From 0558a215c91989fae3d4760aba8b9b7a9aad2ccd Mon Sep 17 00:00:00 2001 From: StefanThoma <40463122+StefanThoma@users.noreply.github.com> Date: Wed, 14 Jun 2023 12:27:04 +0200 Subject: [PATCH 1/9] draft post: cross company development --- .../index/execute-results/html.json | 14 ---- .../index/execute-results/html.json | 14 ---- .../index/execute-results/html.json | 6 +- .../index/execute-results/html.json | 4 +- .../index/execute-results/html.json | 4 +- .../index/execute-results/html.json | 14 ---- .../index/execute-results/html.json | 14 ++++ posts/2023-07-20_cross_company_dev/index.qmd | 63 ++++++++++++++++++ .../shiny_klee.png | Bin 0 -> 2467874 bytes 9 files changed, 83 insertions(+), 50 deletions(-) delete mode 100644 _freeze/posts/2023-01-09_CICD-spelling/index/execute-results/html.json delete mode 100644 _freeze/posts/2023-01-09_CICD_spelling/index/execute-results/html.json delete mode 100644 _freeze/posts/2023-05-16_check_links/index/execute-results/html.json create mode 100644 _freeze/posts/2023-07-20_cross_company_dev/index/execute-results/html.json create mode 100644 posts/2023-07-20_cross_company_dev/index.qmd create mode 100644 posts/2023-07-20_cross_company_dev/shiny_klee.png diff --git a/_freeze/posts/2023-01-09_CICD-spelling/index/execute-results/html.json b/_freeze/posts/2023-01-09_CICD-spelling/index/execute-results/html.json deleted file mode 100644 index 75813ff1..00000000 --- a/_freeze/posts/2023-01-09_CICD-spelling/index/execute-results/html.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "hash": "193780bdcb6f71427bab19165b3d6cb5", - "result": { - "markdown": "---\ntitle: \"Using CICD to check spelling in quarto documents\"\nauthor:\n - name: Stefan Thoma\n\ndescription: \"This post explains how to use GitHub to automatically check the spelling of your quarto document through the process of continuous integration and deployment (CICD).\"\ndate: \"2023-03-07\"\ncategories: [CICD, R, GitHub, Quarto]\nimage: \"xportr_rev.png\"\n---\n\n\n\n\n\n\n\n\n\n\nAs a data scientist or R programmer, you may be familiar with the benefits of version control systems like GitHub for tracking changes to your code base and collaborating with others.\nBut did you know that you can also use GitHub to automate the testing, building, and deployment of your R projects?\nThis process, known as continuous integration and deployment (CICD), can save you time and effort by ensuring that your code is always in a deploy-able state and by automatically delivering new updates to your users.\nIn this blog post, we will show you how to set up CICD for your quarto documents on GitHub, including configuring a build pipeline and integrating a spelling checker.\nBy the end of this tutorial, you will have a workflow in place that helps you catch spelling mistakes before they make it into your final documents.\n\n## CICD\n\nCICD is often used in GitHub projects for package development where it helps to maintain a certain code-quality and style consistency across different contributors and developers.\nFor R projects other than packages CICD is used much less frequently.\nI belief that setting up CICD pipelines for less complex projects with only very few contributors is still useful to ensure consistent style, spelling, and more.\n\nAs I am occasionally involved in creating teaching materials in R using quarto, I wanted to implement some CICD checks for quarto documents.\nAs most out-of-the-box CICD pipelines are designed for package development, existing pipelines needed some adjustment to work with other R projects.\n\n## Aim\n\nWhen creating teaching materials in R I rely on GitHub for version control.\nGenerally, I have a `main`-branch which deploys to a GitHub-page displaying the rendered content.\nThe development of materials happens on the `devel` branch with a pending merge request to the `main` branch.\nWhenever a chapter or a section is ready to be published, I merge the branches.\nI wanted to create a pipeline that runs a spell-check on all my quarto files on the merge request with `main`, i.e.: Whenever I push to `devel` I want GitHub to run the CICD pipeline to check my spelling.\nAs an example, I will show how to implement spelling CICD on this blog-project.\n\n## Spell-check\n\nBecause R is all I know, I would like to use an R-package to do the spell-checking.\nThe `spelling` package is well suited for the task, as it allows to spell-check all files at once.\nBefore we try to implement the CICD pipeline, the spell-checker has to work locally, so we first install and load the package:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ninstall.packages(\"spelling\")\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n── Attaching core tidyverse packages ──────────────────── tidyverse 2.0.0 ──\n✔ dplyr 1.1.2 ✔ readr 2.1.4\n✔ forcats 1.0.0 ✔ stringr 1.5.0\n✔ ggplot2 3.4.2 ✔ tibble 3.2.1\n✔ lubridate 1.9.2 ✔ tidyr 1.3.0\n✔ purrr 1.0.1 \n── Conflicts ────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\nℹ Use the conflicted package () to force all conflicts to become errors\n```\n:::\n\n```{.r .cell-code}\nlibrary(spelling)\n```\n:::\n\n\nIf you are working on a package, you can directly use the function `spell_check_package()` and the `spelling` package will do so.\nIf you are working on any other R project you have to use the `spell_check_files()` function and you have to include a path to the files you want to check.\nLet's check just this file:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# 2023-01-09_CICD-spelling/\nspelling::spell_check_files(path = \"index.qmd\") %>%\n head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n WORD FOUND IN\naff index.qmd:348,349,350\ncallout index.qmd:114\nCICD index.qmd:2,6,8,16,27,28,31,33,34,35,37,38,46,47,53,70,122,139,167,170,172,173,174,176,278,322,411,412,415\ncran index.qmd:103\nde index.qmd:349,350,357,367,391,400,401,414\ndesc index.qmd:261,305,399\n```\n:::\n:::\n\n\nIt looks like there are a few words that spelling did not recognize.\nWe should carefully look through the full list and decide whether any mistakes were made.\nWe would not want the GitHub action to prohibit a merge request for any of these words, as there are no typos present (I hope).\nTherefore, we want to add these words to a file that include words to be ignored by the `spelling` package.\n\nIf you are working on a package, this is easy, you can use the `spelling::update_wordlist()` function.\nWe simply save the list of words as a *.txt* file.\nFor now, we save it in the working directory.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwrite(spelling::spell_check_files(path = \"index.qmd\")[[1]], \"WORDLIST.txt\")\n```\n:::\n\n\nThe file looks like this:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nread_lines(\"WORDLIST.txt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] \"aff\" \"callout\" \"CICD\" \"cran\" \n [5] \"de\" \"desc\" \"dic\" \"djnavarro\" \n [9] \"doch\" \"eval\" \"frami\" \"FRAMI\" \n[13] \"german\" \"github\" \"hört\" \"https\" \n[17] \"hunspell\" \"ìnst\" \"jeder\" \"JetBrains\" \n[21] \"JW\" \"lang\" \"lockfile\" \"md\" \n[25] \"nur\" \"png\" \"pre\" \"qmd\" \n[29] \"qmd's\" \"readme\" \"readr\" \"renv\" \n[33] \"repo\" \"Rproj\" \"Rscript\" \"StefanThoma\" \n[37] \"subfolders\" \"testthat\" \"Thoma\" \"tidyverse\" \n[41] \"ubuntu\" \"verowokjnsthet\" \"versteht\" \"versthet\" \n[45] \"von\" \"wordlist\" \"WORDLIST\" \"xportr\" \n[49] \"yaml\" \"yml\" \"zitat\" \"Zitat\" \n```\n:::\n:::\n\n\nNow we can tell `spelling` to ignore the words in this file from typo-detection:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nspelling::spell_check_files(\n path = \"index.qmd\",\n ignore = read_lines(\"WORDLIST.txt\")\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nNo spelling errors found.\n```\n:::\n:::\n\n\nYou can find a more comprehensive guide to the `spelling` package [in the package manual.](https://cran.r-project.org/web/packages/spelling/spelling.pdf)\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# we can get all qmd's in a project by\nlist.files(\n path = \"../..\", # first setting the path to the project\n recursive = TRUE, # include subfolders\n pattern = \".*.qmd$\"\n) # include only files ending in .qmd\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"index.qmd\" \n[2] \"posts/2023-01-09_CICD-spelling/index.qmd\"\n[3] \"posts/2023-01-09_welcome/index.qmd\" \n```\n:::\n:::\n\n\n::: callout-caution\n## path\n\nThe structure of this project is such that each blog-post `.qmd` file is two folders down from the `.Rproj` file.