My 3rd Book is now officially released

Today 12th September (2016) is the official release date of my 3rd book.

The title of the books is 'Oracle R Enterprise'. Make sure to check it out on Amazon.

It has been a busy 17 months, as you may have noticed that I had another book released a few weeks ago. Check it out here.

Yes, I was working on two books at the same time.

Yes, that was a lot of work, and looking back on it was a lot of fun too.

This new book (Oracle R Enterprise) is a good companion for my first book (Predictive Analytics using Oracle Data Miner), as I now have a book for each of the components of the Oracle Advanced Analytics option.

Here is what is on the back cover of the book.

"Effectively manage your enterprise’s big data and keep complex processes running smoothly using the hands-on information contained in this Oracle Press guide. Oracle R Enterprise: Harnessing the Power of R in Oracle Database shows, step-by-step, how to create and execute large-scale predictive analytics and maintain superior performance. Discover how to explore and prepare your data, accurately model business processes, generate sophisticated graphics, and write and deploy powerful scripts. You will also find out how to effectively incorporate Oracle R Enterprise features in APEX applications, OBIEE dashboards, and Apache Hadoop systems. Learn to: • Install, configure, and administer Oracle R Enterprise • Establish connections and move data to the database • Create Oracle R Enterprise packages and functions • Use the R language to work with data in Oracle Database • Build models using ODM, ORE, and other algorithms • Develop and deploy R scripts and use the R script repository • Execute embedded R scripts and employ ORE SQL API functions • Map and manipulate data using Oracle R Advanced Analytics for Hadoop • Use ORE in Oracle Data Miner, OBIEE, and other applications ... "

This books is ideally suited to people who are starting out with Oracle R Enterprise (ORE) or have some experience with using it, and want to see what you can do with it and how it can be used with other products like APEX, OBIEE, Hadoop and Spark. Yes I touch on these in the book. This book may also be of interest for those who are working with the products I've just listed and want to see how to use ORE.

If you are at Oracle Open World (OOW) next week make sure to check out the book in the Oracle Book Store, and if you buy a copy try to track me down to get me to sign it. The best way to do this is to contact me on Twitter, leave a message at the Oracle Press stand, or you will find me hanging out at the OTN Lounge.

A special thanks to my technical editor, Mark Hornick, who is a Director of Oracle Advanced Analytics Product Management, for Oracle's R Technologies.

Here are quotes from some people about the book.

The book ‘Oracle R Enterprise’, written by Brendan Tierney, is a valuable resource for any data scientist who wants to use the R language with the Oracle Database. It demonstrates very well the many features of Oracle R Enterprise, from performing simple analytics to utilising the many performance features of the Oracle Database, allowing you to work with all your datasets - Big or small. Additionally the book demonstrates how you can use the power of the R language with the SQL language as well as with other Oracle products including APEX and OBIEE, as well as Hadoop and Spark.

- John Donnelly - Regional Director, Oracle Ireland

The new book by Brendan Tierney, Oracle ACE Director, on Oracle R Enterprise details how users can gain maximal value out of the Oracle Database’s tight integration with the popular open source R statistical programming language. The author guides the R community into how they can, through the ease and familiarity of R, tap into the power of the Oracle Database Enterprise Edition with its Oracle Advanced Analytics Option or the Oracle Database Cloud Service. Brendan, an expert in this field, clearly articulates how to get quickly started and provides extensive “how to” examples and R scripts. Readers of the book can learn how they can access data directly in the Database, eliminate data movement while exploiting the openness and flexibility of R. Readers can then tap into the scalability and security of SQL of the Oracle Database and leverage Oracle’s proprietary, parallelized in-database machine learning algorithms and Oracle R Enterprise’s R “push down” to SQL functions. Read this book and learn how to leverage R and reduce model development and enterprise model deployment from days/weeks to minutes/hours!

-Charlie Berger

Sr. Director Product Management, Oracle Advanced Analytics and Machine Learning

"Brendan Tierney conveys very clearly all the aspects required for a successful Data Scientist that wants to work with large Databases and large Big Data clusters. It contains a great articulation of all aspects related to building and deploying Machine Learning algorithms in an Oracle Database environment with an overview on the algorithms on Hadoop clusters, as well as the integration with Business Intelligence dashboards and Applications. This is an essential reference for anyone in the Data Science field today working with Oracle Databases.

Marcos Arancibia, Product Manager, Oracle Data Science.

Change the size of ORE PNG graphics using in-database R functions

In a previous blog post I showed you how create and display a ggplot2 R graphic using SQL. Make sure to check it out before reading the rest of this blog post.
In my previous blog post, I showed and mentioned that the PNG graphic returned by the embedded R execution SQL statement was not the same as what was produced if you created the graphic in an R session.
Here is the same ggplot2 graphic. The first one is what is produced in an R session and the section is what is produced by SQL query and the embedded R execution in Oracle.

As you can see the second image (produced using the embedded R execution) gives a very square image.
The reason for this is that Oracle R Enterprise (ORE) creates the graphic image in PNG format. The default setting from this is 480 x 480. You will find this information when you go digging in the R documentation and not in the Oracle documentation.
So, how can I get my ORE produced graphic to appear like what is produced in R?
What you need to do is to change the height and width of the PNG image produced by ORE. You can do this by passing parameters in the SQL statement used to call the user defined R function, that in turn produces the ggplot2 image.
In my previous post, I gave the SQL statement to call and produce the graphic (shown above). One of the parameters to the rqTableEval function was set to null. This was because we didn't have any parameters to pass, apart from the data set.
We can replace this null with any parameters we want to pass to the user defined R function (demo_ggpplot). To pass the parameters we need to define them using a SELECT statement.

cursor(select 500 as "ore.png.height", 850 as "ore.png.width" from dual),

The full SELECT statement now becomes

select *
from table(rqTableEval( cursor(select * from claims),
                        cursor(select 500 as "ore.png.height", 850 as "ore.png.width" from dual),
                        'PNG',
                        'demo_ggpplot'));

When you view the graphic in SQL Developer, you will get something that looks a bit more like what you would expect or want to see.

For each graphic image you want to produce using ORE you will need to figure out that are the best PNG height and width settings to use. Plus it also depends on what tool or application you are going to use to display the images (eg. APEX etc)

How to get ORE to work with APEX

This blog post will bring you through the steps of how to get Oracle R Enterprise (ORE) to work with APEX.

The reason for this blog posts is that since ORE 1.4+ the security model has changed for how you access and run in-database user defined R scripts using the ORE SQL API functions.

I have a series of blog posts going out on using Oracle Text, Oracle R Enterprise and Oracle Data Mining. It was during one of these posts I wanted to show how easy it was to display an R chart using ORE in APEX. Up to now my APEX environment consisted of APEX 4 and ORE 1.3. Everything worked, nice and easy. But in my new APEX environment (APEX 5 and ORE 1.5), it didn't work. This is the calling of an in-database user defined R script using the SQL API functions didn't work. Here is the error message that is displayed.

So something extra was needed with using ORE 1.5. The security model around the use of in-database user defined R scripts has changed. Extra functions are now available to allow you who can run these scripts. For example we have an ore.grant function where you can grant another user the privilege to run the script.

But the problem was, when I was in APEX, the application was defined on the same schema that the r script was created in (this was the RQUSER schema). When I connect to the RQUSER schema using ORE and SQL, I was able to see and run this R script (see my previous blog post for these details). But when I was in APEX I wasn't able to see the R script. For example, when using the SQL Workshop in APEX, I just couldn't see the R script.

Something strange is going on. It turns out that the view definitions for the in-database ORE scripts are defined with

owner=SYS_CONTEXT('USERENV', 'SESSION_USER');

(Thanks to the Oracle ORE team and the Oracle APEX team for their help in working out what needed to be done)

This means when I'm connected to APEX, using my schema (RQUSER), I'm not able to see any of my ORE objects.

How do you overcome this problem ?

