allowing them to try a wide variety of methods in the pursuit of more insight and better results. The R community as a whole is providing a valued role to the greater analytical community by focusing attention on newer methods that hold the most promise in so many application areas. Data analysts should be encouraged to use the tools available to them in order to drive discovery and JMP can help with that by providing an analytic hub that supports both SAS and R integration.

Ajay-  While you do use R, are there any plans to give back something to the R community in terms of your involvement and participation (say at useR events) or sponsoring contests.

 Kelci- We are certainly open to participating in useR groups. At Predictive Analytics World in NY last October, they didn’t have a local useR group, but they did have a Predictive Analytics Meet-up group comprised of many R users. We were happy to sponsor this. Some of us within the JMP division have joined local R user groups, myself included.  Given that some local R user groups have entertained topics like Excel and R, Python and R, databases and R, we would be happy to participate more fully here. I also hope to attend the useR! annual meeting later this year to gain more insight on how we can continue to provide tools to help both the JMP and R communities with their work.

We are also exploring options to sponsor contests and would invite participants to use their favorite tools, languages, etc. in pursuit of the best model. Statistics is about learning from data and this is how we make the world a better place.

About- Kelci Miclaus

Kelci is a research statistician developer for JMP Life Sciences at SAS Institute. She has a PhD in Statistics from North Carolina State University and has been using SAS products and R for several years. In addition to research interests in statistical genetics, clinical trials analysis, and multivariate analysis/visualization methods, Kelci works extensively with JMP, SAS, and R integration.