The Living Thing / Notebooks :

Digital scientific workbooks

Literate coding for reality

The exploratory-algorithm-person’s IDE-equivalent. Literate coding-meets-science.

Let’s say I want to demonstrate my algorithm to my thesis advisor while he’s off at conference. I need an easily shareable demonstration. that’s why we have the internet, and scientific computation/documentation system such as Rmarkdown/knitr and Jupyter.

See also text editors, citation management, academic writing workflow, python, cloud computing a.k.a. cloud wrangling, open notebook science, scientific computation workflows.


Why do this? To belatedly immanentize the prophecy that the scientific paper is dead. That last link is written for outsiders to the field.

If less eloquent, Yihui Xie is far more useful: Notebook war summarises some philosophical and practical differences between the literate coding/exploratory notebook hybrid tools in use.

Nextjournal is a collaborative coding machine that claims to make this easy for you and your colleagues to write in a workbook style together.


See also

  1. jupyter wherein there are some bonus notes about the technical details of this particular solution.
  2. the R tool, RMarkdown, also supports python. Perhaps you want to give it a try? It supports passing python variables to R, which is a serious win for graphing.


Pweave, by Matti Pastell, is python twin to knitr, which in the lineage of literate coding tools.

Pweave is a scientific report generator and a literate programming tool for Python. It can capture the results and plots from data analysis and works well with numpy, scipy and matplotlib.

Max Masnick gives a detailed set up example.


Chris Sewell has produced a scripted called ipypublish that eases some of the pain points in producing articles starting from jupyter. See the comments for some additional pro-tips for this.


knitr / RMarkdown are complementary r-based entrants in the scientific workbook field. There are several pieces in this toolchain with a complicated relationship, but the user can ignore most of this complexity. The result is such fanciness as automatically rendering and caching graphs, an interactive notebook UI, nearly first-class support for python and julia plus mediocre support for other languages. Here are some guides:

R -e "rmarkdown::render('script.Rmd',output_file='output.html')"

For an intro to the various way to build this into a full reproducible research workflow, see the excellent reproducible analysis workshop.

You can include graphics via markdown native markup or via r itself, which is more powerful if more circuitous.

```{r  out.width = "20%"}

Some miscellaneous tips:


Julia also wants this, right? There is are native options, weave.jl and Literate.jl.

Weave is another literate coding thingy, much like RMarkdown or pweave.

# capturing code output

The code chunk wil be run with default options and the output captured.
using Gadfly
x = linspace(0, 2* pi)
plot(x = x, y = sin(x)

Or you could use RMarkdown in julia mode It’s not clear to me how graphing works in this setup.


Editor/IDE support

Miscellaneous preview support scripts are given in the knitr documentation.


As noted, RStudio has intimate Rmarkdown integration.


Atom supports a number of literate programming tools via language-weave package - you might also want a full typesetting experience via the latex or atom-latex package, which can be made to support literate coding of plain latex. It uses Hydrogen to provide code preview.

Setting up a latex toolchain in atom-latex is not too bad. E.g. here is one for knitr:

    "root": "path/to/my/file.Rnw",
    "toolchain": "Rscript -e \"library(knitr); knit('%DOC.%EXT')\" && latexmk -synctex=1 -interaction=nonstopmode -file-line-error -pdf %DOC",
    "latex_ext": [".Rnw"]

It would probably also work for pweave or Weave.jl.