If you think in terms of linear models instead of going directly to ANOVA you may see something very important in the data. Consider the following plot of the data for looking at the response coke1 as a function of coke0 and sex. You'll notice that the responses for Males are perfectly linear making the use of a linear model moot as we already know the 'line of best fit' since the data is a line.

library(ggplot2)
ggplot(dat) +
  aes(x = coke0, y = coke1, color = sex) + 
  geom_point() + 
  stat_smooth(method = 'lm')

(Being a new member on this site, I cannot post an image yet, but try this link. Once I am allowed to post images I'll be back to post the image in this post.) https://i.sstatic.net/2SfaW.jpg

You may continue with an ANOVA, I would do so as: g <- lm(coke1 ~ sex + coke0, data = dat) plot(g) # graphics to check some model assumptions anova(g)

The ANOVA results should look like: Analysis of Variance Table

Response: coke1
          Df  Sum Sq Mean Sq F value    Pr(>F)     
sex        1 128.000 128.000  72.804 0.0003639 ***
coke0      1  49.209  49.209  27.989 0.0032170 ** 
Residuals  5   8.791   1.758                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

If you think in terms of linear models instead of going directly to ANOVA you may see something very important in the data. Consider the following plot of the data for looking at the response coke1 as a function of coke0 and sex. You'll notice that the responses for Males are perfectly linear making the use of a linear model moot as we already know the 'line of best fit' since the data is a line.

library(ggplot2)
ggplot(dat) +
  aes(x = coke0, y = coke1, color = sex) + 
  geom_point() + 
  stat_smooth(method = 'lm')

You may continue with an ANOVA, I would do so as:

g <- lm(coke1 ~ sex + coke0, data = dat)
plot(g) # graphics to check some model assumptions
anova(g)

The ANOVA results should look like:

Analysis of Variance Table

Response: coke1
          Df  Sum Sq Mean Sq F value    Pr(>F)     
sex        1 128.000 128.000  72.804 0.0003639 ***
coke0      1  49.209  49.209  27.989 0.0032170 ** 
Residuals  5   8.791   1.758                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1