I recently found an interesting example of a website that both makes a lot of money and yet is much more efficient than any free or non profit. It is called ECOSIA
If you see a website that wants to balance administrative costs plus have a transparent way to make the world better- this is a great example.
World’s largest tropical forest reserve (38,867 square kilometers, or about the size of Switzerland)
Home to about 14% of all amphibian species and roughly 54% of all bird species in the Amazon – not to mention large populations of at least eight threatened species, including the jaguar
Includes part of the Guiana Shield containing 25% of world’s remaining tropical rainforests – 80 to 90% of which are still pristine
Holds the last major unpolluted water reserves in the Neotropics, containing approximately 20% of all of the Earth’s water
One of the last tropical regions on Earth vastly unaltered by humans
Significant contributor to climatic regulation via heat absorption and carbon storage
Click per milli (or CPM) gives you a very low low conversion compared to contacting ad sponsor directly.
But its a great data experiment-
as you can monitor which companies are likely to be advertised on your site (assume google knows more about their algols than you will)
which formats -banner or text or flash have what kind of conversion rates
what are the expected pay off rates from various keywords or companies (like business intelligence software, predictive analytics software and statistical computing software are similar but have different expected returns (if you remember your eco class)
NOW- Based on above data, you know whats your minimum baseline to expect from a private advertiser than a public, crowd sourced search engine one (like Google or Bing)
Lets say if you have 100000 views monthly. and assume one out of 1000 page views will lead to a click. Say the advertiser will pay you 1 $ for every 1 click (=1000 impressions)
Then your expected revenue is $100.But if your clicks are priced at 2.5$ for every click , and your click through rate is now 3 out of 1000 impressions- (both very moderate increases that can done by basic placement optimization of ad type, graphics etc)-your new revenue is 750$.
Be a good Samaritan- you decide to share some of this with your audience -like 4 Amazon books per month ( or I free Amazon book per week)- That gives you a cost of 200$, and leaves you with some 550$.
Wait! it doesnt end there- Adam Smith‘s invisible hand moves on .
You say hmm let me put 100 $ for an annual paper writing contest of $1000, donate $200 to one laptop per child ( or to Amazon rain forests or to Haiti etc etc etc), pay $100 to your upgraded server hosting, and put 350$ in online advertising. say $200 for search engines and $150 for Facebook.
Woah!
Month 1 would should see more people visiting you for the first time. If you have a good return rate (returning visitors as a %, and low bounce rate (visits less than 5 secs)- your traffic should see atleast a 20% jump in new arrivals and 5-10 % in long term arrivals. Ignoring bounces- within three months you will have one of the following
1) An interesting case study on statistics on online and social media advertising, tangible motivations for increasing community response , and some good data for study
2) hopefully better cost management of your server expenses
3)very hopefully a positive cash flow
you could even set a percentage and share the monthly (or annually is better actions) to your readers and advertisers.
go ahead- change the world!
the key paradigms here are sharing your traffic and revenue openly to everyone
donating to a suitable cause
helping increase awareness of the suitable cause
basing fixed percentages rather than absolute numbers to ensure your site and cause are sustained for years.
A new 5 page brochure from Revolution Analytics. Not that slick and some marketing under-kill (which frankly is a surprise)- but I guess Revolution Analytics does not have a full time graphics designer to help with it’s collateral.
Take a look if you are curious how and why R is getting more and more ready for business.
SAS just released an updated documentation to SAS/IML language with a special chapter devoted to using R
Here is an example-
CALL EXPORTMATRIXTOR(IMLMatrix, RMatrix );
CALL IMPORTMATRIXFROMR(IMLMatrix, RExpr );
If you have existing SAS licences and existing hardware and loots of data -this may be the best of both worlds- without getting into the mess of technically learning MKL threads/BLAS/Premium Packages/Cloud
Another thought- its a good professional looking help book, which is what more R packages can do (work on improving ease of their help/update vignettes)
Here is a set of very nice, screenshot enabled tutorials from SAP BI. They are a bit outdated (3 years old) but most of it is quite relevant- especially from a Tutorial Design Perspective –
Most people would rather see screenshot based step by step powerpoints, than cluttered or clever presentations , or even videos that force you to sit like a TV zombie. Unfortunately most tutorial presentations I see especially for BI are either slides with one or two points, that abruptly shift to “concepts” or videos that are atleast more than 10 minutes long. That works fine for scripting tutorials or hands on workshops, but cannot be reproduced for later instances of study.
