What is Sexy?
There have been several report on how our society has been changing it's ideal of attractive / sexy over the years. More than one lady I have know, have been concerned about that they were "Fat" or not sexy because they didn't look like the models. Back in 2009 Wired Magazine did an article titled Infoporn: Today's Playmates Are More Like Anime Figures Than Real Humans that looked at how Playboy Playmate's Body Mass Index (BMI) had changed over the years. They showed a steady decrease in the Playmate's BMI. The CDC defines a BMI between 18.5 and 24.9 is healthy. 25 and over is overweight and under 18.5 is underweight.
So I took the data and updated to April 2015 using the data from WeKinglyPigs. Here is what I found:
Bad News
48% of the playmates were underweight!
There was a decreasing trend from 1953 - 1987
The median playmate BMI was below (under weight) 29 of the 35 years (83%) from 1977 - 2012
Good News
The trend started to go upward around 1987
Some of the ladies that have been considered sex symbols were in the normal range
Here is the code to do the 2 graphs in R. Files are attached below.
#Code for ploting the points.
#set the dir to where you data is
setwd("c:/R")
#Load your data. The data is in a spreadsheet named simple with NDs.csv and we are going to call it data in R
mydata <- read.table("playmatebmi.csv", header=TRUE, sep=",",)
attach(mydata)
reg1 <- lm(BMI~Year)
par(cex=1)
#Plots the data but makes nondetects a different color and type based on column D_BMI being a 0 for ND and 1 for detect.
plot(Year, BMI, col=ifelse(D_BMI, "black", "red"),ylab = "BMI", pch=ifelse(D_BMI, 19, 19), cex = 0.5)
# Apply loess smoothing using the default span value of 0.8. You can change the curve by changing the span value.
y.loess <- loess(y ~ x, span=0.8, data.frame(x=Year, y=BMI))
# Compute loess smoothed values for all points along the curve
y.predict <- predict(y.loess, data.frame(x=Year))
# Plots the curve.
lines(Year,y.predict, lwd=3.5, lty=2)
# Plots line for underweight limit of 18.5
abline(a = NULL, b = NULL, h = 18.5, v = NULL, reg = NULL,
coef = NULL, untf = FALSE)
#Add Legend to graph. You can change the size of the box by changing cex = 0.75 Large # makes it larger.
legend("topleft",
c("Smoothing Curve", "Underweight BMI<18.5 ", "Playmate’s BMI
(Red = Under weight)"),
col = c(1, "black","black"),
cex = 0.5,
text.col = "black",
lty = c(1 ,1, -1),
lwd= c(3.5,1,1),
pch = c(-1, -1, 19),
merge = TRUE, bg = 'gray90')
#Add title
title(main="Playboy Playmate BMI 1953 - 2013")
# Done
#Box Plot Code
#set the dir to where you data is
setwd("c:/R")
#Load your data. The data is in a spreadsheet named nd-spreadsheet and we are going to call it data in R
mydata <- read.table("Playmate-decade-data.csv", header=TRUE, sep=",",)
#makes the notched box plot. "mydata" = the name of the data in R.
boxplot(mydata, notch=TRUE,
# Gives the title and axis names
main="Box Plots of Playmate BMI", xlab="Year", ylab="BMI",
#Sets the colors
col=(c("gold","darkgray", "darkorchid1", "cyan", "white", "red","limegreen", "magenta", "chartreuse1", "hotpink1")))
# Adds the line at 18.5
abline(a = NULL, b = NULL, h = 18.5, v = NULL, reg = NULL,
coef = NULL, untf = FALSE)
# Done