Showing posts with label R. Show all posts
Showing posts with label R. Show all posts

Thursday, January 26, 2017

Formatting results from ORE script in a SELECT statement

This blog post looks at how to format the output or the returned returns from an Oracle R Enterprise (ORE), user defined R function, that is run using a SELECT statement in SQL.

Sometimes this can be a bit of a challenge to work out, but it can be relatively easy once you have figured out how to do it. The following examples works through some scenarios of different results sets from a user defined R function that is stored in the Oracle Database.

To run that user defined R function using a SELECT statement I can use one of the following ORE SQL functions.

  • rqEval
  • rqTableEval
  • "rqGroupEval"
  • rqRowEval

For simplicity we will just use the first of these ORE SQL functions to illustrate the problem and how to go about solving it. The rqEval ORE SQL function is a generate purpose function to call a user defined R script stored in the database. The function does not require any input data set and but it will return some data. You could use this to generate some dummy/test data or to find some information in the database. Here is noddy example that returns my name.

      'function() {
         } ');

To call this user defined R function I can use the following SQL.

select *
from table(rqEval(null,
                  'select cast(''a'' as varchar2(50))  from dual',
                  'GET_NAME') );  

For text strings returned you need to cast the returned value giving a size.

If we have a numeric value being returned we can don't have to use the cast and instead use '1' as shown in the following example. This second example extends our user defined R function to return my name and a number.

      'function() {
         res<-data.frame(NAME="Brendan", YEAR=2017)
         } ');

To call the updated GET_NAME function we now have to process two returned columns. The first is the character string and the second is a numeric.

select *
from table(rqEval(null,
                  'select cast(''a'' as varchar2(50)) as "NAME", 1 AS YEAR  from dual',
                  'GET_NAME') );                  

These example illustrate how you can process character strings and numerics being returned by the user defined R script.

The key to setting up the format of the returned values is knowing the structure of the data frame being returned by the user defined R script. Once you know that the rest is (in theory) easy.

Tuesday, November 1, 2016

Creating and Reading SPSS and SAS data sets in R

NOTE: Several people have contacted me to say that using this R package does not work. The data set 
generated is not readable by SAS. If you encounter this problem then get in touch with the creators 
of Haven for help and support. I'm using R version 3.2.0.
All I an say is, it worked for me!

Have you ever been faced with having to generate a data set in the format that is needed by another analytics tool? or having to generate a data set in a particular format but you don't have the software that generates that format? For example, if you are submitting data to the FDA and other bodies, you may need to submit the data in a SAS formatted file. There are a few ways you can go about this.

One option is that you can use the Haven R package to generate your dataset in SAS and SPSS formats. But you can also read in SAS and SPSS formatted files. I have to deal with these formatted data files all the time, and it can be a challenge, but I've recently come across the Haven R package that has just made my life just a little bit/lots easier. Now I can easily generate SAS and SPSS formatted data sets for my data in my Oracle Database, using R and ORE. ORE we can now use the embedded feature to build the generation of these data sets into some of our end-user applications.

Let us have a look at Haven and what it can do.

Firstly there is very little if any documentation online for it. That is ok so we will have to rely on the documentation that comes with the R packages. Again there isn't much to help and that is because the R package mainly consists of functions to Read in these data sets, functions to Write these data sets and some additional functions for preparing data.

For reading in data sets we have the following functions:

# Stata

For writing data sets we have the following functions:

write_sas(mtcars, "mtcars.sas7bdat")
# Stata
write_dta(mtcars, "mtcars.dta")
write_sav(mtcars, "mtcars.sav")

Let us now work through an example of creating a SAS data set. We can use some of the sample data sets that come with the Oracle Database in the SH schema. I'm going to use the data in the CUSTOMER table to create a SAS data set. In the following code I'm using ORE to connect to the database but you can use your preferred method.

> library(ORE)
> # Create your connection to the schema in the DB
> ore.connect(user="sh", password="sh", host="localhost", service_name="PDB12C", 
            port=1521, all=TRUE) 

[1] 55500    23
> names(CUSTOMERS)
 [1] "CUST_ID"                "CUST_FIRST_NAME"        "CUST_LAST_NAME"        
 [7] "CUST_STREET_ADDRESS"    "CUST_POSTAL_CODE"       "CUST_CITY"             
[16] "CUST_CREDIT_LIMIT"      "CUST_EMAIL"             "CUST_TOTAL"            
[19] "CUST_TOTAL_ID"          "CUST_SRC_ID"            "CUST_EFF_FROM"         
[22] "CUST_EFF_TO"            "CUST_VALID"      

Next we can prepare the data, take a subset of the data, reformat the data, etc. For me I just want to use the data as it is. All I need to do now is to pull the data from the database to my local R environment.

dat <- ore.pull(CUSTOMERS)

Then I need to load the Haven library and then create the SAS formatted file.

write_sas(dat, "c:/app/my_customers.sas7bdat")

That's it. Nice and simple.

But has it worked? Has it created the file correctly? Will it load into my SAS tool?

There is only one way to test this and that is to only it in SAS. I have an account on SAS OnDemand with access to several SAS products. I'm going to use SAS Studio.

Well it works! The following image shows SAS Studio after I had loaded the data set with the variables and data shown.


WARNING: When you load the data set into SAS you may get a warning message saying that it isn't a SAS data set. What this means is that it is not a data set generated by SAS. But as you can see in the image above all the data got loaded OK and you can work away with it as normal in your SAS tools.