The mode of tutorials especially for GUI software can vary, it may be Slideshare, Scribd, Google Presentation,Microsoft Powerpoint but a step by step screenshot by screenshot tutorial is much better for understanding than commando line jargon/ Youtub Videos presentations, or Powerpoint with Points.
Have a look at these SAP BI 7 slideshares
and
Speaking of BI, the R Package called Brew is going to brew up something special especially combined with R Apache. However I wish R Apache, or R Web, or RServe had step by step install screenshot tutorials to increase their usage in Business Intelligence.
I tried searching for JMP GUI Tutorials too, but I believe putting all your content behind a registration wall is not so great. Do a Pareto Analysis of your training material, surely you can share a couple more tutorials without registration. It also will help new wanna-migrate users to get a test and feel for the installation complexities as well as final report GUI.
Here is a short list of resources and material I put together as starting points for R and Cloud Computing It’s a bit messy but overall should serve quite comprehensively.
Cloud computing is a commonly used expression to imply a generational change in computing from desktop-servers to remote and massive computing connections,shared computers, enabled by high bandwidth across the internet.
As per the National Institute of Standards and Technology Definition,
Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.
Rweb is developed and maintained by Jeff Banfield. The Rweb Home Page provides access to all three versions of Rweb—a simple text entry form that returns output and graphs, a more sophisticated JavaScript version that provides a multiple window environment, and a set of point and click modules that are useful for introductory statistics courses and require no knowledge of the R language. All of the Rweb versions can analyze Web accessible datasets if a URL is provided.
The paper “Rweb: Web-based Statistical Analysis”, providing a detailed explanation of the different versions of Rweb and an overview of how Rweb works, was published in the Journal of Statistical Software (http://www.jstatsoft.org/v04/i01/).
Ulf Bartel has developed R-Online, a simple on-line programming environment for R which intends to make the first steps in statistical programming with R (especially with time series) as easy as possible. There is no need for a local installation since the only requirement for the user is a JavaScript capable browser. See http://osvisions.com/r-online/ for more information.
Rcgi is a CGI WWW interface to R by MJ Ray. It had the ability to use “embedded code”: you could mix user input and code, allowing the HTMLauthor to do anything from load in data sets to enter most of the commands for users without writing CGI scripts. Graphical output was possible in PostScript or GIF formats and the executed code was presented to the user for revision. However, it is not clear if the project is still active.
Currently, a modified version of Rcgi by Mai Zhou (actually, two versions: one with (bitmap) graphics and one without) as well as the original code are available from http://www.ms.uky.edu/~statweb/.
David Firth has written CGIwithR, an R add-on package available from CRAN. It provides some simple extensions to R to facilitate running R scripts through the CGI interface to a web server, and allows submission of data using both GET and POST methods. It is easily installed using Apache under Linux and in principle should run on any platform that supports R and a web server provided that the installer has the necessary security permissions. David’s paper “CGIwithR: Facilities for Processing Web Forms Using R” was published in the Journal of Statistical Software (http://www.jstatsoft.org/v08/i10/). The package is now maintained by Duncan Temple Lang and has a web page athttp://www.omegahat.org/CGIwithR/.
Rpad, developed and actively maintained by Tom Short, provides a sophisticated environment which combines some of the features of the previous approaches with quite a bit of JavaScript, allowing for a GUI-like behavior (with sortable tables, clickable graphics, editable output), etc.
Jeff Horner is working on the R/Apache Integration Project which embeds the R interpreter inside Apache 2 (and beyond). A tutorial and presentation are available from the project web page at http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RApacheProject.
Rserve is a project actively developed by Simon Urbanek. It implements a TCP/IP server which allows other programs to use facilities of R. Clients are available from the web site for Java and C++ (and could be written for other languages that support TCP/IP sockets).
OpenStatServer is being developed by a team lead by Greg Warnes; it aims “to provide clean access to computational modules defined in a variety of computational environments (R, SAS, Matlab, etc) via a single well-defined client interface” and to turn computational services into web services.
Two projects use PHP to provide a web interface to R. R_PHP_Online by Steve Chen (though it is unclear if this project is still active) is somewhat similar to the above Rcgi and Rweb. R-php is actively developed by Alfredo Pontillo and Angelo Mineo and provides both a web interface to R and a set of pre-specified analyses that need no R code input.
webbioc is “an integrated web interface for doing microarray analysis using several of the Bioconductor packages” and is designed to be installed at local sites as a shared computing resource.
