R as an Analysis Engine

R as an Analysis Engine

RapidNCA, the non-compartmental

analysis workflow tool

Building Apps

•

Java

•

.NET

•

R

Why Build Analytic Applications?

Why Build Analytic Applications

on R?

Why Analytics?

• Analytics answer many questions

• I believe there is no company in the world today who

cannot benefit from analytics in some way

Who is a good driver? What bonus should I pay? How do we win more games?

Why build Analytic Applications?

3 key reasons we see:

•

To deploy analytical tools to decision makers

•

To make an analysts life more efficient

Deploying Analytics

• Adding analytics into a business process

more informed decisions

• Complex analytics shouldn’t be attempted by

non-analysts

• Communication between the decision maker and

Deploying Analytics

If we build an application which:

• is easy for the decision maker to use

• contains the correct analysis to apply

• communicates analytical results in suitable manner this leads to some major benefits!

Benefits for

the Analyst

Benefits for the

Decision Maker

No need to wait for information

Can perform “what if” analysis

Decision not dependent on analyst availability

Less need to perform often-repetitive tasks

Comfortable that the “right” analysis is being run

Can get on with more strategic things?

User Interface Data Analytic Outputs Data Storage Analytic Code Code Mgment

Analytic App Structure

Analytic Engine

Why build Analytic Applications on R?

We want a programmable engine so that it can be readily

extended (i.e. no black boxes please)

R can be extended by the developer as needed

We often want to be able to deploy new algorithms and techniques as they become available

Why build Analytic Applications on R?

Building applications requires installing analytic engine on

desktops, servers, clusters, clouds

R is license free

Building analytic applications involves integrating an analytic engine with other technologies (data sources, UI etc.)

Formal R Development

• Creating sophisticated analytic applications requires a formal development approach

• This mostly means taking standard development practices and applying it to analytics

• Mango’s formal R development procedures and structure has been evolving since its inception ~2004

Project Mgment Requirements Behaviour Driven Code Review Review board StatET testthat roxygen2 Continuous Integration Issue Tracking Quality Manual Dev Procedures Coding Standards Knowledge Mgment TestCoverage

RapidNCA,

the non-compartmental

analysis workflow tool

• Need for RapidNCA • Using .NET

• RapidNCA Structure • Code Quality

• Connections with R.NET

Need for RapidNCA

• Customer needed to send monthly reports to dozens of

trial centres

• Small team, so time limited

• Predefined non-compartmental

analysis

Using .NET

What is .NET?

• Object-oriented environment to develop applications • Safe execution environment

• Choice of programming languages • Framework consisting of:

• runtime

• class library

Using .NET

Visual Studio

• A graphical programming tool (IDE) • Visual Studio Express - free version

Using .NET

Choice of languages

• C# is the main one

• F# is a functional language (similar concepts to OCaml)

• XAML (a Microsoft declarative XML language) for interactive

graphics

• C++/CLI useful for legacy and bespoke parallel processing

(including GPGPU) Other possibilities...

• Vb.Net is very like C# (no advantage over it)

Using .NET

“Ajar Source” Platform

Not exactly open source, but…

• Most CLI third party languages are open

• C# and VB.Net are not, but many open source projects

based on them

• Microsoft have made F# open source • Compiler is free

Using .NET

Performance

Performance is very good

• On graphics (millions of data points will plot with ease and

zoom smoothly)

• Computation is fast enough in C#, calling R adds little overhead • Standard Maths library is limited; third parties and MS maths for

“drawing” are better

• Data parallel computation is possible on the desktop (GPGPU) • F# provides further “big data” capabilities

User Interface Data Analytic Outputs Data Storage Analytic Code Code Mgment Data Service

RapidNCA Structure

Analytic Engine

RapidNCA Structure

MangoNca

Analytic Code

Analyse Element AnalysisDo Get Analysis Unit Tests Data Checks

RapidNCA Structure

Code Quality

Unit Tests

• Ensure product works!

• User/Customer/Payer trust

Code Quality

Run Code, Check Output

• Working Cases

> test1 <- ncaAnalysis(Conc = c(4, 9, 8, 6, 4:1, 1), + Time = 0:8, Dose = 100, Dof = 2)

> checkEquals(test1[1, "ROutput_adjr2"], 0.9714937901, + tol = 1e-8)

[1] TRUE

> require(RUnit)

Code Quality

Error Case Unit Tests

• Use try

• Handled Error Cases

> test7 <- try(AUCLast(Conc = 1:10, Time = 9:0), + silent = TRUE) > checkEquals(test7, + "Error in checkOrderedVector(Time, ... ") [1] TRUE > test26 <- ncaAnalysis(Conc = c(4, 9, 8, 6, 4:1, 1), + Time = 0:8, Dof = 1) > checkEquals(test26[, "ROutput_Error"], + "Error in checkSingleNumeric(Dose, ... ") [1] TRUE

Connections with R.NET

• What will be provided to R? • What will be returned from R?

Connections with R.NET

Using the R Service

• R.NET allows R calls to be submitted to an R service • R.NET connects to R down to Expression level

Connections with R.NET

Data Checks

• Function may be passed data outside its anticipated

structure

> checkOrderedVector(c(0, 1, 3, 2, 4), + description = "Time")

Error in checkOrderedVector(c(0, 1, 3, 2, 4), description = "Time") :

Error: Time is not ordered. Actual value is 0 1 3 2 4

Connections with R.NET

Data Checks

• The tool expects a certain return object

• An error in an R call should be trapped by the

communicating function

• Return object passed as normal

• An error checking element of the return object can report

information about the error

> check01 <- try(checkOrderedVector(Time,

+ description = "Time"), silent = TRUE) > if (is(check01, "try-error")) { return(object) }

Connections with R.NET

_pluginsManager = new RPluginManager(PluginLocation, RLocation); _pluginsManager.SetActivePlugin();

_session = _pluginsManager.GetSession();

bool sessionOk = _pluginsManager.TryMakeSession(out _session);

R is efficiently accessed, via R.Net (as pictured in Visual Studio) via a

Connections with R.NET

User Interface Data Analytic Outputs Data Storage Analytic Code Code Mgment Analytic Engine Data Service R.NET Analytic Engine

Analysis Display Get PK Params Data Service Dialog Service App Logger Status Bar Service App Config Mgment Data Importers Project Wizard Validators Receive R Output Create R Expressns

Connections with R.NET

.NET Data Service

Connections with R.NET

Using the framework

_pluginsManager = new RPluginManager(PluginLocation, RLocation); _pluginsManager.SetActivePlugin();

_session = _pluginsManager.GetSession();

bool sessionOk = _pluginsManager.TryMakeSession(out _session);

_session.SetNumericSymbol("TimePtVector", CheckTimePointData(toAnalyse));

_session.SetNumericSymbol("ConcVector", CheckConcentrationPointData(toAnalyse));

var evalString = string.Format("ncaAnalysis(TimePtVector, ConcVector, …

MathEngineDataRowDto<double> ncaGetBack =

_session.PerformNumericEvaluation(evalString, "ROutput_Error"); _lastErrors = ncaGetBack.ErrorStrings;

_session.FlushConsole();

Complete & Deploy

RapidNCA

• Can users understand how to use tool? • How confident are we in tool output?

• On-going code review • Independent test team • Installation Qualification • Operational Qualification • Performance Qualification

Conclusions

• Great graphical interfaces can be built using .NET • Intuitive interactive features are available

• R.NET allows R analysis to be accessed as a service • Good coding practice will ensure application is robust • Work on a well engineered framework will be rewarded