Category: Analytics

Oracle R Updated!

Interesting message from https://blogs.oracle.com/R/ the latest R blog

 

_——–_

Oracle just released the latest update to Oracle R Enterprise, version 1.1. This release includes the Oracle R Distribution (based on open source R, version 2.13.2), an improved server installation, and much more.  The key new features include:

  • Extended Server Support: New support for Windows 32 and 64-bit server components, as well as continuing support for Linux 64-bit server components
  • Improved Installation: Linux 64-bit server installation now provides robust status updates and prerequisite checks
  • Performance Improvements: Improved performance for embedded R script execution calculations

In addition, the updated ROracle package, which is used with Oracle R Enterprise, now reads date data by conversion to character strings.

We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for R-related software: Oracle R Distribution, Oracle R Enterprise, ROracle, Oracle R Connector for Hadoop.  As always, we welcome comments and questions on the Oracle R Forum.

 

 

Oracle R Distribution 2-13.2 Update Available

Oracle has released an update to the Oracle R Distribution, an Oracle-supported distribution of open source R. Oracle R Distribution 2-13.2 now contains the ability to dynamically link the following libraries on both Windows and Linux:

  • The Intel Math Kernel Library (MKL) on Intel chips
  • The AMD Core Math Library (ACML) on AMD chips

 

To take advantage of the performance enhancements provided by Intel MKL or AMD ACML in Oracle R Distribution, simply add the MKL or ACML shared library directory to the LD_LIBRARY_PATH system environment variable. This automatically enables MKL or ACML to make use of all available processors, vastly speeding up linear algebra computations and eliminating the need to recompile R.  Even on a single core, the optimized algorithms in the Intel MKL libraries are faster than using R’s standard BLAS library.

Open-source R is linked to NetLib’s BLAS libraries, but they are not multi-threaded and only use one core. While R’s internal BLAS are efficient for most computations, it’s possible to recompile R to link to a different, multi-threaded BLAS library to improve performance on eligible calculations. Compiling and linking to R yourself can be involved, but for many, the significantly improved calculation speed justifies the effort. Oracle R Distribution notably simplifies the process of using external math libraries by enabling R to auto-load MKL orACML. For R commands that don’t link to BLAS code, taking advantage of database parallelism usingembedded R execution in Oracle R Enterprise is the route to improved performance.

For more information about rebuilding R with different BLAS libraries, see the linear algebra section in the R Installation and Administration manual. As always, the Oracle R Distribution is available as a free download to anyone. Questions and comments are welcome on the Oracle R Forum.

Easter Eggs in #Rstats

Yes.

Cite-http://en.wikipedia.org/wiki/Easter_egg_(media)

A virtual Easter egg is an intentional hidden message, in-joke, or feature in a work such as a computer program, web page, video game, movie, book, or crossword. The term was coined — according to Warren Robinett — by Atari after they were pointed to the secret message left by Robinett in the game Adventure.[1] It draws a parallel with the custom of the Easter egg hunt observed in many Western nations as well as the last Russian imperial family’s tradition of giving elaborately jeweled egg-shaped creations by Carl Fabergé which contained hidden surprises

In R.

Cite-http://stackoverflow.com/questions/7910270/are-there-any-easter-eggs-in-base-r-or-in-major-packages

I like this

just type

example(readLine)

and these two

on 32 bit R type

memory.limit(4096)

and on any version try four question marks

Perhaps the prettiest eggs are the demos in animation package.

But there is magic in asking for help on internal functions in R

Just type-

?.Internal

and you get the sobering thought that you probably are a R Muggle

Call an Internal Function

Description

.Internal performs a call to an internal code which is built in to the R interpreter.

Only true R wizards should even consider using this function, and only R developers can add to the list of internal functions.

Usage

 .Internal(call)

Arguments

call a call expression

See Also

.Primitive, .External (the nearest equivalent available to users).