Rwui is a web application to create user-friendly web interfaces for R scripts. All code for the web interface is created automatically. There is no need for the user to do any extra scripting or learn any new scripting techniques. Rwui can also be found at http://rwui.cryst.bbk.ac.uk.
Finally, the R.rsp package by Henrik Bengtsson introduces “R Server Pages”. Analogous to Java Server Pages, an R server page is typically HTMLwith embedded R code that gets evaluated when the page is requested. The package includes an internal cross-platform HTTP server implemented in Tcl, so provides a good framework for including web-based user interfaces in packages. The approach is similar to the use of the brew package withRapache with the advantage of cross-platform support and easy installation.
Remote access to R/Bioconductor on EBI’s 64-bit Linux Cluster
Start the workbench by downloading the package for your operating system (Macintosh or Windows), or via Java Web Start, and you will get access to an instance of R running on one of EBI’s powerful machines. You can install additional packages, upload your own data, work with graphics and collaborate with colleagues, all as if you are running R locally, but unlimited by your machine’s memory, processor or data storage capacity.
Most up-to-date R version built for multicore CPUs
Access to all Bioconductor packages
Access to our computing infrastructure
Fast access to data stored in EBI’s repositories (e.g., public microarray data in ArrayExpress)
Using R Google Docs http://www.omegahat.org/RGoogleDocs/run.pdf
It uses the XML and RCurl packages and illustrates that it is relatively quick and easy
to use their primitives to interact with Web services.
Amazon’s EC2 is a type of cloud that provides on demand computing infrastructures called an Amazon Machine Images or AMIs. In general, these types of cloud provide several benefits:
Simple and convenient to use. An AMI contains your applications, libraries, data and all associated configuration settings. You simply access it. You don’t need to configure it. This applies not only to applications like R, but also can include any third-party data that you require.
On-demand availability. AMIs are available over the Internet whenever you need them. You can configure the AMIs yourself without involving the service provider. You don’t need to order any hardware and set it up.
Elastic access. With elastic access, you can rapidly provision and access the additional resources you need. Again, no human intervention from the service provider is required. This type of elastic capacity can be used to handle surge requirements when you might need many machines for a short time in order to complete a computation.
Pay per use. The cost of 1 AMI for 100 hours and 100 AMI for 1 hour is the same. With pay per use pricing, which is sometimes called utility pricing, you simply pay for the resources that you use.
#This example requires you had previously created a bucket named data_language on your Google Storage and you had uploaded a CSV file named language_id.txt (your data) into this bucket – see for details
library(predictionapirwrapper)
Elastic-R is a new portal built using the Biocep-R platform. It enables statisticians, computational scientists, financial analysts, educators and students to use cloud resources seamlessly; to work with R engines and use their full capabilities from within simple browsers; to collaborate, share and reuse functions, algorithms, user interfaces, R sessions, servers; and to perform elastic distributed computing with any number of virtual machines to solve computationally intensive problems.
Also see Karim Chine’s http://biocep-distrib.r-forge.r-project.org/
R for Salesforce.com
At the point of writing this, there seem to be zero R based apps on Salesforce.com This could be a big opportunity for developers as both Apex and R have similar structures Developers could write free code in R and charge for their translated version in Apex on Salesforce.com
Force.com and Salesforce have many (1009) apps at http://sites.force.com/appexchange/home for cloud computing for
businesses, but very few forecasting and statistical simulation apps.
These are like iPhone apps except meant for business purposes (I am
unaware if any university is offering salesforce.com integration
though google apps and amazon related research seems to be on)
Personal Note-Mentioning SAS in an email to a R list is a big no-no in terms of getting a response and love. Same for being careless about which R help list to email (like R devel or R packages or R help)
I am currently playing/ trying out RApache- one more excellent R product from Vanderbilt’s excellent Dept of Biostatistics and it’s prodigious coder Jeff Horner.
I really liked the virtual machine idea- you can download a virtual image of Rapache and play with it- .vmx is easy to create and great to share-
Basically using R Apache (with an EC2 on backend) can help you create customized dashboards, BI apps, etc all using R’s graphical and statistical capabilities.
