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Combining R with Java

Ryan CuprakElsa Cuprak@ctjava cuprak.info

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Combining R with Java

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Agenda

R Overvie

w

R + Java

R + Java EE

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What is R?• Free open-source alternative to Matlab, SAS, Excel, and SPSS

• R is:

• Statistical software

• Language

• Environment

• Ecosystem

• Used by Google, Facebook, Bank of America, etc.

• 2 million users worldwide

• Downloaded URL:

http://www.r-project.org

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What is R?• R Foundation responsible for R.• Sponsored/supported by industry.• Licensed under GPL.• Implementation of the S programming language• Name derived from author’s of R.• First implementation ~1997• Written in C, Fortran, and R

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CRAN• Power of R is packages!• CRAN = Comprehensive R Archive Network• Analogous to (Maven) Central• 6745 packages available

• Database access• Data manipulation• Visualization• Data modeling• Reports• Geospatial data analysis• Time series/financial data

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CRAN Popular Packages• ggplot2 – package for creating graphs• rgl – interactive 3D visualizations• Caret – training regression• Survival – tools for survival analysis• Mgcv – generalized additive models• Maps – polygons for plots• Ggmap – Google maps• Xts – manipulates time series data• Quantmode – downloads financial data, plotting, charting• tidyr – changes layout of datasets

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Uses of R

Calculating Credit Risk

Reporting

Data Analysis Data Visualization

Data Exploration

Clinical Research

Flood ForecastingServer Failure

Modeling

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Why not Java?• Java isn’t “convenient”• Lacks specialized data structures• Limited graphing capabilities• Few statistical libraries available• Statisticians don’t use Java• No interactive tools for data exploration• No built-in support for data import/cleanup• Re-inventing the wheel is expensive…

R is a DSL + Stat Library

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Leveraging R from Java• Two approaches to integration:

• rJava – access R from Java• JRI – call Java from R

• rJava includes JRI.• Installed from CRAN: install.packages(‘rJava’)• Documentation & code:

• http://www.rforge.net/rJava/• https://github.com/s-u/rJava

• R & Java worlds bridged via JNI

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Getting Started with R• Download and install:

• Rhttp://www.r-project.org

• R Studio:http://www.rstudio.com

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Basics of R• Interpreted language• Functional• Dynamic typing• Lexical scoping• R scripts stored in “.R” files• Run R commands interactively in R/R Studio or RScript.• Language

• Object-oriented• Exceptions• Debugging

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R Data Types• Scalar

• Numeric• Decimal• Integer

• Character• Logical – true or false

• Vectors – a sequence of numbers or characters, or higher-dimensional arrays like matrices

• Factors – sequence assigning a category to each index• Lists – collection of objects• Data frames – table-like structure

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NULL & NA• NULL – indicates an object is absent• NA – missing values (Not Available)

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Language Basics• # Comments• Assignment “<-” but “=“ can also be used• Variables rules:

• Letters, numbers, dot (.), underscore (_)• Can start with a letter or a dot but not followed by a number• Valid

.test_testtesttest.today

• Invalid.2test_test_2test

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Vectors• Defining and assigning a vector:

> x <- c(10,20,30,40,50,60)• Multiplying a vector:

> x * 3[1] 30 , 60, 90, 120, 150, 180

• Applying a function to a vector:> sqrt(x)[1] 3.162278 4.472136 5.477226 6.324555 7.071068…

• Access individual elements:> x[1][1] 30

• Appending data to a vector:> x <- c(x,70)[1] 10 20 30 40 50 60 70

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Data Frames• Setup the data for the frame:

boats <- c("Bayou Blue", "Pachyderm", "Spectre" , "Flatline")model <- c("J30" , "Frers 33", "J-125" , "Evelyn 32-2")phrf <- c(135, 108 , -6, 99)finish <- times(c( "19:53:06" , "19:42:18" , "19:38:11" , "19:45:48" ))kts <- c(4.09 , 4.66 , 4.92 , 4.46)

• Construct the data frame:raceDF <- data.frame(boats,model,phrf,finish,kts)

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Data Frames> summary(raceDF) boats model phrf finish kts Bayou Blue:1 Evelyn 32-2:1 Min. : -6.00 Min. :19:38:11 Min. :4.090 Flatline :1 Frers 33 :1 1st Qu.: 72.75 1st Qu.:19:41:16 1st Qu.:4.367 Pachyderm :1 J-125 :1 Median :103.50 Median :19:44:03 Median :4.560 Spectre :1 J30 :1 Mean : 84.00 Mean :19:44:51 Mean :4.532 3rd Qu.:114.75 3rd Qu.:19:47:37 3rd Qu.:4.725 Max. :135.00 Max. :19:53:06 Max. :4.920

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Lists• Generic Vector containing other objects• Example:

wkDays <- c("Monday","Tuesday","Wednesday","Thursday","Friday")dts <- c(15,16,17,18,19)devoxx <- c(FALSE,FALSE,TRUE,TRUE,TRUE)weekSch <- list(wkDays,dts,devoxx)

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Lists• Member slicing:

> weekSch[1][[1]][1] "Monday" "Tuesday" "Wednesday" "Thursday" "Friday"