\nThe working directory of the .qmd blog-post file is where the file is located.\nIf I want to list files or save files in a higher order folder I need to adjust my path to first go two folders up.\nI do this by adding `\"../..\"` to my file paths.\n\nThe working directory of the CICD pipeline is by default on project level, therefore, the `\"../..\"` is not required.\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwordlist <- list.files(\n path = \"../..\",\n recursive = TRUE,\n full.names = TRUE,\n pattern = \".*.qmd$\"\n) %>%\n spelling::spell_check_files()\n```\n:::\n\n\nNow you should take a good look at the output and fix any typos spotted.\n\n\nWhat remains is a list of words to be ignored.\nThey can now be saved into a project level `WORDLIST_EXAMPLE.txt` file to be accessed later by our CICD workflow.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwrite(x = wordlist[[1]], file = \"../../inst/WORDLIST_EXAMPLE.txt\")\n```\n:::\n\n\nCheck again with `WORDLIST_EXAMPLE.txt`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlist.files(\n path = \"../..\",\n recursive = TRUE,\n full.names = TRUE,\n pattern = \".*.qmd$\"\n) %>%\n spelling::spell_check_files(ignore = read_lines(\"../../inst/WORDLIST_EXAMPLE.txt\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nNo spelling errors found.\n```\n:::\n:::\n\n\nLooks like it worked --- great!\n\n### Append WORDLIST\n\nIt makes sense to check the spelling locally before you push to your `develop` branch.\nFor this purpose I create an r-script where I can run the spell-check for the project and where I can also append the `WORDLIST.txt` file if needed.\n\n\n::: {.cell file='CICD-Spellcheck.R'}\n\n```{.r .cell-code}\n#-------------------------- spell-check ----------------------------------------\n\n# create empty wordlist:\n# write(\"\", file = \"../../inst/WORDLIST_EXAMPLE.txt\")\n# check spelling:\nspelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE),\n ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\")\n)\n\n# now check those words and whether or not they are really mistakes.\n# once you fixed all mistaked you can:\nwords <- spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE),\n ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\")\n)\n# now you can add words to the wordlist\n#-- uncomment the following line\n# write(words[[1]], file = \"inst/WORDLIST_EXAMPLE.txt\", append = TRUE)\n\nspelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE),\n ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\")\n)\n```\n:::\n\n\n## Setup CICD Workflow\n\nNow this needs to be implemented in the CICD pipeline.\nTo implement GitHub CICD I create a folder `.github` in the project directory, and the folder `workflows` within the `.github` folder.\nThis is where CICD pipelines are stored.\n\nCICD pipelines are written in `yaml` format, it should look like this:\n\n``` yaml\n#| eval: false\nname: Spellcheck\non:\n pull_request: {branches: ['main']}\njobs:\n Spelling:\n runs-on: ubuntu-latest\n container: {image: \"rocker/tidyverse:4.2.1\"}\n steps:\n - name: Checkout repo\n uses: actions/checkout@v3\n\n - name: Install spelling\n run: if (!require(\"spelling\")) install.packages(\"spelling\")\n shell: Rscript {0}\n\n - name: Run Spelling Check test\n run: spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"))\n shell: Rscript {0}\n```\n\nThe first few lines define the name of the workflow (`Spellcheck`) and when it should be executed.\\\n\nIn this case, the action runs on pull requests to the `main` branch.\n\n``` yaml\n#| eval: false\nname: Spellcheck\non:\n pull_request: {branches: ['main']}\n```\n\nThen, we define the job to run:\n\n``` yaml\njobs:\n Spelling:\n runs-on: ubuntu-latest\n container: {image: \"rocker/tidyverse:4.2.1\"}\n steps:\n - name: Checkout repo\n uses: actions/checkout@v3\n\n - name: Install spelling\n run: if (!require(\"spelling\")) install.packages(\"spelling\")\n shell: Rscript {0}\n\n - name: Run Spelling Check test\n run: spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"))\n shell: Rscript {0}\n \n```\n\nWe run just one job called `Spelling`.\n\nIt is run on a [docker image](https://blog.djnavarro.net/posts/2023-01-01_playing-with-docker/) deployed by GitHub.\nWe use a particular docker image that comes with R and tidyverse pre-installed, this eases the use of R in this image.\n\nThe actual workflow is defined in the `steps` (which can be named) --- here we only have three steps.\n\n- `actions/checkout@v3` loads the GitHub repository so the subsequent steps can reference the repo.\n\n- Next, the *Install spelling* step installs the R package `spelling`.\n This is written in R code, so we need to specify that we run the command in R.\n We do this with the instruction `shell: Rscript {0}`.\n\n- At last, we run the spell check in R.\n By default, the code is executed in the project level directory, so we do not need to adjust the path in the `list.files()` function to go up the project directory.\n The same goes for the `ìnst/WORDLIST_EXAMPLE.txt` file.\n\nNow while this works, it will not throw an error if typos are spotted.\nWe can remedy this by writing code that throws an error if there is a typo. \nThe `testthat` package is designed to test R code for packages. \nWe use its `test_that()` function together with the `expect_equal()` function where we can specify the test we want to conduct. \nOur test is simple: As the `object` argument we run the spell-check from above. \nThe output we expect is a spell-check that did not result in any error. \nWe have to supply such an object representing a flawless spell-check in the `expected` argument. \nTo always get such an object we simply spell-check the `WORDLIST_EXAMPLE.txt` file using itself as the list of words to ignore:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntestthat::test_that(\n desc = \"No Typo\",\n code = testthat::expect_equal(\n object = spelling::spell_check_files(\n path = list.files(\n path = \"../..\", pattern = \".*.qmd$\",\n recursive = TRUE, full.names = TRUE\n ),\n ignore = readr::read_lines(\"../../inst/WORDLIST_EXAMPLE.txt\")\n ),\n expected = spelling::spell_check_files(\n path = \"../../inst/WORDLIST_EXAMPLE.txt\",\n ignore = readr::read_lines(\"../../inst/WORDLIST_EXAMPLE.txt\")\n )\n )\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nTest passed 🌈\n```\n:::\n:::\n\n\nWe can now implement this test into our CICD workflow:\n\n\n::: {.cell}\n\n```{.yaml .cell-code}\nname: Spellcheck\non:\n pull_request: {branches: ['main']}\njobs:\n Spelling:\n runs-on: ubuntu-latest\n container: {image: \"rocker/tidyverse:4.2.1\"}\n steps:\n - name: Checkout repo\n uses: actions/checkout@v3\n\n - name: Install spelling\n run: if (!require(\"spelling\")) install.packages(\"spelling\")\n shell: Rscript {0}\n\n - name: Run Spelling Check test\n run: spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"))\n shell: Rscript {0}\n\n - name: Install testthat\n run: if (!require(\"testthat\")) install.packages(\"testthat\")\n shell: Rscript {0}\n\n - name: test typos\n run: testthat::test_that(desc = \"No Typo\", code = {\n no_problem <- spelling::spell_check_files(path = \"inst/WORDLIST_EXAMPLE.txt\", ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"))\n spellcheck <- spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"))\n testthat::expect_equal(object = spellcheck, expected = no_problem)\n })\n shell: Rscript {0}\n```\n:::\n\n\n\n## Change language\n\nThis works fine for English, but what if we write in German?\nThe `spelling` package depends on the `hunspell` package.\nThis package comes with the English dictionary pre-installed.\nFurther, it looks at the user library for any other dictionaries requested in the `spelling` function call.\n\nWe can add dictionaries to the user library in the OS we are using to locally check the spelling in our projects.\nAs soon as we want to spell-check on GitHub (with CICD) it gets a bit more tricky because we need to reference a library file within the CICD workflow.\n\nLet's write a file that contains a German quote (by *JW von Goethe*).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# zitat <- file(\"Zitat.txt\", encoding = \"UTF-8\")\n# write(x = \"Es hört doch jeder nur, was er verowokjnsthet.\", file = \"Zitat.txt\")\nwrite_lines(\"Es hört doch jeder nur, was er versthet.