Plot graph with more than one value on x and y-axis in R I have a dataset containing two columns and a total of 90 rows. The data is from my experiment where in the first column I have an integer representing the quantity, in the second column I have a percentage. A small example:
Quantity   Percentage
1          53%
1          51%
1          67%
2          73%
2          69%
3          73%
...        ...

As you can see in both columns the numbers can occur more than once. Now I wish to plot this in a graph (I was thinking a scatter plot) in R. I just am a real beginner in using R and statistics so I was hoping someone can help me out how to get a good graph. If someone has an other suggestion that would give a better representation, shoot!
I just need to have a visual representation that shows the correlation between the two values.
 A: If the percentages are ratios of counts, I agree with whuber's concern about the proportions, so it would be good if you could confirm if that's the case. 
As a matter of data visualization, you're dealing with coincident points (a multiplicity of points at some locations) where there's a need to show those points.
Here's an example with 30 points, where you only see 23 because the remaining 7 lie on top of earlier points:

There are numerous techniques for plotting such overlaid points.


*

*Jittering. 
Points can have a small amount of random noise added to the x and y values so they become slightly offset from each other

We can suddenly see there's quite a few points at $(3,3)$ that were not obvious before; this changes the impression of where the centers of the two variables lie. 
A similar approach can be seen for ordered categorical variables here

*Plotting with transparency
If points are plotted with a transparency (alpha) level, a single point looks "faint" while multiple points in one position look more solid, making the greater density of points obvious by a greater density of color.

(here generated with plot(xx,yy,col=rgb(0,100,0,70,maxColorValue=255), pch=16))
[Added in later edit: I somehow seem to have changed my example data after this point. I am not sure how it occurred, but it doesn't especially matter except for the fact that the later plots aren't quite identical to the earlier ones. I am not going to regenerate them all as it doesn't alter the ideas.]

*Symbols to indicate multiplicity
You can plot symbols that directly indicate the value in some way, and through size and weight of symbols attempt to give a rough second impression of the relative density. Here are some that might be used.

So for our data:

A very simple version of that approach is to simply plot a count of the multiplicity ("1", "2", "3" etc). It's very easy to do but it doesn't really convey the visual impression well, and I decided not to include the example, but I can put it up if anyone cares.
More sophisticated versions of this approach can be implemented, such as sunflower plots (see, for example, ?sunflowerplot in R):

The advantage of the sunflower plot is it's a bit more automatic to do, and it can handle high multiplicity without fiddling about with symbols.

*Stacking (This one was suggested by Nick Cox in comments)

While it might run into problems if there were a large range of values on the x-axis (so the space between them might be too small to accommodate a high multiplicity of points), I think this works fairly well for my example data. It should be possible to squeeze the points up a bit more/draw them smaller, and so fit a slightly higher multiplicity in. In cases where there were mostly multiplicity of 1, 2 or 3, I think this is a highly competitive approach - it came out better than I thought.

*Using area to convey point multiplicity

Here again, amount of ink indicates number of points (by making symbol size $\propto\sqrt n$).
