Histograms in R

Hi. In
this video we are going to be talking

about graphical data
summary for numerical data.

We are going to be 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 data set

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

variable today.

So to create a histogram event

of a numeric 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 1 and 1 and a half

and, you know 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.

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.

But if you don't really want this,
our 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, 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 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 0 to 8 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 axes go from

0 to 60.

Now it's 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,

magic number.

Just kind of, to make it look nice is all.

We're really kind of doing.

All right.

So now you can see here maybe 60
was a little too much, but yeah, it works.

But now you can see there's 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,

you 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 see breaks in by bins
and meaning how many like 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 are technically be three, 456789,
ten, 11, 12.

Right.

Now let's
see how well it can do with eight.

If you specify seven it might do 6 or 8.

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, 234,
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 statistics
or 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 1
and 2, one

between 2 and 3, 15 between 3 and 4, 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.

All right.

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 1 and 2, 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 speed 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 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 freq, 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 want to change our y limits.

Notice it's not going from 0 to 60.

And because these are all now percentages
or proportions.

So I'm going to make my y limit be.

From 0 to 1 which is the next that

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.

And 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
or 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

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

for setosa is just for virginica
and just for versicolor.

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

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 length,
but only for the ones that are.

Setosa is.

So what's interesting here is
you can see that

all of the statuses
have really small petal lakes.

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 virginica is

are the biggest of the petal lakes.

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 statuses are all the way over here.

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

These are the versicolor.

And these are kind of more the Virginias.

There is a way to also

in to pass

in other packages and things in order
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.

All right.

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.