\", file = \"Zitat.txt\")\n```\n:::\n\n\nThe `spelling` package does not recognize the language in a file:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nspelling::spell_check_files(\"Zitat.txt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n WORD FOUND IN\ndoch Zitat.txt:1\nhört Zitat.txt:1\njeder Zitat.txt:1\nnur Zitat.txt:1\nversthet Zitat.txt:1\n```\n:::\n:::\n\n\nWe can list the dictionaries that are currently available to the hunspell package.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nhunspell::list_dictionaries()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"en_AU\" \"en_CA\" \"en_GB\" \"en_US\"\n```\n:::\n:::\n\n\nApparently, only English dictionaries are available at the moment.\nYou can download UTF-8 encoded dictionaries from the [LibreOffice GitHub repo fork](https://github.com/JetBrains/hunspell-dictionaries).\nFor me, the easiest way was to download the entire repo as a `.zip` folder and then move the dictionary files manually into the repo in which you want to spell-check using that dictionary.\n\n`hunspell` requires two dictionary files for a language: the `.dic` and the `.aff` file.\nIn this example we take the German dictionary files `de_DE_frami.aff` and `de_DE_frami.dic` and save them in the `inst` folder where our `WORDLIST_EXAMPLE.txt` file is as well.\nI am not sure why, but sometimes `hunspell` will look for the file `de_CH_FRAMI.dic` when we specify `lang = \"inst/de_CH_frami\"` so make sure to rename the `.dic` and `.aff` files as `de_DE_FRAMI.aff` and `de_DE_FRAMI.dic`, just to be sure.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlist.files(\"../../inst\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"de_CH_FRAMI.aff\" \"de_CH_FRAMI.dic\" \"de_DE_FRAMI.aff\" \n[4] \"de_DE_FRAMI.dic\" \"WORDLIST_EXAMPLE.txt\" \"WORDLIST.txt\" \n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nspelling::spell_check_files(\"Zitat.txt\", lang = \"../../inst/de_CH_FRAMI\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n WORD FOUND IN\nversthet. Zitat.txt:1\n```\n:::\n:::\n\n\nNow we just have to fix the error and check again. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nwrite_lines(\"Es hört doch jeder nur, was er versteht.\", file = \"Zitat.txt\")\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nspelling::spell_check_files(\"Zitat.txt\", lang = \"../../inst/de_CH_FRAMI\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nNo spelling errors found.\n```\n:::\n:::\n\n\nThe `.yml` file for the german spell-check would like this: \n\n\n\n::: {.cell}\n\n```{.yml .cell-code}\nname: Spellcheck\non:\n pull_request: {branches: ['main']}\njobs:\n Spelling:\n runs-on: ubuntu-latest\n container: {image: \"rocker/tidyverse:4.2.1\"}\n steps:\n - name: Checkout repo\n uses: actions/checkout@v3\n\n - name: Install spelling\n run: if (!require(\"spelling\")) install.packages(\"spelling\")\n shell: Rscript {0}\n\n - name: Run Spelling Check test\n run: spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST.txt\"), lang = \"inst/de_CH_frami\")\n shell: Rscript {0}\n\n - name: Install testthat\n run: if (!require(\"testthat\")) install.packages(\"testthat\")\n shell: Rscript {0}\n\n - name: test typos\n run: testthat::test_that(desc = \"No Typo\", code = {\n no_problem <- spelling::spell_check_files(path = \"inst/WORDLIST_EXAMPLE.txt\", ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"), lang = \"inst/de_CH_FRAMI\")\n spellcheck <- spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"), lang = \"inst/de_CH_FRAMI\")\n testthat::expect_equal(object = spellcheck, expected = no_problem)\n })\n shell: Rscript {0}\n```\n:::\n\n\n\n## Conclusion\n\nYou should now be able to run a spell-check on your quarto files. \nFurther, you know how to implement a GitHub CICD pipeline for spell-checks in any language with available dictionary files. \nThis also allows you to implement other R-code based CICD pipelines. \n\nFor your (and my) convenience, I have created book-templates for both [English](https://github.com/StefanThoma/en_quarto_template) and [German](https://github.com/StefanThoma/de_quarto_template) quarto books.\nThey include CICD pipelines for both spelling and style check, and also implement a CICD publishing workflow. \nPlease read the respective `readme.md` file for more information.\n\n\n", - "supporting": [], - "filters": [ - "rmarkdown/pagebreak.lua" - ], - "includes": {}, - "engineDependencies": {}, - "preserve": {}, - "postProcess": true - } -} \ No newline at end of file diff --git a/_freeze/posts/2023-01-09_CICD_spelling/index/execute-results/html.json b/_freeze/posts/2023-01-09_CICD_spelling/index/execute-results/html.json deleted file mode 100644 index 0d76c04e..00000000 --- a/_freeze/posts/2023-01-09_CICD_spelling/index/execute-results/html.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "hash": "eacab8e3ee7c18938913b907aff847fb", - "result": { - "markdown": "---\ntitle: \"Using CICD to check spelling in quarto documents\"\nauthor:\n - name: Stefan Thoma\n\ndescription: \"This post explains how to use GitHub to automatically check the spelling of your quarto document through the process of continuous integration and deployment (CICD).\"\ndate: \"2023-03-07\"\ncategories: [CICD, R, GitHub, Quarto]\nimage: \"xportr_rev.png\"\n---\n\n\n\n\n\n\n\n\n\n\nAs a data scientist or R programmer, you may be familiar with the benefits of version control systems like GitHub for tracking changes to your code base and collaborating with others.\nBut did you know that you can also use GitHub to automate the testing, building, and deployment of your R projects?\nThis process, known as continuous integration and deployment (CICD), can save you time and effort by ensuring that your code is always in a deploy-able state and by automatically delivering new updates to your users.\nIn this blog post, we will show you how to set up CICD for your quarto documents on GitHub, including configuring a build pipeline and integrating a spelling checker.\nBy the end of this tutorial, you will have a workflow in place that helps you catch spelling mistakes before they make it into your final documents.\n\n## CICD\n\nCICD is often used in GitHub projects for package development where it helps to maintain a certain code-quality and style consistency across different contributors and developers.\nFor R projects other than packages CICD is used much less frequently.\nI belief that setting up CICD pipelines for less complex projects with only very few contributors is still useful to ensure consistent style, spelling, and more.\n\nAs I am occasionally involved in creating teaching materials in R using quarto, I wanted to implement some CICD checks for quarto documents.\nAs most out-of-the-box CICD pipelines are designed for package development, existing pipelines needed some adjustment to work with other R projects.\n\n## Aim\n\nWhen creating teaching materials in R I rely on GitHub for version control.\nGenerally, I have a `main`-branch which deploys to a GitHub-page displaying the rendered content.\nThe development of materials happens on the `devel` branch with a pending merge request to the `main` branch.\nWhenever a chapter or a section is ready to be published, I merge the branches.\nI wanted to create a pipeline that runs a spell-check on all my quarto files on the merge request with `main`, i.e.: Whenever I push to `devel` I want GitHub to run the CICD pipeline to check my spelling.\nAs an example, I will show how to implement spelling CICD on this blog-project.\n\n## Spell-check\n\nBecause R is all I know, I would like to use an R-package to do the spell-checking.\nThe `spelling` package is well suited for the task, as it allows to spell-check all files at once.\nBefore we try to implement the CICD pipeline, the spell-checker has to work locally, so we first install and load the package:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ninstall.packages(spelling)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n── Attaching core tidyverse packages ─────────────── tidyverse 1.3.2.9000 ──\n✔ dplyr 1.1.0.9000 ✔ readr 2.1.3 \n✔ forcats 0.5.2 ✔ stringr 1.5.0 \n✔ ggplot2 3.4.0 ✔ tibble 3.1.8 \n✔ lubridate 1.9.0 ✔ tidyr 1.2.1 \n✔ purrr 1.0.1 \n── Conflicts ────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\nℹ Use the \u001b]8;;http://conflicted.r-lib.org/\u0007conflicted package\u001b]8;;\u0007 to force all conflicts to become errors\n```\n:::\n\n```{.r .cell-code}\nlibrary(spelling)\n```\n:::\n\n\nIf you are working on a package, you can directly use the function `spell_check_package()` and the `spelling` package will do so.