To fix this problem, I needed to grant the APEX_PUBLIC_USER access to my ORE script.

ore.grant(name = "prepare_tm_data_2", type = "rqscript", user = "APEX_PUBLIC_USER")

Now when I query the ALL_RQ_SCRIPTS view again, using the APEX SQL Workshop, I now get the following.

Great. Now I can see the ORE script in my schema.

Now when I run my APEX application I now get graphic produced by R, running on my DB server, and delivered to my APEX application using SQL (via a BLOB object), displayed on my screen.

Oracle Text, Oracle R Enterprise and Oracle Data Mining - Part 3

This is the third blog post of a series on using Oracle Text, Oracle R Enterprise and Oracle Data Mining. Check out the first and second blog posts of the series, as the data used in this blog post was extracted, processed and stored in a databases table.

This blog post is divided into 3 parts. The first part will build on what was covered in in the previous blog post and will expand the in-database ORE R script to include more data processing. The second part of this blog post will look at how you can use SQL to call our in-database ORE R scripts and to be able to include it in our custom applications, for example using APEX (part 3).

Part 1 - Expanding our in-database ORE R script for Text Mining

In my previous blog post we created an ORE user defined R script, that is stored in the database, and this script was used to perform text mining and to create a word cloud. But the data/text to be mined was processed beforehand and passed into this procedure.

But what if we wanted to have a scenario where we just wanted to say, here is the table that contains the data. Go ahead and process it. To do this we need to expand our user defined R script to include the loop to merge the webpage text into one variable. The following is a new version of our ORE user defined R script.

> ore.scriptCreate("prepare_tm_data_2", function (local_data) { 
  library(tm)
  library(SnowballC)
  library(wordcloud)
  
  tm_data <-""
  for(i in 1:nrow(local_data)) {
    tm_data <- paste(tm_data, local_data[i,]$DOC_TEXT, sep=" ")
  }
    
  txt_corpus <- Corpus (VectorSource (tm_data))
  
  # data clean up
  tm_map <- tm_map (txt_corpus, stripWhitespace) # remove white space
  tm_map <- tm_map (tm_map, removePunctuation) # remove punctuations
  tm_map <- tm_map (tm_map, removeNumbers) # to remove numbers
  tm_map <- tm_map (tm_map, removeWords, stopwords("english")) # to remove stop words
  tm_map <- tm_map (tm_map, removeWords, c("work", "use", "java", "new", "support"))

  # prepare matrix of words and frequency counts
  Matrix <- TermDocumentMatrix(tm_map) # terms in rows
  matrix_c <- as.matrix (Matrix)
  freq <- sort (rowSums (matrix_c)) # frequency data
  
  res <- data.frame(words=names(freq), freq)
  wordcloud (res$words, res$freq, max.words=100, min.freq=3, scale=c(7,.5), random.order=FALSE, colors=brewer.pal(8, "Dark2"))
} ) 

To call this R scipts using the embedded R execution we can use the ore.tableApply function. Our parameter to our new R script will now be an ORE data frame. This can be a table in the database or we can create a subset of table and pass it as the parameter. This will mean all the data process will occur on the Oracle Database server. No data is passed to the client or processing performed on the client. All work is done on the database server. The only data that is passed back to the client is the result from the function and that is the word cloud image.

> res <- ore.tableApply(MY_DOCUMENTS, FUN.NAME="prepare_tm_data_2")
> res

Part 2 - Using SQL to perform R Text Mining

Another way you ccan call this ORE user defined R function is using SQL. Yes we can use SQL to call R code and to produce an R graphic. Then doing this the R graphic will be returned as a BLOB. So that makes it easy to view and to include in your applications, just like APEX.

To call our ORE user defined R function, we can use the rqTableEval SQL function. You only really need to set two of the parameters to this function. The first parameter is a SELECT statement the defines the data set to be passed to the function. This is similar to what I showed above using the ore.tableApply R function, except we can have easier control on what records to pass in as the data set. The fourth parameter gives the name of the ORE user defined R script.

select *
from table(rqTableEval( cursor(select * from MY_DOCUMENTS),
                        null,
                        'PNG',
                        'prepare_tm_data_2'));

This is the image that is produced by this SQL statement and viewed in SQL Developer.

Part 3 - Adding our R Text Mining to APEX

Adding the SQL to call an ORE user defined script is very simple in APEX. You can create a form or a report based on a query, and this query can be the same query that is given above.

Something that I like to do is to create a view for the ORE SELECT statement. This gives me some flexibility with some potential future modifications. This could be as simple as just changing the name of the script. Also if I discover a new graphic that I want to use, all I need to do is to change the R code in my user defined R script and it will automatically be picked up and displayed in APEX. See the images below.

WARNING: Yes I do have a slight warning. Since the introduction of ORE 1.4 and higher there is a slightly different security model around the use of user defined R scripts. Instead of going into the details of this and what you need to do in this blog post, I will have a separate blog post that describes the behaviour and what you need to do allow APEX to use ORE and to call the user defined R scripts in your schema. So look out for this blog post coming really soon.

In this blog post I showed you how you use Oracle R Enterprise and the embedded R execution features of ORE to use the text from the webpages and to create a word cloud. This is a useful tool to be able to see visually what words can stand out most on your webpage and if the correct message is being put across to your customers.

Creating ggplot2 graphics using SQL

Did you read the title of this blog post! Read it again.

Yes, Yes, I know what you are saying, "SQL cannot produce graphics or charts and particularly not ggplot2 graphics".

You are correct to a certain extent. SQL is rubbish a creating graphics (and I'm being polite).

But with Oracle R Enterprise you can now produce graphics on your data using the embedded R execution feature of Oracle R Enterprise using SQL. In this blog post I will show you how.

1. Pre-requisites

You need to have installed Oracle R Enterprise on your Oracle Database Server. Plus you need to install the ggplot2 R package.

In your R session you will need to setup a ORE connection to your Oracle schema.

2. Write and Test your R code to produce the graphic

It is always a good idea to write and test your R code before you go near using it in a user defined function.

For our (first) example we are going to create a bar chart using the ggplot2 R package. This is a basic example and the aim is to illustrate the steps you need to go through to call and produce this graphic using SQL.

The following code using the CLAIMS data set that is available with/for Oracle Advanced Analytics. The first step is to pull the data from the table in your Oracle schema to your R session. This is because ggplot2 cannot work with data referenced by an ore.frame object.

data.subset <- ore.pull(CLAIMS) 

Next we need to aggregate the data. Here we are counting the number of records for each Make of car.

aggdata2 <- aggregate(data.subset$POLICYNUMBER,
                      by = list(MAKE = data.subset$MAKE),
                      FUN = length)

Now load the ggplot2 R package and use it to build the bar chart.

ggplot(data=aggdata2, aes(x=MAKE, y=x, fill=MAKE)) + 
       geom_bar(color="black", stat="identity") +
       xlab("Make of Car") + 
       ylab("Num of Accidents") + 
       ggtitle("Accidents by Make of Car")

The following is the graphic that our call to ggplot2 produces in R.

At this point we have written and tested our R code and know that it works.

3. Create a user defined R function and store it in the Oracle Database

Our next step in the process is to create an in-database user defined R function. This is were we store R code in our Oracle Database and make this available as an R function. To create the user defined R function we can use some PL/SQL to define it, and then take our R code (see above) and in it.

BEGIN
   -- sys.rqScriptDrop('demo_ggpplot');
   sys.rqScriptCreate('demo_ggpplot', 
      'function(dat) {
         library(ggplot2)
         
         aggdata2 <- aggregate(dat$POLICYNUMBER,
                      by = list(MAKE = dat$MAKE),
                      FUN = length)

        g <-ggplot(data=aggdata2, aes(x=MAKE, y=x, fill=MAKE)) + geom_bar(color="black", stat="identity") +
                   xlab("Make of Car") + ylab("Num of Accidents") + ggtitle("Accidents by Make of Car")

        plot(g)
   }');
END;

We have to make a small addition to our R code. We need need to include a call to the plot function so that the image can be returned as a BLOB object. If you do not do this then the SQL query in step 4 will return no rows.

