And then here I'm going to add one so that the values won't be messed up.
And so it didn't really help me this time, but you, in general,
can sort of make different transforms to try to see if the data look good.
You can also plot more than one value at once, so here I'm going to do that
same box plot, but now instead of just selecting out the first column,
I'm going to box plot apply to the entire matrix.
And so when I do that, I actually get one box plot per sample, so
one box plot per column of that data set.
And then I can set the color to be a color that I like.
And then, in order to not have all those outlying points,
I can set range equal to zero, and so then it will make this sort of whisker extend
all the way out to the most extreme outlying point.
So here I can already see again that the data is pretty skewed for
all these different samples.
And you can see it because most of the values are squished down here near zero.
The other thing that you can do is you can make histograms.
So I'm actually going to show two histograms.
And if I want to show plot side by side, I can use the par(mfrow=c(1,2)).
This is basically saying set up the screen so
that it has one row and two columns of plots.
So it's side by side plots.
So I can make a histogram of the values from the first sample.
So again I'm going to do this transform here because I
think it makes it a little easier to see.
And then I'm going to set the color equal to two, so
this is going to make this nice blue histogram.
And so you can see here right away that almost all the values are equal to zero.
And then you see some values out to the right there.
And so, then, I can make that same plot.
But for the second sample, and so, when I do that,