The next step is to test the loading of a SAS data set into R. I'm going to use one of the standard SAS data sets called PVA97NK.SAS7BDAT. If you have worked with SAS products then you will have come across this data set.

When you use Haven to load in your SAS data set, it will create the data in tribble format. This is a slight varient of a data.frame. So if you want the typical format of a data.frmae then you will need to convert the loaded data, as shown in the following code.

> data_read <- read_sas("c:/app/pva97nk.sas7bdat")
> dim(data_read)
[1] 9686   28
> d<-data.frame(data_read)
> class(data_read)
[1] "tbl_df"     "tbl"        "data.frame"
> class(d)
[1] "data.frame"
> head(d)
  TARGET_B       ID TARGET_D GiftCnt36 GiftCntAll GiftCntCard36 GiftCntCardAll
1        0 00014974       NA         2          4             1              3
2        0 00006294       NA         1          8             0              3
3        1 00046110        4         6         41             3             20

I think this package to going to make my life a little bit easier, and if you work with SPSS and SAS data sets then hopefully some of your tasks have become a little bit easier too.

Monday, September 26, 2016

Machine Learning notebooks (and Oracle)

Over the past 12 months there has been an increase in the number of Machine Learning notebooks becoming available.
What is a Machine Learning notebook?
As the name implies it can be used to perform machine learning using one or more languages and allows you to organise your code, scripts and other details in one application.
The ML notebooks provide an interactive environment (sometimes browser based) that allows you to write, run, view results, share/collaborate code and results, visualise data, etc.
Some of these ML notebooks come with one language and others come with two or more languages, and have the ability to add other ML related languages. The most common languages are Spark, Phython and R.
Based on these languages ML notebooks are typically used in the big data world and on Hadoop.
Examples of Machine Learning notebooks include: (Starting with the more common ones)
  • Apache Zeppelin
  • Jupyter Notebook (formally known as IPython Notebook)
  • Azure ML R Notebook
  • Beaker Notebook
  • SageMath
At Oracle Open World (2016), Oracle announced that they are currently working creating their own ML notebook and it is based on Apache Zeppelin. They seemed to indicate that a beta version might be available in 2017. Here are some photos from that presentation, but with all things that Oracle talk about you have to remember and take into account their Safe Habor.
2016 09 22 12 43 41 2016 09 22 12 45 53 2016 09 21 12 16 09
I'm looking forward to getting my hands on this new product when it is available.

Tuesday, September 6, 2016

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.
NewImage NewImage
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),
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)

Monday, August 8, 2016

Oracle Text, Oracle R Enterprise and Oracle Data Mining - Part 2

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

In this blog post I will show 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.

Prerequisites: You will need to load the following R packages into your R environment 'tm', 'word cloud' 'SnowballC'. These are required to process the following R code segments.

install.packages (c( "tm", "wordcloud", "SnowballC"))
library (tm)
library (wordcloud)
library (SnowballC)

Select data from table and prepare: We need to select the data from the table in our schema and to merge it into one variable.

local_data <- ore.pull(MY_DOCUMENTS)

tm_data <-""
for(i in 1:nrow(local_data)) {
  tm_data <- paste(tm_data, local_data[i,]$DOC_TEXT, sep=" ")

Create function to perform Text Mining: In my previous blog post on creating a word cloud I gave the R code. In order to allow for this R code to be run on the database server (using the embedded R execution of ORE) we need to package this text mining R code up into a ORE user defined R script. This is stored in the database.

ore.scriptCreate("prepare_tm_data", function (tm_data) { 
  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"))
} ) 

Before we can run this user define R script, we need to ensure that we have the 'tm', 'SnowballC' and 'wordcloud' R packages installed on the Oracle Database server. On the Oracle Database server you need to rune ORE.

> library(ORE)

Then run the following command to install these R packages

> install_packages(c('tm','wordcloud', 'SnowballC'))

Run the function on the DB Server: You are now ready to run the function. In an earlier step we had gathered the data. Now we can pass this data to the in-database R script.

> res <- ore.doEval(FUN.NAME="prepare_tm_data", tm_data=tm_data)

The ore.doEval function is a general purpose ORE function. In this case we pass it two parameters. The first parameter is the neame of the user defined R script stored in the database, and the second parameter is the data. The function returns and ORE object that contains the word cloud graphic.

Display the results: You can very easily display the results.

> res

This gives us the following graphic.


In my next blog post, of this series, I will show you how you can use the function created above and some other bits and pieces, using some other features of ORE and also in SQL.

Tuesday, July 26, 2016

Oracle Text, Oracle R Enterprise and Oracle Data Mining - Part 1

A project that I've been working on for a while now involves the use of Oracle Text, Oracle R Enterprise and Oracle Data Mining. Oracle Text comes with your Oracle Database licence. Oracle R Enterprise and Oracle Data Mining are part of the Oracle Advanced Analytics (extra cost) option.

What I will be doing over the course of 4 or maybe 5 blog posts is how these products can work together to help you gain a grater insight into your data, and part of your data being large text items like free format text, documents (in various forms e.g. html, xml, pdf, ms word), etc.

Unfortunately I cannot show you examples from the actual project I've been working on (and still am, from time to time). But what I can do is to show you how products and components can work together.

In this blog post I will just do some data setup. As with all project scenarios there can be many ways of performing the same tasks. Some might be better than others. But what I will be showing you is for demonstration purposes.

The scenario: The scenario for this blog post is that I want to extract text from some webpages and store them in a table in my schema. I then want to use Oracle Text to search the text from these webpages.