4. Write the SQL to call it

To call our defined R function we will need to use one of the ORE SQL API functions. In the following example we are using the rqTableEval function. The first parameter for this function passes in the data to be processed. In our case this is the data from the CLAIMS table. The second parameter is set to null. The third parameter is set to the output format and in our case we want this to be PNG. The fourth parameter is the name of the user defined R function.

select *
from table(rqTableEval( cursor(select * from claims),
                        null,
                        'PNG',
                        'demo_ggpplot'));                        

5. How to view the results

The SQL query in Step 4 above will return one row and this row will contain a column with a BLOB data type.

The easiest way to view the graphic that is produced is to use SQL Developer. It has an inbuilt feature that allows you to display BLOB objects. All you need to do is to double click on the BLOB cell (under the column labeled IMAGE). A window will open called 'View Value'. In this window click the 'View As Image' check box on the top right hand corner of the window. When you do the R ggplot2 graphic will be displayed.

Yes the image is not 100% the same as the image produced in our R session. I will have another blog post that deals with this at a later date.

But, now you have written a SQL query, that calls R code to produce an R graphic (using ggplot2) of our data.

6. Now you can enhance the graphics (without changing your SQL)

What if you get bored with the bar chart and you want to change it to a different type of graphic? All you need to do is to change the relevant code in the user defined R function.

For example, if we want to change the graphic to a polar plot. The following is the PL/SQL code that re-defines the user defined R script.

BEGIN
   sys.rqScriptDrop('demo_ggpplot');
   sys.rqScriptCreate('demo_ggpplot', 
      'function(dat) {
         library(ggplot2)
         
         aggdata2 <- aggregate(dat$POLICYNUMBER,
                      by = list(MAKE = dat$MAKE),
                      FUN = length)

         n <- nrow(aggdata2)
         degrees <- 360/n

        aggdata2$MAKE_ID <- 1:nrow(aggdata2)

        g<- ggplot(data=aggdata2, aes(x=MAKE, y=x, fill=MAKE)) + geom_bar(color="black", stat="identity") +
               xlab("Make of Car") + ylab("Num of Accidents") + ggtitle("Accidents by Make of Car") + coord_polar(theta="x") 
        plot(g)
   }');
END;

We can use the exact same SQL query we defined in Step 4 above to call the next graphic.

All done.

Now that was easy! Right?

I kind of is easy once you have been shown. There are a few challenges when working in-database user defined R functions and writing the SQL to call them. Most of the challenges are around the formatting of R code in the function and the syntax of the SQL statement to call it. With a bit of practice it does get easier.

7. Where/How can you use these graphics ?

Any application or program that can call and process a BLOB data type can display these images. For example, I've been able to include these graphics in applications developed in APEX.

Configuring RStudio Server for Oracle R Enterprise

In this blog post I will show you the configurations that are necessary for RStudio Server to work with Oracle R Enterprise on your Oracle Database server. In theory if you have just installed ORE and then RStudio Server, everything should work, but if you encounter any issues then check out the following.

Before I get started make sure to check out my previous blog posts on installing R Studio Server. The first blog post was installing and configuring RStudio Server on the Oracle BigDataLite VM. This is an automated install. The second blog post was a step by step guide to installing RStudio Server on your (Oracle) Linux Database Server and how to open the port on the VM using VirtualBox.

Right. Let's get back to configuring to work with Oracle R Enterprise. The following assumes you have complete the second blog post mentioned above.

1. Edit the rserver.conf files

Add in the values and locations for RHOME and ORACLE_HOME

sudo vi /etc/rstudio/rserver.conf
    rsession-ld-library-path=RHOME/lib:ORACLE_HOME/lib

2. Edit the .Renviron file.

Add in the values for ORACLE_HOME, ORACLE_HOSTNAME and ORACLE_SID

cd /home/oracle
sudo vi .Renviron
    ORACLE_HOME=ORACLE_HOME
    ORACLE_HOSTNAME=ORACLE_HOSTNAME
    ORACLE_SID=ORACLE_SID
 
export ORACLE_HOME
export ORACLE_HOSTNAME
export ORACLE_SID

3. To access the Oracle R Distribution

Add the following to the usr/lib/rstudio-server/R/modules/SessionHelp.R file for the version of Oracle R Distribution you installed prior to installing Oracle R Enterprise.

.rs.addFunction( "httpdPortIsFunction", function() {
   getRversion() >= "3.2"
})

You are all done now with all the installations and configurations.

Installing RStudio Server on an (Oracle) Linux server

In a previous blog post I showed how you can install and get started with using RStudio on a server by using RStudio Server. My previous post showed how you could do that on the Oracle BigDataLite VM. On this VM everything was nicely scripted and set up for you. But when it comes to installing it on a different server, well things can be a bit different.

The purpose of this blog post is to go through the install steps you need to follow on your own server or Oracle Database server. The following is based on a server that is setup with Oracle Linux. (I'm actually using the Oracle DB Developer VM).

1. Download the latest version of RStudio Server.

Use the following link to download RStudio Server. But do a quick check on the RStudio server to get the current version number.

wget https://download2.rstudio.org/rstudio-server-rhel-0.99.892-x86_64.rpm

The following shows you what you will see when you run this command.

--2016-03-16 06:22:30--  https://download2.rstudio.org/rstudio-server-rhel-0.99.892-x86_64.rpm
Resolving download2.rstudio.org (download2.rstudio.org)... 54.192.28.107, 54.192.28.54, 54.192.28.12, ...
Connecting to download2.rstudio.org (download2.rstudio.org)|54.192.28.107|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 38814908 (37M) [application/x-redhat-package-manager]
Saving to: ‘rstudio-server-rhel-0.99.892-x86_64.rpm’

100%[============================================================>] 38,814,908  6.54MB/s   in 6.0s  

2016-03-16 06:22:37 (6.17 MB/s) - ‘rstudio-server-rhel-0.99.892-x86_64.rpm’ saved [38814908/38814908]

2. Install RStudio Server

sudo yum install --nogpgcheck rstudio-server-rhel-0.99.892-x86_64.rpm

when prompted if it is OK to install, enter y (highlighted in bold below)

Loaded plugins: langpacks
Examining rstudio-server-rhel-0.99.892-x86_64.rpm: rstudio-server-0.99.892-1.x86_64
Marking rstudio-server-rhel-0.99.892-x86_64.rpm to be installed
Resolving Dependencies
--> Running transaction check
---> Package rstudio-server.x86_64 0:0.99.892-1 will be installed
--> Finished Dependency Resolution
ol7_UEKR3/x86_64                                                                    | 1.2 kB  00:00:00    
ol7_addons/x86_64                                                                   | 1.2 kB  00:00:00    
ol7_latest/x86_64                                                                   | 1.4 kB  00:00:00    
ol7_optional_latest/x86_64                                                          | 1.2 kB  00:00:00    

Dependencies Resolved

===========================================================================================================
 Package               Arch          Version             Repository                                   Size
===========================================================================================================
Installing:
 rstudio-server        x86_64        0.99.892-1          /rstudio-server-rhel-0.99.892-x86_64        280 M

Transaction Summary
===========================================================================================================
Install  1 Package

Total size: 280 M
Installed size: 280 M

Is this ok [y/d/N]: y

Downloading packages:
Running transaction check
Running transaction test
Transaction test succeeded
Running transaction
  Installing : rstudio-server-0.99.892-1.x86_64                                                        1/1
groupadd: group 'rstudio-server' already exists
rsession: no process found
ln -s '/etc/systemd/system/rstudio-server.service' '/etc/systemd/system/multi-user.target.wants/rstudio-server.service'
rstudio-server.service - RStudio Server
   Loaded: loaded (/etc/systemd/system/rstudio-server.service; enabled)
   Active: active (running) since Wed 2016-03-16 10:46:00 PDT; 1s ago
  Process: 3191 ExecStart=/usr/lib/rstudio-server/bin/rserver (code=exited, status=0/SUCCESS)
 Main PID: 3192 (rserver)
   CGroup: /system.slice/rstudio-server.service
           ├─3192 /usr/lib/rstudio-server/bin/rserver
           └─3205 /usr/lib64/R/bin/exec/R --slave --vanilla -e cat(R.Version()$major,R.Version()$minor,~+~sep=".")

