Lesson learned, and the wheels keep turning (The Killers – The world we live in)

I discovered this site with a huge amount of data waiting to be analyzed. The first thing I’ve done is this simple graph, where you can see relationship between cellular subscribers and obese people. Bubbles are countries and its size depends on the population:

Some quick conclusions:

• The more cellular subscribers, the more obese people
• Pacific islands such as Kiribati, Palau and Tonga are plenty of happy people
• Singapore people are thinner than they should be
• How do Saudi Arabian and Panamanian manage two cellulars?

This is the world we live in.

cellular  =read.csv("UNdata_Export_20140930_cellular.csv",   nrows=193,   header=T, row.names=NULL)
obese     =read.csv("UNdata_Export_20140930_obese.csv",      nrows=567,   header=T, row.names=NULL)
population=read.csv("UNdata_Export_20140930_population.csv", nrows=12846, header=T, row.names=NULL)
require("sqldf")
require("plyr")
population=rename(population, replace = c("Country.or.Area" = "Country"))
population=sqldf("SELECT a.Country, a.Year, a.Value as Population
FROM population a INNER JOIN (SELECT Country, MAX(Year) AS Year FROM population GROUP BY 1) b
      ON (a.Country=b.Country AND a.Year=b.Year)")
cellular=rename(cellular, replace = c("Country.or.Area" = "Country"))
cellular=rename(cellular, replace = c("Value" = "Cellular"))
obese=rename(obese, replace = c("Country.or.Area" = "Country"))
obese=rename(obese, replace = c("Year.s." = "Year"))
obese=sqldf("SELECT a.Country, a.Year, SUBSTR(TRIM(Value), 1, CHARINDEX(' [', TRIM(Value))) as Obeses
FROM obese a INNER JOIN (SELECT Country, MAX(Year) AS Year FROM obese WHERE GENDER='Both sexes' GROUP BY 1) b
ON (a.Country=b.Country AND a.Year=b.Year AND a.GENDER='Both sexes')")
obese$Obeses=as.numeric(obese$Obeses)
data=sqldf("SELECT a.Country, a.Cellular, c.Obeses, b.Population FROM cellular a inner join population b on a.Country = b.Country
      inner join obese c on (a.Country = c.Country) WHERE a.Country NOT IN ('World', 'South Asia')")
require(ggplot2)
require(scales)
opts=theme(
  panel.background = element_rect(fill="gray98"),
  panel.border = element_rect(colour="black", fill=NA),
  axis.line = element_line(size = 0.5, colour = "black"),
  axis.ticks = element_line(colour="black"),
  panel.grid.major = element_line(colour="gray75", linetype = 2),
  panel.grid.minor = element_blank(),
  axis.text.y = element_text(colour="gray25", size=15),
  axis.text.x = element_text(colour="gray25", size=15),
  text = element_text(size=20),
  legend.key = element_blank(),
  legend.position = "none",
  legend.background = element_blank(),
  plot.title = element_text(size = 45)
    )
ggplot(data, aes(x=Cellular/100, y=Obeses/100, size=Population, label=Country), guide=FALSE)+
  geom_point(colour="white", fill="red", shape=21, alpha=.65)+
  scale_size_continuous(range=c(3,35))+
  scale_x_continuous(limits=c(0,2.1), labels = percent)+
  scale_y_continuous(limits=c(0,.6), labels = percent)+
  labs(title="The World We Live In #1: Obesity And Cells",
       x="Cellular Subscribers (per 100 population)",
       y="Adults aged >= 20 years who are obese (%)")+
  geom_text(data=subset(data, Cellular/100 > 1.9 | Obeses/100 > .4 | (Cellular/100 > 1.4 & Obeses/100 < .15)), size=5, colour="gray25", hjust=0, vjust=0)+
  geom_text(aes(.9, .0), colour="blue", hjust=0, label="World's Countries (Source: United Nations Statistics Division. Size of bubble depending on population", size=4)+
  opts