knitr (part 3)

Loading...

From the course by Johns Hopkins University

Reproducible Research

2537 оценки

Johns Hopkins University

Reproducible Research

2537 оценки

Course 5 of 10 in the Specialization Data Science

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

From the lesson

Week 2: Markdown & knitr

This week we cover some of the core tools for developing reproducible documents. We cover the literate programming tool knitr and show how to integrate it with Markdown to publish reproducible web documents. We also introduce the first peer assessment which will require you to write up a reproducible data analysis using knitr.

Coding Standards in R8:58

R Markdown Demonstration7:23

knitr (part 1)7:04

knitr (part 2) 4:10

knitr (part 3) 4:46

knitr (part 4) 9:29

Meet the Instructors

Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health

So how do we create a basic knitr document?

So I'm going to talk about this in the context of R

studio, because I think it's the easiest way to do it.

There are other ways to do this, so this is not really exclusive to R studio

but it's going to, I'll just, the example that I'll show you will be in R studio.

So if you click on the New Document button on the

upper left, you'll see that there are a variety of options.

And you can click the R Markdown option to to

create a new markdown file that'll have your text and codes.

Here's your basic mark, R markdown document.

So R markdown is basically markdown with R code embedded in it.

You can see that what I have here is a

basic symbol that indicates that I'm going to start a code chunk.

So when I have the three back ticks, then the curly braces, and

an r, and then a close curly brace, that indicates that I'm starting

a code chunk.

So as you can see I have three lines of R code here.

Got set seed, that generates a random normal var variables.

And then I just take the mean.

And then I close this code chunk with three back ticks.

And so that and so that's, and so you can see above this shaded area

with the code, I've got text, so it's, the title is my first knitr document.

And then I said, just a line of text that says, this is some text.

And I say, here's a code chunk, and then I start the code chunk.

So in R studio you'll have this document open.

And you'll have the the code chunk and the text chunk there together.

And and the idea is that if you want to

process the knitr document, and create some human-readable output.

So, you want to, the idea is that you want to run the R

code, so that it, so you execute these three lines of R code.

And then you want to put the output from that R code.

In, in, embedded in your document.

And what's the output of this R code? Well there's only one

thing that happens when you calculate the mean.

So it's going to generate the mean, and it'll be whatever

the mean happens to be for that simulated data set.

So it's very easy in R Studio, you can just push

the Knit HTML button, and it will automatically run everything for you.

There's if you're not using our studio the way you

can do this is you can load the knitr package

with a library function you can make sure your working

director is set to wherever the aardvark down document is located

typically aardvark down documents will have a capital RMD extension.

It's not necessary but it's just a good way to identify these types of files.

You can use the knit2html function if you want to create a webpage as your output.

And you will automatically process the r markdown file and run

the r code and put the r code within the file.

And then, within r, if you want to open this

in your web browser, you can use the browseURL function.

It will create a new file.

Sorry, the knit2html function will take your r markdown document, create a html

file with the same name then, then you can open in your web browser.

So here's the output that you would get, this

is the HTML format output that you would get.

You can see there's a title in bold, in large text, there's some regular text.

The code is kind of in the shaded box, and then the output from

that code is directly below, here you can see here the mean is 0.1089.

So that's the output

that came from the mean function.

So in your MarkR, so that happens here is that neither

takes your original RMarkdown document, we can see on the left here.

And it processes it into a regular Markdown document.

As you can see that the, one thing that happens is that, it takes your code.

And then it, it reproduces the code in the regular markdown document.

But you can see that the in the

markdown document there's one extra piece, which is that

the result of the code which is down here at the bottom.

And so the result of evaluating the r code is

added to the markdown document after evaluating the r code.

So so when you create, first couple of

things, first of all when you generate, when you

create the new knitr document in RStudio, it will

populate that document with some kind of generic filler text.

So the first thing you probably want to do just delete it, because

that's not going to be relevant to whatever you want to do, most likely.

Code chunks begin, again, with the three back ticks the curly braces with an r.

And you end a code chunk with three back ticks.

All of your R code will go between these markers.

Now you can have more than one code chunk.

You can have as many code chunks as you want.

So you don't have put all of the R code in within the code chunk.

But any R code that you do have goes in a code chunk indicated by three back ticks.

You can have names for your code chunks so you

can name them after so immediately after the curly brace the

r you can put a name it can be anything you

want and then you can close it with a curly brace.

And then by default

the code in a code chunk will be echoed in the output document so just like in the in

the HTML document you saw that it echoed the code

and then it gave you the result of that code.

By default, you will, all the code chunks you'll be

able to see in the document, the code that was produced.

Ознакомьтесь с нашим каталогом

Присоединяйтесь бесплатно и получайте персонализированные рекомендации, обновления и предложения.

Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.

© Coursera Inc., 2018 Все права защищены.

Загрузить из App Store

Загрузить в Google Play