useR 2014 jskim

The R User Conference 2014
useR 2014
@Ä-
UCLA in LA
6.30 7.3
@Ä- (UCLA in LA) The R User Conference 2014 6.30 7.3 1 / 33

What is useR?
Contents
1 What is useR?
2 1st day: Tutorial
Applied Predictive Modeling in R
Graphical Models and Bayesian Networks with R
3 2nd day
4 3rd day
5 4th day

What is useR?
Since 2004: : :
Main meeting of the R user and developer community.
Invited keynote lectures
broad spectrum of topics ranging from technical and R-related
computing issues to general statistical topics of current interest
User-contributed presentations

What is useR?

What is useR?
Figure. Afternoon tutorial: dplyr

1st day: Tutorial
Contents
1 What is useR?
2 1st day: Tutorial
3 2nd day
4 3rd day
5 4th day

1st day: Tutorial
List
http://user2014.stat.ucla.edu/#tutorials

1st day: Tutorial Applied Predictive Modeling in R
Max Kuhn, Ph.D : P

zer Global RD
http://appliedpredictivemodeling.com
caret package in R
Outline
Conventions in R
Data Splitting and Estimating Performance
Data Pre-Processing
Over{Fitting and Resampling
Training and Tuning Tree Models
Training and Tuning A Support Vector Machine
Comparing Models
Parallel Processing

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Trade-o : Logistic regressiont t@ ¸XÀÌ(Odds ratio), t|
àÑ` D”” Æä. !X UÄ@ Ä| t ¼ÈàÀ logit
@ hÈ ˆä.
XäD D”Ð 0| mŒ À(ex: Log ÀX, scale, centering)
R2, AIC, p-value: : : VS cross validation, bootstrapping, sampling,
ROC curve
2 Supervised machine learning
Regression : simple, glm, PCA, penalized(Ridge, Lasso, elastic-net) : : :
Classi

cation: K{Nearest Neighbors, trees
Common: Boosting, Support Vector Machine (SVM)
3 Parallel Processing

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1 Supervised learning
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Our data| Rt ù` ˆ”???
2 Unsupervised learning
Deep learning: ì5 à½Ý(DNN: Deep Neural Network)
2014D uì0 Q˜. ex)L1 xÝ
(|xX?
https://class.coursera.org/neuralnets-2012-001/lecture

1st day: Tutorial Graphical Models and Bayesian Networks with R
Probability propagation with Bayesian networks (BNs) and their
implementation in the gRain (gRaphical independence networks)
package.
A look under the hood of BNs to understand mechanisms of
probability propagation. Dependency graphs and conditional
independence restrictions.
Log-linear models, graphical models, decompsable models and their
implementation in the gRim (gRaphical independence models)
package.
Model selection with gRim
Converting a decompsable graphical model to a Bayesian network.

1st day: Tutorial Graphical Models and Bayesian Networks with R
The chest clinic narrative
p(V) = p(a)p(tja)p(s)p(l js)p(bjs)p(ejt; l)p(dje; b)p(xje)

2nd day
Contents
1 What is useR?
2 1st day: Tutorial
3 2nd day
4 3rd day
5 4th day

2nd day
Opening Keynote
John Chambers(Stats, Stanford) - Interfaces, Eciency and Big
Data
Rcpp: cpp function ! R
RLLVM, http://www.omegahat.org/Rllvm: RD compilet ôä.
h2o: java baseX machine learining for big data.

