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Histogram in R

During this first week, we show how to download and install R on Windows and the Mac. We review those basics of inferential and descriptive statistics that you'll need during the course.

Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future. Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn. You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself! Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!

Visualiser le programme de cours

Compétences que vous apprendrez

Time Series Forecasting, Time Series, Time Series Models

Enseigné par

Tural Sadigov
Lecturer
William Thistleton
Associate Professor

Transcription

Hello everyone.

In this video lecture we'll learn how to obtain a histogram of a dataset.

How to impose smooth density function over a histogram and

to change colors and line width in a histogram.

So I have entered the following data set and

called it small size data set because it only has 30 data points.

And we will try to find the histogram of this data set.

So histogram routine or command in R is basically hist.

And it's a function, and the argument of the function will be our data set.

If you basically write small.size.dataset here, and

without doing anything, by just entering, we will get our histogram.

But let me mention a few things about this histogram.

By default, on the y-axis, there is a frequency.

For example, there is only one number in my dataset between 0 and 20.

Usually, we'll like to have this as a probability or density.

So we'll change that.

By default the name of this histogram is histogram of the name of the variable name

of the dataset, we can play with it.

And by again, by default x label is just small.size.dataset in

other words it's just the variable name, the name of the dataset.

But we can change this, for example,

we can go back and we can change our x label.

So x label will accept a string, so

that's why we have these quotas, here and

I will, for example, put, My data points.

If I enter, everything stays same, its label has changed.

We can change the title to, so if we go back, and we say main.

Again, main accepts a string, and whatever the string is,

it will be it is title, the title of histogram.

Let me write 'Histogram of my data.'

And now the histogram title has

changed, and I have my labels.

Correct my data points, my title.

Now we can also turn off this frequency.

How do we do that?

We go back into the function, we say freq.

freq here stands for frequency, and it's a boolean variable.

By default, it is true.

That's why in the histogram it comes with the frequency.

But we can turn that off by saying it is FALSE, or just by using capital letter F.

And if I do that, everything stays same, well almost.

The density has changed, so my histogram scaled down.

Now I have probabilities here.

Now, we can change the colors here, we can, for

example, change the colors of this rectangle.

For example, I can go and say, color=,

again, this is going to take it as a string.

Let's say I'm going to write this as green.

And if I do that, I have my histogram, which is green, all green.

We can impose smooth density

function over this histogram.

So density function is found by using this command, density.

So I have to put my data set here.

Now, density to find its density, but

if I want to impose the graph on it, I should use the lines command.

And my argument for the lines is the density command, and

the argument for the density is basically my data set.

If I do that, it basically imposes some smooth density.

Probably distribution function over my histogram.

I can change the color here, color of my density function.

So let's say this time I'm going to use red, and

now it is red, but I can also play with that line width.

So width is lwd and if I increase it, for example, if I make this five.

Now my line width is a little, the width is bigger than the previous one.

So I have this histogram, I have my distribution.

Density function's imposed on my histogram here.

And one last thing I would like to mention is this bin width.

We can play with this bin width, in other words, the width of this rectangles.

By increasing these break points.

For example, I can go back to my histogram command and say breaks.

So breaks takes a lot of values.

It can take a sequence, array, or it can take a number of break points.

I'm going to use this as just a number.

For example, if I put 10 here.

Let’s see what happens.

Well if I put 10 here then I have a smaller bin width,

more break points and my histogram looks like this.

Might or might not be useful to use these breaks.

And if I want to still impose my density over it,

I would just impose my density over this.

Okay, so what have you learned in this video?

You have learned how to find histogram of a dataset with frequencies or

probabilities on the y-axis.

You learned how to change bin width in histogram using breaks.

You have learned how to impose a smooth density over a histogram.

And you have learned how to change colors and line width in a histogram.

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