Histograms in R

NARRATOR: Hi! In this video,

we are going to be talking about
graphical data summary

for numerical data.

We are going to be, um,

using the iris dataset,

for this.

And the first graphical data summary
we're going to talk about are histograms.

So, if you remember from the iris dataset,

there were four different
numerical variables you could choose from.

Sepal length, sepal width,
petal length and petal width.

And I'm going to use
the petal length, um, variable today.

So, to create a histogram of a--
of a numeric var-variable,

you will use the function, "hist()."

And then, you can just go ahead and put in

whatever data you want
to produce a histogram for.

Since this is already
a numerical variable,

it's good to go!

All you need is just this.

And here you can see a histogram,

of all the various petal lengths.

There's a lot of them that are
in between one and one and a half,

and, yknow, fair amount over here.

Definitely not really
normally distributed,

not like a bell curve.

Kind of, got a couple of peaks.

So, if you want to customize
your...histogram,

here are some additional arguments
that you can put in, to make it,

more pretty, I guess.
[chuckles]

So first one is,
similar to categorical data

—when we were doing the graphical.

You can create a title.

So there is a default title here,
"Histogram of..."

But if you don't really want
this R syntax to be showing,

you can totally change it.

So...

Let's do...

"Histogram of Iris Petal Lengths."

There we go, much better.

We can also, um, add,

x-labels for the x-axis,

so if we didn't want
to have that there, as well.

You can also change the y-label,

but I'm going to keep it just like that.

So, and you know,
these are all available to you,

but you don't have to change all of them.

Just kind of whatever you feel, so.

I'm going to call this, "Petal Length."

There we go, looks much better down here.

You can change the limits
of your x-axis and your y-axis.

So obviously, with the Y,

it's not quite reaching
all the way up there.

So I'm going to change that.

And then, I like to always have
a little buffer on the sides, too,

so maybe we could have it
go from zero to eight or something.

So for the x-axis, I'm going to change
my limits to go from 0 to 8.

And my y-axis, I'm going to have
the axis go from 0 to 60.

Now,

you just kind of just play around
and find out which one looks good,

and kind of when you figured out
what you like the best.

There really is no right or wrong, um,

magic number.

Just kind of--to make it look nice,
is all we're really kind of doing.

Alright, so now you can see here.

Maybe 60 was a little too much, but.

Eh, it works.

But now you can see there's a side,
buffers on the side.

You can see it easily,
where it easily starts and ends.

So, I probably actually might take
that in a little more

and squish that down
a little more, but, y'know.

For our purposes, it's probably just fine.

There is another argument that you can do
that's new that's called "breaks,"

where "breaks" kind of specifies
a suggested number of bins.

It can't always perfectly say like,

so if I want to say breaks--

And by bins,
I'm meaning how many boxes,

it's using and how many
to create the histogram.

So if I were to say like eight,

it's going to try and do
its very best to create it,

so there's only eight boxes.
So there's one, two,

There would technically be three,
4, 5, 6, 7, 8, 9, 10, 11, 12,

right now, let's see
how well it can do with eight.

If you specify seven
it might do six or eight,

just kind of depending on how the data is,
it does its very best to match,

whatever number you put there.

So you see here we got
one, two, three, four, five, six.

So yeah, So it definitely
made it so that the shape,

of the graphic looks different.
The you know, the bars are wider.

But it didn't quite do exactly as eight.

So that one is,
you just kind of have to do your very best.

If you want to learn more
about how you can kind of be,

more specific on how you
can set the actual breakpoints,

you should take
statistical visualization,

or, you can research how to do that,

or you can use the "ggplot" package
And that's pretty great as well.

Another thing you can do is
you can say labels is equal to true,

and what this will do is that it
will put the counts in,

in each category on top of the bars.

So you can see that there were
50 in between one and two,

one between two and three,
15 between three and four, etc...

Another, And then the last argument,

or actually the second to last argument,
is you can always change the color.

