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Computational Statistics
Setia Pramana
2015
Computational Statistics 1
Course Outline
• Introduction
– Different Statistical Software
• Data Preparation, Management, Manipulation,
Summarization with:
– SPSS
– R (R Commander)
– Ms. Excel
• Data Tabulation and Visualization
Computational Statistics 2
Course Outline
• Generate Different Statistical Distribution (with
Rcmdr)
• Simple Linear Regression and Correlation
• Basic R Programming
• Developing Simple Graphical User Interface in R
• Resampling Methods
• Statistical Inference (Point and interval
estimation)
Computational Statistics 3
Course Outline
• Hypothesis testing: one, two sample t-test (test
for mean difference, proportion and variance)
• Analysis of Variance (Anova): one and two way
Anova.
• Introduction to Design of Experiment
• Final Project
Computational Statistics 4
Course Workload
• 20% Theory, 80% practice
• Group Project (5 students)
• Presentation every week
• R code would be provided
• Slides can be seen at :
http://www.slideshare.net/hafidztio/
Computational Statistics 5
Reference Books
Computational Statistics 6
Reference Books
• John Maindonald dan W. John Braun. Data Analysis and
Graphics Using R – an Example-Based Approach. 3rd
Edition. Cambridge University Press: Cambridge.2010.
• John Fox. Journal of Statistical Software, The R
Commander : A Basic-Statistics Graphical User Interface
to R.Volume 14, Issue 9, September 2005.
• Chris Beeley. Web Application Development with R
Using Shiny. Packt Publishing: Birmingham.2013.
• SPSS Statistics Base User’s Guide 17.0. Polar
Engineering and Consulting : Chicago, 2007.
Computational Statistics 7
Reference Books
• Jurusan Komputasi Statistik. Modul Mata Kuliah
Komputasi Statistik. 2014
• Kerns, G. Jays. Introduction to Probability and Statistics
Using R. E book. GNU Free Documentation License.
2010.
• Geof H. Givens dan Jennifer A. Hoeting. Computational
Statistics, 2nd edition. John Wiley and Sons : New
Jersey. 2013
• Jochen Voss. Statistical Computing. E book. 2011.
• Brent B. Welch, Ken Jones dan Jeffrey Hobbs. Practical
Programming in Tcl and Tk. 4Th edition. Prentice Hall
PTR: New Jersey.2003.
Computational Statistics 8
Other Materials
• https://sites.google.com/site/biostatinfocor
e/home/rworkshop
• https://sites.google.com/site/biostatinfocor
e/biostatistics-workshop
Computational Statistics 9
Introduction
Computational Statistics 10
Statistics?
Computational Statistics 11
Computational Statistics 12
What is Statistics?
• Statistics: is the science which deals with
collection, classification and tabulation of
numerical facts as the basis for explanation,
description and comparison of phenomenon”.
Computational Statistics 13
Observations on the
Bills of
Mortality (1662)
Recorded Plague
related death for
100 years
Computational Statistics 14
What is Statistics?
• Exploring data: Using graphical and numerical
techniques to study patterns and departures from
patterns (in order to interpreting data)
• Sampling and experimentation: Clarifying the
question, deciding on methods of collection and analysis
to produce valid information.
• Anticipating patterns: Exploring random phenomena
using probability and simulation. Probability is our tool for
anticipating distributions...
• Statistical Inference: Estimating population parameters
and testing hypothesis
Computational Statistics 15
“Statistical thinking will one day be as
necessary for efficient citizenship as the
ability to read and write” HG Well
Computational Statistics 16
Areas of Statistics
Two areas of statistics:
Descriptive Statistics: collection, presentation,
and description of sample data.
Inferential Statistics: making decisions and
drawing conclusions about populations.
Computational Statistics 17
Statistics Descriptive
What is your conclusion?
The fatality rate is:
– 40% in the group of drivers who did not wear seat belts
– 20%in drivers who did wear seat belts
• Seat belts appear to save lives
18Computational Statistics
Inferential Statistics
• Are results applicable to the population of all drivers?
