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



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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)]

#Copy to clipboard
Education    Y2006    Y2009
Elementary     42.5    37.9
High School    47.7    52.2
College    9.7    10

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, =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, =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, =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, =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, =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, =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, =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, =2)
text(0.5,-1, “Data Source: NSCB\nCreated by: ARSalvacion”, adj=c(0,0), cex=0.7)

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