2nd day
1185 Terra Bella Ave, Mountain View, CA 94043 | 650-429-8337 | h2o.ai
.ai
.ai
H2O is the world’s fastest in-memory platform for
machine learning and predictive analytics on big
data. It is the only alternative to combine the power
of highly advanced algorithms, the freedom of open
source, and the capacity of truly scalable in-memory
processing for big data on one or many nodes. Com-bined,
these capabilities make it faster, easier, and
more cost effective to harness big data to maximum
benefit for the business.
With H2O, you can:
• Make better predictions. Harness sophisticated,
ready-to-use algorithms and the processing
power you need to analyze bigger data sets,
more models, and more variables.
• Get started with minimal effort and invest-ment.
H2O is an extensible open source platform
that offers the most pragmatic way to put big
data to work for your business. With H2O, you
can work with your existing languages and tools.
Further, you can extend the platform seamlessly
into your Hadoop environments.
Churn Prediction
• Banking. What are the profiles and usage
patterns of customers who are most likely to
defect?
• Online retail. What are the leading indicators
and patterns of behavior to predict customer
churn? Predict the segment of customers most
likely to churn, and when,
in order to intercept it and
change their behavior.
Fraud Prediction
• Payment processor.
Predict fraudulent activity
using anomaly detection
methods.
• Insurance. Stop fraud
before claims are paid
using real time scoring.
Identify repeat offenders
and score incoming claims
based on fraudulent his-tory
patterns.
Scoring Engine
• Score customers based on purchase history and
analyze the lifetime value of key accounts to
discover upsell and cross-sell opportunities.
Pricing Engine
• Travel. Analyze different cost and promotional
packages to create the most competitive combi-nation
of services.
• Healthcare. Discover new insights and create
competitive services and healthcare programs
by analyzing patient attributes, including envi-ronment,
lifestyle and medical history.
Forecast
• Real estate. Predict property value and forecast
sales by neighborhoods and regional variables.
Analyze larger nationwide datasets vs. smaller
sample sets to realize greater accuracy and find
previously unnoticed patterns.
Key Benefits
BETTER PREDICTIONS
• Ready-to-use, powerful algorithms for
regression, classification, clustering, and
deep learning—along with advanced capa-bilities
for churn prediction, recommenda-tions,
fraud prediction, and more.
SPEED
• In-memory processing provides real-time
responsiveness and enables you to run
more models.
• Fine-grain parallel distribution on big
data—enabling accurate computations
across one or many nodes by moving the
code to the data.
EASE OF USE
• Easy set up and use, either through an
intuitive Web interface or your existing
tools, including R, Java, Scala, and Python.
• Model export in plain Java code for real-time
scoring in any environment.
EXTENSIBILITY
• Seamless Hadoop integration with distrib-uted
data ingestion from HDFS and S3.
Algorithms
EXPLORATORY DATA ANALYTICS (EDA)
• Summary*
• K-Means*
• PCA*
• Data Munging / Transformation*
* Supported in R
ADVANCED ALGORITHMS
• Generalized Linear Model (GLM)—
Poisson, Gamma Tweedie, binomial (logit),
Gaussian*
• Random Forest*
• Gradient Boosted Regression*
• Gradient Boosted Classification*
* Low Latency Java Scoring
SCORING AND PREDICTION ENGINES
• GLM
• Random Forest
• Gradient Boosted Regression
• Gradient Boosted Classification
• K-Means
DEEP LEARNING
• Neural Networks
H2O
The Open Source In-Memory Prediction Engine
What can you do with better
predictions? Expect more
from your data.
Customers
spot a job that should
be stopped and more
quickly iterate to find
the optimal approach.
Native R and
Seamless
Hadoop
Integration
H2O can run as a
standalone platform
or within an existing
Hadoop installation,