Mar 16 10:46:00 localhost.localdomain systemd[1]: Started RStudio Server.
  Verifying  : rstudio-server-0.99.892-1.x86_64                                                        1/1

Installed:
  rstudio-server.x86_64 0:0.99.892-1                                                                      

Complete!

3. Open RStudio using a web browser.

Open your favourite web browser and put in the host name or the IP address of your server. In my example I'm using the Oracle DB Developer VM to demonstrate the install, so I can use localhost, followed by the port number for RStudio Server.

Log in using your Server username and password. This is oracle/oracle on the VM.

4. Use and Enjoy

If you get logged into RStudio Server then you will see a screen something like the following!

Job Done and Enjoy!

5. An Extra Step is using the Oracle DB Developer VM

If you want to use RStudio on the Oracle DB Developer VM from your local OS, then you will need to open the port 8787 on the VM. To do this power down the VM, if you have it open. The open the Network section of the VM settings. I'm using VirtualBox. And then click on the Port Forwarding.

Click on OK to save your Port Forwarding setting and then click on the OK button again to close the Network settings for the VM.

Now start up the VM. When it has loaded and you have the desktop displayed in the VM window, you should now be able to connect to RStudio in the VM, from your local machine.

To do this open your web browser on your local machine and type in

http://localhost:8787

You should now get the RStudio login in screen that is shown in point 3 above. Go ahead, login and enjoy.

6. A little warning

Make sure to log out of RStudio when you are finished using it. If you don't then your R environment may not have been saved and you will get a message when you log in next. Now we don't want that happenings, so just log out of RStudio. You can do that by looking at the top right hand corner of the RStudio Server application.

I will have one more blog post on how you can configure RStudion Server to work with an Oracle Database server that has Oracle R Enterprise installed.

Installing RStudio Server on Oracle BigDataLite VM

A very popular tool for data scientists is RStudio. This tool allows you to interactively work with your R code, view the R console, the graphs and charts you create, manage the various objects and data frames you create, as well shaving easy access to the R help documentation. Basically it is a core everyday tool.

The typical approach is to have RStudio installed on your desktop or laptop. What this really means is that the data is pulled to your desktop or laptop and all analytics is performed there. In most cases this is fine but as your data volumes goes does does the limitations of using R on your local machine.

An alternative is to install a version called RStudio Server on an analytics server or on the database server. You can now use the computing capabilities of this server to overcome some of the limitations of using R or RStudio locally. Now you will use your web browser to access RStudio Server on your database server.

In this blog post I will walk you through how to install and get connected to RStudio Server on the Oracle BigDataLite VM.

After starting up the Oracle BigDataLite VM and logging into the Oracle user (password=welcome1) you will see the Start Here icon on the desktop. You will need to double click on this.

This will open a webpage on the VM that contains details of all the various tools that are installed on the VM or are ready for you to install and configure. This information contains all the http addresses and ports you need to access each of these tools via a web browser or some other way, along with the usernames and passwords you need to use them.

One of the tools lists is for RStudio Server. This product is not installed on the VM but Oracle has provided a script that you can run to perform the install in an automated way. This script is located in:

[oracle@bigdatalite ~]$ cd  /home/oracle/scripts/

Use the following command to run the RStudio Server install script.

[oracle@bigdatalite scripts]$ ./install_rstudio.sh

The following is the output from running this script and it will be displayed in your terminal window. You can use this to monitor the progress of the installation.

Retrieving RStudio
--2016-03-12 02:06:15--  https://download2.rstudio.org/rstudio-server-rhel-0.99.489-x86_64.rpm
Resolving download2.rstudio.org... 54.192.28.12, 54.192.28.54, 54.192.28.98, ...
Connecting to download2.rstudio.org|54.192.28.12|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 34993428 (33M) [application/x-redhat-package-manager]
Saving to: `rstudio-server-rhel-0.99.489-x86_64.rpm'

100%[======================================>] 34,993,428  5.24M/s   in 10s     

2016-03-12 02:06:26 (3.35 MB/s) - `rstudio-server-rhel-0.99.489-x86_64.rpm' saved [34993428/34993428]

Installing RStudio
Loaded plugins: refresh-packagekit, security, ulninfo
Setting up Install Process
Examining rstudio-server-rhel-0.99.489-x86_64.rpm: rstudio-server-0.99.489-1.x86_64
Marking rstudio-server-rhel-0.99.489-x86_64.rpm to be installed
public_ol6_UEKR3_latest                                  | 1.2 kB     00:00     
public_ol6_UEKR3_latest/primary                          |  22 MB     00:03     
public_ol6_UEKR3_latest                                                 568/568
public_ol6_latest                                        | 1.4 kB     00:00     
public_ol6_latest/primary                                |  55 MB     00:12     
public_ol6_latest                                                   33328/33328
Resolving Dependencies
--> Running transaction check
---> Package rstudio-server.x86_64 0:0.99.489-1 will be installed
--> Finished Dependency Resolution

Dependencies Resolved

================================================================================
 Package        Arch   Version       Repository                            Size
================================================================================
Installing:
 rstudio-server x86_64 0.99.489-1    /rstudio-server-rhel-0.99.489-x86_64 251 M

Transaction Summary
================================================================================
Install       1 Package(s)

Total size: 251 M
Installed size: 251 M
Downloading Packages:
Running rpm_check_debug
Running Transaction Test
Transaction Test Succeeded
Running Transaction
  Installing : rstudio-server-0.99.489-1.x86_64                             1/1 
useradd: user 'rstudio-server' already exists
groupadd: group 'rstudio-server' already exists
rsession: no process killed
rstudio-server start/running, process 5037
  Verifying  : rstudio-server-0.99.489-1.x86_64                             1/1 

Installed:
  rstudio-server.x86_64 0:0.99.489-1                                            

Complete!
Restarting RStudio
rstudio-server stop/waiting
rsession: no process killed
rstudio-server start/running, process 5066

When the installation is finished you are now ready to connect to the RStudio Server. So open your web browser and enter the following into the address bar.

http://localhost:8787/

The initial screen you are presented with is a login screen. Enter your Linux username and password. In the case of the BigDataLite VM this will be oracle/welcome1.

Then you will be presented with the RStudio Server application in your web browser, as shown below. As you can see it is very similar to using RStudio on your desktop. Happy Days! You are now setup and able to run RStudio on the database server.

Make sure to log out of RStudio Server before closing down the window.

If you don't log out of RStudio Server then the next time you open RStudio Server your session will automatically open. Perhaps this is not the best for security, so try to remember to log out each time.

By now using RStudio Server on the Oracle Database server I can not get some of the benefits of computing capabilities of this server. Although there are still the typical limitations with of using R. But now I access RStudio on the database server and process the data on the database server, all from my local PC or laptop.

Everything is nicely setup and ready for you to install on the BigDataLite VM (thank you Oracle). But what about when we want to install RStudion Server on a different server. What are the steps necessary to install, configure and log in. Yes they should be similar but I will give a complete list of steps in my next blog post.

Recoding variable values using ore.recode

Oracle R Enterprise comes with a vast array of features that not really documented anywhere. One of these features that I've recently found useful is the ore.recode() function.

The following code illustrates how you can records the values in an existing attributes or (more specifically in this example) how you can create a new attribute based on the values in another attribute.

The data set that I'm using is the White Wine data set that can be found on the UCI Machine Learning Repository Archive website. You can download this data set and load it into a table in your Oracle schema using just two commands.

