R programmingmilano
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This document provides an overview of the R programming language. It describes that R can handle numeric and textual data, perform matrix algebra and statistical functions. While R is not a database, it can connect to external databases. It also summarizes that R has no graphical user interface but can connect to other languages for visualization, and its interpreter can be slow but users can call optimized C/C++ code. The document also contrasts the differences between using R and commercial packages.
![• > 7 + 5 # arithmetic operations
• [1] 12
• > 6 – 3 * ( 8/2 – 1 )
• [1] -3
• > log(10) # commonly used functions
• [1] 2.302585
• > exp(7)
• [1] 1096.633
• > sqrt(2)
• [1] 1.414214](https://image.slidesharecdn.com/r-programmingmilano-180806142245/85/R-programmingmilano-5-320.jpg)
![• > mat <- matrix(seq(1:21) ,nrow=7, byrow=TRUE)
• > mat
• [,1] [,2] [,3]
• [1,] 1 2 3
• [2,] 4 5 6
• [3,] 7 8 9
• [4,] 10 11 12
• [5,] 13 14 15
• [6,] 16 17 18
• [7,] 19 20 21](https://image.slidesharecdn.com/r-programmingmilano-180806142245/85/R-programmingmilano-7-320.jpg)
![A character string can contain arbitrary text. Sometimes it is useful to use a limited
vocabulary, with a small number of allowed words. A factor is a variable that can only
take such a limited number of values, which are called levels.
> a
[1] “A1" “1A" “U!"
[4] "M1" “A1" ”A3"
[7] “C1" “C11" “G1”
> class(a)
[1] "factor"
> as.character(a)
[1] “A1)" “1A" “U!"
[4] "M1" “A1" ”A3"
[7] “C1" “C11" “G1"
> as.integer(a)
[1] 1 2 2 2 2 4 2 3 2
> as.integer(as.character(a))
[1] NA NA NA NA NA NA NA NA NA NA NA NA
Warning message: NAs introduced by coercion](https://image.slidesharecdn.com/r-programmingmilano-180806142245/85/R-programmingmilano-10-320.jpg)
R programmingmilano
- 2. What does and does not ⪫ data handling and storage: numeric, textual ⪫ matrix algebra ⪫ hash tables and regular expressions ⪫ high-level data analytic and statistical functions ⪫ programming language: loops, branching, subroutines ⪫ is not a database, but connects to DBMSs ⪫ has no graphical user interfaces, but connects to Java, TclTk ⪫ language interpreter can be very slow, but allows to call own C/C++ code ⪫ no professional / commercial support
- 3. R Commercial packages Ø Many different datasets (and other “objects”) available at same time Ø Datasets can be of any dimension Ø Functions can be modified Ø Experience is interactive-you program until you get exactly what you want Ø One stop shopping - almost every analytical tool you can think of is available Ø R is free and will continue to exist. Nothing can make it go away, its price will never increase. Ø One datasets available at a given time Ø Datasets are rectangular Ø Functions are proprietary Ø Experience is passive-you choose an analysis and they give you everything they think you need Ø Tend to be have limited scope, forcing you to learn additional programs; extra options cost more and/or require you to learn a different language (e.g., SPSS Macros) Ø They cost money. There is no guarantee they will continue to exist, but if they do, you can bet that their prices will always increase
- 4. • scc:~% R • R version 2.15.3 (2013-03-01) • Copyright (C) 2013 The R Foundation for Statistical Computing • ISBN 3-900051-07-0 • Platform: x86_64-unknown-linux-gnu (64-bit) • >
- 5. • > 7 + 5 # arithmetic operations • [1] 12 • > 6 – 3 * ( 8/2 – 1 ) • [1] -3 • > log(10) # commonly used functions • [1] 2.302585 • > exp(7) • [1] 1096.633 • > sqrt(2) • [1] 1.414214
- 6. sqrt(x), sum(x), sign(x), abs(x), … # trigonometric sin(x), cos(x), tan(x), asin(x), acos(x), … # hyperbolic sinh(x), cosh(x), … # logarithmic and exponent log(x), log10(x), log2(x) or log(x, base=10), exp(x) # factorial and combination functions factorial(n) , choose(n ,m) # built-in constants T, F, pi, LETTERS, letters, month.abb, month.name
- 7. • > mat <- matrix(seq(1:21) ,nrow=7, byrow=TRUE) • > mat • [,1] [,2] [,3] • [1,] 1 2 3 • [2,] 4 5 6 • [3,] 7 8 9 • [4,] 10 11 12 • [5,] 13 14 15 • [6,] 16 17 18 • [7,] 19 20 21
- 9. R Objects x1 x2 x3 x4 x5 x6 1 2 3 4 5 6 7 8 Z <-
- 10. A character string can contain arbitrary text. Sometimes it is useful to use a limited vocabulary, with a small number of allowed words. A factor is a variable that can only take such a limited number of values, which are called levels. > a [1] “A1" “1A" “U!" [4] "M1" “A1" ”A3" [7] “C1" “C11" “G1” > class(a) [1] "factor" > as.character(a) [1] “A1)" “1A" “U!" [4] "M1" “A1" ”A3" [7] “C1" “C11" “G1" > as.integer(a) [1] 1 2 2 2 2 4 2 3 2 > as.integer(as.character(a)) [1] NA NA NA NA NA NA NA NA NA NA NA NA Warning message: NAs introduced by coercion
- 12. 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
- 13. 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
- 14. 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
- 15. 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)
- 16. LEAST SQUARES ESTIMATION βˆˆ Xy =
- 17. For more information, check out R home page http://www.r-project.org Cran home page https://cran.r-project.org/