[scicloj/tablecloth "4.04"]

tech.ml.dataset is a great and fast library which brings columnar dataset to the Clojure. Chris Nuernberger has been working on this library for last year as a part of bigger tech.ml stack.

I’ve started to test the library and help to fix uncovered bugs. My main goal was to compare functionalities with the other standards from other platforms. I focused on R solutions: dplyr, tidyr and data.table.

During conversions of the examples I’ve come up how to reorganized existing tech.ml.dataset functions into simple to use API. The main goals were:

• Focus on dataset manipulation functionality, leaving other parts of tech.ml like pipelines, datatypes, readers, ML, etc.
• Single entry point for common operations - one function dispatching on given arguments.
• group-by results with special kind of dataset - a dataset containing subsets created after grouping as a column.
• Most operations recognize regular dataset and grouped dataset and process data accordingly.
• One function form to enable thread-first on dataset.

Important! This library is not the replacement of tech.ml.dataset nor a separate library. It should be considered as a addition on the top of tech.ml.dataset.

If you want to know more about tech.ml.dataset and dtype-next please refer their documentation:

• tech.ml.dataset walkthrough
• dtype-next overview
• dtype-next cheatsheet

Join the discussion on Zulip

Please refer detailed documentation with examples

(require '[tablecloth.api :as api])

(-> "https://raw.githubusercontent.com/techascent/tech.ml.dataset/master/test/data/stocks.csv"
    (api/dataset {:key-fn keyword})
    (api/group-by (fn [row]
                    {:symbol (:symbol row)
                     :year (tech.v3.datatype.datetime/long-temporal-field :years (:date row))}))
    (api/aggregate #(tech.v3.datatype.functional/mean (% :price)))
    (api/order-by [:symbol :year])
    (api/head 10))

_unnamed [10 3]:

Tablecloth is open for contribution. The best way to start is discussion on Zulip.

Documentation is written in RMarkdown, that means that you need R to create html/md/pdf files. Documentation contains around 600 code snippets which are run during build. There are two files:

• README.Rmd
• docs/index.Rmd

Prepare following software:

1. Install R
2. Install rep, nRepl client
3. Install pandoc
4. Run nRepl
5. Run R and install R packages: install.packages(c("rmarkdown","knitr"), dependencies=T)
6. Load rmarkdown: library(rmarkdown)
7. Render readme: render("README.Rmd","md_document")
8. Render documentation: render("docs/index.Rmd","all")

1. Before commiting changes please perform tests. I ususally do: lein do clean, check, test and build documentation as described above (which also tests whole library).
2. Keep API as simple as possible:
   • first argument should be a dataset
   • if parametrizations is complex, last argument should accept a map with not obligatory function arguments
   • avoid variadic associative destructuring for function arguments
   • usually function should working on grouped dataset as well, accept parallel? argument then (if applied).
3. Follow potemkin pattern and import functions to the API namespace using tech.v3.datatype.export-symbols/export-symbols function
4. Functions which are composed out of API function to cover specific case(s) should go to tablecloth.utils namespace.
5. Always update README.Rmd, CHANGELOG.md, docs/index.Rmd, tests and function docs are highly welcomed
6. Always discuss changes and PRs first

• tests
• tutorials

Copyright (c) 2020 Scicloj

The MIT Licence