> WhiteWine = read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv",
                        sep=";", header=TRUE)
> ore.create(WhiteWine, table="WHITE_WINE")

This data set has an attribute called "quality". This "quality" attribute contains values ranging from 2 to 8, and indicates the quality of the wine.

A typical task you may want to do is to relabel values into attributes to something a bit more meaningful or to group some values into a more standardised value.

To demonstrate this I want to create a new attribute that contains a description of the type of wine (and who I might share it with).

In this case, and to allow for other values in future versions of the data sets I've coded up the following:

quality  grade
-------  ----------------
1        Paint Stripper
2        Vinegar
3        Barely Drinkable
4        For the in-laws
5        For my family
6        To share with friends
7        For cooking
8        To share with my wife
9        Mine all Mine

The next step we need to perform is to gather some information about the values in the "quality" attribute. We can use the table command to quickly perform the aggregations, and then use the marplot function to graph the distributions.

> WHITE_WINE2 <- WHITE_WINE
> table(WHITE_WINE2$quality)
> barplot(table(WHITE_WINE2$quality), xlab="Wine Quality Ranking")

Now we are ready to perform the recoding of the values using the ore.recode() function.

> WHITE_WINE2$grade <- ore.recode(WHITE_WINE2$quality, old=c(1, 2, 3, 4, 5, 6, 7, 8, 9), 
                     new=c("1-Paint Stripper", "2-Vinegar", "3-Barely Drinkable",
                           "4-For the in-laws", "5-For my family", "6-To share with friends", 
                           "7-For cooking", "8-To share with my wife", 
                           "9-Mine all Mine"))

You can now go and inspect the data, perform a frequency count and compare the values with what we had previously.

> head(WHITE_WINE2[,c("quality", "grade")]) 
> table(WHITE_WINE2$grade) 

The final step is to write the newly modified data set back to your Oracle schema into a new table. This is to ensure that the original data is modified so that it can be used or reused later.

> ore.create(WHITE_WINE2, "WHITE_WINE2")

ORE video : Demo Code Part 5

The following is the fifth and final set of demo code from my video on using R in the Oracle Database. Check out the video before using the following code. The blog post for the video will be updated to contain links to all blog posts that have the various demo code.

The following examples illustrate how you can use the Oracle R Enterprise capabilities within SQL and PL/SQL. The following illustrate building a GLM model using the glm algorithm that comes with the R language, and then uses this mode to score or label new data that is stored in a table. The last part of the example illustrates how you can perform What-If analysis using this ORE model

-- Build & save the R script, called Demo_GLM in the DB
--  This builds a GLM  DM model in the DB
--
Begin
   sys.rqScriptDrop('Demo_GLM');
   sys.rqScriptCreate('Demo_GLM',
      'function(dat,datastore_name) {
          mod <- glm(AFFINITY_CARD ~ CUST_GENDER + AGE + CUST_MARITAL_STATUS + COUNTRY_NAME + CUST_INCOME_LEVEL + EDUCATION + HOUSEHOLD_SIZE + YRS_RESIDENCE, dat, family = binomial())
       ore.save(mod, name=datastore_name, overwrite=TRUE)   }');
end;
/

--
-- After creating the script you need to run it to create the GLM model
--
select * 	
from table(rqTableEval(
             cursor(select CUST_GENDER,
                           AGE,
                           CUST_MARITAL_STATUS,
                           COUNTRY_NAME,
                           CUST_INCOME_LEVEL,
                           EDUCATION,
                           HOUSEHOLD_SIZE,
                           YRS_RESIDENCE,
                           AFFINITY_CARD
                    from mining_data_build_v),
             cursor(select 1 as "ore.connect", 'myDatastore' as "datastore_name” from dual),
                  'XML', 'Demo_GLM' ));


--
-- There are 2 ways to use the GLM model : in Batch and in Real-Time mode
--
-- First Step : Build the in-database R script to score you new data
--
Begin
   sys.rqScriptDrop('Demo_GLM_Batch');
   sys.rqScriptCreate('Demo_GLM_Batch',
      'function(dat, datastore_name) {
      ore.load(datastore_name)
      prd <- predict(mod, newdata=dat)
      prd[as.integer(rownames(prd))] <- prd
      res <- cbind(dat, PRED = prd)
      res}');
end;
/

-- 
-- Now you can run the script to score the new data in Batch model
--   The data is located in the table MINING_DATA_APPLY
--
select * from table(rqTableEval(
              cursor(select CUST_GENDER, AGE, CUST_MARITAL_STATUS, COUNTRY_NAME, CUST_INCOME_LEVEL, EDUCATION, HOUSEHOLD_SIZE, YRS_RESIDENCE
                     from   MINING_DATA_APPLY_V
                     where rownum <= 10),
             cursor(select 1 as "ore.connect", 'myDatastore' as "datastore_name" from dual),
             'select CUST_GENDER, AGE, CUST_MARITAL_STATUS, COUNTRY_NAME, CUST_INCOME_LEVEL, EDUCATION, HOUSEHOLD_SIZE, YRS_RESIDENCE, 1 PRED from MINING_DATA_APPLY_V','Demo_GLM_Batch'))
order by 1, 2, 3;

--
-- Now let us use the Demo_GLM_Batch script to score data in Real-Time
--  The data values are passed to the GLM model
--
select * from table(rqTableEval(
              cursor(select 'M' CUST_GENDER,
                            23 AGE,
                            'Married' CUST_MARITAL_STATUS, 
                            'United States of America' COUNTRY_NAME,
                            'B: 30,000 - 49,999' CUST_INCOME_LEVEL, 
                            'Assoc-A' EDUCATION,
                            '3' HOUSEHOLD_SIZE, 
                            5 YRS_RESIDENCE
                     from dual),
              cursor(select 'myDatastore' "datastore_name", 1 "ore.connect" from dual),
                    'select CUST_GENDER, AGE, CUST_MARITAL_STATUS, COUNTRY_NAME, CUST_INCOME_LEVEL, EDUCATION, HOUSEHOLD_SIZE, YRS_RESIDENCE, 1 PRED from MINING_DATA_APPLY',
              'Demo_GLM_Batch')) order by 1, 2, 3; 

ORE video : Demo Code Part 4

The following is the fourth set of demo code from my video on using R in the Oracle Database. Check out the video before using the following code. The blog post for the video will be updated to contain links to all blog posts that have the various demo code.

The following code example illustrate how you can build a Data Mining model using the in-database data mining algorithms. In this example a Decision Tree model is created. This model is then applied to new data, scoring this data with the predicted values.

> #
> # Build am in-database ODM Decision Tree
> #
> dtData <- ore.get("MINING_DATA_BUILD_V")
> # Create a ODM DT model in the DB : Only a temporary model. It is deleted when you logout
> dtModel <- ore.odmDT(AFFINITY_CARD ~ ., dtData)
> # View the details of the ODM model
> #summary(dtModel)
> names(dtModel)
 [1] "name"          "settings"      "attributes"    "costs"         "distributions”
 [6] "nodes"         "formula"       "extRef"        "call"         
> dtModel$name
 [1] "ORE$208_210”
> dtModel$settings
                          value
prep.auto                    on
impurity.metric   impurity.gini
term.max.depth                7
term.minpct.node           0.05
term.minpct.split           0.1
term.minrec.node             10
term.minrec.split            20
> dtModel$attributes
                 name        type data.type data.length precision scale is.target
1       AFFINITY_CARD categorical    number          22         0     0      TRUE
2                 AGE   numerical    number          22        NA    NA     FALSE
3 CUST_MARITAL_STATUS categorical  varchar2          20        NA    NA     FALSE
4           EDUCATION categorical  varchar2          21        NA    NA     FALSE
5      HOUSEHOLD_SIZE categorical  varchar2          21        NA    NA     FALSE
6          OCCUPATION categorical  varchar2          21        NA    NA     FALSE
7       YRS_RESIDENCE   numerical    number          22        NA    NA     FALSE
>