Schema setup: We need to create a table that will store the text from the webpages. We also want to create an Oracle Text index so that this text is searchable.

drop sequence my_doc_seq;
create sequence my_doc_seq;

drop table my_documents;

create table my_documents (
doc_pk number(10) primary key, 
doc_title varchar2(100), 
doc_extracted date, 
data_source varchar2(200), 
doc_text clob);

create index my_documents_ot_idx on my_documents(doc_text) 
indextype is CTXSYS.CONTEXT;

In the table we have a number of descriptive attributes and then a club for storing the website text. We will only be storing the website text and not the html document (More on that later). In order to make the website text searchable in the DOC_TEXT attribute we need to create an Oracle Text index of type CONTEXT.

There are a few challenges with using this type of index. For example when you insert a new record or update the DOC_TEXT attribute, the new values/text will not be reflected instantly, just like we are use to with traditional indexes. Instead you have to decide when you want to index to be updated. For example, if you would like the index to be updated after each commit then you can create the index using the following.

create index my_documents_ot_idx on my_documents(doc_text) 
indextype is CTXSYS.CONTEXT
parameters ('sync (on commit)');

Depending on the number of documents you have being committed to the DB, this might not be for you. You need to find the balance. Alternatively you could schedule the index to be updated by passing an interval to the 'sync' in the above command. Alternatively you might want to use DBMS_JOB to schedule the update.

To manually sync (or via DBMS_JOB) the index, assuming we used the first 'create index' statement, we would need to run the following.

EXEC CTX_DDL.SYNC_INDEX('my_documents_ot_idx');

This function just adds the new documents to the index. This can, over time, lead to some fragmentation of the index, and will require it to the re-organised on a semi-regular basis. Perhaps you can schedule this to happen every night, or once a week, or whatever makes sense to you.


(I could talk a lot more about setting up some basics of Oracle Text, the indexes, etc. But I'll leave that for another day or you can read some of the many blog posts that already exist on the topic.)

Extracting text from a webpage using R: Some time ago I wrote a blog post on using some of the text mining features and packages in R to produce a word cloud based on some of the Oracle Advanced Analytics webpages. I'm going to use the same webpages and some of the same code/functions/packages here. The first task you need to do is to get your hands on the 'htmlToText function. You can download the htmlToText function on github. This function requires the 'Curl' and 'XML' R packages. So you may need to install these. I also use the str_replace_all function ("stringer' R package) to remove some of the html that remains, to remove some special quotes and to replace and occurrences of '&' with 'and'. # Load the function and required R packages source("c:/app/htmltotext.R") library(stringr)
data1 <- str_replace_all(htmlToText(""), "[\r\n\t\"\'\u201C\u201D]" , "")
data1 <- str_replace_all(data1, "&", "and")
data2 <- str_replace_all(str_replace_all(htmlToText(""), "[\r\n\t\"\'\u201C\u201D]" , ""), "&", "and")
data2 <- str_replace_all(data2, "&", "and")
data3 <- str_replace_all(str_replace_all(htmlToText(""), "[\r\n\t\"\'\u201C\u201D]" , ""), "&", "and")
data3 <- str_replace_all(data3, "&", "and")
data4 <- str_replace_all(str_replace_all(htmlToText(""), "[\r\n\t\"\'\u201C\u201D]" , ""), "&", "and")
data4 <- str_replace_all(data4, "&", "and")
We now have the text extracted and cleaned up. Create a data frame to contain all our data: Now that we have the text extracted, we can prepare the other data items we need to insert the data into our table ('my_documents'). The first stept is to construct a data frame to contain all the data.
data_source = c("",
doc_extracted = Sys.Date()
data_text <- c(data1, data2, data3, data4)

my_docs <- data.frame(doc_title, doc_extracted, data_source, data_text)

Insert the data into our database table: With the data in our data fram (my_docs) we can now use this data to insert into our database table. There are a number of ways of doing this in R. What I'm going to show you here is how to do it using Oracle R Enterprise (ORE). The thing with ORE is that there is no explicit functionality for inserting and updating records in a database table. What you need to do is to construct, in my case, the insert statement and then use ore.exec to execute this statement in the database.

ore.connect(user="ora_text", password="ora_text", host="localhost", service_name="PDB12C", 
            port=1521, all=TRUE) 

for(i in 1:nrow(my_docs)) {
  insert_stmt <- "BEGIN insert_tab_document ('"
  insert_stmt <- paste(insert_stmt,  my_docs[i,]$doc_title, sep="")
  insert_stmt <- paste(insert_stmt, "', '",  my_docs[i,]$doc_extracted, "'", sep="")
  insert_stmt <- paste(insert_stmt, ", '",  my_docs[i,]$data_source, sep="")
  insert_stmt <- paste(insert_stmt, "', '",  my_docs[i,]$data_text, "');", " END;", sep="")

You can now view the inserted webpage text using R or using SQL.

In my next blog post in this series, I will look at how you can use the ORE embedded features to read and process this data.

Monday, July 11, 2016

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.

   -- sys.rqScriptDrop('demo_ggpplot');
      'function(dat) {
         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")


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),

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.

      'function(dat) {
         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") 

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.

Thursday, June 30, 2016

googleVis R package for creating google charts in R

I've recently come across the 'googleVis' R package. This allows you to create a variety of different (typical and standard) charts in R but with the look and feel of the charts we can get from a number of different Google sites.

I won't bore you with some examples in the post but I'll point you to a good tutorial on the various charts.

Here is the link to the mini-tutorial.

