Using JMP 9 and R together

An interesting blog post at!.html on using the new JMP 9 with R, and quite possibly using SAS as well.

Example Code-

Here’s the R integration JSL code used to run the bootstrap

rconn = R Connect();
rconn << Submit(“\[

# Load Boot package

RStatFctn <- function(x,d) {return(mean(x[d]))}

b.basic = matrix(data=NA, nrow=1000, ncol=2)
b.normal = matrix(data=NA, nrow=1000, ncol=2)
b.percent =matrix(data=NA, nrow=1000, ncol=2)
b.bca =matrix(data=NA, nrow=1000, ncol=2)

for(i in 1:1000){
rnormdat = rnorm(30,0,1)
b <- boot(rnormdat, RStatFctn, R = 1000), conf =095,type=c(“basic”,”norm”,”perc”,”bca”)) b.basic[i,] =$basic[,4:5]
b.normal[i,] =$normal[,2:3]
b.percent[i,] =$percent[,4:5]
b.bca[i,] =$bca[,4:5]
b_basic= rconn << Get(b.basic);
b_normal = rconn << Get(b.normal);
b_percent= rconn << Get(b.percent);
b_bca = rconn << Get(b.bca);
rconn << Disconnect();

Using the R Connect() JSL command and assigning it to the object “rconn”, the code sends messages to the JSL scriptable object “rconn” to submit R code via the Submit() command and to retrieve R matrices containing the bootstrap confidence intervals back via the Get() commands.

and I also found interesting what the write has to say about using JMP (for visual analysis) and SAS (bigger datasets handling) and R (for advanced statistics) together

Other standard JMP tools such as the Data Filter can help to explore these results in ways that cannot easily and quickly be done in R


With a little JSL and the statistical and graphics platforms of JMP coupled with the breadth and variety of packages and functions in R, one can build complete easy-to-use applications for statistical analysis.

JMP can also integrate with SAS, which adds the ability to work with large-scale data through the file-based system as well as the depth and advanced capabilities of SAS procedures. With these seamless integrations, JMP can become a hub that enables you to connect with both SAS and R, as well as provide unique statistical features such as the JMP Profiler and interactive graphic features such as Graph Builder

and in the meanwhile here is a data visualization of a frequency analysis of various words bundled together from

CommeRcial R- Integration in software

Some updates to R on the commercial side.

Revolution Computing is apparently now renamed Revolution Analytics. Hopefully this and the GUI development will help pay more focused attention on working in R in a mainstream office situation. I am still waiting for David Smith’s cheery hey-guys-we-changed-again blog post though at a new site called or his old blog site at

They probably need to hire more people now – Curt Monash, noted all-things-data software guru has the inside dope here

Techworld writes more here at

The company’s software is priced “aggressively” versus IBM and SAS. A single supported workstation costs $2,000 for an annual subscription. Pricing for server-based licenses varies depending on the implementation.

But Revolution Analytics faces a tough challenge from those larger vendors, as well as the likes of XLSolutions, which offers R training and a competing software package, R-Plus.

SPSS though continues to integrate R solidly and also march ahead with Python (which is likely to be the next gen in statistical programming if it keeps up)

With the release of Version 18 of IBM SPSS Statistics and the Developer product, easy-to-install versions of the Python and R materials are posted.  In particular, look for the R Essentials link on the main page or from the Plugins page.  It installs the R Plugin, the correct version of R, and a bunch of example R integrations as bundles.  It’s much easier to get going with this now.

Netezza , a business intelligence vendor promises more integration and even a training in R based analytics here

R Modeling for TwinFin i-Class

Learn how to use TwinFin i-Class for scaling up the R language.

In this class, you’ll learn how to use R to create models using huge data and how to create R algorithms that exploit our asymmetric massively parallel (AMPP®) architecture. Netezza has seamlessly integrated with R to offload the heavy lifting of the computational processing on TwinFin i-Class. This results in higher performance and increased scalability for R. Sign up for this class to learn how to take advantage of TwinFin i-Class for your R modeling. Topics include:

  1. R CRAN package installation on TwinFin i-Class
  2. Creating models using R on TwinFin i-Class
  3. Creating R algorithms for TwinFin i-Class

Hands-on classroom lecture, lab exercises, tour

Knowledgeable R users – modelers, analytic developers, data miners

Course Length
0.5 day: 12pm-4pm Wednesday, June 23 OR 8am-12pm Thursday, June 24 OR 1pm-5pm Thursday, June 24, 2010

Enzee Universe 2010, Boston, MA

Student Prerequisites

  • Working knowledge of R and parallel computing
  • Have analytic, compute-intensive challenges
  • Understanding of data mining and analytics”

My favourite GUI in stats , JMP (also from SAS Institute) is going to deploy R integration as soon as this September – Read more here-

Also SAS-IML studio is not lagging behind

The next release of SAS/IML will extend R integration to the server environment – enabling users to deploy results in batch mode and access R from SAS on additional platforms, such as UNIX and Linux.

I am kind of happy at one of the best GUI’s integrating with one of the most innovative stats softwares. It’s like two of your best friends getting married. (see screenshots of the softwares)

All in all- R as a platform making good overall progress from all sides of the corporate software spectrum which can only be good for R developers as well as users/students.

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