computer science

Knitr

Knitr is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Knitr rather than just read about it. In short: knitr is a software engine for dynamic report generation with R. It is a package in the programming language R that enables integration of R code into LaTeX, LyX, HTML, Markdown, AsciiDoc, and reStructuredText documents.

Key takeaways

Knitr belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Knitr to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Knitr from memory before moving on to harder problems.

Reference excerpt

knitr is a software engine for dynamic report generation with R. It is a package in the programming language R that enables integration of R code into LaTeX, LyX, HTML, Markdown, AsciiDoc, and reStructuredText documents. The purpose of knitr is to allow reproducible research in R through the means of literate programming. It is licensed under the GNU General Public License. knitr was inspired by Sweave and written with a different design for better modularization, so it is easier to maintain and extend. Sweave can be regarded as a subset of knitr in the sense that all features of Sweave are also available in knitr. Some of knitr's extensions include the R Markdown format (used in reports published on RPubs), caching, TikZ graphics and support to other languages such as Python, Perl, C++, Shell scripts and CoffeeScript, and so on. knitr is officially supported in the RStudio IDE for R, LyX, Emacs/ESS and the Architect IDE for data science.

Workflow of knitr Knitr consists of standard e.g. Markdown document with R-code chunks integrated in the document. The code chunks can be regarded as R-scripts that

load data, performs data processing and creates output data (e.g. descriptive analysis) or output graphics (e.g. boxplot diagram). The implementation of logical conditions in R can provide text elements for the dynamic report depended on the statistical analysis. For example:

The Wilcoxon Sign test was applied as statistical comparison of the average of two dependent samples above. In this case, the calculated P-value was 0.56 and hence greater than the significance level (0.05 by default). This implies that "H0: there is no difference between the results in data1 and data2" cannot be rejected.

The text fragments are selected according to the script's results. In this example, if the P-value was lower than the significance level, different text fragments would be inserted in the dynamic report. In particular, the second sentence would swap "less" for "greater," and the third sentence would be replaced to reflect rejection of the null hypothesis. Using this workflow allows creating new reports simply by supplying new input data, ensuring the methodology is reproduced identically.

See also The R Programming wikibook Reproducible research

References

External links Official website Repository on GitHub Example code on GitHub knitr package on CRAN

Illustrations

Worked examples

Example 1 — a first encounter with Knitr

Start with the simplest possible case. Write down what Knitr claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Knitr before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Knitr ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Knitr

In research: Knitr appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Knitr in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Knitr is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computer programming tool stubs, Free R (programming language) software, Free TeX software, so understanding it makes those chapters shorter.
In everyday life: Look for Knitr outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Knitr in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Knitr means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Knitr out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Knitr in simple terms?

Why does Knitr matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Knitr?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Knitr.