NSCB Sexy Stats Version 2
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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.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
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$Age.Group, col=brewer.pal(6,”Set1″), border=”white”)
par(new=TRUE)
pie(c(1), labels=NA, border=’white’, radius=0.4)
text(0,0,labels=”Percent\nUnemployment\nby Age Group\nYear 2006″, cex=1.5, font=2)
pie(agegroup$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)
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