of, Working With Data & APIS in JavaScript.

Now, we're going to do some real stuff in this video.

We did real stuff in the previous video,

but the previous video was just about practicing with the fetch

API and getting some image files.

We weren't really working with data

and we weren't doing anything with that data yet.

In this video, I want to take a look

at this idea of tabular data.

There are a lot of different file

formats for storing data in a table format, in tabular data.

The one that I want to look at in this video,

and probably I would think is the most common one, is CSV,

or comma-separated value.

Meaning the data, the data in the table,

is literally separated by commas.

The first line of text, after all, this file, the CSV file,

is ultimately just a plain text file.

It might function as something like a hedero.

So it would have the names of the fields

of data you're going to have.

So it might have something like, Item, comma, Cuteness.

So you're going to have a table of things and a cuteness score.

So all the rest of the lines would have the actual items

and their cuteness scores.

So you could have, puppy, comma, 10.

Kitten, comma, 10.

Because we live in a world where everything has a cuteness

curve 10.

But I want to do something with real data, data that's

out there in the world, that I can

grab with the fetch function, load onto my web page,

and do something with, for example, graph.

And so the data set that I'm going to show you,

it comes from NASA, National Aeronautics

and Space Administration, in particular from the Goddard

Institute for Space Studies.

This CSV file includes the combined global average land

surface air and sea surface water temperature

from 1880 all the way to present.

It's stored in sort of a funny way.

The values that are actually in the data set

are the difference from the mean temperature.

And what do I mean by the mean temperature?

Well, I mean the average temperature.

Well, what's the average temperature?

Well, it so happens that there is the average world

temperature, as recorded from 1951 to 1980, which also

recorded an average by NASA, the Earth Observatory website,

at 14 degrees Celsius.

So the data in this CSV file is the difference from the mean,

from 14 degrees Celsius, from combined land surface

air and sea surface water temperature from 1880

to present.

So I want to load that CSV file, parse it, graph it,

and we're done.

I'm going to do this in two parts.

The first part, that you're watching right now,

is just loading the CSV file, parsing it.

I want to be able to see it, maybe

as a console log in the browser, in the Chrome Developer Tools.

And then once I see I have the data there,

then I want to try to graph it.

I'm going to graph it using a particular JavaScript library,

called Chart.js.

I'll talk about some other ways that you can choose

to graph stuff, then, as well.

If you want to follow along with coding,

I'm about to start coding, you're

first going to want to grab that CSV file

and have it stored locally on your computer.

So it's a pretty easy process.

You just want to go to data.giss.nasa.gov/gistemp.

The URL's here and in the video's description.

And then you want to scroll all the way down and find the place

on the web page that says, Tables of Global

and Hemispheric Monthly Means and Zonal Annual Means.

So, there are actually a ton of different data sets on this web

page, and you might explore them,

and perhaps as an exercise try doing

graphing a different data set on this web page.

But the one that I'm using is the last entry

on that section called, Zonal Annual Means,

from 1880 to present.

And I'm using the CSV file format.

You'll notice there's also a TXT file format, that's probably

tab delimited so that the sort of data records

have tabs in between them instead of commas.

Again, there's a variety of different formats,

but the CSV is the one that I'm going to use.

Time to start coding.

So let's check in and see if you--

if you want to follow along, let's check in

and see if you have exactly what I have.

So what I have is some boiler plate HTML.

It's just a plain index of HTML file

with a title, a head, a body, and an empty script tag.

I've got links to where the data is coming from,

just to make sure I'm referencing and crediting

properly in my code.

And then I also have that CSV file itself.

So, yeah, I'm in Visual Studio Code.

And you can see, there's my index of HTML

and there's my CSV file in the same local directory.

But you might be using a different text

editor or a different environment, all of this

will work as long as you have your HTML file

and your CSV file.

Let's take a look at that CSV file.

So here's the CSV file.

You can see that there are a number of columns: Year; Glob,

which I assume stands for global; Nhem ,

northern hemisphere; et cetera, et cetera.

And then you can see that the columns of data each

being separated by commas.

So this isn't really meant to be human readable.