\nIf you are working on any other R project you have to use the `spell_check_files()` function and you have to include a path to the files you want to check.\nLet's check just this file:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# 2023-01-09_CICD-spelling/\nspelling::spell_check_files(path = \"index.qmd\") %>%\n head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n WORD FOUND IN\naff index.qmd:348,349,350\ncallout index.qmd:114\nCICD index.qmd:2,6,8,16,27,28,31,33,34,35,37,38,46,47,53,70,122,139,167,170,172,173,174,176,278,322,411,412,415\ncran index.qmd:103\nde index.qmd:349,350,357,367,391,400,401,414\ndesc index.qmd:261,305,399\n```\n:::\n:::\n\n\nIt looks like there are a few words that spelling did not recognize.\nWe should carefully look through the full list and decide whether any mistakes were made.\nWe would not want the GitHub action to prohibit a merge request for any of these words, as there are no typos present (I hope).\nTherefore, we want to add these words to a file that include words to be ignored by the `spelling` package.\n\nIf you are working on a package, this is easy, you can use the `spelling::update_wordlist()` function.\nWe simply save the list of words as a *.txt* file.\nFor now, we save it in the working directory.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwrite(spelling::spell_check_files(path = \"index.qmd\")[[1]], \"WORDLIST.txt\")\n```\n:::\n\n\nThe file looks like this:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nread_lines(\"WORDLIST.txt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] \"aff\" \"callout\" \"CICD\" \"cran\" \n [5] \"de\" \"desc\" \"dic\" \"djnavarro\" \n [9] \"doch\" \"eval\" \"frami\" \"FRAMI\" \n[13] \"german\" \"github\" \"hört\" \"https\" \n[17] \"hunspell\" \"ìnst\" \"jeder\" \"JetBrains\" \n[21] \"JW\" \"lang\" \"lockfile\" \"md\" \n[25] \"nur\" \"png\" \"pre\" \"qmd\" \n[29] \"qmd's\" \"readme\" \"readr\" \"renv\" \n[33] \"repo\" \"Rproj\" \"Rscript\" \"StefanThoma\" \n[37] \"subfolders\" \"testthat\" \"Thoma\" \"tidyverse\" \n[41] \"ubuntu\" \"verowokjnsthet\" \"versteht\" \"versthet\" \n[45] \"von\" \"wordlist\" \"WORDLIST\" \"xportr\" \n[49] \"yaml\" \"yml\" \"zitat\" \"Zitat\" \n```\n:::\n:::\n\n\nNow we can tell `spelling` to ignore the words in this file from typo-detection:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nspelling::spell_check_files(\n path = \"index.qmd\",\n ignore = read_lines(\"WORDLIST.txt\")\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nNo spelling errors found.\n```\n:::\n:::\n\n\nYou can find a more comprehensive guide to the `spelling` package [in the package manual.](https://cran.r-project.org/web/packages/spelling/spelling.pdf)\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# we can get all qmd's in a project by\nlist.files(\n path = \"../..\", # first setting the path to the project\n recursive = TRUE, # include subfolders\n pattern = \".*.qmd$\"\n) # include only files ending in .qmd\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"index.qmd\" \n[2] \"posts/2023-01-09_CICD_spelling/index.qmd\" \n[3] \"posts/2023-01-09_welcome/index.qmd\" \n[4] \"posts/2023-03-14_hackathon_app/index.qmd\" \n[5] \"posts/2023-03-14_hackathon_writeup/index.qmd\"\n```\n:::\n:::\n\n\n::: callout-caution\n## path\n\nThe structure of this project is such that each blog-post `.qmd` file is two folders down from the `.Rproj` file.\nThe working directory of the .qmd blog-post file is where the file is located.\nIf I want to list files or save files in a higher order folder I need to adjust my path to first go two folders up.\nI do this by adding `\"../..\"` to my file paths.\n\nThe working directory of the CICD pipeline is by default on project level, therefore, the `\"../..\"` is not required.\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwordlist <- list.files(\n path = \"../..\",\n recursive = TRUE,\n full.names = TRUE,\n pattern = \".*.qmd$\"\n) %>%\n spelling::spell_check_files()\n```\n:::\n\n\nNow you should take a good look at the output and fix any typos spotted.\n\n\nWhat remains is a list of words to be ignored.\nThey can now be saved into a project level `WORDLIST_EXAMPLE.txt` file to be accessed later by our CICD workflow.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwrite(x = wordlist[[1]], file = \"../../inst/WORDLIST_EXAMPLE.txt\")\n```\n:::\n\n\nCheck again with `WORDLIST_EXAMPLE.txt`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlist.files(\n path = \"../..\",\n recursive = TRUE,\n full.names = TRUE,\n pattern = \".*.qmd$\"\n) %>%\n spelling::spell_check_files(ignore = read_lines(\"../../inst/WORDLIST_EXAMPLE.txt\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nNo spelling errors found.\n```\n:::\n:::\n\n\nLooks like it worked --- great!\n\n### Append WORDLIST\n\nIt makes sense to check the spelling locally before you push to your `develop` branch.\nFor this purpose I create an r-script where I can run the spell-check for the project and where I can also append the `WORDLIST.txt` file if needed.\n\n\n::: {.cell file='CICD-Spellcheck.R'}\n\n```{.r .cell-code}\n#-------------------------- spell-check ----------------------------------------\n\n# create empty wordlist:\n# write(\"\", file = \"../../inst/WORDLIST_EXAMPLE.txt\")\n# check spelling:\nspelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE),\n ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\")\n)\n\n# now check those words and whether or not they are really mistakes.\n# once you fixed all mistaked you can:\nwords <- spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE),\n ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\")\n)\n# now you can add words to the wordlist\n#-- uncomment the following line\n# write(words[[1]], file = \"inst/WORDLIST_EXAMPLE.txt\", append = TRUE)\n\nspelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE),\n ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\")\n)\n```\n:::\n\n\n## Setup CICD Workflow\n\nNow this needs to be implemented in the CICD pipeline.\nTo implement GitHub CICD I create a folder `.github` in the project directory, and the folder `workflows` within the `.github` folder.\nThis is where CICD pipelines are stored.\n\nCICD pipelines are written in `yaml` format, it should look like this:\n\n``` yaml\n#| eval: false\nname: Spellcheck\non:\n pull_request: {branches: ['main']}\njobs:\n Spelling:\n runs-on: ubuntu-latest\n container: {image: \"rocker/tidyverse:4.2.1\"}\n steps:\n - name: Checkout repo\n uses: actions/checkout@v3\n\n - name: Install spelling\n run: if (!require(\"spelling\")) install.packages(\"spelling\")\n shell: Rscript {0}\n\n - name: Run Spelling Check test\n run: spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"))\n shell: Rscript {0}\n```\n\nThe first few lines define the name of the workflow (`Spellcheck`) and when it should be executed.\\\n\nIn this case, the action runs on pull requests to the `main` branch.\n\n``` yaml\n#| eval: false\nname: Spellcheck\non:\n pull_request: {branches: ['main']}\n```\n\nThen, we define the job to run:\n\n``` yaml\njobs:\n Spelling:\n runs-on: ubuntu-latest\n container: {image: \"rocker/tidyverse:4.2.1\"}\n steps:\n - name: Checkout repo\n uses: actions/checkout@v3\n\n - name: Install spelling\n run: if (!require(\"spelling\")) install.packages(\"spelling\")\n shell: Rscript {0}\n\n - name: Run Spelling Check test\n run: spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"))\n shell: Rscript {0}\n \n```\n\nWe run just one job called `Spelling`.\n\nIt is run on a [docker image](https://blog.djnavarro.net/posts/2023-01-01_playing-with-docker/) deployed by GitHub.\nWe use a particular docker image that comes with R and tidyverse pre-installed, this eases the use of R in this image.\n\nThe actual workflow is defined in the `steps` (which can be named) --- here we only have three steps.\n\n- `actions/checkout@v3` loads the GitHub repository so the subsequent steps can reference the repo.\n\n- Next, the *Install spelling* step installs the R package `spelling`.\n This is written in R code, so we need to specify that we run the command in R.\n We do this with the instruction `shell: Rscript {0}`.\n\n- At last, we run the spell check in R.\n By default, the code is executed in the project level directory, so we do not need to adjust the path in the `list.files()` function to go up the project directory.\n The same goes for the `ìnst/WORDLIST_EXAMPLE.txt` file.