--- title: "Dataset (data frame) manipulation API for the tech.ml.dataset library" output: md_document: variant: gfm --- ```{r setup, include=FALSE} find_nrepl_port_up <- function() { wd <- getwd() while(wd != dirname(wd)) { f <- paste0(wd,"/.nrepl-port") if(file.exists(f)) return(paste0("@",f)) wd <- dirname(wd) f <- NULL } } port_file <- find_nrepl_port_up() if(is.null(port_file)) stop("nREPL port not found") library(knitr) knitr_one_string <- knitr:::one_string nrepl_cmd <- "rep" opts_chunk$set(comment=NA, highlight=TRUE) knit_engines$set(clojure = function(options) { rep_params <- if(isTRUE(options$stdout_only)) { "--print 'out,1,%{out}' --print 'value,1,' -p" } else { "-p" } code <- paste(rep_params, port_file, shQuote(knitr_one_string(options$code))) out <- if (options$eval) { if (options$message) message('running: ', nrepl_cmd, ' ', code) tryCatch( system2(nrepl_cmd, code, stdout = TRUE, stderr = TRUE, env = options$engine.env), error = function(e) { if (!options$error) stop(e) paste('Error in running command', nrepl_cmd) } ) } else '' if (!options$error && !is.null(attr(out, 'status'))) stop(knitr_one_string(out)) engine_output(options, options$code, out)}) ``` [![](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/index.html) ## 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 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](https://www.r-project.org/) 2. Install [rep](https://github.com/eraserhd/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")` ### API file generation `tablecloth.api` namespace is generated out of `api-template`, please run it before making documentation ```{clojure eval=FALSE} (exporter/write-api! 'tablecloth.api.api-template 'tablecloth.api "src/tablecloth/api.clj" '[group-by drop concat rand-nth first last shuffle]) ``` ### 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.Rmd`, `CHANGELOG.md`, `docs/index.Rmd`, tests and function docs are highly welcomed 6. Always discuss changes and PRs first ## TODO * tests * tutorials ## New experimental dev workflow In this branch, we develop a new proposed dev workflow for Tablecloth: - namespace-as-a-notebook documentation using [Kindly](https://scicloj.github.io/kindly) and [Clay](https://scicloj.github.io/clay) - testing the documentation using [note-to-test](https://github.com/scicloj/note-to-test) - coming soon ### Relevant files - [notebooks/draft.clj](notebooks/draft.clj) - the tutorial as a Kindly notebook (developed with Clay) - [dev/conversion.clj](dev/conversion.clj) - the script used to generate the notebook from the original `Rmarkdown` tutorial (up to a few additional manual edits) - [docs/draft.html](docs/draft.html) - the tutorial rendered using Clay and [Quarto](https://quarto.org/) ### Actions - to render the notebook using Clay (assuming you have the Quarto CLI [installed](https://quarto.org/docs/get-started/)): ```clj (require '[scicloj.clay.v2.api :as clay]) (clay/make! {:format [:quarto :html] :source-path "notebooks/draft.clj"}) ``` ## Licence Copyright (c) 2020 Scicloj The MIT Licence