Exercise of HSAUR Chapter 1

The book: A Handbook of Statistical Analyses Using R

	data("Forbes2000", packages = "HSAUR")
	##ex 1.1
	> tmp <- subset(Forbes2000, country==c("United States","United Kingdom","France","Germany"))
	> by(tmp$profits, as.character(tmp$country), FUN=function(x)median(x, na.rm=T))
	INDICES: France
	[1] 0.215
	------------------------------------------------------------------------------
	INDICES: Germany
	[1] 0.245
	------------------------------------------------------------------------------
	INDICES: United Kingdom
	[1] 0.17
	------------------------------------------------------------------------------
	INDICES: United States
	[1] 0.26
	##ex 1.2
	> subset(Forbes2000, country=="Germany" & profits < 0, select = "name")
	name
	350 Allianz Worldwide
	364 Deutsche Telekom
	397 E.ON
	431 HVB-HypoVereinsbank
	500 Commerzbank
	798 Infineon Technologies
	869 BHW Holding
	926 Bankgesellschaft Berlin
	1034 W&W-Wustenrot
	1187 mg technologies
	1477 Nurnberger Beteiligungs
	1887 SPAR Handels
	1994 Mobilcom
	##ex 1.3
	> summary(subset(Forbes2000, country=='Bermuda', select = "category"))
	category
	Insurance :10
	Conglomerates : 2
	Oil & gas operations: 2
	Banking : 1
	Capital goods : 1
	Food drink & tobacco: 1
	(Other) : 3
	==> Insurance
	##ex 1.4
	> o <- order(Forbes2000$profits, decreasing = T)
	> tmp <- Forbes2000[o,]
	> tmp1 <- tmp[1:50,]
	> plot(tmp1$sales, log(tmp1$assets), main = "Forbes2000 Top 50 Profits Company's Sales vs Assets", xlab = "Sales", ylab = "log(Assets)")
	> text(tmp1$sales, log(tmp1$assets), abbreviate(tmp1$country))
	##ex 1.5
	> by(Forbes2000$sales, as.character(Forbes2000$country), FUN = function(x) mean(x, na.rm = T))
	> tmp <- Forbes2000[Forbes2000$profits>5, c("name","country")]
	> summary(tmp$country)

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rle and inverse.rle

rle return a list of run length of the vector, length for the run length, value for it's corresponding value
inverse.rle rollback the original vector

example:
> x <- c(1,0,0,1,1,1,1,1,0,0,0) > (tmp <- rle(x)) Run Length Encoding lengths: int [1:4] 1 2 5 3 values : num [1:4] 1 0 1 0

>inverse.rle(tmp)
[1] 1 0 0 1 1 1 1 1 0 0 0


Application: Finding peaks in vector
getPeaks <- function(x) { tmp <- rle(diff(x)>0) change.index <- cumsum(c(1, tmp$lengths)) ind <- change.index[which(tmp$values ==FALSE)] return(ind) }

> x<-c(rnorm(300),rnorm(700,5,2)) > d1<- density(x) > ind <- getPeaks(d1$y) > plot(d1,xlab="",ylab="") > ind [1] 149 279 > points(d1$x[ind], d1$y[ind], pch = 16, col="red")