knitr (part 2)

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Из курса от партнера Johns Hopkins University

Reproducible Research

3115 оценки

Johns Hopkins University

Reproducible Research

3115 оценки

Курс 5 из 10 — 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.

Из урока

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

Познакомьтесь с преподавателями

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 what is knitr?

So knitr is this tool that I'm going to

talk about that can help you make these reproducible documents.

Knitr is an R package.

It's written by Yihui Xie who, while he was a grad student at Iowa State.

It's available on CRAN.

If you happen to use the package R Studio it's actually built

into R Studio, and so it's,

it's integrated into the graphical user interface.

And so, you don't even have to obtain it separately if you're using R-Studio.

So, it supports for the documentation language.

It supports, something called RMarkdown.

It also supports, LaTeX and HTML.

So these are three languages, that are commonly used.

And then, it can export to PDF and HTML and and you can

also export to other formats if using, using other external tools like Pandoc.

It's built right into RStudio for your convenience, as I just mentioned.

So, what do you need to use knitr? You need a recent version

of R. You need a text editor.

You can use the one that comes with RStudio if you

happen to be using that, otherwise any old text editor will do.

You need some support packages that are available from CRAN.

These will be automatically downloaded if you use the install dot packages function.

You need some knowledge of one of the three documentation languages.

So, you need, need to know Markdown, LaTeX or HTML.

And I will talk about Markdown here

since it's a fairly simple language to understand.

So, what is Markdown?

Well, it's a simplified version of common markup languages.

So LaTeX and HTML could be thought of as markup languages,

where you take regular text and you kind of add things

to it, add tags and other types of annotation to kind

of tell, to indicate what you want to do with the text.

One problem with markup languages is that they can

be kind of like with they can be very

difficult to read cause all this kind of tags

and things that, that, that can, can, that can obscure

the actual text.

So the purpose of Markdown is just kind of

simplify all that, make it easy to read just

the text and only have a few kind of

formatting elements that you need to enter into the document.

There's no special editor required, just a standard text

editor, Notepad, you know, whatever you want to use.

There are simple, intuitive formatting elements and you can

get all of the documentation at this website here.

So, what is knitr good for in terms of creating reproducible documents?

Well, personally, I think it's good for things like manuals.

If you want to instruct someone on how to use a piece of software.

Short or medium length technical documents

so you're explaining some technical concept.

tutorials, if you want to, again, this is like a

manual, but maybe more extended, a tutorial on some topic.

reports, they're especially useful for reports.

If you have to, for example, every week

generate a report on some database or some dataset.

If you're running a study and you

have subjects that are constantly being enrolled

in the study and then maybe every week you generate a report on, it, it

has summary statistics about about the study

that these kinds of live documents are

very useful because you can recalculate all

the summary statistics as you generate the document.

You don't have to do it by hand and then stick it into a report separately.

And then also for data preprocessing.

So, often it's useful if you're going to preprocess

a dataset to create a clean dataset, to create a knitr type document where, that

documents all of the things that you do to the dataset to make it clean.

Maybe you remove some outliers, maybe you fill in some

missing data, maybe you transform a variable here and there.

All of that can be documented, in this kind of knitr's file, you know, where

you simultaneously say what you did and then you actually do it in the code.

What is knitr not good for?

Well in, by, in my opinion, it's not very good for very long research articles,

so if you're writing, writing a very complex,

very involved analysis it, like I said before,

because everything is stored in a single document,

it can get a little bit confusing to

kind of edit this document if there's a lot of code and a lot of text.

If you have, if have very complex or time-consuming computations

then knitr is often not a good format because you have

to recompile this document every time you want to look at it.

And so if your computations are very slow

this will just kind of make everything slow.

And furthermore if you have a document, if you

want to create a document that requires very precise formatting.

So if you have a special layout and the pictures have to

go here and the text has to go here or something like that.

Knitr is, again, not a super great tool for

that because the formatting tends to be very ad hoc.

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