(generalization)
• Does wearing seat belts save lives? (assess strength of
evidence)
• Is the fatality rate of those not wearing seat belts higher than
the fatality rate of those wearing seat belts? (comparison)
• How many lives can be saved by wearing seat belts?
(prediction)
• Do other variables influence the conclusion? For example:
the age of driver, alcohol use, type of car, speed at impact
(ask more questions)
19Computational Statistics
Statistics and the Technology
• The electronic technology has had a tremendous effect
on the field of statistics.
• Many statistical techniques are repetitive in nature:
computers and calculators are good at this.
• Lots of statistical software packages: R, MINITAB,
SYSTAT, STATA, SAS, Statgraphics, SPSS, MS Excel,
and calculators.
Computational Statistics 20
Available Statistical Packages
Computational Statistics 21
Available Statistical Packages
Proprietary
 Excel
 SPSS
 MINITAB
 SAS
 Stata
 Statistica
 Many more ……
Free Software
 LibreOffice Calc
 R
 CS Pro
 WinBugs
 EpiInfo
 Many more……..
Computational Statistics 22
Computational Statistics 23
Computational Statistics 24
Computational Statistics 25
Computational Statistics 26
Microsoft Excel
Computational Statistics 27
Which one do you use?
Why?
Computational Statistics 28
Statistical Software Used
Computational Statistics 29
Statistical Software Used
Computational Statistics 30
R is HOT !
Computational Statistics 31
R is HOT !
• R is HOT !
http://r4stats.com/articles/popularity/Computational Statistics 32
R is HOT !
http://r4stats.com/articles/popularity/Computational Statistics 33
R is HOT !
http://r4stats.com/articles/popularity/Computational Statistics 34
What is R?
• A language and environment for statistical computing and
graphics.
• An integrated suite of software facilities for data
manipulation, calculation and graphical display.
• First appeared in 1996 by Prof. Ross Ihaka and Robert
Gentleman of the University of Auckland, NZ.
• GNU software -> Free. Similar like S language.
• Open source, maintained and developed by a community
of developers.
• Works in Windows, Unix, MacOsComputational Statistics 35
R includes
• Effective data handling and storage facility,
• A suite of operators for calculations on arrays, in particular
matrices
• A large, coherent, integrated collection of intermediate
tools for data analysis,
• Graphical facilities for data analysis and display either on-
screen or on hardcopy
• Well-developed, simple and effective programming
language which includes conditionals, loops, user-defined
recursive functions and input and output facilities.
http://www.r-project.org/
Computational Statistics 36
Why R?
• It is not only statistical software but
also a language
• 5000 add-on packages  lots of pre-
prepared packages (http://cran.r-
project.org/web/packages/)
• With many applications http://cran.r-
project.org/web/views/,
http://www.revolutionanalytics.com/r-
language-features-applications-and-
extensions#thirdparty .
• Access to powerful, cutting-edge
analytics Computational Statistics 37
Why R?
• Flexible (complex or standard statistical practices, bayesian
modelling, GIS map building, building interactive web
applications, building interactive tests, etc. )
• We can make our own package and publish it
• Great Graphics and data visualization
• Can be used for High Performance Computer Clusters
• Well Supported by R Community (http://www.inside-r.org/r-
resources-web)
• And many more…..
Computational Statistics 38
Why R?
• Can be integrated with other languages (C/C++,
Java).
• R can interact with many data sources and other
statistical packages (SAS, Stata, SPSS, and Minitab).
• For the high performance computing task 
multiple cores, either on a single machine or across a
network.
39Computational Statistics
But…..
• R has no warranty
• Command Line Interface : difficult for some users.
• Users must learn a new way of thinking about data
and data analysis sequence
• That’s all ….. I guess
Computational Statistics 40
Companies using R in 2013
• The New York Times routinely uses R for interactive and print data
visualization.
• Google has more than 500 R users.
• The FDA supports the use of R for clinical trials of new drugs.
• The National Weather Service uses R to predict the extent of flooding
events.