\n\nNow while this works, it will not throw an error if typos are spotted.\nWe can remedy this by writing code that throws an error if there is a typo. \nThe `testthat` package is designed to test R code for packages. \nWe use its `test_that()` function together with the `expect_equal()` function where we can specify the test we want to conduct. \nOur test is simple: As the `object` argument we run the spell-check from above. \nThe output we expect is a spell-check that did not result in any error. \nWe have to supply such an object representing a flawless spell-check in the `expected` argument. \nTo always get such an object we simply spell-check the `WORDLIST_EXAMPLE.txt` file using itself as the list of words to ignore:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntestthat::test_that(\n desc = \"No Typo\",\n code = testthat::expect_equal(\n object = spelling::spell_check_files(\n path = list.files(\n path = \"../..\", pattern = \".*.qmd$\",\n recursive = TRUE, full.names = TRUE\n ),\n ignore = readr::read_lines(\"../../inst/WORDLIST_EXAMPLE.txt\")\n ),\n expected = spelling::spell_check_files(\n path = \"../../inst/WORDLIST_EXAMPLE.txt\",\n ignore = readr::read_lines(\"../../inst/WORDLIST_EXAMPLE.txt\")\n )\n )\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nTest passed 🌈\n```\n:::\n:::\n\n\nWe can now implement this test into our CICD workflow:\n\n\n::: {.cell}\n\n```{.yaml .cell-code}\nname: Spellcheck\non:\n pull_request: {branches: ['main']}\njobs:\n Spelling:\n runs-on: ubuntu-latest\n container: {image: \"rocker/tidyverse:4.2.1\"}\n steps:\n - name: Checkout repo\n uses: actions/checkout@v3\n\n - name: Install spelling\n run: if (!require(\"spelling\")) install.packages(\"spelling\")\n shell: Rscript {0}\n\n - name: Run Spelling Check test\n run: spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"))\n shell: Rscript {0}\n\n - name: Install testthat\n run: if (!require(\"testthat\")) install.packages(\"testthat\")\n shell: Rscript {0}\n\n - name: test typos\n run: testthat::test_that(desc = \"No Typo\", code = {\n no_problem <- spelling::spell_check_files(path = \"inst/WORDLIST_EXAMPLE.txt\", ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"))\n spellcheck <- spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"))\n testthat::expect_equal(object = spellcheck, expected = no_problem)\n })\n shell: Rscript {0}\n```\n:::\n\n\n\n## Change language\n\nThis works fine for English, but what if we write in German?\nThe `spelling` package depends on the `hunspell` package.\nThis package comes with the English dictionary pre-installed.\nFurther, it looks at the user library for any other dictionaries requested in the `spelling` function call.\n\nWe can add dictionaries to the user library in the OS we are using to locally check the spelling in our projects.\nAs soon as we want to spell-check on GitHub (with CICD) it gets a bit more tricky because we need to reference a library file within the CICD workflow.\n\nLet's write a file that contains a German quote (by *JW von Goethe*).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# zitat <- file(\"Zitat.txt\", encoding = \"UTF-8\")\n# write(x = \"Es hört doch jeder nur, was er verowokjnsthet.\", file = \"Zitat.txt\")\nwrite_lines(\"Es hört doch jeder nur, was er versthet.\", file = \"Zitat.txt\")\n```\n:::\n\n\nThe `spelling` package does not recognize the language in a file:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nspelling::spell_check_files(\"Zitat.txt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n WORD FOUND IN\ndoch Zitat.txt:1\nhört Zitat.txt:1\njeder Zitat.txt:1\nnur Zitat.txt:1\nversthet Zitat.txt:1\n```\n:::\n:::\n\n\nWe can list the dictionaries that are currently available to the hunspell package.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nhunspell::list_dictionaries()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"en_AU\" \"en_CA\" \"en_GB\" \"en_US\"\n```\n:::\n:::\n\n\nApparently, only English dictionaries are available at the moment.\nYou can download UTF-8 encoded dictionaries from the [LibreOffice GitHub repo fork](https://github.com/JetBrains/hunspell-dictionaries).\nFor me, the easiest way was to download the entire repo as a `.zip` folder and then move the dictionary files manually into the repo in which you want to spell-check using that dictionary.\n\n`hunspell` requires two dictionary files for a language: the `.dic` and the `.aff` file.\nIn this example we take the German dictionary files `de_DE_frami.aff` and `de_DE_frami.dic` and save them in the `inst` folder where our `WORDLIST_EXAMPLE.txt` file is as well.\nI am not sure why, but sometimes `hunspell` will look for the file `de_CH_FRAMI.dic` when we specify `lang = \"inst/de_CH_frami\"` so make sure to rename the `.dic` and `.aff` files as `de_DE_FRAMI.aff` and `de_DE_FRAMI.dic`, just to be sure.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlist.files(\"../../inst\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"de_CH_FRAMI.aff\" \"de_CH_FRAMI.dic\" \"de_DE_FRAMI.aff\" \n[4] \"de_DE_FRAMI.dic\" \"WORDLIST_EXAMPLE.txt\" \"WORDLIST.txt\" \n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nspelling::spell_check_files(\"Zitat.txt\", lang = \"../../inst/de_CH_FRAMI\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n WORD FOUND IN\nversthet. Zitat.txt:1\n```\n:::\n:::\n\n\nNow we just have to fix the error and check again. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nwrite_lines(\"Es hört doch jeder nur, was er versteht.\", file = \"Zitat.txt\")\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nspelling::spell_check_files(\"Zitat.txt\", lang = \"../../inst/de_CH_FRAMI\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nNo spelling errors found.\n```\n:::\n:::\n\n\nThe `.yml` file for the german spell-check would like this: \n\n\n\n::: {.cell}\n\n```{.yml .cell-code}\nname: Spellcheck\non:\n pull_request: {branches: ['main']}\njobs:\n Spelling:\n runs-on: ubuntu-latest\n container: {image: \"rocker/tidyverse:4.2.1\"}\n steps:\n - name: Checkout repo\n uses: actions/checkout@v3\n\n - name: Install spelling\n run: if (!require(\"spelling\")) install.packages(\"spelling\")\n shell: Rscript {0}\n\n - name: Run Spelling Check test\n run: spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST.txt\"), lang = \"inst/de_CH_frami\")\n shell: Rscript {0}\n\n - name: Install testthat\n run: if (!require(\"testthat\")) install.packages(\"testthat\")\n shell: Rscript {0}\n\n - name: test typos\n run: testthat::test_that(desc = \"No Typo\", code = {\n no_problem <- spelling::spell_check_files(path = \"inst/WORDLIST_EXAMPLE.txt\", ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"), lang = \"inst/de_CH_FRAMI\")\n spellcheck <- spelling::spell_check_files(list.files(pattern = \".*.qmd$\", recursive = TRUE), ignore = readr::read_lines(\"inst/WORDLIST_EXAMPLE.txt\"), lang = \"inst/de_CH_FRAMI\")\n testthat::expect_equal(object = spellcheck, expected = no_problem)\n })\n shell: Rscript {0}\n```\n:::\n\n\n\n## Conclusion\n\nYou should now be able to run a spell-check on your quarto files. \nFurther, you know how to implement a GitHub CICD pipeline for spell-checks in any language with available dictionary files. \nThis also allows you to implement other R-code based CICD pipelines. \n\nFor your (and my) convenience, I have created book-templates for both [English](https://github.com/StefanThoma/en_quarto_template) and [German](https://github.com/StefanThoma/de_quarto_template) quarto books.\nThey include CICD pipelines for both spelling and style check, and also implement a CICD publishing workflow. \nPlease read the respective `readme.md` file for more information.\n\n\n", - "supporting": [], - "filters": [ - "rmarkdown/pagebreak.lua" - ], - "includes": {}, - "engineDependencies": {}, - "preserve": {}, - "postProcess": true - } -} \ No newline at end of file diff --git a/_freeze/posts/2023-01-09_welcome/index/execute-results/html.json b/_freeze/posts/2023-01-09_welcome/index/execute-results/html.json index b4f4e919..ba6569f2 100644 --- a/_freeze/posts/2023-01-09_welcome/index/execute-results/html.json +++ b/_freeze/posts/2023-01-09_welcome/index/execute-results/html.json @@ -1,10 +1,8 @@ { "hash": "cd9910b9f24e1b3dc76d68ee668046f2", "result": { - "markdown": "---\ntitle: \"Hello pharmaverse\"\nauthor:\n - name: Ben Straub\n\ndescription: \"What am I doing here?