NSCB Sexy Stats Version 2

This was a revised version of my previous post about the NSCB article. With the suggestion from Tal Galili, below were the new pie charts and the R codes to produce these plots by directly scrapping the data from the webpage using XML and RColorBrewer pagkage.

Unemployment by Age Group

Unemployment by Gender

Unemployment by Civil Status

Unemployment by Educational Level

	library (XML)
	library(RColorBrewer)
	url<-"http://www.nscb.gov.ph/sexystats/2012/Filipinoversion/SS20121022_joblessness_filver.asp"
	unemployment<-readHTMLTable(url, header=T, which=2,stringsAsFactors=F)
	agegroup<-unemployment[3:8,c(1,3,5)]
	gender<-unemployment[12:13,c(1,3,5)]
	civil<-unemployment[17:20,c(1,3,5)]
	education<-unemployment[25:31,c(1,3,5)]
	Education <- c("Elementary", "High School", "College")
	Y2006 <- c(42.5, 47.7, 9.7)win.graph(w=14.3,h=7)
	Y2009 <- c(37.9, 52.2, 10)
	educ <- data.frame(Education, Y2006, Y2009)
	rm(Education, Y2006, Y2009)
	educ<-read.table("clipboard", header=T, sep="\t")
	colnames(agegroup)<-c("Age.Group","Y2006","Y2009")
	colnames(gender)<-c("Gender","Y2006","Y2009")
	colnames(civil)<-c("Civil.Status","Y2006","Y2009")
	colnames(education)<-c("Education","Y2006","Y2009")
	agegroup$Age.Group[6]<-"65 & Up"
	agegroup$Y2006<-as.numeric(agegroup$Y2006)
	agegroup$Y2009<-as.numeric(agegroup$Y2009)
	gender$Gender[2]<-"Female"
	gender$Gender[1]<-"Male"
	gender$Y2006<-as.numeric(gender$Y2006)
	gender$Y2009<-as.numeric(gender$Y2009)
	civil$Y2006<-as.numeric(civil$Y2006)
	civil$Y2009<-as.numeric(civil$Y2009)
	cs<-c("Single","Married","Widowed", "Divorced")
	win.graph(w=14.3,h=7)
	par(mfrow=c(1,2), oma=c(1,0,1,1) , mar=c(1,1,0,1))
	#Chart 1
	pie(agegroup$Y2006,label=agegroup$Age.Group, col=brewer.pal(6,"Set1"), border="white")
	par(new=TRUE)
	pie(c(1), labels=NA, border='white', radius=0.4)
	text(0,0,labels="Percent\nUnemployment\nby Age Group\nYear 2006", cex=1.5, font=2)
	pie(agegroup$Y2009,label=agegroup$Age.Group, col=brewer.pal(6,"Set1"), border="white")
	par(new=TRUE)
	pie(c(1), labels=NA, border='white', radius=0.4)
	text(0,0,labels="Percent\nUnemployment\nby Age Group\nYear 2009", cex=1.5, font=2)
	text(0.5,-1, "Data Source: NSCB\nCreated by: ARSalvacion", adj=c(0,0), cex=0.7)
	#Chart 2
	pie(gender$Y2006,label=gender$Gender, col=brewer.pal(2,"Set1"), border="white", cex=1.5)
	par(new=TRUE)
	pie(c(1), labels=NA, border='white', radius=0.4)
	text(0,0,labels="Percent\nUnemployment\nby Gender\nYear 2006", cex=1.5, font=2)
	pie(gender$Y2009,label=gender$Gender, col=brewer.pal(2,"Set1"), border="white", cex=1.5)
	par(new=TRUE)
	pie(c(1), labels=NA, border='white', radius=0.4)
	text(0,0,labels="Percent\nUnemployment\nby Gender\nYear 2009", cex=1.5, font=2)
	text(0.5,-1, "Data Source: NSCB\nCreated by: ARSalvacion", adj=c(0,0), cex=0.7)
	#Chart 3
	pie(civil$Y2006,label=cs, col=brewer.pal(4,"Dark2"), border="white")
	par(new=TRUE)
	pie(c(1), labels=NA, border='white', radius=0.4)
	text(0,0,labels="Percent\nUnemployment\nby Civil Status\nYear 2006", cex=1.5, font=2)
	pie(civil$Y2009,label=cs, col=brewer.pal(4,"Dark2"), border="white")
	par(new=TRUE)
	pie(c(1), labels=NA, border='white', radius=0.4)
	text(0,0,labels="Percent\nUnemployment\nby Civil Status\nYear 2009", cex=1.5, font=2)
	text(0.5,-1, "Data Source: NSCB\nCreated by: ARSalvacion", adj=c(0,0), cex=0.7)
	#Chart 4
	pie(educ$Y2006,label=educ$Education, col=brewer.pal(3,"Dark2"), border="white", cex=1.5)
	par(new=TRUE)
	pie(c(1), labels=NA, border='white', radius=0.4)
	text(0,0,labels="Percent\nUnemployment by\nEducational Level\nYear 2006", cex=1.5, font=2)
	pie(educ$Y2009,label=educ$Education, col=brewer.pal(3,"Dark2"), border="white", cex=1.5)
	par(new=TRUE)
	pie(c(1), labels=NA, border='white', radius=0.4)
	text(0,0,labels="Percent\nUnemployment by\nEducational Level\nYear 2009", cex=1.5, font=2)
	text(0.5,-1, "Data Source: NSCB\nCreated by: ARSalvacion", adj=c(0,0), cex=0.7)

view raw NSCB Stats.r hosted with ❤ by GitHub

NSCB Sexy Statistics (Unemployment)

Recently, my friend posted on her Facebook account about the article published by the National Statistical Coordination Board (NSCB) about poverty and unemployment in the country.  Looking at the report I saw a lot of tables, so I thought why not use R to make some graphs out of these tables (with some inspiration from the mazmascience).


Percent Unemployed Per Age Group

Percent Unemployed by Sex Group

 Percent Unemployed by Civil Status