13 Exploration and simple univariate measures

path <- file.choose()    # look for BRFSS-subset.csv

stopifnot(file.exists(path))
brfss <- read.csv(path)

13.1 Clean data

R read Year as an integer value, but it’s really a factor

brfss$Year <- factor(brfss$Year)

13.2 Weight in 1990 vs. 2010 Females

Create a subset of the data

brfssFemale <- brfss[brfss$Sex == "Female",]
summary(brfssFemale)

##       Age            Weight           Sex            Height     
##  Min.   :18.00   Min.   : 36.29   Female:12039   Min.   :105.0  
##  1st Qu.:37.00   1st Qu.: 57.61   Male  :    0   1st Qu.:157.5  
##  Median :52.00   Median : 65.77                  Median :163.0  
##  Mean   :51.92   Mean   : 69.05                  Mean   :163.3  
##  3rd Qu.:67.00   3rd Qu.: 77.11                  3rd Qu.:168.0  
##  Max.   :99.00   Max.   :272.16                  Max.   :200.7  
##  NA's   :103     NA's   :560                     NA's   :140    
##    Year     
##  1990:5718  
##  2010:6321  
##             
##             
##             
##             
## 

Visualize

plot(Weight ~ Year, brfssFemale)

Statistical test

t.test(Weight ~ Year, brfssFemale)

## 
##  Welch Two Sample t-test
## 
## data:  Weight by Year
## t = -27.133, df = 11079, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -8.723607 -7.548102
## sample estimates:
## mean in group 1990 mean in group 2010 
##           64.81838           72.95424

13.3 Weight and height in 2010 Males

Create a subset of the data

brfss2010Male <- subset(brfss,  Year == 2010 & Sex == "Male")
summary(brfss2010Male)

##       Age            Weight           Sex           Height      Year     
##  Min.   :18.00   Min.   : 36.29   Female:   0   Min.   :135   1990:   0  
##  1st Qu.:45.00   1st Qu.: 77.11   Male  :3679   1st Qu.:173   2010:3679  
##  Median :57.00   Median : 86.18                 Median :178              
##  Mean   :56.25   Mean   : 88.85                 Mean   :178              
##  3rd Qu.:68.00   3rd Qu.: 99.79                 3rd Qu.:183              
##  Max.   :99.00   Max.   :278.96                 Max.   :218              
##  NA's   :30      NA's   :49                     NA's   :31

Visualize the relationship

hist(brfss2010Male$Weight)

hist(brfss2010Male$Height)

plot(Weight ~ Height, brfss2010Male)

Fit a linear model (regression)

fit <- lm(Weight ~ Height, brfss2010Male)
fit

## 
## Call:
## lm(formula = Weight ~ Height, data = brfss2010Male)
## 
## Coefficients:
## (Intercept)       Height  
##    -86.8747       0.9873

Summarize as ANOVA table

anova(fit)

## Analysis of Variance Table
## 
## Response: Weight
##             Df  Sum Sq Mean Sq F value    Pr(>F)    
## Height       1  197664  197664   693.8 < 2.2e-16 ***
## Residuals 3617 1030484     285                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plot points, superpose fitted regression line; where am I?

plot(Weight ~ Height, brfss2010Male)
abline(fit, col="blue", lwd=2)
points(180, 88, col="red", cex=4, pch=20)

Class and available ‘methods’

class(fit)                 # 'noun'
methods(class=class(fit))  # 'verb'

Diagnostics

plot(fit)
?plot.lm