BNOSAC will be giving from June 08 up to June 12 a 5-day crash course on the use of R using Oracle R Enterprise. The course is given together with our Oracle Partner in Leuven, Belgium. If you are interested in attending, contact us for further details.
For R users who aren't aware of this yet. Oracle has embedded R into it's database which allows R users to transparently run R code inside the database – yes really transparently. The Oracle R Enterprise is part of the Oracle Advanced Analytics stack which basically consists of the following elements for R users:
- ROracle: supported native DBI driver to connect R with Oracle which is open source and available at CRAN (link).
- Oracle R Enterprise (ORE): This consists of an Oracle released version of R which is up to date with the current version of R and supported by Oracle and next to that a number of R packages which are available for download at the ORE website. These packages embed R into Oracle.
- Oracle Data Mining (ODM): a set of distributed data mining algorithms accessible from R
- Oracle Advanced Analytics for Hadoop (ORAAH) : a set of R packages which allow R users to connect with Hadoop and run data mining models and map reduce jobs on top of Hadoop. (link)
During the 5-day course, you will learn how to use R alongside the Oracle DB. The course covers some base R, advanced R usage and functionality from the Oracle R Enterprise (ORE) suite of packages.
Module 1: Introduction to base R
What is R, packages available (CRAN, R-Forge, …), R documentation search, finding help, RStudio editor, syntax, Data types (numeric/character/factor/logicals/NA/Dates/Times), Data structures (vector/data.frame/matrix/lists and standard operations on these), Saving (RData) & importing data from flat files, csv, Excel, Oracle, MS SQL Server, SAS, SPSS, Creating functions, data manipulation (subsetting, adding variables, ifelse, control flow, recoding, rbind, cbind) and aggregating and reshaping, Plotting in R using base and lattice functionality (dot plots, barcharts, graphical parameters, legends, devices), Basic statistics in R (mean, variance, crosstabs, quantile, correlation, distributions, densities, histograms, boxplot, t-tests, wilcoxon test, non-parametric tests)
Module 2: Advanced R programming & data manipulation with base R
vectorisation, writing your own functions, control flow, aggregating and data.table – fast group by, joining and data.table programming tricks, reshaping from wide to long format, miscellaneous usefull functions, apply family of functions & split-apply-combine strategy, do.call, parallel execution of code, handling of errors and exceptions, debugging code, other goodies: basic regular expressions, data manipulations, rolling data handling, S3 classes, generics and basic S4 methodology
Module 3: ROracle and Oracle R Enterprise (ORE) – transparancy layer
• ROracle – getting and sending SQL queries from Oracle
• Installing Oracle R Enterprise (ORE)
• Basic database connectivity: ore.exec, ore.ls, ore.synch, ore.push, ore.pull, ore.create, ore.drop, ore.get
• ORE data types: ore.character, ore.factor, ore.logical, ore.number, ore.datetime, ore.numeric. Conversion between data types
• ORE data structures: ore.matrix, ore.frame, ore.vector
• ORE transparancy data operations on ore.frame/ore.vector (subset, ncol, nrow, head, ifelse, paste, is.na, sd, mean, tapply, by, c, %in%, …) and indexing and overwriting in-database ore.vectors
• Save R objects in Oracle ore.save, ore.load, ore.datastore and ORE data store handling
• Basic statistics with ORE (ore.univariate, ore.summary, ore.crosstab, ore.corr, exponential smoothing, t.test, wilcoxon, IQR)
Module 4: Oracle R Enterprise – advanced data manipulation
• Running R functions parallel inside the database: ore.doEval, ore.groupApply, ore.indexApply, ore.rowApply, ore.tableApply
• Creating R scripts inside the database and accessing ORE stored procedures
• Embedding R scripts in production database applications
• Embedded (parallel) R execution within ORE using the R Interface as well as the SQL Interface
Module 5: Data mining models inside Oracle R Enterprise (ORE) and Oracle Data Mining (ODM)
In this session you will become acquainted with some of the most common data mining methods and learn how to use these algorithms in ORE. The following algorithms will be covered.
• principal component analysis and factor analysis
• kmeans clustering and orthogonal partitioning
• data reduction using Minimum Description Length attribute importance
• linear models and generalized linear models
• naive bayes, neural networks, decision tree and support vector machines
• market basket analysis / recommendation engines (apriori)
• bagging
If you are interested in attending, contact us for further details.
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