This document outlines courses in computational statistics that utilize various statistical software packages like R, SPSS, and Excel. The courses cover topics ranging from data preparation and visualization to statistical modeling techniques like linear regression, resampling methods, and hypothesis testing. They emphasize hands-on practice over theory, involve group projects, and provide reference materials for further learning.

1. Computational Statistics
Setia Pramana
2015
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2. Course Outline
• Introduction
– Different Statistical Software
• Data Preparation, Management, Manipulation,
Summarization with:
– SPSS
– R (R Commander)
– Ms. Excel
• Data Tabulation and Visualization
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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)
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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
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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/
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6. Reference Books
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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.
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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.
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9. Other Materials
• https://sites.google.com/site/biostatinfocor
e/home/rworkshop
• https://sites.google.com/site/biostatinfocor
e/biostatistics-workshop
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10. Introduction
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11. Statistics?
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12. Computational Statistics 12

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”.
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14. Observations on the
Bills of
Mortality (1662)
Recorded Plague
related death for
100 years
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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
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16. “Statistical thinking will one day be as
necessary for efficient citizenship as the
ability to read and write” HG Well
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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.
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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
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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)
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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.
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21. Available Statistical Packages
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22. Available Statistical Packages
Proprietary
 Excel
 SPSS
 MINITAB
 SAS
 Stata
 Statistica
 Many more ……
Free Software
 LibreOffice Calc
 R
 CS Pro
 WinBugs
 EpiInfo
 Many more……..
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27. Microsoft Excel
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28. Which one do you use?
Why?
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29. Statistical Software Used
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30. Statistical Software Used
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31. R is HOT !
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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/
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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…..
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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.
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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
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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
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43. My R Packages
• IsoGene
• IsoGeneGUI
• nea
• neaGUI
• biclustGUI
• OCRME
• More detail: http://setiopramono.wordpress.com/r-
programming/
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44. R For Cutting Edge
Technologies
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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………
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46. R Graphics
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47. R Graphics
RCircos
https://gjabel.wordpress.com/ 47Computational Statistics

48. R Graphics
A map of worldwide email traffic
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49. R Graphics
Facebook connections between city centers around the world
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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
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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).
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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.
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53. R Studio
• Download from
Rstudio.com
• Powerfull IDE
(Integrated
Development
Environment) for R.
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54. RGUI Developed using tcltk
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55. RGUI: RCommander
• Rcommander.com
• Helpful for R beginner
• Install inside R
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56. RGUI using C#: Wires
• Developed by STIS
students
• For Spatial Data
Analysis
• Still developing…
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57. RGUI using C#: Wires
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58. RGUI: Web Based App
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59. WebBUGS
• Conducting Bayesian
Statistical Analysis
Online
• Combines
OpenBUGS and R
www.webbugs.psychstat.org
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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/
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61. RGUI using Shiny: FAST
Figure 5. FAST main page
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62. Dynamic Report Generation
• Sweave
• knitr
• markdown
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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
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64. Need Help?
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Number of R- or SAS-related posts to Stack Overflow by week.