• Zillow uses R to model housing prices.
• The Consumer Financial Protection Bureau uses R and other open
source tools.
• Twitter uses R for data science applications on the Twitter database.
• FourSquare uses R to develop its recommendation engine.
• Facebook uses R to model all sorts of user behaviour.
Source: RevolutionanalyticsComputational Statistics 41
R Library/packages
R Base Packages
lme4
IsoGene
foreign
survival
zoo
ggplot2
zoo
reshape2
nlme
Computational Statistics 42
My R Packages
• IsoGene
• IsoGeneGUI
• nea
• neaGUI
• biclustGUI
• OCRME
• More detail: http://setiopramono.wordpress.com/r-
programming/
Computational Statistics 43
R For Cutting Edge
Technologies
44Computational Statistics
R Graphics and Visualization
• R provides wide range graphics and visualizations
• Basic Plots: bar plots, basic 3D plots, heatmap.,etc
• Geographic Maps
• Projection Maps
• Social Network Graphs
• Animated graphics and movies (animation)
• Motion Charts (GoogleViz)
• Interactive Graphics (rggobi)
• Image format: BMP, JPEG, PDF, PNG etc…
• and….many more………
Computational Statistics 45
R Graphics
Computational Statistics 46
R Graphics
RCircos
https://gjabel.wordpress.com/ 47Computational Statistics
R Graphics
A map of worldwide email traffic
Computational Statistics 48
R Graphics
Facebook connections between city centers around the world
Computational Statistics 49
R Graphical User Interfaces
• R uses Command line interface and it is preferred for
advanced users  allows direct control, more accurate,
flexible and the analysis is reproducible.
• Requires good knowledge of the language  difficult for
beginners or less frequent users.
• R provides tools for building GUIs  RGUI
Computational Statistics 50
R GUI Projects
• Integrated development environment (IDE)/Script
Editors aimed to provide feature-rich environments to
edit R scripts and code: Rstudio (www.rstudio.com),
and architect (www.Openanalytics.eu)
• Web based application: the Rweb (Banfield, 1999),
R.Net (www.u.arizona.edu/~ryckman/Net.php),
or gWidgetsWWW (Verzani, 2012).
51Computational Statistics
R GUI Projects
• Python: OpenMeta-Analyst (Wallace et al, 2012)
• Java: JGR (Java GUI for R), Deducer (Fellows, 2012),
and Glotaran (Snellenburg, 2012).
• Php: R-php (http://dssm.unipa.it/R-php/)
• Other extensions connect R to graphical toolboxes for
developing menus and dialog boxes: Tcltk, Gtk.
52Computational Statistics
R Studio
• Download from
Rstudio.com
• Powerfull IDE
(Integrated
Development
Environment) for R.
Computational Statistics 53
RGUI Developed using tcltk
Computational Statistics 54
RGUI: RCommander
• Rcommander.com
• Helpful for R beginner
• Install inside R
Computational Statistics 55
RGUI using C#: Wires
• Developed by STIS
students
• For Spatial Data
Analysis
• Still developing…
Computational Statistics 56
RGUI using C#: Wires
Computational Statistics 57
RGUI: Web Based App
Computational Statistics 58
WebBUGS
• Conducting Bayesian
Statistical Analysis
Online
• Combines
OpenBUGS and R
www.webbugs.psychstat.org
Computational Statistics 59
RGUI: Shiny
• A new package from Rstudio to build interactive web
applications with R.
• Really Easy!
• Build useful web applications with only a few lines of
code—no JavaScript required.
• Self learning: http://shiny.rstudio.com/
• http://www.showmeshiny.com/
Computational Statistics 60
RGUI using Shiny: FAST
Figure 5. FAST main page
61Computational Statistics
Dynamic Report Generation
• Sweave
• knitr
• markdown
Computational Statistics 62
Want to Learn R? Need Help?
