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Graphical Anlaysis of Trends in Name Data
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()
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