using r for statistical training: an application to six sigma methodology for process improvement

Using R forStatistical Training

17/04/2012

EL Cano,JM Moguerza,

A Redchuk

Statistical Training

The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Using R for Statistical TrainingAn Application to Six Sigma Methodology

for Process Improvement.

Emilio L. Cano, Andres Redchuk and JavierM. Moguerza

Departamento de Estadıstica e Investigacion OperativaUniversidad Rey Juan Carlos (Madrid)

XXXIII Congreso Nacional de Estadıstica eInvestigacion Operativa

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Contenido

1 Statistical TrainingThe ProblemApproaches

2 The R ChoiceThe R frameworkSweave

3 ApplicationSix SigmaExamplesEnvironments

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Contenido




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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

The ProblemElements of Statistical Training

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Copy-paste ApproachApproaches

Inconsistencies

Errors

Out-of-date

non-reproducible

Painful changes

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A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Reproducible Research ApproachApproaches

Reproducible ResearchThe goal of reproducible research is to tiespecific instructions to data analysis andexperimental data so that scholarship can berecreated, better understood and verified

Literate ProgrammingLiterate programming is a methodology thatcombines a programming language with adocumentation language

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Reproducible ResearchWorkflow

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Contenido




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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

The R SystemChoosing R

What is R?R is a language and environment for statisticalcomputing and graphics.

Open Source

Platform independent

Huge community

Extensible

3 730 availablepackages

http://www.r-project.org

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http://www.r-project.org


17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

LATEX, Beamer, PDFChoosing R

LATEXLaTeX is a high-quality typesetting system; itincludes features designed for the productionof technical and scientific documentation

BeamerBeamer is a LaTeX class for creatingpresentations that are held using a projector,but it can also be used to create transparencyslides

LATEXFiles can easily be converted to PDF.SEIO 2012 10/28

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Sweave DocumentsAn Efficient Framework

SweaveA Sweave document is a plain-text file whichmerges LATEX code and R code. The Rfunction Sweave() converts the Sweavedocument (*.Rnw) into a LATEXfile (*.tex).The code chunks are executed and the resultsembedded into the LATEX file.

SEIO 2012 11/28

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Contenido




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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Methodology at a GlanceSix Sigma

The EssenseThe application of the Scientific Method toprocess improvement, using an easy language.

DMAIC CycleDefineMeasureAnalyzeImproveControl

RolesChampionMaster Black BeltBlack BeltGreen Belt

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

SixSigma PackageSix Sigma

Six Sigma with R | Paper Helicopter template

cut

fold ↑ fold ↓tape

?cu

t

fold

↓↓

cut

fold

↑↑

cut

tape

?

tape

?

clip?

min

(6.5cm)

std

(8cm)

max

(9.5cm)

← b

ody

leng

th →

← body width →min

(4cm)

min

(4cm)

max

(6cm)

max

(6cm)

min

(6.5cm)

std

(8cm)

max

(9.5cm)

← w

ings

leng

th →

Using packagesManuals

Data sets

Templates

Learn-by-Code

Six Sigma Process Map

Paper Helicopter Project

INPUTSX

operators tools raw material facilities

INSPECTION

INP

UT

S

sheets...

Param.(x): width NCoperator CMeasure pattern Pdiscard P

Featur.(y): ok

ASSEMBLY

INP

UT

S

sheets

Param.(x): operator Ccut Pfix Protor.width Crotor.length Cpaperclip Ctape C

Featur.(y): weight

TEST

INP

UT

S

helicopter

Param.(x): operator Cthrow Pdiscard Penvironment N

Featur.(y): time

LABELING

INP

UT

S

helicopter

Param.(x): operator Clabel P

Featur.(y): label

OUTPUTSY

helicopter LEGEND(C)ontrollable(Cr)itical(N)oise(P)rocedure

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

BookSix Sigma

Six Sigma with RA live example: The entire book has beenproduced using Sweave.

The roadmap: TheDMAIC Cycle

The case study: paperhelicopter

SixSigma package: datasets, functions

Easy explanations,further readings

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Sweave Example ISix Sigma Application

\documentclass[a4paper ]{ article}

\usepackage{Sweave}

\title{Design of Experiments}

\author{EL Cano and JM Moguerza and A Rechuk}

\begin{document}

\maketitle

\section{Introduction}

Design of experiments is the most important took in the Improve phase of the

DMAIC cycle \ldots.

