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A similar philosophy has made the "tidyverse" a much-loved extension of the statistical language R.

Compare the following 2 equivalent snippets. Which one seems more understandable?

    iris_data %>%
        names() %>%
        tolower() %>%
        gsub(".", "_", ., fixed=TRUE) %>%
        paste0("(", ., ")")
or:

    paste0("(", gsub(".", "_", tolower(names(iris_data)), fixed=TRUE), ")")



Yup, I'm working on Oil shell and I wrote an article about data frames, comparing tidyverse, Pandas, and raw Python:

What Is a Data Frame? (In Python, R, and SQL)

http://www.oilshell.org/blog/2018/11/30.html

The idea is essentially to combine tidyverse and shell -- i.e. put structured data in pipes. I use both shell and R for data cleaning.

My approach is different than say PowerShell because data still gets serialized; it's just more strictly defined and easily parseable. It's more like JSON than in-memory objects.

The left-to-right syntax is nicer and more composable IMO, and many functional languages are growing this feature (Scala, maybe Haskell?). Although I think Unix pipes serve a distinct use case.




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