> ## Compute the Compusion Matrix
> dtResults <- predict(dtModel, dtData, "AFFINITY_CARD")
> with(dtResults, table(AFFINITY_CARD, PREDICTION))
             PREDICTION
AFFINITY_CARD    0    1
            0 1056   64
            1  201  179
> ## How do you persist the model in the DB
> ##     Rename and save the model in the database
> dtModel$name
 [1] "ORE$208_210"

> ## Save the ODM model in the in-database R datastore
> ore.save(dtModel, name = "ORE_MODELS", overwrite=TRUE)
> ore.load(name = "ORE_MODELS")
 [1] "dtModel"

> ## Score new data using the DM Model
> ore.sync(table = c("MINING_DATA_APPLY"))
> ore.ls()
 [1] "DEMO_R_APPLY_RESULT"   "DEMO_R_TABLE"          "DEMO_SUBSET_TABLE"    
 [4] "INSUR_CUST_LTV_SAMPLE" "MINING_DATA_APPLY"     "MINING_DATA_BUILD_V"  
 [7] "MINING_DATA_TEST_V"   > dtApply <- ore.get("MINING_DATA_APPLY")
> dim(dtApply)
 [1] 1500   18
> class(dtApply)
 [1] "ore.frame”
 attr(,"package")
 [1] "OREbase”
> DTAPPLY <- ore.push(dtApply)
> dtApplyResult <- predict(dtModel, DTAPPLY)

> dtApplyResult <- predict(dtModel, DTAPPLY)
> head(dtApplyResult)
             '0'        '1' PREDICTION
100001 0.9521912 0.04780876          0
100002 0.9521912 0.04780876          0
100003 0.9521912 0.04780876          0
100004 0.9521912 0.04780876          0
100005 0.2633745 0.73662551          1
100006 0.9521912 0.04780876          0
> dim(dtApplyResult)
 [1] 1500    3
> dim(dtApply)
 [1] 1500   18
> dtResults <- cbind(dtApply, dtApplyResult)
> dim(dtResults)
 [1] 1500   21
> ore.drop(table = "DEMO_R_APPLY_RESULT")
> ore.create(dtApplyResult, table="DEMO_R_APPLY_RESULT")
> ## Run the following for the first time you will rename a mode
> # ore.exec(paste("BEGIN> 
  #                  DBMS_DATA_MINING.RENAME_MODEL(model_name => '", dtModel$name, "',> 
  #                      new_model_name => 'DEMO_R_DT_MODEL'); END;",sep=""))> 
  ## Run the following to refresh an existing model
> ore.exec(paste("BEGIN
+ DBMS_DATA_MINING.DROP_MODEL('DEMO_R_DT_MODEL');
+ DBMS_DATA_MINING.RENAME_MODEL(model_name => '", dtModel$name,"',
+ new_model_name => 'DEMO_R_DT_MODEL');
+ END;",sep=""))

ORE video : Demo Code Part 3

The following is the third set of demo code from my video on using R in the Oracle Database. Check out the video before using the following code. The blog post for the video will be updated to contain links to all blog posts that have the various demo code.

The following code is illustrates some simple examples of using Oracle R Enterprise. In these example you will see how to connect to the Oracle Database, how to query and process some of the tables and views in the Oracle Database, how to check that you are working with objects in the database, how to move data to the database and query it.

> library(ORE)
> # ore.connect(user="rquser", sid="orcl", host="localhost", password="rquser", port=1521, all=TRUE);
> ore.connect(user="dmuser", sid="orcl", host="localhost", password="dmuser", port=1521, all=FALSE);
> # Test the connection
> ore.is.connected()
 [1] TRUE
> # List all the tables and views
> ore.ls()
character(0)
> # Use ore.sync to only include the tables and views listed
> ore.sync()
> ore.ls()
 [1] "DEMO_R_APPLY_RESULT"      "DEMO_R_TABLE"             "INSUR_CUST_LTV_SAMPLE"    "MINING_DATA_APPLY"       
 [5] "MINING_DATA_APPLY_V"      "MINING_DATA_BUILD_V"      "MINING_DATA_TEST_V"       "MINING_DATA_TEXT_APPLY_V”
 [9] "MINING_DATA_TEXT_BUILD_V" "MINING_DATA_TEXT_TEST_V" 
> # Disconnect and reattached with no meta-data sync
> ore.disconnect()
> ore.connect(user="dmuser", sid="orcl", host="localhost", password="dmuser", port=1521, all=FALSE);
> ore.sync(table = c("MINING_DATA_BUILD_V", "MINING_DATA_TEST_V", "INSUR_CUST_LTV_SAMPLE"))
> ore.ls()
 [1] "INSUR_CUST_LTV_SAMPLE" "MINING_DATA_BUILD_V"   "MINING_DATA_TEST_V"   
> # Check for the existance of a table or view
> ore.exists("MINING_DATA_BUILD_V")
 [1] TRUE
> # list the objects in the DMUSER schema
> ore.ls("DMUSER")
 [1] "INSUR_CUST_LTV_SAMPLE" "MINING_DATA_BUILD_V"   "MINING_DATA_TEST_V" 
> #
> # Load data from a file into a new table
> ore.exists("DEMO_R_TABLE")
 [1] TRUE
> ore.drop(table='DEMO_R_TABLE')
> ore.ls()
 [1] "INSUR_CUST_LTV_SAMPLE" "MINING_DATA_BUILD_V"   "MINING_DATA_TEST_V"   
> titanic <- read.table("c:/R/titanic2.txt", header=T, sep="\t")
> ore.create(titanic, table="DEMO_R_TABLE")
> tData <- ore.get("DEMO_R_TABLE")
> head(tData)
                 NAME PCLASS AGE    SEX SURVIVED
1 Fynney, Mr Joseph J    2nd  35   male        0
2      Gale, Mr Harry    2nd  35   male        0
3   Gale, Mr Shadrach    2nd  38   male        0
4 Garside, Miss Ethel    2nd  24 female        1
5  Gaskell, Mr Alfred    2nd  16   male        0
6  Gavey, Mr Lawrence    2nd  26   male        0
> # Use ORE to pull data from the Database to local R
> # ore.pull  -- United States of America
> mdbv <- ore.get("MINING_DATA_BUILD_V")
> mdbv_data <- ore.pull(mdbv)
Warning message:ORE object has no unique key - using random order 
> head(mdbv_data,3)
  CUST_ID CUST_GENDER AGE CUST_MARITAL_STATUS             COUNTRY_NAME    CUST_INCOME_LEVEL EDUCATION OCCUPATION
1  101501           F  41              NeverM United States of America J: 190,000 - 249,999   Masters      Prof.
2  101502           M  27              NeverM United States of America I: 170,000 - 189,999     Bach.      Sales
3  101503           F  20              NeverM United States of America H: 150,000 - 169,999   HS-grad    Cleric.
  HOUSEHOLD_SIZE YRS_RESIDENCE AFFINITY_CARD BULK_PACK_DISKETTES FLAT_PANEL_MONITOR HOME_THEATER_PACKAGE
1              2             4             0                   1                  1                    1
2              2             3             0                   1                  1                    0
3              2             2             0                   1                  0                    0
  BOOKKEEPING_APPLICATION PRINTER_SUPPLIES Y_BOX_GAMES OS_DOC_SET_KANJI
1                       1                1           0                0
2                       1                1           1                0
3                       1                1           1                0
> class(mdbv_data)
[1] "data.frame”
> summary(mdbv_data)

ORE Video : Demo Code part 1

In a previous blog post I posted a video on using R with the Oracle Database and using Oracle R Enterprise. This is a part 1 extension of that blog post that gives the first set of demo code.

This first set of demonstration code is for using RJDBC to connect to the Oracle Database. Using RJDBC relies on using the JDBC jar file for Oracle. It is easily found in various installations of Oracle products and will be called something like ojdbc.jar. I like to take a copy of this file and place it in the root/home directory.