I liked that I could see the actual internal functions in svn at http://svn.r-project.org/R/trunk/src/main/names.c

The opening of the internals document floored me.

It must have been a curious year in 2003-4 when the copyright of R was held (briefly it seems) by the R Foundation and also by the R Development Core Team. (which sounds better?)

*  R : A Computer Language for Statistical Data Analysis
 *  Copyright (C) 1995, 1996  Robert Gentleman and Ross Ihaka
 *  Copyright (C) 1997--2012  The R Development Core Team
 *  Copyright (C) 2003, 2004  The R Foundation

My contribution

R help discourages for loop

Try ??for or ?for

you go into a loop till you hit escape

If you want more-just write
 .Internal(inspect(ls())) at the end of your  R program.

 

 

 

 

 

 

Color Palettes in R using RColorBrewer #rstats

The lovely colors at http://ColorBrewer.org can be used for much better color palettes in R.

library(RColorBrewer)

display.brewer.all()

and we use the function brewer.pal(N,”Name”) as the col  parameter for the new color palettes

where we can see name of palettes  from the list above

data(VADeaths)
par(mfrow=c(2,3))
 hist(VADeaths,col=brewer.pal(3,"Set3"),main="Set3 3 colors")
 hist(VADeaths,col=brewer.pal(3,"Set2"),main="Set2 3 colors")
 hist(VADeaths,col=brewer.pal(3,"Set1"),main="Set1 3 colors")
 hist(VADeaths,col=brewer.pal(8,"Set3"),main="Set3 8 colors")
 hist(VADeaths,col=brewer.pal(8,"Greys"),main="Greys 8 colors")
 hist(VADeaths,col=brewer.pal(8,"Greens"),main="Greens 8 colors")
Created by Pretty R at inside-R.org

Rplot7

• Colors from [http://www.ColorBrewer.org] by Cynthia A. Brewer, Geography, Pennsylvania State University
• Erich Neuwirth (2011). RColorBrewer: ColorBrewer palettes. R package version 1.0-5. [http://CRAN.R-project.org/package=RColorBrewer]
Note-ColorBrewer is Copyright (c) 2002 Cynthia Brewer, Mark Harrower, and The Pennsylvania State University. All rights reserved. The ColorBrewer palettes have been included in the R package with permission of the copyright holder.

Cricinfo StatsGuru Database for Statistical and Graphical Analysis

Data from the ESPN Cricinfo website is available from the STATSGURU website.

The url is of the form-

http://stats.espncricinfo.com/ci/engine/stats/index.html?class=1;team=6;template=results;type=batting

http://stats.espncricinfo.com/ci/engine/stats/index.html?

class=1;team=6;template=results;type=batting

If you break down this URL to get more statistics on cricket, you can choose the following parameters.
class
1=Test
2=ODI
3=T20I
11=Test+ODI+T20I
team
1=England
2=Australia
3=South America
4-West Indies
5=New Zealand
6=India ,7=Pakistan and 8=Sri Lanka

type
batting
bowling
fielding
allround
fow
official
team
aggregate

 

ESPN Terms of Use are here-you may need to  check this before trying any web scraping.

http://www.espncricinfo.com/ci/content/site/company/terms_use.html

 

However ESPN has unleashed the API (including both free and premium)for Developers at http://developer.espn.com/docs.

and especially these sports http://developer.espn.com/docs/headlines#parameters

/sports News across all sports/sections
/sports/baseball/mlb Major League Baseball (MLB)
/sports/basketball/mens-college-basketball NCAA Men’s College Basketball
/sports/basketball/nba National Basketball Association (NBA)
/sports/basketball/wnba Women’s National Basketball Association (WNBA)
/sports/basketball/womens-college-basketball NCAA Women’s College Basketball
/sports/boxing Boxing
/sports/football/college-football NCAA College Football
/sports/football/nfl National Football League (NFL)
/sports/golf Golf
/sports/hockey/nhl National Hockey League (NHL)
/sports/horse-racing Horse Racing
/sports/mma Mixed Martial Arts
/sports/racing Auto Racing
/sports/racing/nascar NASCAR Racing
/sports/soccer Professional soccer (US focus)
/sports/tennis Tennis

 

I wonder when this can be enabled for Cricket as well (including APIs  free,academic,premium,partner ).

