Here we illustrate markdown, knitr, and ggplot2 to motivate what’s to come. We’ll use a dataset on 10000 measurements of height and weight for men and women available through the book Machine Learning for Hackers, Drew Conway & John Myles-White, O’Reilly Media.

We begin by loading packages.

setwd("~/Documents/Computing with Data/2_Motivation/")
library(ggplot2)

First load the data.

ht_weight_df <- read.csv(file="../Data/01_heights_weights_genders.txt")
# str is short for structure(). It reports what's in the data.frame
str(ht_weight_df)
## 'data.frame':    10000 obs. of  3 variables:
##  $ Gender: Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Height: num  73.8 68.8 74.1 71.7 69.9 ...
##  $ Weight: num  242 162 213 220 206 ...
head(ht_weight_df)
##   Gender Height Weight
## 1   Male  73.85  241.9
## 2   Male  68.78  162.3
## 3   Male  74.11  212.7
## 4   Male  71.73  220.0
## 5   Male  69.88  206.3
## 6   Male  67.25  152.2

Relationship between height and weight

We certainly expect dependence between height and weight. Let’s plot the points on a plane and see what it looks like.

plot(x= ht_weight_df$Height, y=ht_weight_df$Weight)

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It really does look like there is a strong linear relationship. Execute an lm fit to see.

lm_ht_weight <- lm(Weight ~ Height, data = ht_weight_df)
summary(lm_ht_weight)
## 
## Call:
## lm(formula = Weight ~ Height, data = ht_weight_df)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -51.93  -8.24  -0.12   8.26  46.84 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -350.7372     2.1115    -166   <2e-16 ***
## Height         7.7173     0.0318     243   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.2 on 9998 degrees of freedom
## Multiple R-squared:  0.855,  Adjusted R-squared:  0.855 
## F-statistic: 5.9e+04 on 1 and 9998 DF,  p-value: <2e-16

There is an extremely strong linear relationship.

How do height and weight depend on gender?

We expect that they do. Inspect the quantiles of height after restricting to each geneder.

# Subset the full data.frame by genders
male_df <- subset(ht_weight_df, Gender == "Male")
female_df <- subset(ht_weight_df, Gender == "Female")
# Get the summary values of height
summary(male_df$Height)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    58.4    67.2    69.0    69.0    71.0    79.0
summary(female_df$Height)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    54.3    61.9    63.7    63.7    65.6    73.4

Comparing the densities by gender

We see that the means and other order statistics are shifted. Are the entire distributions shifted, or is there some skewing by geneder?

plot(density(male_df$Height))

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plot(density(female_df$Height))

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For men there is a little bump around 6 feet. (Maybe the measurers generously rounded up the guys who are 5’11.5“.) However the distributions look pretty close. It would be best if we could overlay the plots.

Using ggplot2 to overlay plots

The package ggplot2 has had a significant impact on visualization in statistics. It will change the way you work.

dens_by_gender <- ggplot(data = ht_weight_df, aes(x=Height, color=Gender)) + geom_density()
dens_by_gender

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This makes it more clear. It looks like these are two normal distributions with different means. We can check a Q-Q plot.

qq_by_gender <- ggplot(data = ht_weight_df, aes(sample=Height)) + 
  geom_point(stat="qq") + facet_wrap(~ Gender)
qq_by_gender