So in this tutorial we're going to be
talking about different types of plots
that could be useful for data analysis.
So the first type of plot if you've ever
used Excel or watched a PowerPoint
presentation, you've probably seen this
probably seen most of these actually, so
the bar plot. So suppose we have some
sequence of data let's, just say x equals
1 to 10, and we want to plot this on a
bar chart. We just call the method bar,
pass in X, and we see the bar chart. So
this does pretty much the exact same
thing as a plot function except instead
of connecting them all in a line
it connects well it doesn't connect the
points at all it gives them each their
own individual bar. So, just for reference
let's do let's do our sine example again.
Ok, so we're going to do bar( X, Y),
right, so this is a bar chart of the sine
function.
Ok, so the next type of plot that I want
to talk about is the histogram. So if
you've ever seen, if you've ever studied
probability you know about probability
distributions and given a data set we
might want to figure out what kind of
distribution does it follow, so let's
generate some data where we know the
distribution. So i'm going to create a
thousand points that are normally
distributed. So if I plot this it doesn't
do much it just looks like noise, so I
won't be able to see much from that. What
you want to do is you want to call a
function called hist which will
automatically create a histogram out of
your data. So i'm going to call the
simplest version which is hist of
just the data right, and so you see that
it's pretty much normally distributed.
There's a little bit more weight on the
right bar than the left bar in the
center and so the the resolution for
this chart is not that great. So what you
can do is you can set the number of bins
right, so if you have a lot of data, in
our case a thousand points, you can set
the bins to maybe say 50, ok, and so this
gives us a much more granular
representation of the data. So now we can
see it still looks normal,
you know there's a little less data than
expected kind of right here at zero,
and then a little more to the right of
that. But, generally speaking this plot is
pretty much what we expect.
The next type of plot that I want to
talk about, again if you use Excel you've
probably seen this or PowerPoint, is a
pie chart. So we're going to work with
some simple data again let's say x is
1 to 5, and all I do is call a function
called pie, pass in the data right, and so
it automatically splits up the data. So
the dark blue is the first element, light
blue is the second element, green is the
third element, and so on. And so there are
ways that you can label this to to make
your visualization better and so when
you show it to people they know what
everything stands for,
so there's a lot of documentation on the
matlab website for that. The last type of
plot that I want to talk about, probably
the most useful for data analysis, is
called the scatterplot. So we're going to
again return to our sine example, so
x = linspace(0,2*pi,1000);
So now i'm going to do
something a little different, i'm going
to set it y = 10*sin(X) + randn(0,1000)
and then I'm going to add some random
noise with variants 1 centered at 0,
and so the reason why I want to scale it
by 10 is so that the sine wave is big
compared to the noise
otherwise the noise will just take over
the shape of the sine wave. I did that
wrong.
Ok, so now if I were to just plot x and y
so you can see a noisy sine wave which
is ok, if I do it in a scatterplot I can
see that pattern pretty much just as
well. So the nice thing about our sine X
example was that the data was ordered.
Now suppose that my data is not ordered
so instead of having linspace my X is
just a bunch of random points. So X is
normally distributed with a standard
deviation of 2, and so Y is going to
be let's say 5*sin(X) plus some
noise.
Ok, so if I plot x and y now I don't
really see anything it just looks like a
bunch of craziness, but scattering so a
scatterplot that will just plot all the
dots so all the individual data points
right, and so I can now very clearly see
the sine wave that I'm supposed to see.
So if you're doing data analysis and your
points are not ordered, you want to use a
scatterplot.