bringing in-memory
performance to
Hadoop. H2O works
with data in HDFS and
supports familiar pro-gramming
tools, such
as Hive and Pig. In
addition, the solution
can be efficiently run
in Amazon Web Ser-vices
environments.
Fine-Grain Distributed
Processing on Big Data at
Speeds Up to 100x Faster
Faster H2O lets you model interactively using
in-memory processing, and delivers paral-lel
distributed scalability required to support
your big data production environments. The
solution combines the responsiveness of in-memory
processing with the ability to run fast
serialization between nodes and clusters—so
you can support the size requirements of
your large data sets. Further, H2O does this
distributed processing with fine-grain parallel-ism,
which enables optimal efficiency, without
introducing degradation in computational
accuracy.
Join the H2O Movement
H2O brings better algorithms to big data.
H2O is a fast open source in-memory predic-tion
engine and machine learning platform.
With H2O enterprises can use all of their data
(instead of sampling) in real-time for better
predictions. Users can model data quickly and
make better data-driven decisions faster by
running advanced algorithms such as Deep
Learning, Classification, Regression, Decision
Trees, Forests, Gradient Boosting, GLM, PCA
and more. Data Scientists can take both simple
sophisticated models to production from the
same interactive platform used for modeling
within R and JSON.
Our earliest customers have built powerful
domain specific predictive engines for Recom-mendations,
Pricing, Outlier Detection and
Fraud Prediction for Insurance and Ad Plat-forms.
H2O is nurturing a grassroots movement
of math, systems and data scientists to herald
the new wave of Discovery with Big Data
Science. H2O is on CRN’s 10 Coolest Big Data
Products of 2013. www.h2o.ai
For latest features and updates, go to H2O
Open Source Github Repository
http://0xdata.github.io/h2o/
H2O Billion Row Machine Learning Benchmark
GLM Logistic Regression
Hadoop/Mahout
H2O 16 EC2
nodes
H2O 16 EC2
nodes
H2O 48 EC2
nodes
H2O 48 EC2
nodes
34.9 sec, 3 itera
ons
numerical and categorical
16.5 sec, 2 itera
ons
numerical
14.2 sec, 3 itera
ons
numerical and categorical
5.6 sec, 2 itera
ons
numerical
Compute Hardware: AWS EC2 c3.2xlarge - 8 cores and 15 GB per node, 1 GbE interconnect
Airline Dataset 1987-2013, 42 GB CSV, 1 billion rows, 12 input columns, 1 outcome column
9 numerical features, 3 categorical features with cardinali
es 30, 376 and 380
Work with R, Familiar Tools and
Intuitive Interfaces
Through its intuitive Web interface and inte-gration
with common tools, H2O makes it fast
and easy to get started with big data analyt-ics.
The solution works seamlessly with R and
R Studio. For example, using the R interface,
you can forward workflows to H2O for big data
processing, and work in a familiar interface
while running algorithms on data sets that are
hundreds of times larger than what would be
possible on a user machine. H2O also features
native support for Java, Scala, and Python.
The solution’s interface is driven by JSON APIs,
which makes it easy to plug into your organiza-tion’s
existing tools and processes to train your
data and continuously improve your models
and predictive accuracy.
In-Memory Processing
Responsiveness
With H2O, your organization can harness the
responsiveness of highly optimized in-memory
processing, so you can operationalize many
more models and gain real-time intelligence in
business transactions and interactions. With
model export as plain Java code, you gain light-ning
fast real-time scoring in any environment.
In addition, the solution enables data scientists
to view partial query results while longer pro-cesses
are running, so they can immediately
Copyright © H20 All rights reserved. All trademarks referenced herein belong to their respective companies.
.ai 1185 Terra Bella Ave, Mountain View, CA 94043 | 650-429-8337 | h2o.ai

