11–20 of 308 posts
It offers some nifty things including, well, observables where cells of the scratchpad can automatically update by observing changes from other cells.
What was the earliest of these tools? Mathcad? Mathematica? Maple?
The real question is when (and whether) new social scientist stats courses will start teaching Python stats toolchains, rather than R. That seemed to be an inflection point for R (as folks moved away from SAS), and could be for stats-centric Python too.
I recently got a Jupyter Notebook, and found it's a large JSON document, with some sections in markdown and some in Python. A browser could omit the Python, and an interpreter could omit the markdown. Would this work for other languages? Maybe JavaScript or Powershell instead of Python?
I've switched largely to Jupyter / Python for computational linguistics / psycholinguistics because of the pandas / numpy /numba stack, decent off-the-shelf NLP (spacy and gensim), and the ease of moving data into an R kernel for specific analyses and plots. Also nice that any reasonably sized notebook will render on GitHub (and access can be controlled through the accounts system, until something is ready to be publ…
What was the earliest of these tools? Mathcad? Mathematica? Maple?
Also: https://www.theatlantic.com/science/archive/2018/04/the-scie...
“The notebook interface was the brainchild of Theodore Gray, who was inspired while working with an old Apple code editor. Where most programming environments either had you run code one line at a time, or all at once as a big blob, the Apple editor let you highlight any part of your code and run just that part. Gray brought the same basic concept to Mathematica, with help refining the design from none other than Steve Jobs.”
Link to the deck by Joel Grus' talk that is mentioned in the article: https://docs.google.com/presentation/d/1n2RlMdmv1p25Xy5thJUh...
And all JupyterCon 2018 talks if anyone is interested: https://www.youtube.com/playlist?list=PL055Epbe6d5b572IRmYAH...
RStudio with using Rmarkdown is also popular. Both workflows are language agnostic.
It's easy to share data between the R and python session, and calling python from R, or R from python is straightforward.