Before you can use the package you will need to install it. The simplest way is to run the following in your R session.

> install.packages("googleVis")

Depending on your version of R you may need to upgrade.

Here is a selection of some of the charts you can create, and there are many, many more.


Some of you might be familiar with the presenting that Hans Rosling gives. Some of the same technology is behind these bubble charts from Google, as they bought the software years ago. Hans typically uses a data set that consists of GDP, Population and Life Expectancy for countries around the World. You too can use this same data set and is available from rdatamarket. The following R codes will extract this data set to you local R session and you can then use it as input to the various charts in the googleVis functions.


# Pull in life expectancy and population data
life_expectancy <- dmlist("15r2!hrp")
population <- dmlist("1cfl!r3d")

# Pull in the yearly GDP for each country
gdp <- dmlist("15c9!hd1")

# Load in the plyr package

# Rename the Value for each dataset
names(gdp)[3] <- "GDP"

# Use plyr to join your three data frames into one: development 
gdp_life_exp <- join(gdp, life_expectancy)
names(gdp_life_exp)[4] <- "LifeExpectancy"
development <- join(gdp_life_exp, population)
names(development)[5] <- "Population"

Here is an example of the bubble chart using this data set.


There are a few restrictions with using this package. All the results will be displayed in a web browser, so you need to make sure that this is possible. Some of the charts are require flash. Again you need to make sure you are the latest version and/or you many have restrictions in your workplace on using it.

Friday, April 29, 2016

Accessing the R datasets in ORE and SQL

When you install R you also get a set of pre-compiled datasets. These are great for trying out many of the features that are available with R and all the new packages that are being produced on an almost daily basis.

The exact list of data sets available will depend on the version of R that you are using.

To get the list of available data sets in R you can run the following.

> library(help="datasets")

This command will list all the data sets that you can reference and start using immediately.

I'm currently running the latest version of Oracle R Distribution version 3.2. See the listing at the end of this blog post for the available data sets.

But are these data sets available to you if you are using Oracle R Enterprise (ORE)? The answer is Yes of course they are.

But are these accessible on the Oracle Database server? Yes they are, as you have R installed there and you can use ORE to access and use the data sets.

But how? how can I list what is on the Oracle Database server using R? Simple use the following ORE code to run an embedded R execution function using the ORE R API.

What? What does that mean? Using the R on your client machine, you can use ORE to send some R code to the Oracle Database server. The R code will be run on the Oracle Database server and the results will be returned to the client. The results contain the results from the server. Try the following code.

ore.doEval(function() library(help="datasets")) 

# let us create a functions for this code
myFn <- function() {library(help="datasets")}

# Now send this function to the DB server and run it there.

# create an R script in the Oracle Database that contains our R code
ore.scriptCreate("inDB_R_DemoData", myFn)
# Now run the R script, stored in the Oracle Database, on the Database server
#   and return the results to my client

Simple, Right!

Yes it is. You have shown us how to do this in R using the ORE package. But what if I'm a SQL developer. Can I do this in SQL? Yes you can. Connect you your schema using SQL Developer/SQL*Plus/SQLcl or whatever tool you will be using to run SQL. Then run the following SQL.

select * 
from table(rqEval(null, 'XML', 'inDB_R_DemoData'));

This SQL code will return the results in XML format. You can parse this to extract and display the results and when you do you will get something like the following listing, which is exactly the same that is produced when you run the R code that I gave above.

So what this means is that evening if you have an empty schema with no data in it, and as long as you have the privileges to run embedded R execution, you actually have access to all these different data sets. You can use these to try our R using the ORE SQL APIs too.

		Information on package ‘datasets’


Package:       datasets
Version:       3.2.0
Priority:      base
Title:         The R Datasets Package
Author:        R Core Team and contributors worldwide
Maintainer:    R Core Team 
Description:   Base R datasets.
License:       Part of R 3.2.0
Built:         R 3.2.0; ; 2015-08-07 02:20:26 UTC; windows