(Note you can use R packages XML , RCurl , rjson, to get data from the web among others).

Plotting is best done using ggplot2 http://had.co.nz/ggplot2/ or d3.js at http://mbostock.github.com/d3/, and the current status of cricket graphics can surely look a change- they are mostly a single radial plot of shots played /runs scored or a combined barplot/line graph.

Doing RFM Analysis in R



RFM is a method used for analyzing customer behavior and defining market segments. It is commonly used in database marketing and direct marketing and has received particular attention in retail.




RFM stands for




    

  • Recency – How recently did the customer purchase?
    • 

  • Frequency – How often do they purchase?
    • 

  • Monetary Value – How much do they spend?
    • 





To create an RFM analysis, one creates categories for each attribute. For instance, the Recency attribute might be broken into three categories: customers with purchases within the last 90 days; between 91 and 365 days; and longer than 365 days. Such categories may be arrived at by applying business rules, or using a data mining technique, such as CHAID, to find meaningful breaks.


from-http://en.wikipedia.org/wiki/RFM


If you are new to RFM or need more step by step help, please read here


https://decisionstats.com/2010/10/03/ibm-spss-19-marketing-analytics-and-rfm/


and here is R code- note for direct marketing you need to compute Monetization based on response rates (based on offer date) as well






##Creating Random Sales Data of the format CustomerId (unique to each customer), Sales.Date,Purchase.Value

sales=data.frame(sample(1000:1999,replace=T,size=10000),abs(round(rnorm(10000,28,13))))

names(sales)=c("CustomerId","Sales Value")

sales.dates <- as.Date("2010/1/1") + 700*sort(stats::runif(10000))

#generating random dates

sales=cbind(sales,sales.dates)

str(sales)

sales$recency=round(as.numeric(difftime(Sys.Date(),sales[,3],units="days")) )

library(gregmisc)

##if you have existing sales data you need to just shape it in this format

rename.vars(sales, from="Sales Value", to="Purchase.Value")#Renaming Variable Names

## Creating Total Sales(Monetization),Frequency, Last Purchase date for each customer

salesM=aggregate(sales[,2],list(sales$CustomerId),sum)

names(salesM)=c("CustomerId","Monetization")

salesF=aggregate(sales[,2],list(sales$CustomerId),length)

names(salesF)=c("CustomerId","Frequency")

salesR=aggregate(sales[,4],list(sales$CustomerId),min)

names(salesR)=c("CustomerId","Recency")

##Merging R,F,M

test1=merge(salesF,salesR,"CustomerId")

salesRFM=merge(salesM,test1,"CustomerId")

##Creating R,F,M levels 

salesRFM$rankR=cut(salesRFM$Recency, 5,labels=F) #rankR 1 is very recent while rankR 5 is least recent

salesRFM$rankF=cut(salesRFM$Frequency, 5,labels=F)#rankF 1 is least frequent while rankF 5 is most frequent

salesRFM$rankM=cut(salesRFM$Monetization, 5,labels=F)#rankM 1 is lowest sales while rankM 5 is highest sales

##Looking at RFM tables


table(salesRFM[,5:6])
table(salesRFM[,6:7])
table(salesRFM[,5:7])








Code Highlighted by Pretty R at inside-R.org


Note-you can also use quantile function instead of cut function. This changes cut to equal length instead of equal interval. or  see other methods for finding breaks for categories.


 

			


	





	

		
		
send email by R
	


	
	
	

		
For automated report delivery I have often used send email options in BASE SAS. For R, for scheduling tasks and sending me automated mails on completion of tasks I have two R options and 1 Windows OS scheduling option. Note red font denotes the parameters that should be changed. Anything else should NOT be changed.