Lots of Self learning Resources
http://www.rdatamining.com/resources/onlinedocs
Blogs:
Software # Blogs Blogs Source
R 550 R-Bloggers.com
Python 60 SciPy.org
SAS 40 PROC-X.com, sasCommunity.org Planet
Stata 11 Stata-Bloggers.com
User Group: Stockholm R User group, etc…
Indonesia/Jakarta?
https://sites.google.com/site/biostatinfocore/introduction-to-r
Computational Statistics 63
Need Help?
Computational Statistics 64
Number of R- or SAS-related posts to Stack Overflow by week.

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Introduction to Computational Statistics

  • 2. Course Outline • Introduction – Different Statistical Software • Data Preparation, Management, Manipulation, Summarization with: – SPSS – R (R Commander) – Ms. Excel • Data Tabulation and Visualization Computational Statistics 2
  • 3. Course Outline • Generate Different Statistical Distribution (with Rcmdr) • Simple Linear Regression and Correlation • Basic R Programming • Developing Simple Graphical User Interface in R • Resampling Methods • Statistical Inference (Point and interval estimation) Computational Statistics 3
  • 4. Course Outline • Hypothesis testing: one, two sample t-test (test for mean difference, proportion and variance) • Analysis of Variance (Anova): one and two way Anova. • Introduction to Design of Experiment • Final Project Computational Statistics 4
  • 5. Course Workload • 20% Theory, 80% practice • Group Project (5 students) • Presentation every week • R code would be provided • Slides can be seen at : http://www.slideshare.net/hafidztio/ Computational Statistics 5
  • 7. Reference Books • John Maindonald dan W. John Braun. Data Analysis and Graphics Using R – an Example-Based Approach. 3rd Edition. Cambridge University Press: Cambridge.2010. • John Fox. Journal of Statistical Software, The R Commander : A Basic-Statistics Graphical User Interface to R.Volume 14, Issue 9, September 2005. • Chris Beeley. Web Application Development with R Using Shiny. Packt Publishing: Birmingham.2013. • SPSS Statistics Base User’s Guide 17.0. Polar Engineering and Consulting : Chicago, 2007. Computational Statistics 7
  • 8. Reference Books • Jurusan Komputasi Statistik. Modul Mata Kuliah Komputasi Statistik. 2014 • Kerns, G. Jays. Introduction to Probability and Statistics Using R. E book. GNU Free Documentation License. 2010. • Geof H. Givens dan Jennifer A. Hoeting. Computational Statistics, 2nd edition. John Wiley and Sons : New Jersey. 2013 • Jochen Voss. Statistical Computing. E book. 2011. • Brent B. Welch, Ken Jones dan Jeffrey Hobbs. Practical Programming in Tcl and Tk. 4Th edition. Prentice Hall PTR: New Jersey.2003. Computational Statistics 8
  • 9. Other Materials • https://sites.google.com/site/biostatinfocor e/home/rworkshop • https://sites.google.com/site/biostatinfocor e/biostatistics-workshop Computational Statistics 9
  • 13. What is Statistics? • Statistics: is the science which deals with collection, classification and tabulation of numerical facts as the basis for explanation, description and comparison of phenomenon”. Computational Statistics 13
  • 14. Observations on the Bills of Mortality (1662) Recorded Plague related death for 100 years Computational Statistics 14
  • 15. What is Statistics? • Exploring data: Using graphical and numerical techniques to study patterns and departures from patterns (in order to interpreting data) • Sampling and experimentation: Clarifying the question, deciding on methods of collection and analysis to produce valid information. • Anticipating patterns: Exploring random phenomena using probability and simulation. Probability is our tool for anticipating distributions... • Statistical Inference: Estimating population parameters and testing hypothesis Computational Statistics 15
  • 16. “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write” HG Well Computational Statistics 16
  • 17. Areas of Statistics Two areas of statistics: Descriptive Statistics: collection, presentation, and description of sample data. Inferential Statistics: making decisions and drawing conclusions about populations. Computational Statistics 17