<<>>=

library(SixSigma)

doe.model1 <- lm(score ~ flour + salt + bakPow +

flour * salt + flour * bakPow +

salt * bakPow + flour * salt * bakPow ,

data = ss.data.doe1)

summary(doe.model1)

@

This is the general model:

\begin{equation}

\label{eq:doe:model}

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Sweave Example IISix Sigma Application

y_{ijkl }=\mu+\ alpha_i +\ beta_j +\ gamma_k +(\ alpha\beta)_{ij}+

(\alpha\gamma)_{ik}+(\ beta\gamma)_{kl}+(\ alpha\beta\gamma)_{ijk}+

\varepsilon_{ijkl},

\end{equation}

And here we have a plot of effects:

<<maineff , echo=FALSE , fig=TRUE >>=

plot(c(-1, 1), ylim = range(ss.data.doe1$score),

coef(doe.model1 )[1] + c(-1, 1) * coef(doe.model1 )[2],

type="b", pch =16)

abline(h=coef(doe.model1 )[1])

@

%\input{section2}

\end{document}

SEIO 2012 17/28

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Design of Experiments

EL Cano and JM Moguerza and A Rechuk

April 10, 2012

1 Introduction

Design of experiments is the most important took in the Improve phase of theDMAIC cycle . . . .

> library(SixSigma)

> doe.model1 <- lm(score ~ flour + salt + bakPow +

+ flour * salt + flour * bakPow +

+ salt * bakPow + flour * salt * bakPow,

+ data = ss.data.doe1)

> summary(doe.model1)

Call:

lm(formula = score ~ flour + salt + bakPow + flour * salt + flour *

bakPow + salt * bakPow + flour * salt * bakPow, data = ss.data.doe1)

Residuals:

Min 1Q Median 3Q Max

-0.5900 -0.2888 0.0000 0.2888 0.5900

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 5.5150 0.3434 16.061 2.27e-07 ***

flour+ 1.8350 0.4856 3.779 0.005398 **

salt+ -0.8350 0.4856 -1.719 0.123843

bakPow+ -2.9900 0.4856 -6.157 0.000272 ***

flour+:salt+ 0.1700 0.6868 0.248 0.810725

flour+:bakPow+ 0.8000 0.6868 1.165 0.277620

salt+:bakPow+ 1.1800 0.6868 1.718 0.124081

flour+:salt+:bakPow+ 0.5350 0.9712 0.551 0.596779

---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4856 on 8 degrees of freedom

Multiple R-squared: 0.9565, Adjusted R-squared: 0.9185

F-statistic: 25.15 on 7 and 8 DF, p-value: 7.666e-05

This is the general model:

yijkl = µ+ αi + βj + γk + (αβ)ij + (αγ)ik + (βγ)kl + (αβγ)ijk + εijkl, (1)

1

And here we have a plot of effects:

●

●

−1.0 −0.5 0.0 0.5 1.0

34

56

7

c(−1, 1)

coef

(doe

.mod

el1)

[1] +

c(−

1, 1

) * c

oef(d

oe.m

odel

1)[2

]

2


17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Project ExampleDivide and Conquer!

StrategiesPartial Sweave files can be compiled to getpartial LATEX files. R scripts can Sweave .Rnwfiles and “source” .R files. The final documentis obtained by compiling the “master”LATEX file.

> source("code/myoptions.R")

> source("code/myfunctions.R")

> source("code/mydata.R")

> Sweave("rnw/theorem01.Rnw")

> Sweave("rnw/lesson01.Rnw")

> Sweave("rnw/exercises01.Rnw")

> ...

> texi2pdf("master.tex")

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Some useful extensionsPackages

knitr, pgfSweave: enhanced options forSweave

RGIFT: Automatic generation ofquestionnaires for Moodle

exams: Automatic generation of printableexams

odfWeave: Open Document formatdocuments generation

More in the “Reproducible Research” TaskView at CRAN.http://cran.r-project.org/web/views/

ReproducibleResearch.htmlSEIO 2012 21/28

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http://cran.r-project.org/web/views/ReproducibleResearch.html

http://cran.r-project.org/web/views/ReproducibleResearch.html


17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

R GUIIntegrated Environments

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

R StudioIntegrated Environments

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

EMACS + ESSIntegrated Environments

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Eclipse + StatETIntegrated Environments

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Summary

Statistical training entail some challengesregarding contents and materials.

R is the perfect partner for statisticaltraining.

Reproducible research and literateprogramming enhance training materialsquality.

The use of R and LATEX through Sweave,comprise a complete framework forstatistical documentation generation.

Extensions and integrated environmentsmake easy exploiting the R capabilities.

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17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Acknowledgements

R Core Team and R enthusiasts in general.Springer

This work has been partially funded by the projects:AGORANET project (IPT-430000-2010-32)VRTUOSI www.vrtuosi.org: 502869-LLP-1-2009-ES-ERASMUS-EVC)HAUS: IPT-2011-1049-430000EDUCALAB: IPT-2011-1071-430000DEMOCRACY4ALL: IPT-2011-0869-430000CORPORATE COMMUNITY: IPT-2011-0871-430000

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www.vrtuosi.org


17/04/2012


A Redchuk


The Problem

Approaches

The R Choice

The R framework

Sweave

Application

Six Sigma

Examples

Environments

Discussion

Thanks for yourattention !

SEIO 2012 28/28

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using r for statistical training: an application to six sigma methodology for process improvement

Education

problemapproaches r

r code

r foraltex

statistical trainingcontenido

statistical trainingcopy

pdf statistical training

andrs redchuk

packages seio