\"\ndate: \"2024-04-03\"\ncategories: [Blogging]\nimage: \"admiral.png\"\n\n---\n\n\n\n\n\n\n\n\n\n\nThe structure of this Blog is based on the [GitHub repo](https://github.com/djnavarro/quarto-blog) of [the fantastic blog by Danielle Navarro](https://blog.djnavarro.net/), please check it out! \n\nHi there, and welcome to this blog.\nI am a statistical programmer. \nI write this blog to have a reference of what I have done previously, and how I did it. \nMaking this public encourages me to write more explicitly, which in turn will make it easier for me to understand the next time I try to do something similar. \n\n\n\n\n\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n## Last updated {.appendix}\n \n2023-06-12 12:48:39 UTC\n \n## Details {.appendix}\n \n[source code](https://github.com/StefanThoma/quarto-blog/tree/main/posts/2023-01-09_welcome/index.qmd), [R environment](https://github.com/StefanThoma/quarto-blog/tree/main/posts/2023-01-09_welcome/renv.lock)\n:::\n:::\n", - "supporting": [ - "index_files" - ], + "markdown": "---\ntitle: \"Hello pharmaverse\"\nauthor:\n - name: Ben Straub\n\ndescription: \"What am I doing here?\"\ndate: \"2024-04-03\"\ncategories: [Blogging]\nimage: \"admiral.png\"\n\n---\n\n\n\n\n\n\n\n\n\n\nThe structure of this Blog is based on the [GitHub repo](https://github.com/djnavarro/quarto-blog) of [the fantastic blog by Danielle Navarro](https://blog.djnavarro.net/), please check it out! \n\nHi there, and welcome to this blog.\nI am a statistical programmer. \nI write this blog to have a reference of what I have done previously, and how I did it. \nMaking this public encourages me to write more explicitly, which in turn will make it easier for me to understand the next time I try to do something similar. \n\n\n\n\n\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n## Last updated {.appendix}\n \n2023-06-14 12:26:20.784873\n \n## Details {.appendix}\n \n[source code](https://github.com/StefanThoma/quarto-blog/tree/main/posts/2023-01-09_welcome/index.qmd), [R environment](https://github.com/StefanThoma/quarto-blog/tree/main/posts/2023-01-09_welcome/renv.lock)\n:::\n:::\n", + "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" ], diff --git a/_freeze/posts/2023-03-14_hackathon_app/index/execute-results/html.json b/_freeze/posts/2023-03-14_hackathon_app/index/execute-results/html.json index 59fdde92..6c2d82e7 100644 --- a/_freeze/posts/2023-03-14_hackathon_app/index/execute-results/html.json +++ b/_freeze/posts/2023-03-14_hackathon_app/index/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "03d4e4efdeb79de4e29c4b36eaa8b76d", + "hash": "de5022c40d0330cc70d194e02f08691d", "result": { - "markdown": "---\ntitle: \"Hackathon Feedback Application\"\nauthor:\n - name: Stefan Thoma\ndescription: \"Going through the process of creating a shiny app for the admiral hackathon. The shiny app allows users to check their solutions autonomously, gives feedback, and rates their results.\"\ndate: \"2023-03-14\"\ncategories: [R, shiny, teaching, WIP]\nimage: \"shiny_klee.png\"\neditor: \n markdown: \n wrap: sentence\n---\n\n\n\n\n\n\n\n\n\n\n\n\n\nWe recently created a shiny application for the `admiral` hackathon in February 2023.\nThe `admiral` hackathon was an event designed to make statistical programmers from the pharmaceutical industry more comfortable with the `admiral` R package which allows users to efficiently transform data from one data standard (sdtm) to another (adam).\n\nHackathon participants formed groups of up to five people and were then tasked to create R-scripts that map the `sdtm` data to `adam` according to specifics defined in the metadata.\n\nThe purpose of the shiny app was threefold:\n\n1. Allow participants to easily register as a team.\n\n2. Let participants upload their R-scripts, so that the `admiral` team can see how the `admiral` package is being used -- and how the functions and the documentation could be improved.\n\n3. Run participants R-scripts on the cloud and compare the produced file to a solution file.\n Let participants autonomously get feedback on their work.\n\nIn this blog post I want to highlight some of the thoughts that went into this application.\nPlease keep in mind that this work was done under tight time restraints.\n\nThe [hackathon application](https://zxqguo-stefan0pascal-thoma.shinyapps.io/data_upload/?_ga=2.75919894.2036952320.1679990144-1584712930.1674550185) is still online (although data-upload is switched off) and the [GitHub repository](https://github.com/StefanThoma/hackathon_app) is publicly available.\nI have also uploaded to GitHub a `.zip` file of the workspace to which hackathon participants had access via [posit cloud](https://posit.cloud).\nFor more context you can watch [recordings of the hackathon-meetings](https://www.cdisc.org/events/webinar/admiral-hackathon-kickoff).\n\n## Permanent Data\n\nThe biggest challenge you have to consider for this app is the permanent data storage.\nShiny apps run on a server.\nAlthough we can write files on this server, whenever the app restarts, the files are lost.\nTherefore, a [persistent data storage solution](https://shiny.rstudio.com/articles/persistent-data-storage.html#local-vs-remote) is required.\n\n### Google drive\n\nI decided to leverage Google drive using the `googledrive` package.\nThis allowed me to save structured data (the team registry and the submission scores) as well as unstructured data (their R-script files).\n\n::: callout-note\n## Authentication\n\nTo access Google drive using the `googledrive` package we need to authenticate.\nThis can be done interactively using the command `googledrive::drive_auth()` which takes you to the Google login page.\nAfter login you receive an authentication token requested by R.\n\nFor non-interactive authentication this token must be stored locally.\nIn our case where the shiny app must access the token once deployed, the token must be stored on the project level.\n\nI have included the authentication procedure I followed in the `R` folder in `google_init.R`.\nYou can find more extensive [documentation of the non-interactive authentication](https://googledrive.tidyverse.org/reference/drive_auth.html).\n:::\n\nThe initial concept was: Each team gets their own folder including the most recent submission for each task, and a `.csv` file containing team information.\nTo keep track of the submissions and the respective scores we wrote a `.csv` file in the `mock-hackathon` folder, so one folder above the team folders.\n\nSaving the team info as a `.csv` file worked fine as each team received their own file which -- once created -- was not touched anymore.\nAs each upload for every team should simply add a row to the `submissions.csv` file, appending the file would be ideal.\nThis was not possible using the `googledrive` package.\nInstead, for each submission, the `submissions` file was downloaded, appended, and uploaded again.\nUnfortunately, this lead to a data loss, as the file was continuously overwritten, especially when two teams would submit simultaneously.\n\n::: callout-note\n## Recover the Lost Data\n\nWhenever the `submissions.csv` file was uploaded, the previous version was sent to the Google drive bin.\nWe ended up with over 3000 `submissions.csv` files containing a lot of redundant information.\nI had to write the following chunk to first get the unique file IDs of the 3000 `submissions.csv` files, create an empty `submissions` data-frame, and then download each file and add its information to the `submisisons` data-frame.\nTo keep the data-frame as light as possible, after each append I deleted all duplicate submissions.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# get all task_info.csv ID's\n# each row identifies one file in the trash\ntask_info_master <- drive_find(\n pattern = \"task_info.csv\",\n trashed = TRUE\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# set up empty df to store all submissions\norigin <- tibble(\n score = numeric(),\n task = character(),\n team = character(),\n email = character(),\n time = character()\n)\n\n# downloads, reads, and returns one csv file given a file id\nget_file <- function(row) {\n tf <- tempfile()\n row %>%\n as_id() %>%\n drive_download(path = tf)\n new <- read_csv(tf) %>%\n select(score, task, team) %>%\n distinct()\n}\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# quick and dirty for loop to subsequently download each file, extract information\n# merge with previous information and squash it (using distinct()).