2nd day
Session 1: Bayesian
tÀH µÄ Ä°Ð approximation
Spatial analysis ©.
tÀH Œ¸è´..

2nd day
Session 2
R For Improving Consumer Engagement and Health Outcomes
ActiveHealth Management, Inc
internal member data + externally-purchased lifestyle behavioral
data
K-Means Clustering and CART classi

cation trees
Response rate øùÐ 0| ätä. Þ¤ ©.
Shiny: R made interactive
http://shiny.rstudio.com/gallery/kmeans-example.html
Fostering the next generation of open science with R
http://ropensci.org/

2nd day
Invited Talk
Martin Maechler(Math, Zurich)- Good Practices in R Programming
¡ä T)¤¬ä.. (ex: VS =, ü, D´ð0 ñ..)

2nd day
Session 3
dplyr,data.table: High performance in data step
PivotalR: A Package for Machine Learning on Big Data
https://github.com/gopivotal/PivotalR

2nd day
Poster Session 1: 28 posters
Reproducible Research in Public Health
Jinseob Kim1, Joohon Sung1,?
1. Complex Disease and Genetic Epidemiology Branch, Graduate School of Public Health, Seoul National University
Objectives
In this study, we aimed to construct pipelines
of reproducible statistical analysis in health
research. The development of pipelines in this
study consists of
• Automatic suggestions of a summary table
describing the general characteristics of the
study
• Univariate analysis of both explanatory and
outcome variables of a study
• Graphical presentations of summary and
univariate analyses
• Automatic analysis and tabulations of main
results
based on frequently used analytical methods
in the health research area (e.g., multiple re-gression,
logistic regression, survival analysis,
multilevel analysis, genome-wide association
study(GWAS)).
Background
For most health reasearchers who learned and ap-plied
the statistical methods properly had spent
long time for learning statistics. Addtionally, sta-tistical
methods are ever evolving and updating
their knowledge and analytic skills will require the
most precious resources-the time.
Methods
With xtable packages in R and tex4ht in LATEX,
researcher will get PDF or odt(open document
text) version of descriptive statistics when they se-lect
data, variables(factor or numeric), strata vari-able(
sex, region, etc. . . )[1, 2]. In next, among var-ious
analysis, we present GWAS’ example using
FASTA method in GenABEL R package or fas-tassoc
method in MERLIN. If they select lists of
phenotypes, covariates and kinship matrix, PDF
or odt files including each phenotype’s name, nar-row
sense heritability, top SNPs in GWAS re-sults,
qqplot and manhattan plot were created
automatically[3, 4]. Example dataset is TG, LDL
phenotypes and chromosome 21 in Healthy Twin
Study, Korea[5].
Key Methods
Researchers can obtain tables and figures if they select data set and dependent variables of interest,
and define the nature of each variables (e.g. continuous, binomial, count), explanatory variables, and
group variable (e.g., sex, region, unit of random effects or family structure).
Example: GWAS-TG
Table : PDF file- Descriptive statistics of TG
Variable: Mean (SD) or N (%) Male Female P-value P-value:NP
Age 44.6 (13.6) 43.95 (12.73) 0.208 0.294
Smoke 0.001 0.001
No 278 (26.18) 1506 (90.29)
Past 290 (27.31) 53 (3.18)
Current 494 (46.52) 109 (6.53)
TG 140.34 (92.34) 98.8 (62.82) 0.001 0.001
NP: non-parametric
Figure : PDF file- GWAS results of TG
TG (h2 = 0.48)
SNP Chromosome Position A1 A2 N MAF B FASTA SE FASTA P FASTA B fassoc SE fassoc P fassoc
rs12626621 21 23496579 C T 1838 0.167 13.61 3.46 8.41E-05 -12.82 3.68 5.00E-04
rs1702393 21 30866791 C T 1832 0.353 10.67 2.72 8.76E-05 -11.51 2.96 9.99E-05
rs12627596 21 30867101 T A 1840 0.352 10.06 2.72 2.10E-04 -11.06 2.95 1.81E-04
rs128592 21 30878280 C T 1841 0.352 10.04 2.72 2.18E-04 -11.06 2.95 1.80E-04
rs198935 21 30867998 C T 1799 0.358 10.13 2.74 2.19E-04 -11.42 2.98 1.26E-04
rs11702393 21 39304254 G A 1832 0.297 10.71 2.91 2.35E-04 -12.55 3.13 6.23E-05
rs382004 21 19226026 T C 1840 0.035 25.36 7.00 2.92E-04 -22.86 7.63 2.75E-03
