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Okay, so now we're going
to start with a simple bar

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plot of the MIT
international student data.

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So first, let's load the
ggplot library, ggplot2,

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and load the data frame.

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So intl = read.csv("intl.csv").

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Now, the structure of this
date frame is very simple.

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There are two columns,
two variables.

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The first one, the
region, and the second one

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is the percentage of
international students

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who came from that region.

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So making a bar plot from
this data isn't too hard.

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We start off with
a ggplot command,

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of course, the first argument
being the data frame.

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The aesthetic in this case is
to have Region on the x-axis,

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and on the y-axis, to
have the percentage

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of international students.

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Now, the geometry
we're going to use

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is, as you might
guess, bar, geom_bar.

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We have to pass one
argument to this geom_bar,

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and it's called
stat = "identity" .

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I'm going to come back and
explain what that means.

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I also want to label
my bars with the value,

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so it's easy to read
in closer detail.

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So I'm going to use
geom_text to do that.

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And the aesthetic of
our text is simply

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to have the value of a
label, the text of a label,

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to be the value of
our percentages.

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Let's look at that.

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So yes, we have a
bar for each region.

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The values are
between zero and one,

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which looks kind of strange.

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The labels are
actually lying over

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the top of the columns,
which isn't very nice,

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and the regions
aren't really ordered

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in any way that's useful.

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They're actually ordered
in alphabetical order,

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but I think it would be much
more interesting to have them

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in descending order.

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So we're going to work on this.

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First of all, though, what
is this stat = "identity"?

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Well, it's pretty simple.

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Geometry bar has multiple
modes of operation.

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And stat = "identity" says,
use the value of the y variable

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as is, which is what we want.

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The height of the bar is
the value of the y variable.

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Now, there are other
modes, including

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one that counts
the number of rows

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for each value of x,
and plots that instead.

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So you can look at the
documentation for ggplot

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to see the different
options and how they work.

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But stat = "identity" is
what we want right now.

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Now, the x-axis is out of order.

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And the reason for this
is that ggplot defaults

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to alphabetical
order for the x-axis.

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What we need to do is make
Region an ordered factor

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instead of an unordered factor.

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We can do this with
the reorder command

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and the transform command.

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So let's write this out.

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So we're going to transform
the international data frame.

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And what we're going to
do is say, Region, it's

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going to be a
reordering of itself,

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based on decreasing
order of PercentOfIntl.

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So if we look at the structure
of the data frame now,

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we see there's something going
on in the Region column that

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wasn't going before.

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And that's that ordering.

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So you might have also noticed
that I put a negative sign

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in front of PercentOfIntl.

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So that negative sign
means decreasing order.

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If we had left that out,
it would have actually

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ordered them in
increasing order.

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So unknown or stateless
would have been first,

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and Oceania would have
been second, and so on.

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So that's one thing fixed.

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Another thing we didn't
like was that the numbers

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were between zero and one,
which looks a little bit messy.

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So let's just simply multiply
all the values by 100.

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So intl$PercentOfIntl =
intl$PercentOfIntl*100.

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And now the other
things we have to fix,

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like the text overlying and
the x-axis being all bunched up

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like that, we're going to do
that in a new ggplot command.

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So I'm going to break it
across multiple lines.

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So we start up with the ggplot
command, as we did before,

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actually identical to
what we had before.

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So the aesthetic is
x-axis is the region,

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and the y-axis is the percentage
of international students.

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We break it into multiple lines,
so put the plus at the end

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there, and press Shift Enter.

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We're going to do a bar plot.

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The stat =
"identity", as before.

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And this time
though, we're going

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to manually specify a fill.

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I'm going to say "dark blue".

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I quite like how that looks.

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Now, we need the text,
and the aesthetic of that

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is to have the label equal
the value of the column.

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I'm going to add one
more thing to this.

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I'm going to say vjust = -0.4.

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And what this does is, it moves
the labels up a little bit

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and off the top of the bars.

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You can play with that.

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So a positive value
will move it down,

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and a negative value
will move it up.

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Next, I'm going to
set the y-axis label

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to be something a
bit more sensible--

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so "Percent of
International Students".

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And finally, I'd like
to fix up that x-axis.

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So I want to get rid of the
word "Region," because it's

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pretty obvious
these are regions.

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And I also want to rotate the
text at a bit of an angle,

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so you can read it all
on a plot like this.

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That's done with
the theme command.

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So the theming
we're going to do is

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we're going to say the
axis title, the x-axis,

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should be blank.

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And the axis text on the
x-axis should be rotated,

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so it's a text element
that's angle is 45.

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And I'll move it sideways
just a little bit-- hjust = 1.

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And there we go.

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So we've got our labels
vjust-ed above the columns.

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The bars themselves
are dark blue.

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The numbers are now
between 0 and 100,

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instead of zero and one.

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We can read all the text labels.

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And it's generally
a lot more readable

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than the pie plot or our
original ggplot, at that.

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Let's go back to the
slides now and talk

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about what we've just done.