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

Rainfall Amount Flooding Quezon City Philippines

The rainfall received by Quezon City, Philippines was almost double of what the city normally receive for the entire month of August, causing flooding and land slide to various villages in the area.

August 6-7 Rainfall on Metro Manila

Majority of Metro Manila is affected by floods. Looking at the hourly data from PAGASA weather  station located at Bicutan, Taguig, the graph below will gave the viewers of this blog on the rainfall situation in Manila from August 6 (12:00am)-7(11:15 pm), 2012.

Provincial Monthly Rainfall of the Philippines from WORLDCLIM

Preparing for a future conference on climate change, I downloaded and extracted average monthly rainfall in the Philippines from worldclim.org. Using maptools, raster, and animation package in R, I produced an animation of average monthly rainfall of the country.

Provincial Map using GADM

This blog demonstrates how to produce political/provincial boundary map (below) using R maptools and raster packages.


 

	## Load required packages
	library(maptools)
	library(raster)
	## Download data from gadm.org
	adm <- getData('GADM', country='PHL', level=2)
	mar<-(adm[adm$NAME_1=="Marinduque",])plot(mar, bg="dodgerblue", axes=T)
	##Plot downloaded data
	plot(mar, lwd=10, border="skyblue", add=T)
	plot(mar,col="green4", add=T)
	grid()
	box()
	invisible(text(getSpPPolygonsLabptSlots(mar), labels=as.character(mar$NAME_2), cex=1.1, col="white", font=2))
	mtext(side=3, line=1, "Provincial Map of Marinduque", cex=2)
	mtext(side=1, "Longitude", line=2.5, cex=1.1)
	mtext(side=2, "Latitude", line=2.5, cex=1.1)
	text(122.08,13.22, "Projection: Geographic\nCoordinate System: WGS 1984\nData Source: GADM.org\nCreated by: ARSsalvacion", adj=c(0,0), cex=0.7, col="grey20")

view raw Marinduque.r hosted with ❤ by GitHub

Trend and Spatial Pattern of Poverty in the Philippines

In a teaching demo that I have conducted, I discussed on how R can be used to analyze trends and spatial pattern of poverty incidence in the Philippines. Playing on the data I got from the National Statistical Coordination Board below is what I got.

 Scatter  Plot Matrix

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Histogram

 

Poverty Incidence Map

 

 

Spatial Pattern

 

 

Colored 3D Map

In my previous post, I showed how to make a 3D view of an area using the persp function. However, I felt this was not a complete representation, especially for digital elevation. While looking for some reference for my presentation on the use of R for data analysis and visualization, I came across a thread discussing " 3d plot with different levels done in different colors (solution)".  In this thread, a co-blogger Tamas K Papp's shared his levelpersp function. Using this function I tried to plot 3D view of Marinduque, Philippines using terrain and topo color.

3D persp using terrain color

3D persp using topo color

Negros Quake Animation in R

Taken from a previous post in other website, maps below show the locations of epicenters and sequence of earthquakes that struck Negros last February 6, 2012. The bottom image is the animation these maps using animation package in R. Data was taken from United State Geological Survey.