This is the first in a series of blog posts in which I use the R package GGPlot2 to examine real world data. In this post, I construct a line graph of U.S. shoe consumption from 1995 to 2007.
A recent survey conducted by Shop Smart magazine found that the average woman in the US owns approximately 17 pairs of shoes. Furthermore, a survey conducted in 2007 found that the average male in the US owns 3 shoes, with younger men owning more shoes that their older counterparts. It’s no wonder that the US shoe market is $13 billion-per-year industry. In the following graph, you see the increase in US Shoe consumption from 1995 to 2007. The one factor that has really contributed to the increase in shoe sales has been the growth in people using athletic shoes for casual wear. Prior to the 1980′s, athletic shoes were not regularly worn as peoples’ primary shoes for everyday usage. In many ways, the emergence of hip hop music during the 1980′s had a significant impact in making athletic shoes and sneakers a more prominent part of how people dressed. It would be very interesting to find data on sneaker sales in the U.S. from the mid-1970′s to date, and it would likely show a huge increase in the consumption of those types of shoes. Unfortunately, I only have data on shoe consumption for this limited time period. In any case, the R code that I used to construct this plot can be found below.
ggplot(dat, aes(year)) + geom_line(aes(y = consumption), size = 1) + opts(title = "Shoe Consumption in the US (1995-2007)", size=12) + opts(plot.title = theme_text(size = 12, face = "bold")) + ylim(c(0,3)) + xlab("") + ylab("Number of Shoes (in billions)") + opts(axis.text.y = theme_text(family = "sans", face = "bold", size = 10)) + opts(axis.text.x = theme_text(family = "sans", face = "bold", size = 10)) + opts(plot.margin = unit(c(0.3, 0.3, 0.3, 0.1), "lines"))