• Member referencing:> weekSch[[1]][1] "Monday" "Tuesday" "Wednesday" "Thursday" "Friday”

• Labeling entries:> names(weekSch) <- c("Days","Dates","Devoxx Events")

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Matrices• Defining a matrix:

myMatrix <- matrix(1:10 , nrow = 2) [,1] [,2] [,3] [,4] [,5][1,] 1 3 5 7 9[2,] 2 4 6 8 10

• Printing out dimensions:> dim(myMatrix)[1] 2 5

• Multiplying matrixes:> myMatrix + myMatrix

[,1] [,2] [,3] [,4] [,5][1,] 2 6 10 14 18[2,] 4 8 12 16 20

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Factors• Vector whose elements can take on one of a specific set of values.• Used in statistical modeling to assign the correct number of degrees

of freedom.> factor(x=c("High School","College","Masters","Doctorate"), levels=c("High School","College","Masters","Doctorate"), ordered=TRUE)[1] High School College Masters Doctorate Levels: High School < College < Masters < Doctorate

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Defining Functions• Created using function() directive.• Stored as objects of class function.

F <- function(<arguments>) {# do something

}• Functions can be passed as arguments.• Functions can be nested in other functions.• Return value is the last expression to be evaluated.• Functions can take an arbitrary number of arguments.• Example:

double.num <- function(x) {x * 2

}

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Built-in Datasetsdata()

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Dem

o

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Review: Linear RegressionLinear regression model: a type of regression model, in which the

response is continuous variable, and is linearly related with the predictor variable(s).

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Review: Linear RegressionWhat can a linear regression do?• Find linear relationship between height and weight.• Predict a person's weight based on his/ her height.Example:

Given the observations, weight (Y) and height (X), the parameters in the model can be estimated.

response intercept coefficientpredictor

error

Assumptions of the linear regression model: 1) the errors have constant variance2) the errors have zero mean3) the errors come from the same normal distribution

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Review: Linear Regression

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Review: Linear Regression

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Review: Linear Regression

Setup the data…

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Review: Linear Regression

Perform the linear regression…

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Review: Linear Regression

Plot the results…

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Considerations1. Do you want to re-implement that logic in Java?2. How would you test your implementation?3. What would the ramifications of incorrect calculations?

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R + Java = rJava

• rJava provides a Java API to R.• JRI – ability to call from R back into Java code.• Runs R inside of the JVM process via JNI.• Single-threaded – R can be accessed ONLY by one thread!• Native library can be loaded only ONCE.

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<dependency><groupId>org.nuiton.thirdparty</groupId><artifactId>JRI</artifactId><version>0.9-6</version></dependency>

rJava and Maven

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Configuring Project (non-Maven/SE)

Folder containing JNI

library

• Use R.home() to locate the installation directory.

• rJava under library/rJava

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Runtime Parameters

-DR_HOME -Djava.library.path-Denv.R_HOME

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Starting R

• Interact with R via Rengine.• Initialize Rengine with instance of RMainLoopCallbacks.

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Simple rJava Example

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Advanced rJava Example

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R Scripts

Wait – I have to embed all of my R code in Java??

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Java EE + R

JSR 352 - Batching

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Java EE Container Integration

• Add following libraries to container lib: (glassfish4/glassfish/domains/<domain>/lib)• JRI.java• JRIEngine.jar• Libjri.jnilib native code!• Rengine.jar

Do NOT include rJava dependencies in your WAR/EAR!

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Java EE Container Integration

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JSR 352 Basic Concepts

Job Operator

Job Step

Job Repository

ItemReader

ItemProcessor

ItemWriter

Batchlet

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JSR 353 Basic Concepts

• Job – encapsulates the entire batch process.• JobInstance – actual execution of a job.• JobParameters – parameters passed to a job.• Step – encapsulates an independent, sequential phase of a batch

job.• Batch checkpoints:

• Bookmarking of progress so that a job can be restarted. • Important for long running jobs

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JSR 352 Basic Concepts

• Step Models:• Chunk – comprised of Reader/Writer/Procesor• Batchlet – task oriented step (file transfer etc.)

• Partitioning – mechanism for running steps in parallel• Listeners – provide life-cycle hooks

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Initializing R in Singleton Bean

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Example: Road Race Statistics

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Example Batch Job: 5k Racing

Process overview• ResultRetrieverBatchlet – Downloads data raw data from website.• RaceResultsReader – Extracts individual runners from the raw data.• RaceResultsProcessor – Parses a runner’s results.• RaceResultsWriter – Writes the statistics to the database.• RaceAnalysisBatchlet – Uses R to analyze race results.Notes:• JAX-RS used to retrieve the results from the website.• JPA to persist the results.• R script extracts the results from PostgeSQL (not passed in)

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Example Batch Job: 5k Racing

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Example Batch Job: 5k Racing

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Example Batch Job: 5k Racing

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Example Batch Job: 5k Racing

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Challeges

• R can be memory hog!• Crashes takes down R + Java + Container!• Solution: R scripts ‘externally’• Note: plotting requires X!

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Sum

mar

y

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Q &

A

Questions

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rcuprak@gmail.com (Java)actuary.elsa@gmail.com (Stats)@ctjava