In R, basic correlation tests are executed with two commands: cor() and lm() (where lm stands for linear model).

Calculating correlation

To calculate product moment correlation coefficient between Maxle and Maxwi for bronze spears:

> Bronze = subset(spearhead, subset=Mat=="1")
> cor(Bronze$Maxle, Bronze$Maxwi)
[1] 0.6892216

To calculate Spearman's rank correlation coefficient between Date and Weight for bronze spears:

> cor(Bronze$Date, Bronze$Weight, method="spearman")
[1] 0.1269293

Plotting correlation

To draw a scatterplot for Maxle and Maxwi:

> plot(Bronze$Maxle, Bronze$Maxwi)

The scatterplot by itself is already interesting, but R gives us another interesting function with the lm() command (where lm stands for linear model).

> result <- lm(Bronze$Maxwi ~ Bronze$Maxle)
> result

lm(formula = Maxwi ~ Maxle)

(Intercept)        Maxle  
     1.5053       0.1277  

  1. note that the order of arguments to lm() is inverse: the basic use is lm(y ~ x) (with y as dependent variable)
  2. the result of lm() is a rect. You can see by yourself plotting it over the scatterplot
> abline(result$coefficients, col="blue")

The composition of scatterplot and linear model

Plotting the lm() result by itself like

> plot(result)

gives you more informative graphs about the linear model, but their content is beyond the scope of this tutorial.