
15. 05. 29
For persons interested in advancing their knowledge on R and data science with R, BNOSAC offers a range of courses for R users.
These cover courses on
 R analytics (Statistical Machine Learning with R, Text mining with R, Applied Spatial modelling with R)
 R programming (R for starters, Common data manipulation for R programmers, Reporting with R, Creating R packages and R repositories, Managing R processes, Using SVN/git with RStudio, Data connectivity using R, Integration of R into web applications)
 Oracle R Enterprise
For more information, go to http://bnosac.be/images/activities/bnosac_courses_r.pdf
UseRs interested in one of these courses can follow one of the scheduled courses listed below or request for handson training sessions at your site. The scheduled courses are given in Belgium.
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Statistical courses in cooperation with LStat:
 Statistical Machine Learning with R: 28/10/2015 & 29/10/2015
 Text mining with R: 17/11/2015 (new)
 Applied Spatial statistics with R: 13/04/2016 & 14/04/2016 (new)
Programming courses in cooperation with LStat:
 Advanced R programming topics: 17/02/2016 & 18/02/2016
> course containing common data manipulation for R programmers, reporting with R, creating R packages and repositories
1week bootcamp on using R with Oracle R Enterprise, in cooperation with Tripwire Solutions. This includes:
 R for starters: 0812/06/2015
 Common data manipulation for R programmers: 0812/06/2015
 ROracle and Oracle R Enterprise (ORE)  transparancy layer: 0812/06/2015
 Oracle R Enterprise  advanced data manipulation: 0812/06/2015
 Data mining models inside Oracle R Enterprise (ORE) and Oracle Data Mining (ODM): 0812/06/2015
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Make sure you register early for the Statistical courses offered jointly with LStat.
For the previous years the room on machine learning and advanced r programming was overbooked.
If there is a lot of interest in certain courses, an extra session can be set up.
Interested?, get into contact.

15. 05. 21
BNOSAC will be giving from June 08 up to June 12 a 5day 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 5day 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, RForge, ...), 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, ttests, wilcoxon test, nonparametric 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 & splitapplycombine 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 indatabase 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.

15. 05. 13
We will be showcasing our RMOA package at the next R User conference in Aalborg.
For the R users who are unfamiliar with streaming modelling and want to be ahead of the Gartner Hype cycle or want to evaluate existing streaming machine learning models, RMOA allows to build, run and evaluate streaming classification models which are built in MOA (Massive Online Learning).
For an introduction to RMOA and MOA and the type of machine learning models which are possible in MOA  see our previous blog post or scroll through our blog page.
In this example below, we showcase the RMOA package by using streaming JSON data which can come from whatever noSQL database that spits out json. For this example, package jsonlite provides a nice stream_in function (an example is shown here) which handles streaming json data. Plugging in streaming machine learning models with RMOA is a breeze.
Let's dive into the R code immediately where we show how to run, build and evaluate a streaming boosted classification model.
require(jsonlite) require(data.table) require(RMOA) require(ROCR) ## ## Use a dataset from Jeroen Ooms available at jeroenooms.github.io/data/diamonds.json ## myjsondataset < url("http://jeroenooms.github.io/data/diamonds.json") datatransfo < function(x){ ## Setting the target to predict x$target < factor(ifelse(x$cut == "Very Good", "Very Good", "Other"), levels = c("Very Good", "Other")) ## Making sure the levels are the same across all streaming chunks x$color < factor(x$color, levels = c("D", "E", "F", "G", "H", "I", "J")) x } ## ## Read 100 lines of an example dataset to see how it looks like ## x < readLines(myjsondataset, n = 100, encoding = "UTF8") x < rbindlist(lapply(x, fromJSON)) x < datatransfo(x) str(x) ###################################### ## Boosted streaming classification ##  set up the boosting options ###################################### ctrl < MOAoptions(model = "OCBoost", randomSeed = 123456789, ensembleSize = 25, smoothingParameter = 0.5) mymodel < OCBoost(control = ctrl) mymodel ## Train an initial model on 100 rows of the data myboostedclassifier < trainMOA(model = mymodel, formula = target ~ color + depth + x + y + z, data = datastream_dataframe(x)) ## Update the model iteratively with streaming data stream_in( con = myjsondataset, handler = function(x){ x < datatransfo(x) ## Update the trained model with the new chunks myboostedclassifier < trainMOA(model = myboostedclassifier$model, formula = target ~ color + depth + x + y + z, data = datastream_dataframe(x), reset = FALSE) ## do not reset what the model has learned already }, pagesize = 500) ## Do some prediction to test the model predict(myboostedclassifier, x) table(sprintf("Reality: %s", x$target), sprintf("Predicted: %s", predict(myboostedclassifier, x))) ## Do a streaming prediction stream_in(con = myjsondataset, handler = function(x){ x < datatransfo(x) myprediction < predict(myboostedclassifier, x) ## Basic evaluation by extracting accuracy print(round(sum(myprediction == x$target) / length(myprediction), 2)) }, pagesize = 100)
For more information on RMOA or streaming modelling, get into contact.

15. 04. 14
For those of you who are interested in using R alongside Google Maps by using the packages geonames (www.geonames.org), RgoogleMaps, ggmap, loa and plotKML. Enjoy the slides of our presentation on this topic during the last RBelgium meetup.
If you are interested in applying Spatial modelling on your data, get in touch: bnosac.be/index.php/contact/mailus

15. 04. 14
For those of you who are interested in using R alongside Robotics applications and want to use ROS (Robot Operating System) together with R.
Enjoy the slides of our presentation on this topic during the last RBelgium meetup.
If you are interested in applying realtime analysis of your data streams or sensor data with R and ROS, we can help  get in touch at bnosac.be/index.php/contact/mailus