  • 18. Statistics Descriptive What is your conclusion? The fatality rate is: – 40% in the group of drivers who did not wear seat belts – 20%in drivers who did wear seat belts • Seat belts appear to save lives 18Computational Statistics
  • 19. Inferential Statistics • Are results applicable to the population of all drivers? (generalization) • Does wearing seat belts save lives? (assess strength of evidence) • Is the fatality rate of those not wearing seat belts higher than the fatality rate of those wearing seat belts? (comparison) • How many lives can be saved by wearing seat belts? (prediction) • Do other variables influence the conclusion? For example: the age of driver, alcohol use, type of car, speed at impact (ask more questions) 19Computational Statistics
  • 20. Statistics and the Technology • The electronic technology has had a tremendous effect on the field of statistics. • Many statistical techniques are repetitive in nature: computers and calculators are good at this. • Lots of statistical software packages: R, MINITAB, SYSTAT, STATA, SAS, Statgraphics, SPSS, MS Excel, and calculators. Computational Statistics 20
  • 22. Available Statistical Packages Proprietary  Excel  SPSS  MINITAB  SAS  Stata  Statistica  Many more …… Free Software  LibreOffice Calc  R  CS Pro  WinBugs  EpiInfo  Many more…….. Computational Statistics 22
  • 28. Which one do you use? Why? Computational Statistics 28
  • 31. R is HOT ! Computational Statistics 31
  • 32. R is HOT ! • R is HOT ! http://r4stats.com/articles/popularity/Computational Statistics 32
  • 33. R is HOT ! http://r4stats.com/articles/popularity/Computational Statistics 33
  • 34. R is HOT ! http://r4stats.com/articles/popularity/Computational Statistics 34
  • 35. What is R? • A language and environment for statistical computing and graphics. • An integrated suite of software facilities for data manipulation, calculation and graphical display. • First appeared in 1996 by Prof. Ross Ihaka and Robert Gentleman of the University of Auckland, NZ. • GNU software -> Free. Similar like S language. • Open source, maintained and developed by a community of developers. • Works in Windows, Unix, MacOsComputational Statistics 35
  • 36. R includes • Effective data handling and storage facility, • A suite of operators for calculations on arrays, in particular matrices • A large, coherent, integrated collection of intermediate tools for data analysis, • Graphical facilities for data analysis and display either on- screen or on hardcopy • Well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities. http://www.r-project.org/ Computational Statistics 36
  • 37. Why R? • It is not only statistical software but also a language • 5000 add-on packages  lots of pre- prepared packages (http://cran.r- project.org/web/packages/) • With many applications http://cran.r- project.org/web/views/, http://www.revolutionanalytics.com/r- language-features-applications-and- extensions#thirdparty . • Access to powerful, cutting-edge analytics Computational Statistics 37
  • 38. Why R? • Flexible (complex or standard statistical practices, bayesian modelling, GIS map building, building interactive web applications, building interactive tests, etc. ) • We can make our own package and publish it • Great Graphics and data visualization • Can be used for High Performance Computer Clusters • Well Supported by R Community (http://www.inside-r.org/r- resources-web) • And many more….. Computational Statistics 38
  • 39. Why R? • Can be integrated with other languages (C/C++, Java). • R can interact with many data sources and other statistical packages (SAS, Stata, SPSS, and Minitab). • For the high performance computing task  multiple cores, either on a single machine or across a network. 39Computational Statistics
  • 40. But….. • R has no warranty • Command Line Interface : difficult for some users. • Users must learn a new way of thinking about data and data analysis sequence • That’s all ….. I guess Computational Statistics 40