rs1888516 21 41219289 G A 1837 0.014 38.42 10.61 2.93E-04 -34.38 11.09 1.92E-03
rs9306107 21 44798662 G A 1827 0.008 51.02 14.36 3.82E-04 -45.77 15.16 2.53E-03
rs426803 21 19225690 T C 1839 0.037 24.02 6.78 3.98E-04 -22.16 7.38 2.67E-03
rs198936 21 30869966 C T 1837 0.351 9.41 2.73 5.54E-04 -10.69 2.96 3.10E-04
rs1702405 21 30914735 A G 1841 0.493 -9.05 2.63 5.86E-04 10.97 2.86 1.23E-04
rs2257149 21 36213059 G A 1806 0.360 -9.39 2.75 6.24E-04 10.86 3.00 2.96E-04
rs174897 21 30919037 A G 1835 0.492 -8.74 2.63 8.89E-04 10.62 2.85 1.92E-04
rs198871 21 30932579 G A 1819 0.492 -8.76 2.64 8.91E-04 10.42 2.85 2.59E-04
Table 1: GWAS: TG
(a) QQplot-FASTA: TG (b) QQplot-Fassoc: TG
Figure 1: QQplot: TG
(a) Manhattan plot-FASTA: TG
(b) Manhattan plot-Fassoc: TG
Figure 2: Manhattan plot: TG
1
Example: GWAS:LDL
Table : PDF file- Descriptive statistics of LDL
Variable: Mean (SD) or N (%) Male Female P-value P-value:NP
Age 44.6 (13.6) 43.95 (12.73) 0.208 0.294
FBS 97.12 (19.67) 91.36 (16.54) 0.001 0.001
tCholesterol 191.08 (34.99) 188.63 (35.83) 0.077 0.031
HDL 46.45 (11.24) 52.26 (12.72) 0.001 0.001
LDL 112.84 (30.7) 108.76 (30.3) 0.001 0.001
NP: non-parametric
Figure : PDF file- GWAS results of LDL
LDL (h2 = 0.47)
SNP Chromosome Position A1 A2 N MAF B FASTA SE FASTA P FASTA B fassoc SE fassoc P fassoc
rs4818418 21 18802344 C T 1823 0.276 4.87 1.13 1.53E-05 -4.54 1.22 1.93E-04
rs1735790 21 18788001 A G 1792 0.274 4.90 1.15 2.10E-05 -5.20 1.24 2.82E-05
rs2824856 21 18793090 G A 1811 0.278 4.82 1.14 2.21E-05 -4.71 1.22 1.15E-04
rs2824898 21 18813513 T C 1841 0.280 4.70 1.12 2.50E-05 -4.46 1.21 2.34E-04
rs2252190 21 18792169 A G 1839 0.277 4.74 1.13 2.54E-05 -4.81 1.22 7.94E-05
rs2824899 21 18813559 T C 1840 0.280 4.69 1.12 2.68E-05 -4.46 1.21 2.34E-04
rs914244 21 46340779 C T 1839 0.408 -4.38 1.05 2.88E-05 4.78 1.15 3.07E-05
rs2824857 21 18793134 G T 1837 0.279 4.67 1.12 3.26E-05 -4.71 1.22 1.09E-04
rs2026211 21 18806617 C T 1842 0.276 4.64 1.12 3.51E-05 -4.38 1.21 3.10E-04
rs2824880 21 18807606 A G 1842 0.276 4.64 1.12 3.51E-05 -4.38 1.21 3.10E-04
rs2838534 21 44498077 T C 1811 0.352 3.94 1.06 2.04E-04 -4.69 1.16 5.22E-05
rs456164 21 27761178 T C 1842 0.395 -3.82 1.05 2.85E-04 4.46 1.14 9.88E-05
Table 1: GWAS: LDL
(a) QQplot-FASTA: LDL (b) QQplot-Fassoc: LDL
Figure 1: QQplot: LDL
(a) Manhattan plot-FASTA: LDL
(b) Manhattan plot-Fassoc: LDL
Figure 2: Manhattan plot: LDL
1
Conclusion
Using xtable package in R, LATEX and tex4ht pack-age
in LATEX with various statistical packages in
R, we developed a automatic words describing the
result tables and figures with PDF or opendocu-ment
format directly[1, 2]. Though we presented
only descriptive statistics and GWAS examples,
pipelines of other analysis(e.g., survival analysis,
multilevel analysis, etc. . . ) were also made us-ing
similar packages above and some additional
packages. This automated statistical pipeline tools
will help individual researcher in health-related or
broader arena to help to reduce their analytical bur-dens,
as well as to conduct appropriate statistical
analysis much faster and reliable manner.
References
[1] David B. Dahl. xtable: Export tables to LaTeX or HTML,
2014. R package version 1.7-3.
[2] Emma Cliffe. Methods to produce flexible and accessible
learning resources in mathematics: overview document.
2012.
[3] GenABEL project developers. GenABEL: genome-wide
SNP association analysis, 2013. R package version 1.8-0.
[4]Wei-Min Chen and Gonçalo R Abecasis. Family-based
association tests for genomewide association scans. The
American Journal of Human Genetics, 81(5):913–926,
2007.
[5] Joohon Sung, Sung-Il Cho, Yun-Mi Song, Kayoung Lee,
Eun-Young Choi, Mina Ha, Jihae Kim, Ho Kim, Yeonju
Kim, Eun-Kyung Shin, et al. Do we need more twin stud-ies?
the healthy twin study, korea. International journal
of epidemiology, 35(2):488–490, 2006.
Contact Information
•Web: http://snugepi.snu.ac.kr
• Email: kimjinseob@snu.ac.kr
• Phone: +82-2-880-2743

