Very brief introduction to R software that I have presented at UNISZA. No R codes and No Statistical Contents. Basically for those who just heard about R software for the first time

1. 1https://xkcd.com/1478/

2. Introducing the R software: Free
statistics at your fingertips
Kamarul Imran Musa
MD, M Community Medicine
Associate Professor (Epidemiology and Statistics)
Dept of Community Medicine, School of Medical Sciences,
Universiti Sains Malaysia, Health Campus
Email: drki.musa@gmail.com
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3. Overview of presentation
• A bit on ‘Data’ and people dealing with ‘Data’
• Statistical software – choices
• Our main course ---- R -----
• Different flavors of R
• Our experiences with R at Health Campus
• Data analysis – now and future
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4. Data as for now … Data in future?
• What is data?
– Facts and statistics collected together for reference and analysis
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5. 5
https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/
(Harvard Business Review)

6. Scientist and Data
• Are you a scientist?
• Do we work with data?
• Scientist + Data
• Are we data scientist?
• What is new for data science in health and medicine?
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8. What does a data scientist do?
• Data scientists are inquisitive:
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http://www-01.ibm.com/software/data/infosphere/data-
scientist/

9. Scientists use tools to work with
• We need tools in science
• What is the right tool for a data scientist?
• In my case, tools to deal with data
– Scientists use tool or tools to ‘manipulate’ data giving them results that
they have to make sense of the findings
• Which tools are available to help scientist best work with their
data?
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10. Choices of statistical software – many. So don’t get
spoiled
• The normal questions for scientist dealing with data analysis
– What choices do you have?
– Which one are you familiar with?
– Popularity?
– Cost?
– Capability?
– After-sale support?
– Meet scientific rigor?
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11. IBM SPSS – everyone knows
• Popular, easy and user-friendly
• How about the cost? When does the license expire?
Usually for USM, every July.
• What does it do when it expires? NOTHING works
• http://www.ibm.com/marketplace/cloud/spss-
statistics/us/en-us?step=Plan
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12. STATA – less people know it, but it is amazing
• Do not expire but upgradeable
• Much cheaper than SPSS
• Balanced between
– Codes use
– Point-and-click use
• Powerful
• http://www.stata.com/order/new/edu/single-user-licenses/
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13. Who are using what software?
• Number of scholarly articles found in the most recent complete
year (2014) for each software package.
• In order of # of articles:
1. SPSS
2. SAS
3. R
4. STATA
• http://r4stats.com/articles/popularity/
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14. The number of scholarly articles found in each year by
Google Scholar.
Only the top six “classic” statistics packages are shown.
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15. No so new kid on the block – R
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16. What (almost) everybody knows about R?
• R is :
– ‘GNU S’, a freely available language and environment for
statistical computing and graphics which provides a wide
variety of statistical and graphical techniques: linear and
nonlinear modelling, statistical tests, time series
analysis, classification, clustering, etc.
• Questions:
1. What can R do?
2. What is special about R?
3. Does R have future?
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17. R and R-gui
• https://cran.r-project.org/
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18. Revolution-R
• Microsoft owns Revolution-R
http://www.revolutionanalytics.com/revolution-r-enterprise 18

19. Revolution R Ent and Revolution R Open
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20. Rstudio IDE
• https://www.rstudio.com/
• Highly recommend to start
with Rstudio IDE
• It is an interface for R
• Requires users to download
and install R first from CRAN
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21. RStudio IDE- Features
• Clean interface
• Organized
• Integrated with many brilliant in-built
tools
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22. DEMO
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23. How does R fit into data analysis now and in the
future?
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24. Recognition
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25. Reproducibility (DEMO)
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26. Reproducibility
• Reproducibility in research
• The Associate Editor for reproducibility (AER) will handle
submissions of reproducible articles.
– Data: The analytic data from which the principal results were derived are
made available on the journal's Web site.
– Code: Any computer code, software, or other computer instructions that
were used to compute published results are provided.
– Reproducible: An article is designated as reproducible if the AER succeeds
in executing the code on the data provided and produces results matching
those that the authors claim are reproducible.
– http://biostatistics.oxfordjournals.org/content/10/3/405.full
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27. On the fly report using R-markdown (DEMO)
• Produce report on a fly
• In HTML or PDF formats
• Benefits
– Save time
– Reduce error – no more copy paste
– Pretty
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28. Integration with other software (DEMO)
• Latex
• Stata
• WinBUGS
• SPSS
• SAS
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29. Our experience with R
• No experience with undergraduate
• Started teaching R for DrPH candidates this academic session
• Personally introduced R, 2 years ago
• Common resistance
– Totally command-driven
– Steep learning curve
– Limited resources esp books on R --- that was 2 years ago. Not a problem
now
– You need to know your statistics
– Not for data entry
– Very difficult to view and manipulate variables
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30. How’s the feedback from users?
• No formal study or assessment on their experience
• Users seem to like R because it opens up creativity
• R pushes users to explore more and challenge themselves
• R is not boring like point-and-click (menu driven) software
• They seem to like R-markdown
– On-the-fly report
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31. The BIG question--- stick to R? .. And R only?
• Yes, you may
• Hmm, maybe not
– Specialized software for data entry
– Software for data cleaning
– Software for data mining
• But yes, 1 software is enough for 95% of us
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32. Embrace R and abandon others?
• I love R
– Lots of data analysis – Creating publication : HTML, PDF
– Spatial data analysis
– Bayesian
• WINBUGS
• INLA
• But I do love Stata too
– Data cleaning
– Variable manipulation
• And I use Epidata for data entry
• But yes, I have left SPSS
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33. • East-coast data science user group
• My blog :
– https://designdataanalysis.wordpress.com
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