Introduction to R
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R is an open source programming language and software environment for statistical analysis and graphics. It provides functions for data manipulation, calculation, and graphical displays. Some key aspects covered in the document include loading and manipulating data from files, performing common mathematical and statistical operations, creating arrays and matrices, and accessing online communities for using and developing R.
![• Arrays: dim(z) <- c(3,2,2)
• z[1,1,1]
• While entering data match length
• z[1,1,]
• z <- array(1:20, dim=c(4,5))
• Z <- array(data_vector, dim_vector)](https://image.slidesharecdn.com/introductiontor-130108043430-phpapp01/85/Introduction-to-R-8-320.jpg)
Introduction to R
- 1. Introduction to R • http://cran.r-project.org/ • The Comprehensive R Archive Network • The Base System • The idea of Packages • Open source Implementation of S • Commercial implementation is S Plus -Sujoy Bhattacharya
- 2. • Default Prompt is ‘>’ • setwd("C:/Documents and Settings/USER/My Documents") • getwd() • ?(plot) • R is case sensitive
- 3. • Objects in R: variables, arrays of numbers, character strings, functions . • function c() • assignment operator (‘<-’) • Create an object z such that: x=1 to 20, y= 30 to 35, z= concatenation of x and y
- 4. • common arithmetic functions: log, exp, sin, cos, tan, sqrt • range() • mean(y)=sum(y)/ length(y) • sort(object): elements are arranged in increasing order
- 5. • seq(from=5, to=50) • seq(-4, 4, by=.1) • z<- rep(x, times=4) • rep(x, each=4)
- 6. Logical Vectors • values :TRUE, FALSE, and NA • <, <=, >, >=, ==(equality) != ( inequality) • z <- x > 20 • z1 & z2 (intersection) • z1 | z2 (union) • !z negation of z
- 7. • NA and NaN • Do 0/0 and z<- c(2,5,NA) • Operations on NA are NA • Do z<- c(1:100,NA) • t<- 100*z
- 8. • Arrays: dim(z) <- c(3,2,2) • z[1,1,1] • While entering data match length • z[1,1,] • z <- array(1:20, dim=c(4,5)) • Z <- array(data_vector, dim_vector)
- 9. • object <- array(data_vector, dim_vector) • dd <- array(data=c(1:125),c(5,5,5)) • cbind() and rbind() • z<- cbind(c(1:10),c(1:5)) • transpose of an array: t(z) • Matrix product : x %*% y
- 10. • x<- array(data=c(1:6),c(2,3)) • Matrix inversion :solve(z) • x<- cbind(1, y, z): y,z must have equal rows.
- 11. • read.csv(file.choose(), header=T) • reg1 <- lm(y ~ x1 + x2, data = nse) • plot(object) • residuals(object) • plot(x, y)
- 12. R communities • www.r-project.org • www.r-forge.r-project.org • www.rstudio.com