> library(RJDBC)
> # Create connection driver and open 
> connectionjdbcDriver <- JDBC(driverClass="oracle.jdbc.OracleDriver", classPath="c:/ojdbc6.jar")
> jdbcConnection <- dbConnect(jdbcDriver, "jdbc:oracle:thin:@//localhost:1521/orcl", "dmuser", "dmuser")
> #list the tables in the schema
> #dbListTables(jdbcConnection)
> #get the DB connections details - it get LOTS of info - Do not run unless it is really needed
> dbGetInfo(jdbcConnection)
> # Query on the Oracle instance name.
> #instanceName <- dbGetQuery(jdbcConnection, "SELECT instance_name FROM v$instance")
              TABLE_NAME1 
1  INSUR_CUST_LTV_SAMPLE2            
2              OUTPUT_1_2
> #print(instanceName)tableNames <- dbGetQuery(jdbcConnection, "SELECT table_name from user_tables where  
                                                 table_name not like 'DM$%' and table_name not like 'ODMR$%'")
> print(tableNames)
> viewNames <- dbGetQuery(jdbcConnection, "SELECT view_name from user_views")print(viewNames)
1       MINING_DATA_APPLY_V
2       MINING_DATA_BUILD_V
3        MINING_DATA_TEST_V
4  MINING_DATA_TEXT_APPLY_V
5  MINING_DATA_TEXT_BUILD_V
6   MINING_DATA_TEXT_TEST_V

> v <- dbReadTable(jdbcConnection, "MINING_DATA_BUILD_V")
> names(v)
[1] "CUST_ID"                 "CUST_GENDER"             "AGE"                     
[4] "CUST_MARITAL_STATUS"     "COUNTRY_NAME"            "CUST_INCOME_LEVEL"       
[7] "EDUCATION"               "OCCUPATION"              "HOUSEHOLD_SIZE"         
[10] "YRS_RESIDENCE"           "AFFINITY_CARD"           "BULK_PACK_DISKETTES"    
[13] "FLAT_PANEL_MONITOR"      "HOME_THEATER_PACKAGE"    "BOOKKEEPING_APPLICATION”
[16] "PRINTER_SUPPLIES"        "Y_BOX_GAMES"             "OS_DOC_SET_KANJI" 
> dim(v)
[1] 1500   18
> summary(v)
    CUST_ID       CUST_GENDER             AGE        CUST_MARITAL_STATUS COUNTRY_NAME       
Min.   :101501   Length:1500        Min.   :17.00   Length:1500         Length:1500        
1st Qu.:101876   Class :character   1st Qu.:28.00   Class :character    Class :character   
Median :102251   Mode  :character   Median :37.00   Mode  :character    Mode  :character   
Mean   :102251                      Mean   :38.89                                          
3rd Qu.:102625                      3rd Qu.:47.00                                          
Max.   :103000                      Max.   :90.00                                          
CUST_INCOME_LEVEL   EDUCATION          OCCUPATION        HOUSEHOLD_SIZE     YRS_RESIDENCE    
Length:1500        Length:1500        Length:1500        Length:1500        Min.   : 0.000   
Class :character   Class :character   Class :character   Class :character   1st Qu.: 3.000   
Mode  :character   Mode  :character   Mode  :character   Mode  :character   Median : 4.000                                                                               
                                                                            Mean   : 4.089                                                                               
                                                                            3rd Qu.: 5.000                                                                               
                                                                            Max.   :14.000 
> hist(v$RESIDENCE)
> hist(v$AGE)
> dbDisconnect(jdbcConnection)

Make sure to check out the other demonstration scripts that are shown in the video.

Oracle R Enterprise 1.5 (new release)

The Oracle Santa had a busy time just before Christmas with the release of several new version of products. One of these was Oracle R Enterprise version 1.5.

Oracle R Enterprise (1.5) is part of the Oracle Advanced Analytics option for the enterprise edition of the Oracle Database.

As with every new release of a product there are a range of bug fixes. But with ORE 1.5 there are also some important new features. These important new features include:

• New Random Forest specific for ORE.
• New ORE Data Store functions and privileges.
• Partitioning on multiple columns for ore.groupApply.
• Multiple improvements to ore.summary.
• Now performs parallel in-database execution for functions prcomp and svd.
• BLOB and CLOB data types are now supported in some of the ORE functions.

Check out the ORE 1.5 Release Notes for more details on the new features.

ORE 1.5 is only certified (for now) on R 3.2.x in both the open source version and the Oracle R Distribution version 3.2.

Check out the ORE 1.5 Documentation.

You can download ORE 1.5 Server side and Client side software here.

Running R in the Oracle Database video

Earlier this year I was asked by the Business Analysics & Big Data SIG (of the UKOUG) to give a presentation on Oracle R Enterprise. Unfortunately I had already committed to giving the same presentation at the OUG Norway conference on the same day.

But then they asked me if I could record a video of the presentation and they would show it at the SIG. The following video is what I recorded.

At the UKOUG annual (2015) conferences I was supposed to give a 2 hour presentation during their Super Sunday event. Unfortunately due to a storm passing over Ireland on the Saturday all flights going to the UK were cancelled. This meant that I would miss my 2 hour presentation.

Instead of trying to find an alternative speaker for my presentation slot at such sort notice, the committee suggested that they would show the video.

Based on the feedback and the people who thanked me in person during the rest of the conference, I've decided to make it available to everyone. Hopefully you will find it useful.

The following are the links to the demo code that is shown or referred to in the video.

• Demo Code 1
• Demo Code 2
• Demo Code 3
• Demo Code 4
• Demo Code 5

People have been asking me if the demo scripts I used in video are available. You will probably find some of these on various blog posts. So to make it easier for everyone I will post the demo scripts in one or more blog posts over the coming weeks. When these are available I will update this blog post with the links.

I have a few new presentations on Oracle R Enterprise in 2016 so watch out for these at an Oracle User Group conference.

KScope 2016 Acceptances

I've never been to KScope. Yes never.

I've always wanted to. Each year you hear of all of these stories about how much people really enjoy KScope and how much they learn.

So back in October I decided to submit 5 presentations to KScope. 4 of these presentations are solo presentations and 1 joint presentation.

This week I have received the happy news that 2 of my solo presentations have been accepted, plus my joint presentation with Kim Berg Hansen.

So at the end of June 2016 I will be making my way to Chicago for a week of Oracle geekie fun at KScope.

My presentations will be:

• Is Oracle SQL the best language for Statistic?
• Running R in your Oracle Database using Oracle R Enterprise

and my join presentations is called

Forecasting in Oracle using the Power of SQL (this will talk about ROracle, Forecasting in R, Using Oracle R Enterprise and SQL)

I was really hoping that one of my rejected presentations would have been accepted. I really enjoy this presentation and I get to share stories about some of my predictive analytics projects. Ah well, maybe in 2017.

The last time I was in Chicago was over 15 years ago when I sent 5 days in Cellular One (The brand was sold to Trilogy Partners by AT&T in 2008 shortly after AT&T had completed its acquisition of Dobson Communications). I was there to kick off a project to build them a data warehouse and to build their first customer churn predictive model. I stayed in a hotel across the road from their office which was famous because a certain person had stayed in it why one the run. Unfortunately I didn't get time to visit downtown Chicago.

Error when trying to use GLM in ORE

If you have tried to use the ore.odmGLM function in ORE and some other functions in ORE and you are using the Oracle 12.1.0.2 Database you will be getting an error something like the following in R.

> GLMmodel <- ore.odmGLM(AFFINITY_CARD ~., ANALYTIC_RECORD)
Error in .oci.GetQuery(conn, statement, data = data, prefetch = prefetch,  : 
  ORA-29400: data cartridge error
ORA-40024: solution to least squares problem could not be found
ORA-06512: at "SYS.DBMS_DATA_MINING", line 2153
ORA-06512: at line 1

or you get something like the following when using ore.glm

Error in .External(C_pt, q, df, lower.tail, log.p) :
  NULL value passed as symbol address

Similarly if you are using these functions with embedded R execution when the R code is warpped in SQL, you will see similar errors.

