Introducing The R Software
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The document provides an overview of data science and the use of R software for statistical analysis in health and medicine. It discusses various statistical tools, the popularity of R, and its features like reproducibility and integration with other software. The author shares personal experiences with R and emphasizes its advantages over other software options, despite some challenges for new users.
Introducing The R Software
- 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 2
- 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 3
- 4. Data as for now … Data in future? • What is data? – Facts and statistics collected together for reference and analysis 4
- 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? 6
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- 8. What does a data scientist do? • Data scientists are inquisitive: 8 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? 9
- 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? 10
- 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 11
- 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/ 12
- 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/ 13
- 14. The number of scholarly articles found in each year by Google Scholar. Only the top six “classic” statistics packages are shown. 14
- 15. No so new kid on the block – R 15
- 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? 16
- 17. R and R-gui • https://cran.r-project.org/ 17
- 18. Revolution-R • Microsoft owns Revolution-R http://www.revolutionanalytics.com/revolution-r-enterprise 18
- 19. Revolution R Ent and Revolution R Open 19
- 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 20
- 21. RStudio IDE- Features • Clean interface • Organized • Integrated with many brilliant in-built tools 21
- 22. DEMO 22
- 23. How does R fit into data analysis now and in the future? 23
- 24. Recognition 24
- 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 26
- 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 27
- 28. Integration with other software (DEMO) • Latex • Stata • WinBUGS • SPSS • SAS 28
- 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 29
- 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 30
- 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 31
- 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 32
- 33. • East-coast data science user group • My blog : – https://designdataanalysis.wordpress.com 33