And I'm going to pick
a color called sky blue.

Doesn't that look pretty.
Alright,

the one other thing we will talk about
is sometimes you could have,

a frequency histogram
where you just say how many were,

in between one and two,
two and three and put them in there.

Or you can do a, based off
of kind of probability densities.

So meaning this is,

probably, you know,
what proportion of the data lies in here.

So instead of having these be,

frequency numbers they would be like
proportion of the whole.

So you know this has got quite a bit.

So this could be like, you know,
almost a third of the data,

or a quarter of the data could be
all in just this section.

And so you can also make a
histogram be like that as well.

So I'm going to,

copy this,

and make it be very similar.

But I'm going to kind of just
say it's a density function now.

I'm going to temporarily
take out the "y" lim argument.

And then we'll kind of see,

how that works right now.

And then the only other argument you
would need to change is just say freq,

or frequency "f" "r" "e" "q",

say it's equal to false meaning
do not make it a frequency table,

and see what that does.

So now you can see that
this is about a third of the data.

Oh it's getting cut off a little bit because
we'll want to change our "y" limits.

Notice it's not going from zero to 60,

and because these are all now
percentages or proportions.

So I'm going to make my "y" limit be,

From zero to one which is the next
that it kind of can be so.

Yeah that was probably a little too high
but I mean it still gets to point across.

So as you can see, almost
a third of the data is all contained here.

then we've got, 0.7% of the data is here.

If you were to add up all of these numbers
together, it would sum to one.

Just like the, a probability
distribution function.

The area under all of its curve,

and it's in, it's kind of, in
its range is, needs to sum up to one.

So this is a way that you can kind of
show that, with your own data.

Now if you want to, create
a histogram of the petal length variable,

but remember that there were three
different species as well.

There was setosa,
versicolor and virginica.

If you wanted to show a histogram
of just the petal length for like,

just the setosa variable
of the setosa species,

excuse me, there isn't a
quick and easy way to do that.

You just kind of have to create
a separate histogram for each. So,

say, like you wanted a histogram
of just the petal length,

for setosas just for virginicas
and just for versicolors.

This is how we can do that.

I'm going to copy and paste
some code that I have,

just so you can see.

So histograms for the
petal length variable,

I didn't quite do all of the different
specifications and customizations,

but I did enough to make it look good.

So this is what it looks like
for all of the petal lengths,

not for separated by species.

If you want to separate them out
by species, you can do that,

by using bracket notation
and logical operators.

So bracket notation
is how you subset data.

So what we're doing here is we're
saying I want the petal length,

the variable, the numerical
variable to be plotted.

But I only want the values
that meet a certain criteria.

And the criteria is we want it,

the only the ones where the
species is equal to setosa.

So this will now plot a,

histogram of all of the petal lengths,
but only for the ones that are setosas.

So what's interesting here is
you can see that,

all of the setosas have
really small petal lengths.

And I do the exact same thing,

here, I just change each of these
to versicolor and virginica,

and the colors to.

And I have it so that they're all
on the same scale as well,

so that it's easier to compare.

That's also really important
to make sure you do,

if you're going to make comparative
histograms make sure they're all,

on the same scale.

Here's what versicolor would look like.

So the versicolor ones are all right here.

And the virginicas, are the
biggest of the petal lengths.

So as you can kind of see
here they start at about 4.5.

These start they have some in the 4.5,
but it's definitely in this middle range.

And then the petal,
the setosas are all the way over here.

And so if you compare that to this
this is obviously the setosas.

These are the versicolors and
these are kind of more the virginicas.

There is a way to also,

in to poss-

in other packages and things in "R"
for you to even specify it even further.

So you could maybe even show that
this is setosa, versicolor, virginica.

But for right now we're just going to show
you how you can do each one separately.

This is probably the easiest way
to go about doing that.

Alright, that is everything,

You should need to know about creating
histograms for your numerical data in "R".

And we will see you in the next video.