What you need to do is to download and install the stand alond Patch 20173897.

Alternatively this patch is included in the latest Oracle 12.1.2 Database patch.

Managing ORE in-database Data Stores using SQL

When working with ORE you will end up creating a number of different data stores in the database. Also as your data science team increases the number of data stores can grow to a very large number.

When you install Oracle R Enterprise you will get a number of views that are made available to ORE users to see what ORE Data stores they have and what objects exist in them. All using SQL.

Perhaps some of the time the ORE developers and data analysts will use the set of ORE functions to manage the in-database ORE Data stores. These include:

When using these ORE function the schema user/data scientist can see what ORE Data stores they have. You can use the ore.delete to delete an ORE Data store when it is no longer needed.

But the problem here is that over time your schemas can get a bit clogged up with ORE Data stores. Particularly when the data scientist is not longer working on the project or there is no need to maintain ORE Data stores. This is common on data science projects when you might have a number of data scientists work in/sharing the one database schema.

For a DBA, who's role will be to clean up the ORE Data store that are no longer needed, you have 4 options.

The first of these, is if all the ORE Data stores exist in the data scientists schema and nothing else in the schema is needed then you can just go ahead and drop the schema.

The second option is to log into the schema using SQL and drop the ORE Data stores. See an example of this below.

The third option is to connect to the Oracle schema using R and ORE and then use the ore.delete function to drop the ORE Data stores.

The fourth option is to connect to the RQSYS schema. This schema is the owner of the views used to query the ORE Data stores in each schema. After the RQSYS schema was created it was locked as part of the ORE installation. You as the DBA will need to unlock and then connect.

The following SQL lists the ORE Data stores that were created for that schema.

column dsname format a20
column description format a35

SELECT * FROM rquser_DataStoreList;

DSNAME                     NOBJ     DSSIZE CDATE     DESCRIPTION
-------------------- ---------- ---------- --------- -----------------------------------
ORE_DS                        2       5104 04-AUG-15 Example of ORE Datastore
ORE_FOR_DELETION              1       1675 14-AUG-15 Need to Delete this ORE Datastore
ORE_DS2                       5   51466509 04-AUG-15 DS for all R Env Data

You can also view what objects have saved in the ORE Data store.

column objname format a15
column class format a15
SELECT * FROM rquser_DataStoreContents;
 
DSNAME               OBJNAME         CLASS              OBJSIZE     LENGTH       NROW       NCOL
-------------------- --------------- --------------- ---------- ---------- ---------- ----------
ORE_DS               CARS_DATA       ore.frame             1306         11         32         11
ORE_DS               cars_ds         data.frame            3798         11         32         11
ORE_DS2              cars_ds         data.frame            3798         11         32         11
ORE_DS2              cars_ore_ds     ore.frame             1675         11         32         11
ORE_DS2              sales_ds        data.frame        51455575          7     918843          7
ORE_DS2              usa_ds          ore.frame             2749         23      18520         23
ORE_DS2              usa_ds2         ore.frame             2712         23      18520         23
ORE_FOR_DELETION     cars_ore_ds     ore.frame             1675         11         32         11

To drop an ORE Data store for you current schema you can use the rqDropDataStore SQL function.

BEGIN
   rqDropDataStore('ORE_FOR_DELETION');
END;
/

For the DBA when you unlock and connect to the RQSYS schema you will be able to see all the ORE Data stores in the data. The views will contain an additional column.

But if you use the above SQL function to delete an ORE Data store it will not work. This because this SQL function will only drop and ORE Data store if it exists in your schema. If we have connected to the RQSYS schema we will not have any ORE Data stores in it.

We can create a procedure that will allow use to delete/drop any ORE Data store in any schema.

create or replace PROCEDURE my_ORE_Datastore_Drop(
  ds_owner  in VARCHAR2,
  ds_name  IN VARCHAR2
  )
IS
  del_objIds rqNumericSet;
BEGIN
  del_objIds := rq$DropDataStoreImpl(ds_owner, ds_name);
  IF del_objIds IS NULL THEN
    raise_application_error(-20101, 'DataStore ' ||
                            ds_name || ' does not exist');
  END IF;

  -- remove from rq$datastoreinventory
  BEGIN
    execute immediate
       'delete from RQ$DATASTOREINVENTORY c where c.objID IN (' ||
       'select column_value from table(:del_objIds))' using del_objIds;
     COMMIT;
  EXCEPTION WHEN others THEN null;
  END;
END;

We are the DBA, logged into the RQSYS schema can now delete any ORE Data store in the database, using the following.

BEGIN
   my_ORE_Datastore_Drop('ORE_USER', 'ORE_FOR_DELETION');
END;
/

V506 of Oracle OBIEE SampleApp Virtual Machine

A few days ago Oracle released the latest version of the Virtual Machine for OBIEE SampleApp. The current version has a number of new features and new product versions (see below).

To get this latest version go to the following link to download the VM files and to install. As always this is a beast of a VM and you should only consider the install and setup if you have the space and in particular you have 16G RAM.

Oracle Business Intelligence Enterprise Edition Samples on OTN.

v506 New Features

• DB 12.1.0.2 with the In-Memory option
• Load and process JSON data
• Integrates with Big Data SQL
• Connects to Impala
• Session tracking in UT
• Exalytics Aggregation Functions
• Lots of new Visualizations
• Custom Style Features
• Hierarchical Session Variables
• etc.

Software on V506

• Oracle Enterprise Linux 6.5 x64
• OBIEE 11.1.1.9GA two distinct OBIEE instances, Essbase 11.1.2.4, updated BIMAD
• Oracle MapViewer11.1.1.9.1
• Oracle BICS Data Sync v1
• Oracle Database 12c IMDB 12.1.0.2, PDB Install, AWM 12.1.0.2a, APEX 4.2.6 & ORDS 2.0.1, ODM, Oracle Spatial and Graph
• ORE 1.4.1 & R 3.1.1
• ENDECA 3.1, Server 7.6.1, Studio 3.1, Provisioning Services
• Cloudera CDH 5.1.2, Oracle BigData SQL, Oracle BigData Connectors
• Plug and Play Companions : EPM 11.1.2.3, BIApps Demos
• Utils: Start scripts, MapBuilder, SQLDev 4.1

Oracle Advanced Analytics sessions at OOW14

With Oracle Open World just a few days away now, I was going through the list of presentations that are focused on using the Oracle Advanced Analytics Option. These will cover Oracle Data Miner and Oracle R Enterprise.

So I've decided to share this list with you :-) and hopefully I will get to see you are some or all of these sessions.

Date	Time	Location	Presentation Title
Sunday 28th Sept.	9:00-9:45	Moscone South Room 304	What Are They Thinking? With Oracle Application Express and Oracle Data Miner [UGF2861]. (This is my presentation with Roel Hartman.)
Tuesday 30th Sept.	17:00-17:45	Intercontinental - Grand Ballroom C	Advanced Predictive Analytics for Database Developers on Oracle [CON7977]
Tuesday 30th Sept.	18:00-18:45	Moscone South - 303	Oracle’s Big Data Management System [MTE9350]
Wednesday 1st Oct.	10:15-11:00	Moscone South - 301	Big Data and Predictive Analytics: Fiserv Data Mining Case Study [CON8631]
Wednesday 1st Oct.	10:30-10:50	Big Data Theater, Moscone South, Big Data Showcase	Big Data: Maximize the Business Impact with Oracle Advanced Analytics [THT10395]
Wednesday 1st Oct.	11:30-12:15	Moscone South - 300	A Perfect Storm: Oracle Big Data Science for Enterprise R and SAS Users [CON8331]
Wednesday 1st Oct.	12:45-13:30	Moscone West - 3002	Predictive Analytics with Oracle Data Mining [CON8596]
Wednesday 1st Oct.	14:00-14:45	Moscone South - 308	Developing Relevant Dining Visits with Oracle Advanced Analytics at Olive Garden [CON2898]

If I have missed any sessions then do please let me know and I can update the list above.