![Accessing Variables
• edit(<mydataobject>)
• Subscripts essential tools
– x[1] identifies first element in vector x
– y[1,] identifies first row in matrix y
– y[,1] identifies first column in matrix y
• $ sign for lists and data frames
– myframe$age gets age variable of myframe
– attach(dataframe) -> extract by variable name](https://image.slidesharecdn.com/17641-231015173043-c246df39/85/17641-ppt-18-320.jpg)
![Subset Data
• Using subset function
– subset() will subset the dataframe
• Subscripting from data frames
– myframe[,1] gives first column of myframe
• Specifying a vector
– myframe[1:5] gives first 5 rows of data
• Using logical expressions
– myframe[myframe[,1], < 5,] gets all rows of the
first column that contain values less than 5](https://image.slidesharecdn.com/17641-231015173043-c246df39/85/17641-ppt-19-320.jpg)
![Multivariate Techniques
• Several Libraries available
– mva, hmisc, glm,
– MASS: discriminant analysis and multidim
scaling
• Econometrics packages
– dse (multivariate time series, state-space
models), ineq: for measuring inequality, poverty
estimation, its: for irregular time series, sem:
structural equation modeling, and so on…
[http://www.mayin.org/ajayshah/]](https://image.slidesharecdn.com/17641-231015173043-c246df39/85/17641-ppt-30-320.jpg)
17641.ppt
- 1. Introduction to R Arin Basu MD MPH DataAnalytics dataanalytics@rediffmail.com http://dataanalytics.objectis.net
- 2. We’ll Cover • What is R • How to obtain and install R • How to read and export data • How to do basic statistical analyses • Econometric packages in R
- 3. What is R • Software for Statistical Data Analysis • Based on S • Programming Environment • Interpreted Language • Data Storage, Analysis, Graphing • Free and Open Source Software
- 4. Obtaining R • Current Version: R-2.0.0 • Comprehensive R Archive Network: http://cran.r-project.org • Binary source codes • Windows executables • Compiled RPMs for Linux • Can be obtained on a CD
- 5. Installing R • Binary (Windows/Linux): One step process – exe, rpm (Red Hat/Mandrake), apt-get (Debian) • Linux, from sources: $ tar –zxvf “filename.tar.gz” $ cd filename $ ./configure $ make $ make check $ make install
- 6. Starting R Windows, Double-click on Desktop Icon Linux, type R at command prompt $ R
- 7. Strengths and Weaknesses • Strengths – Free and Open Source – Strong User Community – Highly extensible, flexible – Implementation of high end statistical methods – Flexible graphics and intelligent defaults • Weakness – Steep learning curve – Slow for large datasets
- 8. Basics • Highly Functional – Everything done through functions – Strict named arguments – Abbreviations in arguments OK (e.g. T for TRUE) • Object Oriented – Everything is an object – “<-” is an assignment operator – “X <- 5”: X GETS the value 5
- 9. Getting Help in R • From Documentation: – ?WhatIWantToKnow – help(“WhatIWantToKnow”) – help.search(“WhatIWantToKnow”) – help.start() – getAnywhere(“WhatIWantToKnow”) – example(“WhatIWantToKnow”) • Documents: “Introduction to R” • Active Mailing List – Archives – Directly Asking Questions on the List
- 10. Data Structures • Supports virtually any type of data • Numbers, characters, logicals (TRUE/ FALSE) • Arrays of virtually unlimited sizes • Simplest: Vectors and Matrices • Lists: Can Contain mixed type variables • Data Frame: Rectangular Data Set
- 11. Data Structure in R Linear Rectangular All Same Type VECTORS MATRIX* Mixed LIST DATA FRAME
- 12. Running R • Directly in the Windowing System (Console) • Using Editors – Notepad, WinEdt, Tinn-R: Windows – Xemacs, ESS (Emacs speaks Statistics) • On the Editor: –source(“filename.R”) – Outputs can be diverted by using • sink(“filename.Rout”)
- 13. R Working Area This is the area where all commands are issued, and non-graphical outputs observed when run interactively
- 14. In an R Session… • First, read data from other sources • Use packages, libraries, and functions • Write functions wherever necessary • Conduct Statistical Data Analysis • Save outputs to files, write tables • Save R workspace if necessary (exit prompt)
- 15. Specific Tasks • To see which directories and data are loaded, type: search() • To see which objects are stored, type: ls() • To include a dataset in the searchpath for analysis, type: attach(NameOfTheDataset, expression) • To detach a dataset from the searchpath after analysis, type: detach(NameOfTheDataset)
