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Reading from The Web

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Getting and Cleaning Data

Johns Hopkins University

4.6 (6,087 ratings) | 120K Students Enrolled

Course 1 of 10 in the Data Science Specialization

Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

Skills You'll Learn

Data Manipulation, Regular Expression (REGEX), R Programming, Data Cleansing

Reviews

4.6 (6,087 ratings)

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AR

May 14, 2018

Loved the structure of the course. Learned a lot. The course project seemed a little funky , especially creating the codebook for an already existing set of data but was a useful teaching aid.

VV

Nov 26, 2017

A lot of insight and practical knowledge of cleaning data that is available in many places in the Internet. I loved this course and it took me 2 tries to pass the peer graded assignment. ;)

From the lesson

Week 2

Welcome to Week 2 of Getting and Cleaning Data! The primary goal is to introduce you to the most common data storage systems and the appropriate tools to extract data from web or from databases like MySQL.

Reading from MySQL14:44

Reading from HDF56:45

Reading from The Web6:47

Reading From APIs7:57

Reading From Other Sources4:43

Taught By

Jeff Leek, PhD
Associate Professor, Biostatistics
Roger D. Peng, PhD
Associate Professor, Biostatistics
Brian Caffo, PhD
Professor, Biostatistics

This lecture is about reading data from the web.

There are a large number ways that you can read data from the web.

This is primarily going to focus on scraping data out of websites and maybe

doing a little bit with working with APIs and a little bit about authentication.

Later we'll have a lecture that specifically focuses on just the API

component of getting data out of the web.

So webscraping is programatically extracting data from

HTML code of websites or from URLs.

It can be a great way to get data.

For example, this link that I have here is an article about

how somebody scraped all of the categories that Netflix assigns movies to and

then analyzed them in a very interesting way.

And that ended up being a story that went viral because it collected an interesting

set of data just by programmatically extracting data from websites.

I would say almost all websites have information that you might want to

programmatically read in some way if you're interested

in getting the data off that site.

In some cases this is against the terms of service for the website.

So some websites don't specifically say that they do not wanna be scraped.

And so when you do that,

if you try to scrape the data from them, you're at your own risk.

If you attempt to read too many pages too quickly,

a very common consequence is that you can have your IP address blocked.

If you read a lot of proprietary information off of websites,

you can get into even bigger trouble, so you should be a little bit careful when

you're deciding to scrape data off of websites.

But in general, it can be a very good way to collect a lot of data very quickly.

So I'm gonna give you one example of how to programmatically extract some data and

so that comes from my Google Scholar page.

So this is that Google Scholar page and there's the link down there at the bottom.

That's the web link.

And so this is actually a page that tells you about the papers that I've

published and something about how often they've been cited,

which is the kind of data that academics care about.

And so what we're gonna do is, first we're gonna use the read_lines

command to get some data off the Internet, that's one way.

And so what you can do is you can open a connection to a particular URL and

you can do that using this URL function.

So you just pass it the name of the URL that you would like to open

a connection to.

And then you can use the readLines function to read out

some of the data from that connection.

And then just like when you were working with the database,

you're gonna wanna be sure to close the connection after you've used it.

So if you look at the htmlCode that we've collected from this,

it's gonna look like this.

It's gonna look very, like one big long string of letters.

You can send readLines a set number of lines that you'd like it to read but

it'll still come out unformatted in this way.

So it's a little bit hard to read.

One way to deal with that is to, as we've seen before is to use the XML package.

So again, we could use this same URL.

Use the XML package, and parse the HTML again,

using the InternalNodes to get the complete structure out.

Then if I wanted to get say, get the title of the page, I could look for

the node that's in the title tags.

And get the title of the page.

Or I could get sort of the number of times my papers were cited by looking

at particular table elements of that table.

Another approach to getting data is actually with the GET command.

This is the httr package.

For doing things like this where there's an open,

easily accessible website, accessing it with

connections like we talked about before might be the easiest way.

But we'll talk in a minute about why the httr package can be very useful in other

settings.

So here I've loaded the httr package, and then what I do is I take that

same URL I was using before, and I just GET the URL with html2.

And then what I have to do now is actually have to extract the content from that

HTML page.

So I do that, and I say I'm gonna extract the content as a text.

Just one big text string.

And then I can use the htmlParse command to parse out that text, and

get the parsed HTML.

And so this parsed HTML is gonna look exactly like what I would

have got if I had used the XML package to extract the data directly.

And so then I can use xpathSApply to extract out the title

of the page again like I did before.

So that's how you can use get just to do the exact same sort of

exercise that you would have done with the XML package.

In some other cases, you might have to do something a little bit more complicated.

So if you navigate to this webpage with your browser,

you'll see that it requires a username and password input.

And so if I just try to get that page using the GET command from the httr

package, I get a response that says Status: 401 and

that's because I wasn't able to login because I haven't been authenticated.

So what you could do with the HTTR package is you can actually authenticate yourself

for websites, and so

you can do this by assigning to this what we're gonna call the handle.

We can go and get that website and so we pass at the URL again, but

then we use this authenticate command, and we give it the username and the password.

In this case, this is just a test website, and so

the username is user and the password is password.

And so you can test out things like this to see if you can get access.

In this case now, the response is Status: 200, which means we

actually were able to get access to the file and to even authenticate it.

And now we can look at the names of this pg2,

which gives you all the different components.

We included the cookies that we have for this file, and

then the handle that we used to access it and all that.

And so, we can then use the content function to extract the content from that

website, after having logged in through r.

So make sure that you use handles because if you use handles then you can

actually access.

You can sort of save the authentication across multiple accesses to a website.

So if you set Google to be a handle where that Google is a particular website,

then what you can do is you can tell GET to go and get that handle and

you can say go get it for a specific path or you can set it a different path.

So for example, if you authenticate this handle one time

then the cookies will stay with that handle and you'll be authenticated.

You won't have to keep authenticating over and over again as you access that website.

So, r-bloggers has a lot of good examples on how to scrape data from the Web, and

so if you search for Web scraping on r-bloggers,

you'll be able to get all of those tutorials.

The httr help file also has a bunch of useful examples,

primarily with sort of toy examples.

So if you couple that with some of the more applied examples

on r-bloggers you can get quite a ways.

And then see later lectures on APIs on how to programmatically interact with

APIs of websites.

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