### R examples

 fio <- read.csv(file="C:\\Users\\Kyle\\Desktop\\a_Delphix\\fio\\fio.csv",head=TRUE,sep=",") library(scatterplot3d) attach(fio) s3d <-scatterplot3d(users, MB,ms,pch=16, highlight.3d=TRUE,  type="h", main="3D Scatterplot")s3d <-scatterplot3d(users, MB,ms,pch=16, highlight.3d=TRUE,  type="h", main="3D Scatterplot", x.ticklabs=c(1,8,16,24,32,40,48,56,64) , xlim=c(0,60) )s3d <-scatterplot3d(users, MB,ms,pch=16, highlight.3d=TRUE,  type="h", main="3D Scatterplot",  xlim=c(1,64) )s3d <-scatterplot3d(users, MB,ms,pch=16, highlight.3d=TRUE,  type="h", main="3D Scatterplot", lab=c(3,4 ), lab.z=c(4) ,  xlim=c(1,64)  )fit <- lm(mpg ~ wt+disp) s3d\$plane3d(fit)scatter3d(users, MB,ms)```## Script for Part 1: Orientation ## ## John Fox ## ## An Introduction to R ## ## UCLA Feb. 2005 ## ##------------------------------------## # Basics # arithmetic, interacting with the interpreter # basic arithmetic operations 2+3 2-3 2*3 2/3 2^3 # precedence of operators 4^2-3*2 (4^2) - (3*2) # use parentheses and spaces to group, clarify -2--3 -2 - -3 1 - 6 + 4 2^-3 # functions, arguments to functions, obtaining help log(100) log(100, base=10) log(100, b=10) help(log) ?log apropos("log") help.search("log") log(100,10) "+"(2,3) # vectors c(1,2,3,4) # combine 1:4 # sequence operator 4:1 -1:2 # note precedence seq(1,4) seq(2, 8, by=2) seq(0, 1, by=.1) seq(0, 1, length=11) # vectorized arithmetic c(1,2,3,4)/2 c(1,2,3,4)/c(4,3,2,1) log(c(0.1,1,10,100), 10) c(1,2,3,4) + c(4,3) c(1,2,3,4) + c(4,3,2) # variables x <- c(1,2,3,4) x x/2 y <- sqrt(x) y x <- rnorm(100) x summary(x) # a "generic" function # basic indexing x[21] x[11:20] x[-(11:100)] # careful here! z <- x[1:10] z z < -0.5 z > 0.5 z < -0.5 | z > 0.5 # | is vectored "or", & is "and" abs(z) > 0.5 z[abs(z) > 0.5] # indexing by a logical vector # user-defined functions mean(x) sum(x)/length(x) my.mean <- function(x) sum(x)/length(x) my.mean(x) my.mean(y) my.mean(1:100) x # Duncan example # creating a data frame from data stored in a file Duncan <- read.table('http://socserv.socsci.mcmaster.ca/jfox/Courses/UCLA/Duncan.txt', header=TRUE) Duncan summary(Duncan) # attaching a data frame prestige attach(Duncan) prestige # distributions and bivariate relationships hist(prestige) plot(income, education) noteworthy <- identify(income, education, row.names(Duncan)) noteworthy row.names(Duncan)[noteworthy] pairs(cbind(prestige,income,education), panel=function(x,y){ points(x,y) abline(lm(y~x), lty=2) lines(lowess(x,y)) }, diag.panel=function(x){ par(new=TRUE) hist(x, main="", axes=FALSE) } ) # fitting a regression duncan.model <- lm(prestige ~ income + education) duncan.model summary(duncan.model) # regression diagnostics library(car) hist(rstudent(duncan.model)) qq.plot(duncan.model, labels=row.names(Duncan), simulate=TRUE)  ``````graphs http://www.statmethods.net/graphs/scatterplot.html ````csv files````http://www.cyclismo.org/tutorial/R/input.html#read ``````http://socserv.mcmaster.ca/jfox/Courses/UCLA/Part-1-script.R plot(hatvalues(duncan.model)) abline(h = c(2,3)*3/45) identify(1:45, hatvalues(duncan.model), row.names(Duncan)) plot(cookd(duncan.model)) abline(h = 4/(45-2-1)) identify(1:45, cookd(duncan.model), row.names(Duncan)) av.plots(duncan.model, labels=row.names(Duncan)) cr.plots(duncan.model) # refitting the model which.names(c('minister', 'conductor'), Duncan) duncan.model.2 <- update(duncan.model, subset=-c(6, 16)) summary(duncan.model.2)```