\nfor (i in 1:nrow(task_info_master)) {\n origin <- rbind(origin, get_file(row = task_info_master[i, ])) %>%\n distinct()\n\n # save progress in a separate file after every 100 downloaded and merged sheets\n if (i %% 100 == 0) {\n print(i)\n write_csv(origin, paste(\"prog_data/task_info_prog_\", i, \".csv\", sep = \"\"))\n # update on progress\n message(i / nrow(task_info_master) * 100)\n }\n}\n```\n:::\n\n\nWhen doing such a time-intensive task, make sure to try it first with only a couple of files to see whether any errors are produced.\nI am not quite sure how long this took but when I returned from my lunch break everything had finished.\n:::\n\nIf you want to stay in the Google framework, I recommend using the `googlesheets4` package for structured data.\n`googlesheets4` allows appending new information to an already existing sheet without the need to download the file first.\nAs both packages follow the same style, going from one to the other is really simple.\n`googlesheets4` requires authentication as well.\nHowever, you can reuse the cached token from the `googledrive` authentication by setting `gs4_auth(token = drive_token())`.\n\n### Security Concerns\n\nConnecting a public shiny app to your Google account introduces a security vulnerability in general.\nEspecially so because we implemented the upload of files to Google drive.\nAnd even more problematic: We run a user generated script and display some of its output.\nA malicious party might be able to extract the authentication token of our Google account or could upload malware to the drive.\n\nTo reduce the risk, I simply created an un-associated Google account to host the drive.\nThere are certainly better options available, but this seemed a reasonable solution achieved with very little effort.\n\n## Register Team\n\nWe wanted to allow users to sign up as teams using the shiny app.\nThe app provides a simple interface where users could input a team name and the number of members.\nThis in turn would open two fields for each user to input their name and email address.\n\nWe do simple checks to make sure at least one valid email address is supplied, and that the group name is acceptable.\nThe group name cannot be empty, already taken, or contain vulgar words.\n\nThe team registration itself was adding the team information to the Google sheets file `event_info` into the sheet `teams` and to create a team folder in which to store the uploaded R files.\n\nThe checks and registration is implemented in the `register_team()` function stored in `interact_with_google.R`.\n\n![Screenshot of the register team interface](register_team.png)\n\nThe challange here was to adapt the number of input fields depending on the number of team members.\nThis means that the team name and email interface must be rendered: \nFirst, we check how many team members are part of the group, this is stored in the `input$n_members` input variable.\nThen we create a `tagList` with as many elements as team members. \nEach element contains two columns, one for the email, one for the member name. \nThis `tagList` is then returned and displayed to the user.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n # render email input UI of the register tab\n output$name_email <- shiny::renderUI({\n # create field names\n N <- input$n_members\n NAME <- sapply(1:N, function(i) {\n paste0(\"name\", i)\n })\n EMAIL <- sapply(1:N, function(i) {\n paste0(\"email\", i)\n })\n \n output <- tagList()\n\n \n firstsecondthird <- c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth\")\n for (i in 1:N) {\n output[[i]] <- tagList()\n output[[i]] <- fluidRow(\n shiny::h4(paste(firstsecondthird[i], \" Member\")),\n column(6,\n textInput(NAME[i], \"Name\"),\n value = \" \" # displayed default value\n ),\n column(6,\n textInput(EMAIL[i], \"Email\"),\n value = \" \"\n )\n )\n }\n output\n })\n```\n:::\n\nThe team information is then uploaded to Google drive. \nBecause some teams have more members than others, we have to create the respective data-frame with the number of team members in mind. \n\nThe following chunk creates the registration data. \nNoteworthy here the creation of the `NAME` and `EMAIL` variables which depend on the number of members in this team.\nFurther, the user input of these fields is extracted via `input[[paste0(NAME[i])]]` within a for-loop.\n\nWe also make the data-creation dependent on the press of the `Register Group` button and cache some variables. \n\n::: {.cell}\n\n```{.r .cell-code}\n ## registration \n registrationData <-\n reactive({\n N <- input$n_members\n NAME <- sapply(1:N, function(i) {\n paste0(\"name\", i)\n })\n EMAIL <- sapply(1:N, function(i) {\n paste0(\"email\", i)\n })\n names <- character(0)\n emails <- character(0)\n\n for (i in 1:N) {\n names[i] <- input[[paste0(NAME[i])]]\n emails[i] <- input[[paste0(EMAIL[i])]]\n }\n # create df\n dplyr::tibble(\n team_name = input$team_name,\n n_members = N,\n member_name = names,\n member_email = emails\n )\n }) %>%\n bindCache(input$team_name, input$n_members, input$name1, input$email1) %>%\n bindEvent(input$register) # wait for button press\n```\n:::\n\n\n## Upload & Source Script\n\nTo upload a script, participants had to select their team first.\nThe input options were based on the existing folders on the Google-drive in the `mock_hackathon` folder.\nTo upload a particular script participants had to also select the task to be solved.\nThe uploaded script is then uploaded to the team folder following a standardised script naming convention.\n\nThere are different aspects to be aware of when sourcing scripts on a shiny server.\nFor example, you have to anticipate the packages users will include in their uploaded scripts, as their scripts will load but not install packages.\nFurther, you should keep the global environment of your shiny app separate from the environment in which the script is sourced.\nThis is possible by supplying an environment to the `source()` function, e.g: `source(path_to_script, local = new.env())`\n\nAnother thing we had to consider was to replicate the exact folder-structure on the shiny server that participants were working with when creating the scripts, as they were required to source some scripts and to save their file into a specific folder.\nThis was relatively straight forward as we provided participants with a folder structure in the posit cloud instance they were using.\nThey had access to the `sdtm` folder in which the data was stored, and the `adam` folder into which they saved their solutions.\nThe structure also included a folder with metadata which was also available on the shiny server.\nFor some tasks, participants required some `adam`-datasets stored in the `adam` folder, essentially the output from previous tasks.\n\nThis was achieved by first creating a list mapping tasks to the required adam datasets: \n\n::: {.cell}\n\n```{.r .cell-code}\ndepends_list <- list(\n \"ADADAS\" = c(\"ADSL\"),\n \"ADAE\" = c(\"ADSL\"),\n \"ADLBC\" = c(\"ADSL\"),\n \"ADLBH\" = c(\"ADSL\"),\n \"ADLBHY\" = c(\"ADSL\"),\n \"ADSL\" = NULL,\n \"ADTTE\" = c(\"ADSL\", \"ADAE\"),\n \"ADVS\" = c(\"ADSL\")\n)\n```\n:::\n\nThis list is sourced from the `R/parameters.R` file when initiating the application.\nWe then call the `get_depends()` function sourced from `R/get_depends.R` which copies the required files from the `key` folder (where our solutions to the tasks were stored) to the `adam` folder. \nAfter sourcing the uploaded script the content in the `adam` folder is deleted.\n\n## Compare to Solution File\n\nWe want to compare the file created by participants with our solution (key) file stored in the `key` folder.\nThe `diffdf::diffdf()` function allows for easy comparison of two data-frames and directly provides extensive feedback for the user:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(dplyr)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n\nAttaching package: 'dplyr'\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nThe following objects are masked from 'package:stats':\n\n filter, lag\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nThe following objects are masked from 'package:base':\n\n intersect, setdiff, setequal, union\n```\n:::\n\n```{.r .cell-code}\ndf1 <- tibble(\n numbers = 1:10,\n letters = LETTERS[1:10]\n)\ndf2 <- tibble(\n numbers = 1:10,\n letters = letters[1:10]\n)\n\ndiffdf::diffdf(df1, df2)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning in diffdf::diffdf(df1, df2): \nNot all Values Compared Equal\n```\n:::\n\n::: {.cell-output .cell-output-stdout}\n```\nDifferences found between the objects!