So to make sure I have it right - you have data set A, you want to see if data set X, Y, or Y best correlates to data set A?
There are a 2 ways to do this...The first, and simplest, is to do a simple correlation - which is basically your line chart. In excel the function is =CORREL(). The output the r value which tells you how strong the corellation is. The values are 0 to 1. 0 means there's no correlation. 1 means a perfect correlation. If you square the r you get a more sensible estimation of the strength of the correlation. You could fill in this sentence: Variable X accounts for <R^2>% of the variation in variable A.
The other thing you could do is a bit more powerful, but more complex. It's called multiple regression. The point of multiple regression is drop all of the variables into one (generally) linear formula to predict the dependent variables...Basically, you drop X, Y, and Z into the regression and you get the following formula that predicts A:
A = B(sub 0) * Constant + B(sub1) * X + B(sub 2) * Y + B(sub 3) * Z
This should look familiar - it's the basis for your old middle school y = mX + B.
The output will tell you the r value and p value (significance) of the model. The Beta values are the unique correllation coeficients for the data sets. They each have their unique r's and p values that tells you if they're actually significant unique predictors of variation in the model or just dead weight.
This method is more complex, but it is also more powerful. It really shouldn't be TOO hard in R. I've never used R before though, so I'm not totally sure how to walk you through it. I'd be happy to explain more if you'd like - but school just let out and it's birthday burgers and whiskey time!