AirPassengers           Monthly Airline Passenger Numbers 1949-1960
BJsales                 Sales Data with Leading Indicator
BOD                     Biochemical Oxygen Demand
CO2                     Carbon Dioxide Uptake in Grass Plants
ChickWeight             Weight versus age of chicks on different diets
DNase                   Elisa assay of DNase
EuStockMarkets          Daily Closing Prices of Major European Stock
                        Indices, 1991-1998
Formaldehyde            Determination of Formaldehyde
HairEyeColor            Hair and Eye Color of Statistics Students
Harman23.cor            Harman Example 2.3
Harman74.cor            Harman Example 7.4
Indometh                Pharmacokinetics of Indomethacin
InsectSprays            Effectiveness of Insect Sprays
JohnsonJohnson          Quarterly Earnings per Johnson & Johnson Share
LakeHuron               Level of Lake Huron 1875-1972
LifeCycleSavings        Intercountry Life-Cycle Savings Data
Loblolly                Growth of Loblolly pine trees
Nile                    Flow of the River Nile
Orange                  Growth of Orange Trees
OrchardSprays           Potency of Orchard Sprays
PlantGrowth             Results from an Experiment on Plant Growth
Puromycin               Reaction Velocity of an Enzymatic Reaction
Theoph                  Pharmacokinetics of Theophylline
Titanic                 Survival of passengers on the Titanic
ToothGrowth             The Effect of Vitamin C on Tooth Growth in
                        Guinea Pigs
UCBAdmissions           Student Admissions at UC Berkeley
UKDriverDeaths          Road Casualties in Great Britain 1969-84
UKLungDeaths            Monthly Deaths from Lung Diseases in the UK
UKgas                   UK Quarterly Gas Consumption
USAccDeaths             Accidental Deaths in the US 1973-1978
USArrests               Violent Crime Rates by US State
USJudgeRatings          Lawyers' Ratings of State Judges in the US
                        Superior Court
USPersonalExpenditure   Personal Expenditure Data
VADeaths                Death Rates in Virginia (1940)
WWWusage                Internet Usage per Minute
WorldPhones             The World's Telephones
ability.cov             Ability and Intelligence Tests
airmiles                Passenger Miles on Commercial US Airlines,
airquality              New York Air Quality Measurements
anscombe                Anscombe's Quartet of 'Identical' Simple Linear
attenu                  The Joyner-Boore Attenuation Data
attitude                The Chatterjee-Price Attitude Data
austres                 Quarterly Time Series of the Number of
                        Australian Residents
beavers                 Body Temperature Series of Two Beavers
cars                    Speed and Stopping Distances of Cars
chickwts                Chicken Weights by Feed Type
co2                     Mauna Loa Atmospheric CO2 Concentration
crimtab                 Student's 3000 Criminals Data
datasets-package        The R Datasets Package
discoveries             Yearly Numbers of Important Discoveries
esoph                   Smoking, Alcohol and (O)esophageal Cancer
euro                    Conversion Rates of Euro Currencies
eurodist                Distances Between European Cities and Between
                        US Cities
faithful                Old Faithful Geyser Data
freeny                  Freeny's Revenue Data
infert                  Infertility after Spontaneous and Induced
iris                    Edgar Anderson's Iris Data
islands                 Areas of the World's Major Landmasses
lh                      Luteinizing Hormone in Blood Samples
longley                 Longley's Economic Regression Data
lynx                    Annual Canadian Lynx trappings 1821-1934
morley                  Michelson Speed of Light Data
mtcars                  Motor Trend Car Road Tests
nhtemp                  Average Yearly Temperatures in New Haven
nottem                  Average Monthly Temperatures at Nottingham,
npk                     Classical N, P, K Factorial Experiment
occupationalStatus      Occupational Status of Fathers and their Sons
precip                  Annual Precipitation in US Cities
presidents              Quarterly Approval Ratings of US Presidents
pressure                Vapor Pressure of Mercury as a Function of
quakes                  Locations of Earthquakes off Fiji
randu                   Random Numbers from Congruential Generator
rivers                  Lengths of Major North American Rivers
rock                    Measurements on Petroleum Rock Samples
sleep                   Student's Sleep Data
stackloss               Brownlee's Stack Loss Plant Data
state                   US State Facts and Figures
sunspot.month           Monthly Sunspot Data, from 1749 to "Present"
sunspot.year            Yearly Sunspot Data, 1700-1988
sunspots                Monthly Sunspot Numbers, 1749-1983
swiss                   Swiss Fertility and Socioeconomic Indicators
                        (1888) Data
treering                Yearly Treering Data, -6000-1979
trees                   Girth, Height and Volume for Black Cherry Trees
uspop                   Populations Recorded by the US Census
volcano                 Topographic Information on Auckland's Maunga
                        Whau Volcano
warpbreaks              The Number of Breaks in Yarn during Weaving
women                   Average Heights and Weights for American Women

Tuesday, March 22, 2016

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

2. Edit the .Renviron file.


cd /home/oracle
sudo vi .Renviron

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.

Thursday, March 17, 2016

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.


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

--2016-03-16 06:22:30--
Resolving (,,, ...
Connecting to (||: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
 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/'
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

  rstudio-server.x86_64 0:0.99.892-1                                                                      


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.

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

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.

NewImage NewImage

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


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.

Monday, March 14, 2016

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]$ ./
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--
Resolving,,, ...
Connecting to||: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
 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 

  rstudio-server.x86_64 0:0.99.489-1                                            

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.



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.

Wednesday, January 6, 2016

ORE video : Demo Code Part 2

The following is the second 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 gives a very quick demonstration of using the RORACLE R package to access the data in your Oracle schema. ROracle has a number of advantages over using RJDBC and most of the advantages are about the performance improvements. Typically when using ROracle you will see a many fold improvement with selecting data and moving it to your R client, processing data in the database and also writing data back to the Oracle Database. In some tests you can see a 7 times improvement in performance over RJDBC. Now that is a big difference.

But the problem with ROracle is that it is only available on certain platforms/OS. For example it is not officially available for the Mac. But if you google this issue carefully you will find unofficial ways over coming this problem.

ROracle is dependent on Oracle Client. So you will need to have Oracle Client installed on you machine and have it available on the search path.

When you have Oracle Client installed and the ROracle R package installed you are ready to start using it.

So here is the demo code from the video.

