180 lines
7.7 KiB
Plaintext
Vendored
180 lines
7.7 KiB
Plaintext
Vendored
---
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title: "Dataset (data frame) manipulation API for the tech.ml.dataset library"
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output:
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md_document:
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variant: gfm
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---
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```{r setup, include=FALSE}
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find_nrepl_port_up <- function() {
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wd <- getwd()
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while(wd != dirname(wd)) {
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f <- paste0(wd,"/.nrepl-port")
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if(file.exists(f)) return(paste0("@",f))
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wd <- dirname(wd)
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f <- NULL
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}
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}
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port_file <- find_nrepl_port_up()
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if(is.null(port_file)) stop("nREPL port not found")
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library(knitr)
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knitr_one_string <- knitr:::one_string
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nrepl_cmd <- "rep"
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opts_chunk$set(comment=NA, highlight=TRUE)
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knit_engines$set(clojure = function(options) {
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rep_params <- if(isTRUE(options$stdout_only)) {
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"--print 'out,1,%{out}' --print 'value,1,' -p"
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} else {
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"-p"
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}
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code <- paste(rep_params, port_file, shQuote(knitr_one_string(options$code)))
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out <- if (options$eval) {
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if (options$message) message('running: ', nrepl_cmd, ' ', code)
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tryCatch(
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system2(nrepl_cmd, code, stdout = TRUE, stderr = TRUE, env = options$engine.env),
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error = function(e) {
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if (!options$error) stop(e)
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paste('Error in running command', nrepl_cmd)
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}
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)
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} else ''
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if (!options$error && !is.null(attr(out, 'status'))) stop(knitr_one_string(out))
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engine_output(options, options$code, out)})
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```
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[](https://clojars.org/scicloj/tablecloth)
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[](https://travis-ci.org/github/scicloj/tablecloth)
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[](https://clojurians.zulipchat.com/#narrow/stream/236259-tech.2Eml.2Edataset.2Edev/topic/api)
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## Versions
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### tech.ml.dataset 7.x (master branch)
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[](https://clojars.org/scicloj/tablecloth)
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### tech.ml.dataset 4.x (4.0 branch)
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`[scicloj/tablecloth "4.04"]`
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## Introduction
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[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.
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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/).
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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:
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* Focus on dataset manipulation functionality, leaving other parts of `tech.ml` like pipelines, datatypes, readers, ML, etc.
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* Single entry point for common operations - one function dispatching on given arguments.
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* `group-by` results with special kind of dataset - a dataset containing subsets created after grouping as a column.
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* Most operations recognize regular dataset and grouped dataset and process data accordingly.
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* One function form to enable thread-first on dataset.
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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`.
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If you want to know more about `tech.ml.dataset` and `dtype-next` please refer their documentation:
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* [tech.ml.dataset walkthrough](https://techascent.github.io/tech.ml.dataset/walkthrough.html)
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* [dtype-next overview](https://cnuernber.github.io/dtype-next/overview.html)
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* [dtype-next cheatsheet](https://cnuernber.github.io/dtype-next/cheatsheet.html)
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Join the discussion on [Zulip](https://clojurians.zulipchat.com/#narrow/stream/236259-tech.2Eml.2Edataset.2Edev/topic/api)
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## Documentation
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Please refer [detailed documentation with examples](https://scicloj.github.io/tablecloth/index.html)
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## Usage example
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```{clojure results="hide"}
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(require '[tablecloth.api :as tc])
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```
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```{clojure results="asis"}
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(-> "https://raw.githubusercontent.com/techascent/tech.ml.dataset/master/test/data/stocks.csv"
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(tc/dataset {:key-fn keyword})
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(tc/group-by (fn [row]
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{:symbol (:symbol row)
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:year (tech.v3.datatype.datetime/long-temporal-field :years (:date row))}))
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(tc/aggregate #(tech.v3.datatype.functional/mean (% :price)))
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(tc/order-by [:symbol :year])
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(tc/head 10))
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```
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## Contributing
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`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).
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### Development tools for documentation
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Documentation is written in RMarkdown, that means that you need R to create html/md/pdf files.
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Documentation contains around 600 code snippets which are run during build. There are two files:
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* `README.Rmd`
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* `docs/index.Rmd`
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Prepare following software:
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1. Install [R](https://www.r-project.org/)
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2. Install [rep](https://github.com/eraserhd/rep), nRepl client
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3. Install `pandoc`
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4. Run nRepl
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5. Run R and install R packages: `install.packages(c("rmarkdown","knitr"), dependencies=T)`
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6. Load rmarkdown: `library(rmarkdown)`
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7. Render readme: `render("README.Rmd","md_document")`
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8. Render documentation: `render("docs/index.Rmd","all")`
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### API file generation
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`tablecloth.api` namespace is generated out of `api-template`, please run it before making documentation
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```{clojure eval=FALSE}
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(exporter/write-api! 'tablecloth.api.api-template
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'tablecloth.api
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"src/tablecloth/api.clj"
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'[group-by drop concat rand-nth first last shuffle])
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```
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### Guideline
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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).
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2. Keep API as simple as possible:
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- first argument should be a dataset
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- if parametrizations is complex, last argument should accept a map with not obligatory function arguments
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- avoid variadic associative destructuring for function arguments
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- usually function should working on grouped dataset as well, accept `parallel?` argument then (if applied).
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3. Follow `potemkin` pattern and import functions to the API namespace using `tech.v3.datatype.export-symbols/export-symbols` function
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4. Functions which are composed out of API function to cover specific case(s) should go to `tablecloth.utils` namespace.
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5. Always update `README.Rmd`, `CHANGELOG.md`, `docs/index.Rmd`, tests and function docs are highly welcomed
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6. Always discuss changes and PRs first
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## TODO
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* tests
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* tutorials
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## New experimental dev workflow
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In this branch, we develop a new proposed dev workflow for Tablecloth:
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- namespace-as-a-notebook documentation using [Kindly](https://scicloj.github.io/kindly) and [Clay](https://scicloj.github.io/clay)
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- testing the documentation using [note-to-test](https://github.com/scicloj/note-to-test) - coming soon
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### Relevant files
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- [notebooks/draft.clj](notebooks/draft.clj) - the tutorial as a Kindly notebook (developed with Clay)
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- [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)
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- [docs/draft.html](docs/draft.html) - the tutorial rendered using Clay and [Quarto](https://quarto.org/)
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### Actions
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- to render the notebook using Clay (assuming you have the Quarto CLI [installed](https://quarto.org/docs/get-started/)):
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```clj
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(require '[scicloj.clay.v2.api :as clay])
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(clay/make! {:format [:quarto :html]
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:source-path "notebooks/draft.clj"})
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```
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## Licence
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Copyright (c) 2020 Scicloj
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The MIT Licence
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