Option 1-


Use the mail package at


http://cran.r-project.org/web/packages/mail/mail.pdf


> library(mail)


Attaching package: ‘mail’


The following object(s) are masked from ‘package:sendmailR’:


sendmail


>

> sendmail(“ohri2007@gmail.com“, subject=”Notification from R“,message=“Calculation finished!”, password=”rmail”)

[1] “Message was sent to ohri2007@gmail.com! You have 19 messages left.”


Disadvantage- Only 20 email messages by IP address per day. (but thats ok!)


Option 2-


use sendmailR package at http://cran.r-project.org/web/packages/sendmailR/sendmailR.pdf


install.packages()

library(sendmailR)

from <- sprintf(“<sendmailR@%s>”, Sys.info()[4])

to <- “<ohri2007@gmail.com>”

subject <- “Hello from R”

body <- list(“It works!”, mime_part(iris))

sendmail(from, to, subject, body,control=list(smtpServer=”ASPMX.L.GOOGLE.COM”))


 


 


BiocInstaller version 1.2.1, ?biocLite for help

> install.packages(“sendmailR”)

Installing package(s) into ‘/home/ubuntu/R/library’

(as ‘lib’ is unspecified)

also installing the dependency ‘base64’


trying URL ‘http://cran.at.r-project.org/src/contrib/base64_1.1.tar.gz&#8217;

Content type ‘application/x-gzip’ length 61109 bytes (59 Kb)

opened URL

==================================================

downloaded 59 Kb


trying URL ‘http://cran.at.r-project.org/src/contrib/sendmailR_1.1-1.tar.gz&#8217;

Content type ‘application/x-gzip’ length 6399 bytes

opened URL

==================================================

downloaded 6399 bytes


BiocInstaller version 1.2.1, ?biocLite for help

* installing *source* package ‘base64’ …

** package ‘base64’ successfully unpacked and MD5 sums checked

** libs

gcc -std=gnu99 -I/usr/local/lib64/R/include -I/usr/local/include -fpic -g -O2 -c base64.c -o base64.o

gcc -std=gnu99 -shared -L/usr/local/lib64 -o base64.so base64.o -L/usr/local/lib64/R/lib -lR

installing to /home/ubuntu/R/library/base64/libs

** R

** preparing package for lazy loading

** help

*** installing help indices

** building package indices …

** testing if installed package can be loaded

BiocInstaller version 1.2.1, ?biocLite for help


* DONE (base64)

BiocInstaller version 1.2.1, ?biocLite for help

* installing *source* package ‘sendmailR’ …

** package ‘sendmailR’ successfully unpacked and MD5 sums checked

** R

** preparing package for lazy loading

** help

*** installing help indices

** building package indices …

** testing if installed package can be loaded

BiocInstaller version 1.2.1, ?biocLite for help


* DONE (sendmailR)


The downloaded packages are in

‘/tmp/RtmpsM222s/downloaded_packages’

> library(sendmailR)

Loading required package: base64

> from <- sprintf(“<sendmailR@%s>”, Sys.info()[4])

> to <- “<ohri2007@gmail.com>”

> subject <- “Hello from R”

> body <- list(“It works!”, mime_part(iris))

> sendmail(from, to, subject, body,

+ control=list(smtpServer=”ASPMX.L.GOOGLE.COM”))

$code

[1] “221”


$msg

[1] “2.0.0 closing connection ff2si17226764qab.40”


Disadvantage-This worked when I used the Amazon Cloud using the BioConductor AMI (for free 2 hours) at http://www.bioconductor.org/help/cloud/


It did NOT work when I tried it use it from my Windows 7 Home Premium PC from my Indian ISP (!!) .


It gave me this error


or in wait_for(250) :

SMTP Error: 5.7.1 [180.215.172.252] The IP you’re using to send mail is not authorized


 


PAUSE–


ps Why do this (send email by R)?