- 16. Reading data into R • R not well suited for data preprocessing • Preprocess data elsewhere (SPSS, etc…) • Easiest form of data to input: text file • Spreadsheet like data: – Small/medium size: use read.table() – Large data: use scan() • Read from other systems: – Use the library “foreign”: library(foreign) – Can import from SAS, SPSS, Epi Info – Can export to STATA
- 17. Reading Data: summary • Directly using a vector e.g.: x <- c(1,2,3…) • Using scan and read.table function • Using matrix function to read data matrices • Using data.frame to read mixed data • library(foreign) for data from other programs
- 18. Accessing Variables • edit(<mydataobject>) • Subscripts essential tools – x[1] identifies first element in vector x – y[1,] identifies first row in matrix y – y[,1] identifies first column in matrix y • $ sign for lists and data frames – myframe$age gets age variable of myframe – attach(dataframe) -> extract by variable name
- 19. Subset Data • Using subset function – subset() will subset the dataframe • Subscripting from data frames – myframe[,1] gives first column of myframe • Specifying a vector – myframe[1:5] gives first 5 rows of data • Using logical expressions – myframe[myframe[,1], < 5,] gets all rows of the first column that contain values less than 5
- 20. Graphics • Plot an object, like: plot(num.vec) – here plots against index numbers • Plot sends to graphic devices – can specify which graphic device you want • postscript, gif, jpeg, etc… • you can turn them on and off, like: dev.off() • Two types of plotting – high level: graphs drawn with one call – Low Level: add additional information to existing graph
- 21. High Level: generated with plot()
- 22. Low Level: Scattergram with Lowess
- 23. Programming in R • Functions & Operators typically work on entire vectors • Expressions surrounded by {} • Codes separated by newlines, “;” not necessary • You can write your own functions and use them
- 24. Statistical Functions in R • Descriptive Statistics • Statistical Modeling – Regressions: Linear and Logistic – Probit, Tobit Models – Time Series • Multivariate Functions • Inbuilt Packages, contributed packages
- 25. Descriptive Statistics • Has functions for all common statistics • summary() gives lowest, mean, median, first, third quartiles, highest for numeric variables • stem() gives stem-leaf plots • table() gives tabulation of categorical variables
- 26. Statistical Modeling • Over 400 functions – lm, glm, aov, ts • Numerous libraries & packages – survival, coxph, tree (recursive trees), nls, … • Distinction between factors and regressors – factors: categorical, regressors: continuous – you must specify factors unless they are obvious to R – dummy variables for factors created automatically • Use of data.frame makes life easy
- 27. How to model • Specify your model like this: – y ~ xi+ci, where – y = outcome variable, xi = main explanatory variables, ci = covariates, + = add terms – Operators have special meanings • + = add terms, : = interactions, / = nesting, so on… • Modeling -- object oriented – each modeling procedure produces objects – classes and functions for each object
- 28. Synopsis of Operators nesting only no specific %in% limiting interaction depths exponentiation ^ interaction only sequence : main effect and nesting division / main effect and interactions multiplication * add or remove terms add or subtract + or - In Formula means Usually means Operator
- 29. Modeling Example: Regression carReg <- lm(speed~dist, data=cars) carReg = becomes an object to get summary of this regression, we type summary(carReg) to get only coefficients, we type coef(carReg), or carReg$coef don’t want intercept? add 0, so carReg <- lm(speed~0+dist, data=cars)
- 30. Multivariate Techniques • Several Libraries available – mva, hmisc, glm, – MASS: discriminant analysis and multidim scaling • Econometrics packages – dse (multivariate time series, state-space models), ineq: for measuring inequality, poverty estimation, its: for irregular time series, sem: structural equation modeling, and so on… [http://www.mayin.org/ajayshah/]
- 31. Summarizing… • Effective data handling and storage • large, coherent set of tools for data analysis • Good graphical facilities and display – on screen – on paper • well-developed, simple, effective programming
- 32. For more resources, check out… R home page http://www.r-project.org R discussion group http://www.stat.math.ethz.ch/mailman/listinfo/r-help Search Google for R and Statistics
- 33. For more information, contact dataanalytics@rediffmail.com