Combining R With Java For Data Analysis (Devoxx UK 2015 Session)
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Java is a general-purpose language and is not particularly well suited for performing statistical analysis. Special languages and software environments have been created by and for statisticians to use. Statisticians think about programming and data analysis much different from Java programmers. These languages and tools make it easy to perform very sophisticated analyses on large data sets easily. Tools, such as R and SAS, contain a large toolbox of statistical tools that are well tested, documented and validated. For data analysis you want to use these tools. In this session we will provide an overview of how to leverage the power of R from Java. R is the leading open source statistical package/language/environment. The first part of the presentation will provide an overview of R focusing on the differences between R and Java at the language level. We’ll also look at some of the basic and more advanced tests to illustrate the power of R. The second half of the presentation will cover how to integrate R and Java using rJava. We’ll look at leverage R from the new Java EE Batching (JSR 352) to provide robust statistical analysis for enterprise applications.
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Combining R With Java For Data Analysis (Devoxx UK 2015 Session)
- 1. @ctjava#r+java Combining R with Java Ryan Cuprak Elsa Cuprak @ctjava cuprak.info
- 2. @ctjava#r+java Combining R with Java
- 3. @ctjava#r+java Agenda R Overview R + Java R + Java EE
- 4. @ctjava#r+java 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
- 5. @ctjava#r+java 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
- 6. @ctjava#r+java 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
- 7. @ctjava#r+java 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
- 8. @ctjava#r+java Uses of R Calculating Credit Risk Reporting Data Analysis Data Visualization Data Exploration Clinical Research Flood ForecastingServer Failure Modeling
- 9. @ctjava#r+java 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
- 10. @ctjava#r+java 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
- 11. @ctjava#r+java Getting Started with R • Download and install: • R http://www.r-project.org • R Studio: http://www.rstudio.com
- 12. @ctjava#r+java 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
- 13. @ctjava#r+java 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
- 14. @ctjava#r+java NULL & NA • NULL – indicates an object is absent • NA – missing values (Not Available)
- 15. @ctjava#r+java 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 _test test test.today • Invalid .2test _test _2test
- 16. @ctjava#r+java 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
- 17. @ctjava#r+java 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)
- 18. @ctjava#r+java 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
- 19. @ctjava#r+java 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)
- 20. @ctjava#r+java 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")
- 21. @ctjava#r+java 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
- 22. @ctjava#r+java 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
- 23. @ctjava#r+java 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 }
- 26. @ctjava#r+java Review: Linear Regression Linear regression model: a type of regression model, in which the response is continuous variable, and is linearly related with the predictor v a r i a b l e ( s ) .
- 27. @ctjava#r+java Review: Linear Regression What 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 coefficient predictor error Assumptions of the linear regression model: 1) the errors have constant variance 2) the errors have zero mean 3) the errors come from the same normal distribution
- 30. @ctjava#r+java Review: Linear Regression Setup the data…
- 31. @ctjava#r+java Review: Linear Regression Perform the linear regression…
- 32. @ctjava#r+java Review: Linear Regression Plot the results…
- 33. @ctjava#r+java Considerations 1. 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?
- 34. @ctjava#r+java 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.
- 36. @ctjava#r+java Configuring Project (non-Maven/SE) Folder containing JNI library • Use R.home() to locate the installation directory. • rJava under library/rJava
- 38. @ctjava#r+java Starting R • Interact with R via Rengine. • Initialize Rengine with instance of RMainLoopCallbacks.
- 41. @ctjava#r+java R Scripts Wait – I have to embed all of my R code in Java??
- 42. @ctjava#r+java Java EE + R JSR 352 - Batching
- 43. @ctjava#r+java 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!
- 44. @ctjava#r+java Java EE Container Integration
- 45. @ctjava#r+java JSR 352 Basic Concepts Job Operator Job Step Job Repository ItemReader ItemProcesso r ItemWriter Batchlet
- 46. @ctjava#r+java 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
- 47. @ctjava#r+java 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
- 48. @ctjava#r+java Initializing R in Singleton Bean
- 49. @ctjava#r+java Example: Road Race Statistics
- 50. @ctjava#r+java 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)
- 51. @ctjava#r+java Example Batch Job: 5k Racing
- 52. @ctjava#r+java Example Batch Job: 5k Racing
- 53. @ctjava#r+java Example Batch Job: 5k Racing
- 54. @ctjava#r+java Example Batch Job: 5k Racing
- 55. @ctjava#r+java Challeges • R can be memory hog! • Crashes takes down R + Java + Container! • Solution: R scripts ‘externally’ • Note: plotting requires X!
- 58. @YourTwitterHandle#DVXFR14{session hashtag} @ctjava#r+java rcuprak@gmail.com (Java) actuary.elsa@gmail.com (Stats) @ctjava