Graphical Anlaysis of Trends in Name Data

gistfile1.txt

require(plyr)
require(ggplot2)
require(scales)

# Download data from:
# http://www.ssa.gov/oact/babynames/names.zip
setwd("C:/Data/SS-names/")
files<-list.files()
files<-files[grepl(".txt",files)]

###### Reading files
namedata <- matrix(0,ncol=4,nrow=0)

for (i in 1:length(files))
  namedata<-rbind(namedata,
    cbind(read.csv(files[i],header=F), substr(files[i],4,7)))

colnames(namedata)<-c("name","gender","count", "year")

dim(namedata)
# 1.8 million rows

Mdata<-namedata[namedata$gender=="M",]
Fdata<-namedata[namedata$gender=="F",]

Msums <- ddply(Mdata, .(name), summarize, sum=sum(count))
Fsums <- ddply(Fdata, .(name), summarize, sum=sum(count))

nrow(Msums); nrow(Fsums)
# There are 38601 male names and 64089 female names

Morder <- Msums[order(Msums[,2], decreasing = TRUE),]
Forder <- Fsums[order(Fsums[,2], decreasing = TRUE),]

c <- ggplot(Morder[1:20,], aes(x = name, y = sum, size=sum))
c + geom_point() + coord_flip() + theme(legend.position="none")+
  ggtitle("20 Most Popular Male Names Since 1880")+
  xlab("")+scale_y_continuous(name="Names Recorded With Social Security Administration", labels = comma)
# Figure 1
  
c <- ggplot(Forder[1:20,], aes(x = name, y = sum, size=sum))
c + geom_point() + coord_flip() + theme(legend.position="none")+
  ggtitle("20 Most Popular Female Names Since 1880")+
  xlab("")+scale_y_continuous(name="Names Recorded With Social Security Administration", labels = comma)
# Figure 2

Mdata$order <- Fdata$torder <- NA # Create a variable for 
Mdata$prop <- Fdata$prop <- NA

for (i in 1880:2013) {
  Mdata[Mdata$year==i, "torder"] <- 
    order(-Mdata[Mdata$year==i, "count"])
  Mdata[Mdata$year==i, "prop"] <- 
    (Mdata[Mdata$year==i, "count"])/
    sum((Mdata[Mdata$year==i, "count"]))
  Fdata[Fdata$year==i, "torder"] <- 
    order(-Fdata[Fdata$year==i, "count"])
  Fdata[Fdata$year==i, "prop"] <- 
    (Fdata[Fdata$year==i, "count"])/
    sum((Fdata[Fdata$year==i, "count"]))  
}

top <- 7

Mrestricted <- Mdata[Mdata$name%in%Morder[1:top,1],]
Frestricted <- Fdata[Fdata$name%in%Forder[1:top,1],]

ggplot(Mrestricted, aes(x=year, y=count, group=name, color=name))+
  geom_line(size=1)+scale_x_discrete(breaks=seq(1880,2010,20))

ggplot(Mrestricted, aes(x=year, y=prop, group=name, color=name))+
  geom_line(size=1)+scale_x_discrete(breaks=seq(1880,2010,20))+
  ylab("Proportion of Total Names")

ggplot(Mrestricted, 
       aes(x=year, y=torder, group=name, color=name, size=torder))+
  geom_line()+scale_x_discrete(breaks=seq(1880,2010,20))+
  ylab("Order of Total Names That Year (log10)")+scale_y_log10()

ggplot(Frestricted, aes(x=year, y=count, group=name, color=name))+
  geom_line(size=1)+scale_x_discrete(breaks=seq(1880,2010,20))

ggplot(Frestricted, aes(x=year, y=prop, group=name, color=name))+
  geom_line(size=1)+scale_x_discrete(breaks=seq(1880,2010,20))+
  ylab("Proportion of Total Names")

ggplot(Frestricted, 
       aes(x=year, y=torder, group=name, color=name, size=torder))+
  geom_line()+scale_x_discrete(breaks=seq(1880,2010,20))+
  ylab("Order of Total Names That Year (log10)")+scale_y_log10()