2nd day
Visualizationt 8?
mx 2…: ð8YP XüY(ˆ8), 1àYP(õ?)

3rd day
Contents
1 What is useR?
2 1st day: Tutorial
3 2nd day
4 3rd day
5 4th day

3rd day
Invited Talk
Dirk Eddelbuettel - R, C++ and Rcpp
Rcpp : RX ¸h + cppX speed
RÐ èXŒ(pointerD”ÆL) cpp h Ìàä.
cppTÜÐ R h ½…¥.
R () cpp ¬ Tää: : :
Docker : È´ Á8à
0tX Á8à : ø¨ + |t

ì¬ only
Only for linux

3rd day
Sponsors Talk
Revolution Analytics
ÁÅ© R.
SASôä «ä.
Oracle: ROracle
R+ Oracle database
Google
SAS Æä.
ÔÀÈ´, °, µÄY õ lx.

3rd day
Session 4
R in the Midst of Exploding Stars: Distributed, Time-Domain Transient
Classi

cation
NASAÐÄ RD ôä.
à1 ñ Ä „XÐ machine learning(clustering..) t©.
Imputation of Missing Values with the R Package VIM
ä‘ Imputation method ÀÐ.
Imputation°ü| ø˜= ÀÐ.
PSAboot: An R Package for Bootstrapping Propensity Score Analysis
http://github.com/jbryer/psa
Risk of bias due to unobserved covariates
ä‘ )•(5À) bootstrapD t©Xì PSA ‰h.
Permutation Tests in Multidimensional Scaling
ä(Ð ô ! Áx p¬ .
smacof package in R
Permutation test: dissimilarity randomXŒ „ì(null) VS pt0X „ì

3rd day
Invited Talk
David Diez(Openintro) - Textbooks struggle where software
succeeds
http://www.openintro.org
Open source textbook: paperback $10
Labs, videos, for teachers(slides..)
statsTeachR.org, OpenStaxCollege.org,
https://www.coursera.org

3rd day
Session 5: Biology/Ecology
Simulating In
uenza Transmission with Real Network Data
Network data: High school, chip??, statnet package: graph
Simulation: various incubation period, infection period, transmission probability: : :
Enhancing Medical Reporting by Combining Electronic Health Records with
REDCap: Applications of the REDCap API
http://www.project-redcap.org
pt0 ¨D L chart review X˜X˜ ` D” Æt X¬ ì8| ` X|
°tä.
Simulations for regulatory decision making: How many simulations do we need to
run?
Simulationt Ü0Ð ”Xä(ex: FDA).
Simulation@ complex model(not analytically tractable)Ð - D”Xä.
1,000ˆ, 10,000ˆ Ä” ´¼Æ” ½° ˆä. 400Ìˆ tÁLÀÄ.. R
parallel computing: : :
Monitoring Patients with Ongoing Reduced Kidney Function

useR 2014 jskim

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