> library(ROracle)
> drv <- dbDriver("Oracle")
> # Create the connection string
> host <- "localhost"
> port <- 1521
> sid <- "orcl"
>connect.string <- paste("(DESCRIPTION=”, "(ADDRESS=(PROTOCOL=tcp)(HOST=", host, ")(PORT=", port, "))",
>    "(CONNECT_DATA=(SID=", sid, ")))", sep = "")

> con <- dbConnect(drv, username = "dmuser", password = "dmuser",dbname=connect.string)

> rs <- dbSendQuery(con, "select view_name from user_views")
> # fetch records from the resultSet into a data.frame
> data <- fetch(rs)
> # extract all rows
> dim(data)
[1] 6 1
> data
> dbCommit(con)
> dbClearResult(rs)
> dbDisconnect(con)

Wednesday, December 30, 2015

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$instance")
1  INSUR_CUST_LTV_SAMPLE2            
2              OUTPUT_1_2
> #print(instanceName)tableNames <- dbGetQuery(jdbcConnection, "SELECT table_name from user_tables where  
                                                 table_name not like 'DM$%' and table_name not like 'ODMR$%'")
> print(tableNames)
> viewNames <- dbGetQuery(jdbcConnection, "SELECT view_name from user_views")print(viewNames)

> v <- dbReadTable(jdbcConnection, "MINING_DATA_BUILD_V")
> names(v)
[1] "CUST_ID"                 "CUST_GENDER"             "AGE"                     
[7] "EDUCATION"               "OCCUPATION"              "HOUSEHOLD_SIZE"         
[10] "YRS_RESIDENCE"           "AFFINITY_CARD"           "BULK_PACK_DISKETTES"    
[16] "PRINTER_SUPPLIES"        "Y_BOX_GAMES"             "OS_DOC_SET_KANJI" 
> dim(v)
[1] 1500   18
> summary(v)
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                                          
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.

Tuesday, December 29, 2015

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.

Friday, August 14, 2015

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.


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
  del_objIds rqNumericSet;
  del_objIds := rq$DropDataStoreImpl(ds_owner, ds_name);
  IF del_objIds IS NULL THEN
    raise_application_error(-20101, 'DataStore ' ||
                            ds_name || ' does not exist');

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

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

   my_ORE_Datastore_Drop('ORE_USER', 'ORE_FOR_DELETION');

Tuesday, July 28, 2015

Charting Number of R Packages over time (Part 3)

This is the third and final blog post on analysing the number of new R packages that have been submitted over time.

Check out the previous blog posts:

In this blog post I will show you how you can perform Forecasting on our data to make some predictions on the possible number of new packages over the next 12 months.

There are 2 important points to note here:

  1. Only time will tell if these predictions are correct or nearly connect. Just like with any other prediction techniques.
  2. You cannot use just one of the Forecasting techniques in isolation to make a prediction. You need to use a number of functions/algorithms to see which one suits your data best.

The second point above is very important with all prediction techniques. Sometimes you see people/articles talking about them only using algorithm X. They have not considered any of the other techniques/algorithms. It is their favourite or preferred method. But that does not mean it works or is suitable for all data sets and all scenarios.

In this blog post I'm going to use 3 different forecasting functions, the in-build Forecast function in R, using HoltWinters and finally using ARIMA. Yes there are many more (it is R after all) and I'll leave these for you to explore.

1. Convert data set to Time Series data format

The first thing I need to do is to convert the data I want analyze into TimeSeries format (ts). This looks to have one record or instance for each data point.

So you cannot not have any missing data, or in my case any missing dates. Yes (for my data set) we could have some months where we do not have any submissions. What I could do is to work out mean values (or things like that) and fill for the missing months. But I'm feeling a bit lazy and after examining the data I see that we have a continuous set of data from September 2009 onwards. This is fine as most of the growth up to that point is flat.

So I need to subset the data to only include cases greater than or equal to September 2009 and less than or equal to June 2015. I wanted to explore July 2015 as the number for this month is incomplete.

The following code builds on the work we did in the second blog post in the series


# Subset the data
sub_data <- subset(data.sum, >= as.Date("2009-08-01", "%Y-%m-%d"))
sub_data <- subset(sub_data, <= as.Date("2015-06-01", "%Y-%m-%d"))

# Subset again to only take out the data we want to use in the time series
sub_data2 <- sub_data[,c("R_NUM")]

# Create the time series data, stating that it is monthly (12) and giving the start and end dates
ts_data <- ts(sub_data2, frequency=12, start=c(2009, 8), end=c(2015, 6))

# View the time series data
     Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2009                               2   3   4   3   4
2010   5   5   1  11   5   2   5   4   4   5   1   3
2011  11   4   3   6   6   5   9  15   5   8  23  18
2012  33  17  51  28  37  33  50  71  41 231  51  60
2013  75  67  76  81  76  74  77  89 111  96 111 200
2014 155 129 175 140 145 133 155 207 232 162 229 310
2015 308 343 332 378 418 558    

We now have the data prepared for input to our Forecasting functions in R.

2. Using Forecast in R

For the Forecast function all you need to do is pass in the Time Series dataset and tell the function how my steps into the future you want it to predict. In all my examples I'll ask the functions to predict for the next 12 months.

ts_forecast <- forecast(ts_data, h=12)

         Point Forecast     Lo 80     Hi 80 Lo 95     Hi 95
Jul 2015       447.1965  95.67066  784.3163     0  958.6669
Aug 2015       499.4344 115.94329  873.1822     0 1069.8689
Sep 2015       551.7875 123.88426  952.0773     0 1230.4707
Oct 2015       603.7212 156.89486 1078.0395     0 1370.2069
Nov 2015       654.7647 143.29718 1179.4903     0 1603.8335
Dec 2015       704.5162 135.76829 1352.8925     0 1844.7230
Jan 2016       752.6447 151.09936 1502.6088     0 2100.9708
Feb 2016       798.8877 156.37383 1652.0847     0 2575.3715
Mar 2016       843.0474 159.67095 1848.1703     0 2888.1738
Apr 2016       884.9849 154.59456 2061.7990     0 3281.6062
May 2016       924.6136 148.04651 2325.9060     0 3891.5064
Jun 2016       961.8922 138.67935 2531.7578     0 4395.6033