Rapache embeds the R interpreter inside the Apache 2 web server. By doing this, Rapache realizes the full potential of R and its facilities over the web. R programmers configure appache by mapping Universal Resource Locaters (URL’s) to either R scripts or R functions. The R code relies on CGI variables to read a client request and R’s input/output facilities to write the response.
One advantage to Rapache’s architecture is robust multi-process management by Apache. In contrast to Rserve and RSOAP, Rapache is a pre-fork server utilizing HTTP as the communications protocol. Another advantage is a clear separation, a loose coupling, of R code from client code. With Rserve and RSOAP, the client must send data and R commands to be executed on the server. With Rapache the only client requirements are the ability to communicate via HTTP. Additionally, Rapache gains significant authentication, authorization, and encryption mechanism by virtue of being embedded in Apache.
Existing Demos of Architechture based on R Apache-
You can download version 1.1.10 of rApache now. There
are only two significant changes and you don’t have to edit your
apache config or change any code (just recompile rApache and
reinstall):
1) Error reporting should be more informative. both when you
accidentally introduce errors in the Apache config, and when your code
introduces warnings and errors from web requests.
I’ve struggled with this one for awhile, not really knowing what
strategy would be best. Basically, rApache hooks into the R I/O layer
at such a low level that it’s hard to capture all warnings and errors
as they occur and introduce them to the user in a sane manner. In
prior releases, when ROutputErrors was in effect (either the apache
directive or the R function) one would typically see a bunch of grey
boxes with a red outline with a title of RApache Warning/Error!!!.
Unfortunately those grey boxes could contain empty lines, one line of
error, or a few that relate to the lines in previously displayed
boxes. Really a big uninformative mess.
The new approach is to print just one warning box with the title
“”Oops!!! <b>rApache</b> has something to tell you. View source and
read the HTML comments at the end.” and then as the title implies you
can read the HTML comment located at the end of the file… after the
closing html. That way, you’re actually reading how R would present
the warnings and errors to you as if you executed the code at the R
command prompt. And if you don’t use ROutputErrors, the warning/error
messages are printed in the Apache log file, just as they were before,
but nicer 😉
2) Code dispatching has changed so please let me know if I’ve
introduced any strange behavior.
This was necessary to enhance error reporting. Prior to this release,
rApache would use R’s C API exclusively to build up the call to your
code that is then passed to R’s evaluation engine. The advantage to
this approach is that it’s much more efficient as there is no parsing
involved, however all information about parse errors, files which
produced errors, etc. were lost. The new approach uses R’s built-in
parse function to build up the call and then passes it of to R. A
slight overhead, but it should be negligible. So, if you feel that
this approach is too slow OR I’ve introduced bugs or strange behavior,
please let me know.
FUTURE PLANS
I’m gaining more experience building Debian/Ubuntu packages each day,
so hopefully by some time in 2011 you can rely on binary releases for
these distributions and not install rApache from source! Fingers
crossed!
Development on the rApache 1.1 branch will be winding down (save bug
fix releases) as I transition to the 1.2 branch. This will involve
taking out a small chunk of code that defines the rApache development
environment (all the CGI variables and the functions such as
setHeader, setCookie, etc) and placing it in its own R package…
unnamed as of yet. This is to facilitate my development of the ralite
R package, a small single user cross-platform web server.
The goal for ralite is to speed up development of R web applications,
take out a bit of friction in the development process by not having to
run the full rApache server. Plus it would allow users to develop in
the rApache enronment while on windows and later deploy on more
capable server environments. The secondary goal for ralite is it’s use
in other web server environments (nginx and IIS come to mind) as a
persistent per-client process.
And finally, wiki.rapache.net will be the new www.rapache.net once I
translate the manual over… any day now.
Thats the world’s most widely read marketing textbook in slideshare format slides. You think you are a marketing guru expert at selling or promoting software- well spend 10 minutes flipping for a fun reading
and a presentation trying to be the worlds best presentation by putting social causes, geeky languages, hot looks in the same slides – Hi It is BO (not Barack Obama)
and if you are like me and suck at presentations , but unlike me would like to get better at presentations
if you are still reading this you probably have too much time on a Friday, so here is one YouTube poetry video I created while in a graphics design course in Vol State- it’s a mashuo of 12 poems, some Prezi, some music by that big proft making Google machine called You Tub
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DECISION STATS 