Note you can add either of the two programs of the end of the code that you want to be notified automatically. (like daily tasks)


This is mostly done for repeated business analytics tasks (like reports and analysis that need to be run at specific periods of time)


pps- What else can I do with this?


Can be modified to include sms or tweets  or even blog by email by modifying the   “to”  location appropriately.


3) Using Windows Task Scheduler to run R codes automatically (either the above)


or just sending an email


got to Start>  All Programs > Accessories >System Tools > Task Scheduler ( or by default C:Windowssystem32taskschd.msc)


Create a basic task




Now you can use this to run your daily/or scheduled R code  or you can send yourself email as well.




and modify the parameters- note the SMTP server (you can use the ones for google in example 2 at ASPMX.L.GOOGLE.COM)




and check if it works!




 


Related


 Geeky Things , Bro


–


Configuring IIS on your Windows 7 Home Edition-


note path to do this is-


Control Panel>All Control Panel Items> Program and Features>Turn Windows features on or off> Internet Information Services




and


http://stackoverflow.com/questions/709635/sending-mail-from-batch-file


 

	


	





	

		
		
R for Predictive Modeling- PAW Toronto
	


	
	
	

		
A nice workshop on using R for Predictive Modeling by Max Kuhn Director, Nonclinical Statistics, Pfizer is on at PAW Toronto.


Workshop


Monday, April 23, 2012 in Toronto

Full-day: 9:00am – 4:30pm


R for Predictive Modeling:

A Hands-On Introduction


Intended Audience: Practitioners who wish to learn how to execute on predictive analytics by way of the R language; anyone who wants “to turn ideas into software, quickly and faithfully.”


Knowledge Level: Either hands-on experience with predictive modeling (without R) or hands-on familiarity with any programming language (other than R) is sufficient background and preparation to participate in this workshop.




What prior attendees have exclaimed




Workshop Description


This one-day session provides a hands-on introduction to R, the well-known open-source platform for data analysis. Real examples are employed in order to methodically expose attendees to best practices driving R and its rich set of predictive modeling packages, providing hands-on experience and know-how. R is compared to other data analysis platforms, and common pitfalls in using R are addressed.


The instructor, a leading R developer and the creator of CARET, a core R package that streamlines the process for creating predictive models, will guide attendees on hands-on execution with R, covering:


    

  • A working knowledge of the R system
    • 

  • The strengths and limitations of the R language
    • 

  • Preparing data with R, including splitting, resampling and variable creation
    • 

  • Developing predictive models with R, including decision trees, support vector machines and ensemble methods
    • 

  • Visualization: Exploratory Data Analysis (EDA), and tools that persuade
    • 

  • Evaluating predictive models, including viewing lift curves, variable importance and avoiding overfitting
    • 



Hardware: Bring Your Own Laptop

Each workshop participant is required to bring their own laptop running Windows or OS X. The software used during this training program, R, is free and readily available for download.


Attendees receive an electronic copy of the course materials and related R code at the conclusion of the workshop.




Schedule



    

  • Workshop starts at 9:00am
    • 

  • Morning Coffee Break at 10:30am – 11:00am
    • 

  • Lunch provided at 12:30 – 1:15pm
    • 

  • Afternoon Coffee Break at 2:30pm – 3:00pm
    • 

  • End of the Workshop: 4:30pm
    • 





Instructor


Max Kuhn, Director, Nonclinical Statistics, Pfizer


Max Kuhn is a Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He has been applying models in the pharmaceutical industries for over 15 years.


He is a leading R developer and the author of several R packages including the CARET package that provides a simple and consistent interface to over 100 predictive models available in R.


Mr. Kuhn has taught courses on modeling within Pfizer and externally, including a class for the India Ministry of Information Technology.


Source-


http://www.predictiveanalyticsworld.com/toronto/2012/r_for_predictive_modeling.php