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https://github.com/hugetim/nbstata

A Jupyter kernel for Stata built on pystata
https://github.com/hugetim/nbstata

jupyter jupyter-kernels jupyter-notebook jupyterlab nbdev notebook python stata

Last synced: 23 days ago
JSON representation

A Jupyter kernel for Stata built on pystata

Host: GitHub
URL: https://github.com/hugetim/nbstata
Owner: hugetim
License: gpl-3.0
Created: 2022-11-09T20:52:11.000Z (about 2 years ago)
Default Branch: master
Last Pushed: 2024-10-25T19:20:27.000Z (3 months ago)
Last Synced: 2024-10-26T00:16:41.945Z (3 months ago)
Topics: jupyter, jupyter-kernels, jupyter-notebook, jupyterlab, nbdev, notebook, python, stata
Language: Jupyter Notebook
Homepage: https://hugetim.github.io/nbstata/
Size: 9.94 MB
Stars: 28
Watchers: 3
Forks: 2
Open Issues: 13
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE

Awesome Lists containing this project

best-of-jupyter - GitHub - 24% open · ⏱️ 05.11.2024): (Jupyter Kernels)

README

        # nbstata: a new Stata kernel

 

*nbstata* is a [Jupyter

kernel](https://docs.jupyter.org/en/latest/projects/kernels.html) for

[Stata](https://www.stata.com/why-use-stata/) built on top of

[pystata](https://www.stata.com/python/pystata18/index.html).

**[*For the User Guide, click

here.*](https://hugetim.github.io/nbstata/user_guide.html)**

 

## What is Jupyter?

A Jupyter notebook allows you to combine interactive code and results

with [Markdown](https://daringfireball.net/projects/markdown/basics) in

a single document. Though it is named after the three core programming

languages it supports (Julia, Python, and R), it can be used with with a

wide variety of languages.

*nbstata* allows you to create Stata notebooks (as opposed to [using

Stata within a *Python*

notebook](https://www.stata.com/python/pystata18/notebook/Example2.html),

which is needlessly clunky if you are working primarily with Stata).

### Key *nbstata* features

- [x] [Easy

  setup](https://hugetim.github.io/nbstata/user_guide.html#install)

- [x] Works with Stata 17+ (only).

- [x] Displays Stata output without the redundant ‘echo’ of (multi-line)

  commands

- [x] Autocompletion for variables, macros, matrices, and file paths

- [x] DataGrid widget with `browse`-like capabilities (e.g., interactive

  filtering)

- [x] Variable and data properties available in a ‘contextual help’ side

  panel

- [x] Interactive/richtext help files accessible within notebook

- [x] `#delimit ;` interactive support (along with all types of

  comments)

- [x] Quarto [inline

  code](https://quarto.org/docs/computations/inline-code.html) support

The video below demonstrates using Stata in a Jupyter notebook. In

addition to the

[NBClassic](https://nbclassic.readthedocs.io/en/stable/notebook.html)

application shown there, *nbstata* can also be used with

[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html),

[VS

Code](https://code.visualstudio.com/docs/datascience/jupyter-notebooks),

or [Quarto](https://quarto.org/).



### What can you do with Stata notebooks…

…that you can’t do with the [official Stata

interface](https://www.stata.com/features/overview/graphical-user-interface/)?

- Exploratory analysis that is both:

  - interactive

  - preserved for future reference/editing

- Present results in a way that interweaves:[^1]

  - code

  - results (including graphs)

  - rich text:

    1.  lists

    2.  **Headings**

    3.  

    4.  [links](https://hugetim.github.io/nbstata/)

    5.  math: $y_{it}=\beta_0+\varepsilon_{it}$

## Contributing

*nbstata* is being developed using

[nbdev](https://nbdev.fast.ai/blog/posts/2022-07-28-nbdev2/#whats-nbdev).

The `/nbs` directory is where edits to the source code should be made.

(The python code is then exported to the `/nbdev` library folder.)

For more, see

[CONTRIBUTING.md](https://github.com/hugetim/nbstata/blob/master/CONTRIBUTING.md).

## Acknowledgements

Kyle Barron authored the original *stata_kernel*, which works for older

versions of Stata. Vinci Chow created a Stata kernel that instead uses

[pystata](https://www.stata.com/python/pystata18/), which first became

available with Stata 17. *nbstata* was originally derived from his

[*pystata-kernel*](https://github.com/ticoneva/pystata-kernel), but much

of the docs and newer features are derived from *stata_kernel*.

[^1]: Stata [dynamic

    documents](https://www.stata.com/manuals/rptdynamicdocumentsintro.pdf)

    can do this part, though with a less interactive workflow. (See

    also: [markstat](https://grodri.github.io/markstat/),

    [stmd](https://www.ssc.wisc.edu/~hemken/Stataworkshops/stmd/Usage/stmdusage.html),

    and

    [Statamarkdown](https://ssc.wisc.edu/~hemken/Stataworkshops/Statamarkdown/stata-and-r-markdown.html))

    Using *nbstata* with

    [Quarto](https://www.statalist.org/forums/forum/general-stata-discussion/general/1703835-ado-files-and-literate-programming)

    instead gives you a similar workflow, with greater flexibility of

    output.