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=" ")
}
tm_data

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.scriptDrop("prepare_tm_data")
ore.scriptCreate("prepare_tm_data", function (tm_data) { 
  library(tm)
  library(SnowballC)
  library(wordcloud)
  
  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.