There are ways of viewing a CSV that's more human readable.

For example, here's how that same data

looks in spreadsheet format.

You might notice here, however, that it is colored.

Each column has a different color.

This is because I'm using a Visual Studio Code

extension called Rainbow CSV, which it says if it

was like tailor made for me.

And I'll include a link in this video's scripture

if you want to install that extension as well so you

can have things color coded.

Another thing I really like to do

when I'm working with a data set for the first time,

is I like to give myself a test file that

has much less stuff in it.

Because if I want a console, log, and check stuff,

sometimes this big file-- this isn't that big of a file,

it's just 1880 to 2018.

But, potentially, I could have like a really, really big data

file.

So something I'm going to do is, I'm

going to just quickly do a Save As and call this test.csv.

And so now you can see that I have a separate test.csv file.

And I'm going to just leave two years in there.

So I'm going to scroll all the way down and delete everything.

And now I have a CSV file that just has three rows in it:

the header row and then the data for 1880 and the data for 1881.

So I'm going to work with this first.

And once I have the parsing and everything

I want to do working properly, then I'll load the real data.

So the first step is exactly what you might think,

fetch test.csv.

Let's write that code.

I'm going to write, fetch test.csv.

And then remember, fetch returns a promise,

and I can handle the resolution of that promise

when it is finished loading the data with dot vin

and any errors with dot catch.

But I prefer to use the await and async syntax,

so I'm actually going to put this in a function called,

get data.

I might think of a different name for that later.

And I'm going to say the response equals

a weight fetch test.csv.

So I'm rating a function-- oh, and this function,

I almost forgot, has to have the keyword async because it's

an asynchronous function that's making asynchronous calls

with the await keyword.

So the response equals await fetch dot test CSV.

Now you might remember, on the web

fetch API there are a variety of kinds of data streams

that might come in.

There is a blob, there's a JSON, there's an array buffer,

there's text.

And this is what I want to actually use, raw text.

Even though it's tabular data in CSV format,

I'm going to do the parsing of it manually in my own code.

So I just want to receive it as text.

And that means--

I'm going to say--

I'm just going to say table equal--

maybe I'll just call this all data,

equals await a rate response dot text.

So let's console log that data.

And let's call the function, get data here.

And then let's go and see this running actually

in the browser.

And here it is.

And you can see, there we go, the data

has been logged to the console.

Now, ultimately, here there are a variety

of JavaScript libraries that will parse a CSV for you.

And by I mean, parse, I mean figure out

where all the commas are and break up the data

and put it into objects and make it usable for you.

D3, which is a JavaScript library for data visualization,

has a parser in it. p5 js, which is a JavaScript library

that I use a lot on this channel,

has a load table function, which will actually

parse the CSV for you.

And there are many others.

So I'll include some links to those in the video description.

But I think it's a useful exercise right now.

It's a simple enough data for us to do the parsing manually

with the split function.

What?

What split function?

What are you talking about?

So the JavaScript string class, any time

you have a piece of text in a variable in JavaScript

it's a string object, has a function called split.

And that function allows you to take any arbitrary text

and split it up into different elements of an array.

And that's basically what we want to do.

We want to split up all the rows, and then each row,

we want to split up all the columns.

The split function requires a single argument, a separator

or otherwise known as a delimiter.

And in this case, we have two kinds of delimiters.

For each row, the delimiter, the thing

that differentiates one row from another, is a line break.

So first let's call split with line break.

Going to my code, I can say, and I'm going to call these rows,

the rows equals data dot split.

And I'm going to split by backslash n.

So backslash n is an escape character sequence

that indicates a line break or new line.

Depending on your file format, you

might need a slash r, also which is like a carriage return.

You can also use something called a regular expression

here.

This should also work.

Instead of in quotes, if I have forward slashes,

the delimiter is a regular expression.

What's a regular expression.

So that's beyond the scope of what

we're doing in this particular video,

but I think expressions are so useful when doing string

parsing, that I will also link in this video description

to a whole series of videos that I have about that.

But for now, just the backslash n in single quotes

should do for us.

So I'm going to say console.log rows,

just to make sure that works.