For this we get a very large range of values and very wide predictive intervals. If we limit the y axis we can get a better picture of the actual predictions.

plot(ts_forecast, ylim=range(0:1000))

3. Using HoltWinters

For HoltWinters we can use the in-built R function for this. All we need to do is to pass in the Time Series data set. The first part we can plot the HoltWinters for the existing data set

hw <- HoltWinters(ts_data)

Now we want to predict for the next 12 months

forecast <- predict(hw, n.ahead = 12, prediction.interval = T, level = 0.95)

              fit       upr      lwr
Jul 2015 519.9304  599.8097 440.0512
Aug 2015 560.1083  648.4183 471.7983
Sep 2015 601.4528  701.0163 501.8892
Oct 2015 643.9639  757.3750 530.5529
Nov 2015 681.5168  811.0727 551.9608
Dec 2015 724.7363  872.4508 577.0218
Jan 2016 773.8308  941.4768 606.1848
Feb 2016 809.8836  999.0401 620.7272
Mar 2016 847.1448 1059.2371 635.0525
Apr 2016 898.4476 1134.7795 662.1158
May 2016 933.8755 1195.6532 672.0977
Jun 2016 972.3866 1260.7376 684.0356

plot(hw, forecast)

4. Using ARIMA

For ARIMA we need to perform a simple conversion of the Time Series data into ARIMA format and then perform the forecase

fc_arima <- auto.arima(ts_data)
fc_fc_arima  <- forecast(fc_arima, h=12)

         Point Forecast    Lo 80     Hi 80    Lo 95     Hi 95
Jul 2015       524.4758 476.2203  572.7314 450.6753  598.2764
Aug 2015       567.1156 513.2301  621.0012 484.7048  649.5265
Sep 2015       609.7554 548.3239  671.1869 515.8041  703.7068
Oct 2015       652.3952 581.6843  723.1062 544.2522  760.5383
Nov 2015       695.0350 613.5293  776.5408 570.3828  819.6873
Dec 2015       737.6748 644.0577  831.2920 594.4998  880.8499
Jan 2016       780.3147 673.4319  887.1974 616.8516  943.7777
Feb 2016       822.9545 701.7797  944.1292 637.6337 1008.2752
Mar 2016       865.5943 729.2004 1001.9881 656.9978 1074.1907
Apr 2016       908.2341 755.7718 1060.6963 675.0631 1141.4050
May 2016       950.8739 781.5559 1120.1918 691.9244 1209.8233
Jun 2016       993.5137 806.6027 1180.4246 707.6581 1279.3693

plot(fc_fc_arima, ylim=range(0:800))

As you can see there are very different results from each of these forecasting techniques. If this was a real life project on real data then we would go about exploring a lot more of the Forecasting function available in R. The reason for this is to identify which R function and Forecasting algorithm works best for our data.

Which Forecasting technique would you choose from the selection above?

But will this function and algorithm always work with our data? The answer is NO. As our data evolves so may the algorithm that works best for our data. This is why the data science/analytics world is iterative. We need to recheck/revalidate the functions/algorithms to see if we need to start using something else or not. When we do need to use another function/algorithm you need to ask yourself why this has happened, what has changed in the data, what has changed in the business, etc.

Wednesday, July 22, 2015

Charting Number of R Packages over time (Part 2)

This is the second blog post on charting the number of new R Packages over time.

Check out the first blog post that looked at getting the data, performing some simple graphing and then researching some issues that were identified using the graph.

In this blog post I will look at how you can aggregate the data, plot it, get a regression line, then plot it using ggplot2 and we will include a trend line using the geom_smooth.

1. Prepare the data

In my previous post we extracted and aggregated the data on a daily bases. This is the plot that was shown in my previous post. This gives us a very low level graph and perhaps we might get something a little bit more useable is we aggregated the data. I have the data in an Oracle Database so it would be easy for me to write another query to perform the necessary aggregation. But let's make things a little bit trickier. I'm going to use R to do the aggregation.

Our data set is in the data frame called data. What I want to do is to aggregate it up to monthly level. The first thing I did was to create a new column that contains the values of the new aggregate level.

data$R_MONTH <- format(rdate2, "%Y%m01")
data$R_MONTH <- as.Date(data$R_MONTH3, "%Y%m%d")
data.sum <- aggregate(x = data[c("R_NUM")],
                    FUN = sum,
                    by = list( = data$R_MONTH)

2. Plot the Data

We now have the data aggregated at monthly level. We can now plot the graph. Ignore the last data point on the chart. This is for July 2015 and I extracted the data on the 9th of July. So we do not have a full months of data here.

plot(as.Date(data.sum$, data.sum$R_NUM, type="b", xaxt="n", cex=0.75 , ylab="Num New Packages", main="Number of New Packages by Month")
axis(1, as.Date(data.sum$, "%Y-%d"), as.Date(data.sum$, "%Y-%d"), cex.axis=0.5, las=1)

This gives us the following graph.


3. Plot the data using ggplot2

The basic plot function of R is great and allows us to quickly and easily get some good graphs produced. But it is a bit limited and perhaps we want to create something that is a bit more elaborate. ggplot2 is a very popular package that can allow us to create a graph, building it up in a number of steps and layers to give something that is a lot more professional.

In the following example I've kept things simple and Yes I could have done so much more. I'll leave that as an exercise for you go off an do.

The first step is to use the qplot function to produce a basic plot using ggplot2. This gives us something similar to what we got from the plot function.

qplot(x=factor(data.sum$, y=data.sum$R_NUM, data=data.sum, 
       xlab="Year/Month", ylab='Num of New Packages', asp=0.5)

This gives us the following graph.


