# Tablecloth

Dataset (data frame) manipulation API for the tech.ml.dataset library


[![](https://img.shields.io/clojars/v/scicloj/tablecloth)](https://clojars.org/scicloj/tablecloth)
[![](https://api.travis-ci.org/scicloj/tablecloth.svg?branch=master)](https://travis-ci.org/github/scicloj/tablecloth)
[![](https://img.shields.io/badge/zulip-discussion-yellowgreen)](https://clojurians.zulipchat.com/#narrow/stream/236259-tech.2Eml.2Edataset.2Edev/topic/api)

## Versions

### tech.ml.dataset 7.x (master branch)

[![](https://img.shields.io/clojars/v/scicloj/tablecloth)](https://clojars.org/scicloj/tablecloth)

### tech.ml.dataset 4.x (4.0 branch)

`[scicloj/tablecloth "4.04"]`

## Introduction

[tech.ml.dataset](https://github.com/techascent/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](https://dplyr.tidyverse.org/), [tidyr](https://tidyr.tidyverse.org/) and [data.table](https://rdatatable.gitlab.io/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](https://techascent.github.io/tech.ml.dataset/walkthrough.html)
* [dtype-next overview](https://cnuernber.github.io/dtype-next/overview.html)
* [dtype-next cheatsheet](https://cnuernber.github.io/dtype-next/cheatsheet.html)

Join the discussion on [Zulip](https://clojurians.zulipchat.com/#narrow/stream/236259-tech.2Eml.2Edataset.2Edev/topic/api)

## Documentation

Please refer [detailed documentation with examples](https://scicloj.github.io/tablecloth).

The old documentation (till the end of 2023) is [here](https://scicloj.github.io/tablecloth/old).

## Usage example

```{clojure results="hide"}
(require '[tablecloth.api :as tc])
```

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

## Contributing

`Tablecloth` is open for contribution. The best way to start is discussion on [Zulip](https://clojurians.zulipchat.com/#narrow/stream/236259-tech.2Eml.2Edataset.2Edev/topic/api).

### Development tools for documentation

Documentation is written in the [Kindly](https://scicloj.github.io/kindly/) convention and is rendered using [Clay](https://scicloj.github.io/clay/) composed with [Quarto](https://quarto.org/).

The old documentation was written in RMarkdown and is kept under [docs/old/](./docs/old/).

Documentation contains around 600 code snippets which are run during build. There are three relevant source files:

* [README-source.md](./README-source.md) for README.md
* [notebooks/index.clj](./notebooks/index.clj) for the detailed documentation
* [clay.edn](./clay.edn) for some styling options of the docs

(`notebooks/index.clj` was generated by [dev/conversion.clj](dev/conversion.clj) from the earlier Rmarkdown-based `index.Rmd` with asome additional manual editing. Starting at 2024, it will diverge from that source, that will no longer be maintained.)

### README generation

To generate `README.md`, run the `generate!` function at the [dev/readme_generation.clj](./dev/readme_generation.clj) script.

### Detailed documentation generation

To generate the detailed documentation, call the following. You will need the Quarto CLI [installed](https://quarto.org/docs/get-started/) in your system.

Currently (April 2024), we use Quarto's [v1.5.10 pre-release](https://github.com/quarto-dev/quarto-cli/releases/tag/v1.5.10) (specifically this version, not the later ones) due to some Quarto bugs.

```{clojure eval=FALSE}
(require '[scicloj.clay.v2.api :as clay])
(clay/make! {:format [:quarto :html]
             :source-path "notebooks/index.clj"})
```

### Code Generation

To build this project fully we need to perform some code generation operations. These are listed below:

1. Build the `tablecloth.api.operators` namespace

    The `tablecloth.api.operators` namespace is generated by
`tablecloth.api.lift_operators`. To build that namespace, you need to
load the `tablecloth.api.lift_operators` namespace, and then execute
the code surrounded by a comment at the bottom of the file.

2. Build the `tablecloth.api` (aka the Dataset API)

    The `tablecloth.api` namespace is generated out of `api-template`. To
build that namespace you need to load the
`tablecloth.api.api-template` namespace, and then evaluate the code
contained in the comment section at the bottom of the file. This will
re-generate the `tablecloth.api` namespace.

3. Build the `tablecloth.column.api.operators`  namespace

    The `tablecloth.column.api.operators` namespace is generated by
`tablecloth.column.api.lift_operators`. To build that namespace, you
need to load the `tablecloth.api.lift_operators` namespace, and then
execute the code surrounded by a comment at the bottom of the file.

4. Build the `tablecloth.column.api` (aka the Column API) 

    The `tablecloth.column.api` namespace is generated out of
`api-template`. To build that namespace you need to load the
`tablecloth.column.api.api-template` namespace, and then evaluate the
code contained in the comment section at the bottom of the file. This
will re-generate the `tablecloth.column.api` namespace.


### Guideline

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-source.md`, `CHANGELOG.md`, `notebooks/index.clj`, tests and function docs are highly welcomed.
6. Always discuss changes and PRs first

### Tests

Tests are written and run using [midje](https://github.com/marick/Midje/). To run a test, evaluate a midje form. If it passes, it will return `true`, if it fails details will be printed to the REPL.

## TODO

* elaborate on tests
* tutorials

## Licence

Copyright (c) 2020 Scicloj

The MIT Licence