  • 41. Companies using R in 2013 • The New York Times routinely uses R for interactive and print data visualization. • Google has more than 500 R users. • The FDA supports the use of R for clinical trials of new drugs. • The National Weather Service uses R to predict the extent of flooding events. • Zillow uses R to model housing prices. • The Consumer Financial Protection Bureau uses R and other open source tools. • Twitter uses R for data science applications on the Twitter database. • FourSquare uses R to develop its recommendation engine. • Facebook uses R to model all sorts of user behaviour. Source: RevolutionanalyticsComputational Statistics 41
  • 42. R Library/packages R Base Packages lme4 IsoGene foreign survival zoo ggplot2 zoo reshape2 nlme Computational Statistics 42
  • 43. My R Packages • IsoGene • IsoGeneGUI • nea • neaGUI • biclustGUI • OCRME • More detail: http://setiopramono.wordpress.com/r- programming/ Computational Statistics 43
  • 44. R For Cutting Edge Technologies 44Computational Statistics
  • 45. R Graphics and Visualization • R provides wide range graphics and visualizations • Basic Plots: bar plots, basic 3D plots, heatmap.,etc • Geographic Maps • Projection Maps • Social Network Graphs • Animated graphics and movies (animation) • Motion Charts (GoogleViz) • Interactive Graphics (rggobi) • Image format: BMP, JPEG, PDF, PNG etc… • and….many more……… Computational Statistics 45
  • 48. R Graphics A map of worldwide email traffic Computational Statistics 48
  • 49. R Graphics Facebook connections between city centers around the world Computational Statistics 49
  • 50. R Graphical User Interfaces • R uses Command line interface and it is preferred for advanced users  allows direct control, more accurate, flexible and the analysis is reproducible. • Requires good knowledge of the language  difficult for beginners or less frequent users. • R provides tools for building GUIs  RGUI Computational Statistics 50
  • 51. R GUI Projects • Integrated development environment (IDE)/Script Editors aimed to provide feature-rich environments to edit R scripts and code: Rstudio (www.rstudio.com), and architect (www.Openanalytics.eu) • Web based application: the Rweb (Banfield, 1999), R.Net (www.u.arizona.edu/~ryckman/Net.php), or gWidgetsWWW (Verzani, 2012). 51Computational Statistics
  • 52. R GUI Projects • Python: OpenMeta-Analyst (Wallace et al, 2012) • Java: JGR (Java GUI for R), Deducer (Fellows, 2012), and Glotaran (Snellenburg, 2012). • Php: R-php (http://dssm.unipa.it/R-php/) • Other extensions connect R to graphical toolboxes for developing menus and dialog boxes: Tcltk, Gtk. 52Computational Statistics
  • 53. R Studio • Download from Rstudio.com • Powerfull IDE (Integrated Development Environment) for R. Computational Statistics 53
  • 54. RGUI Developed using tcltk Computational Statistics 54
  • 55. RGUI: RCommander • Rcommander.com • Helpful for R beginner • Install inside R Computational Statistics 55
  • 56. RGUI using C#: Wires • Developed by STIS students • For Spatial Data Analysis • Still developing… Computational Statistics 56
  • 57. RGUI using C#: Wires Computational Statistics 57
  • 58. RGUI: Web Based App Computational Statistics 58
  • 59. WebBUGS • Conducting Bayesian Statistical Analysis Online • Combines OpenBUGS and R www.webbugs.psychstat.org Computational Statistics 59
  • 60. RGUI: Shiny • A new package from Rstudio to build interactive web applications with R. • Really Easy! • Build useful web applications with only a few lines of code—no JavaScript required. • Self learning: http://shiny.rstudio.com/ • http://www.showmeshiny.com/ Computational Statistics 60
  • 61. RGUI using Shiny: FAST Figure 5. FAST main page 61Computational Statistics
  • 62. Dynamic Report Generation • Sweave • knitr • markdown Computational Statistics 62
  • 63. Want to Learn R? Need Help? Lots of Self learning Resources http://www.rdatamining.com/resources/onlinedocs Blogs: Software # Blogs Blogs Source R 550 R-Bloggers.com Python 60 SciPy.org SAS 40 PROC-X.com, sasCommunity.org Planet Stata 11 Stata-Bloggers.com User Group: Stockholm R User group, etc… Indonesia/Jakarta? https://sites.google.com/site/biostatinfocore/introduction-to-r Computational Statistics 63
  • 64. Need Help? Computational Statistics 64 Number of R- or SAS-related posts to Stack Overflow by week.