Now if we use ggplot2 then we need to specify a lot more information. Here is the equivalent plot using ggplot2 (with a line plot).


4. Include a trend line

We can very easily include a trend line in a ggplot2 graph using the geom_smooth command. In the following example we have the same chart and include a linear regression line.

plt <- ggplot(data.sum, aes(x=factor(data.sum$, y=data.sum$R_NUM)) + geom_line(aes(group=1)) +
  theme(text = element_text(size=7),
        axis.text.x = element_text(angle=90, vjust=1)) + xlab("Year / Month") + ylab("Num of New Packages") +
  geom_smooth(method='lm', se=TRUE, size = 0.75, fullrange=TRUE, aes(group=20))


We can tell a lot from this regression plot.

But perhaps we would like to see a trend line on the chart, with something like a moving averages plot. Plus I've added in a bit of scaling to help with representing the data at a monthly level.

plt <- ggplot(data.sum, aes(x=as.POSIXct(data.sum$, y=data.sum$R_NUM)) + geom_line() + geom_point() +
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=90, vjust=1)) + xlab("Year / Month") + ylab("Num of New Packages") +
  geom_smooth(method='loess', se=TRUE, size = 0.75, fullrange=TRUE) +
  scale_x_datetime(breaks = date_breaks("months"), labels = date_format("%b"))

In the third blog post on this topic I will look at how we can use some of the forecasting and predicting functions available in R. We can use these to see help us visualize what the future growth patterns might be for this data. I have some interesting things to show.

Monday, July 20, 2015

the R Consortium

On the 30th June (2015) a number of companies came together to form the R Consortium. The aim of the R Consortium is to support the R community and to help it evolve.


In a way the formation of this group is not surprising as there is a growing list of companies who have their own support implementation of R that provides a number of additional and very important features. Most of these features evolve around making R more useable within applications and for easier production deployment.

Founding companies and organizations of the R Consortium include The R Foundation, Platinum members Microsoft and RStudio; Gold member TIBCO Software Inc.; and Silver members Alteryx, Google, HP, Mango Solutions, Ketchum Trading and Oracle.

It is important to note about this group is that they won't be looking at the R language but will be looking at the infrastructure that supports R.

The big boys are now onboard and promoting R and it will be interesting to see what directions they will come up with.

This is something worth keeping an eye on.

Wednesday, July 15, 2015

Charting Number of R Packages over time (Part 1)

This is the first of a three part blog post on charting and analysing the number of R package submissions.

(I will update this blog post with links to the other two posts as they come available)

I'm sure most of you have heard of the R programming language. If not then perhaps it is something that you might want to go off an learn a bit about. Why? well it is one of the most popular languages for performing various types of statistics, advanced topics on statistics and machine learning and for generating lots of cool looking graphs.

If this is not something that you might be interested then it is time to go to another website/blog.

In this blog post I'm going to chart the number of packages submitted to R and are available for download and installation.

Why am I doing this? I got bored one day after coming back from my vacation and I though it would be a useful thing to do. Then after doing this I decided to use these graphs somewhere else, but you will have to wait until 2016 to find out!

The R website has a listing of all the packages and the dates that they were submitted.


There are a variety of tools available that you can use to extract the information on this webpage and there are lots of examples or R code too. I'll leave that as a little exercise for you to do.

I extracted all of this information and stored it in a table in my Oracle Database (of course I did as I work with Oracle databases day in day out). This will allow me to easily reuse this data whenever I need it plus I can update this table with new packages from time to time.


The following R code:

  1. Setups up and ROracle connection to my schema in my database
  2. Connects to the database
  3. Setups up a query to extract the data from the table
  4. Fetches this data into an R data frame called data
  5. Reformat the date columns to remove the time element to it
  6. Plot the data

drv <- dbDriver("Oracle")
# Create the connection string
host <- "localhost"
port <- 1521
service <- "pdb12c"
connect.string <- paste(
  "(ADDRESS=(PROTOCOL=tcp)(HOST=", host, ")(PORT=", port, "))",
  "(CONNECT_DATA=(SERVICE_NAME=", service, ")))", sep = "")

con <- dbConnect(drv, username = "brendan", password = "brendan",dbname=connect.string)

res<-dbSendQuery(con, "select r_date, count(*) r_num from r_packages
                       group by r_date order by 1 asc")
data <- fetch(res)

rdate<- data$R_DATE

plot(data$R_NUM~rdate2, data, type="l" , xaxt="n")
axis(1, rdate2, format(rdate2, "%b %y"), cex.axis=.7, las=1)

After I run the above code I get the following plot.


(Yes I could have done a better job on laying out the chart with all sorts of labels and colors etc)

This chart gives us a plot of the number of new submissions by day.

There are 2 very obvious things that stand out from this graph. The easiest one to deal with is that we can see that there has been substantical growth in new submissions over the past 3 years. Perhaps we need to examine these a bit closer and when you do you will find that a lot of these are existing packages that have been resubmitted with updates.

There is a very obvious peak just over half ways along the chart. We really need to investigate this to understand what has happended. This peak occurs on the 29th October 2012. What happened on the 29th October 2012 as this is clearly an anomaly with the rest of the data. Well on this date R version 2.15.2 was release and a there was a lot of update pagackes got resubmitted.

Check out my next two blog posts were I will explore this data in a bit more detail.

Part 2 blog post

Part 3 blog post