\n\nA summary is given below.\n\nNot all Values Compared Equal\nAll rows are shown in table below\n\n =============================\n Variable No of Differences \n -----------------------------\n letters 10 \n -----------------------------\n\n\nAll rows are shown in table below\n\n ========================================\n VARIABLE ..ROWNUMBER.. BASE COMPARE \n ----------------------------------------\n letters 1 A a \n letters 2 B b \n letters 3 C c \n letters 4 D d \n letters 5 E e \n letters 6 F f \n letters 7 G g \n letters 8 H h \n letters 9 I i \n letters 10 J j \n ----------------------------------------\n```\n:::\n:::\n\n\n## Score\n\nTo compare submissions between participants we implemented a simple scoring function (`score_f()`) based on the table comparison by `diffdf()`.\nThe function can be found in the `compare_dfs.R` file:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nscore_f <- function(df_user, df_key, keys) {\n score <- 10\n diff <- diffdf::diffdf(df_user, df_key, keys = keys)\n if (!diffdf::diffdf_has_issues(diff)) {\n return(score)\n }\n\n # check if there are any differences if the comparison is not strict:\n if (!diffdf::diffdf_has_issues(diffdf::diffdf(df_user,\n df_key,\n keys = keys,\n strict_numeric = FALSE,\n strict_factor = FALSE\n ))) {\n # if differences are not strict, return score - 1\n return(score - 1)\n \n }\n \n return(round(min(max(score - length(diff) / 3, 1), 9), 2))\n}\n```\n:::\n\n\nEvery comparison starts with a score of `10`.\nWe then subtract the length of the comparison object divided by a factor of `3`.\nThe length of the comparison object is a simplified way to represent the difference between the two data-frames by one value.\nFinally, the score is bounded by `1` using `max(score, 1)`.\n\nThe score is not a perfect capture of the quality of the script uploaded but: 1.\nhelped participants get an idea of how close their data-frame is to the solution file 2.\nallowed us to raffle prizes based on the merit of submitted r-scripts\n\n## Reactiveness\n\nSome of the app functions can take quite some time to execute, e.g. running the uploaded script. \nOther tasks, e.g. registering a team, do not intrinsically generate user facing outputs. \nThis would make the app using really frustrating, as users would not know whether the app is correctly working or whether it froze. \n\nWe implemented two small features that made the app more responsive. \nOne is simple loading icons that integrate into the user interface and show that output is being computed -- that something is working. \nThe other is a pop up window which communicates whether team registration was successful, and if not, why not. \nWe further aimed to forward errors generated by the uploaded scripts to the user interface, but errors generated by the application itself should be concealed.\n\n## Conclusion\n\nAlthough the application was continuously improved during the hackathon it proved to be a useful resource for participants from day one as it allowed groups to set their own pace. \nIt further allowed `admiral` developers to gain insights on package usage of a relatively large sample of potential end users.\nFrom our perspective, the application provided a great added value to the hackathon and eased the workload of guiding the participants through all the tasks. \n\n\n\n\n\n::: {.cell}\n\n:::\n", + "markdown": "---\ntitle: \"Hackathon Feedback Application\"\nauthor:\n - name: Stefan Thoma\ndescription: \"Going through the process of creating a shiny app for the admiral hackathon. The shiny app allows users to check their solutions autonomously, gives feedback, and rates their results.\"\ndate: \"2023-03-14\"\ncategories: [R, shiny, teaching, WIP]\nimage: \"shiny_klee.png\"\neditor: \n markdown: \n wrap: sentence\n---\n\n\n\n\n\n\n\n\n\n\n\n\n\nWe recently created a shiny application for the `admiral` hackathon in February 2023.\nThe `admiral` hackathon was an event designed to make statistical programmers from the pharmaceutical industry more comfortable with the `admiral` R package which allows users to efficiently transform data from one data standard (sdtm) to another (adam).\n\nHackathon participants formed groups of up to five people and were then tasked to create R-scripts that map the `sdtm` data to `adam` according to specifics defined in the metadata.\n\nThe purpose of the shiny app was threefold:\n\n1. Allow participants to easily register as a team.\n\n2. Let participants upload their R-scripts, so that the `admiral` team can see how the `admiral` package is being used -- and how the functions and the documentation could be improved.\n\n3. Run participants R-scripts on the cloud and compare the produced file to a solution file.\n Let participants autonomously get feedback on their work.\n\nIn this blog post I want to highlight some of the thoughts that went into this application.\nPlease keep in mind that this work was done under tight time restraints.\n\nThe [hackathon application](https://zxqguo-stefan0pascal-thoma.shinyapps.io/data_upload/?_ga=2.75919894.2036952320.1679990144-1584712930.1674550185) is still online (although data-upload is switched off) and the [GitHub repository](https://github.com/StefanThoma/hackathon_app) is publicly available.\nI have also uploaded to GitHub a `.zip` file of the workspace to which hackathon participants had access via [posit cloud](https://posit.cloud).\nFor more context you can watch [recordings of the hackathon-meetings](https://www.cdisc.org/events/webinar/admiral-hackathon-kickoff).\n\n## Permanent Data\n\nThe biggest challenge you have to consider for this app is the permanent data storage.\nShiny apps run on a server.\nAlthough we can write files on this server, whenever the app restarts, the files are lost.\nTherefore, a [persistent data storage solution](https://shiny.rstudio.com/articles/persistent-data-storage.html#local-vs-remote) is required.\n\n### Google drive\n\nI decided to leverage Google drive using the `googledrive` package.\nThis allowed me to save structured data (the team registry and the submission scores) as well as unstructured data (their R-script files).\n\n::: callout-note\n## Authentication\n\nTo access Google drive using the `googledrive` package we need to authenticate.\nThis can be done interactively using the command `googledrive::drive_auth()` which takes you to the Google login page.\nAfter login you receive an authentication token requested by R.\n\nFor non-interactive authentication this token must be stored locally.\nIn our case where the shiny app must access the token once deployed, the token must be stored on the project level.\n\nI have included the authentication procedure I followed in the `R` folder in `google_init.R`.\nYou can find more extensive [documentation of the non-interactive authentication](https://googledrive.tidyverse.org/reference/drive_auth.html).\n:::\n\nThe initial concept was: Each team gets their own folder including the most recent submission for each task, and a `.csv` file containing team information.\nTo keep track of the submissions and the respective scores we wrote a `.csv` file in the `mock-hackathon` folder, so one folder above the team folders.\n\nSaving the team info as a `.csv` file worked fine as each team received their own file which -- once created -- was not touched anymore.\nAs each upload for every team should simply add a row to the `submissions.csv` file, appending the file would be ideal.\nThis was not possible using the `googledrive` package.\nInstead, for each submission, the `submissions` file was downloaded, appended, and uploaded again.\nUnfortunately, this lead to a data loss, as the file was continuously overwritten, especially when two teams would submit simultaneously.\n\n::: callout-note\n## Recover the Lost Data\n\nWhenever the `submissions.csv` file was uploaded, the previous version was sent to the Google drive bin.\nWe ended up with over 3000 `submissions.csv` files containing a lot of redundant information.\nI had to write the following chunk to first get the unique file IDs of the 3000 `submissions.csv` files, create an empty `submissions` data-frame, and then download each file and add its information to the `submisisons` data-frame.\nTo keep the data-frame as light as possible, after each append I deleted all duplicate submissions.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# get all task_info.csv ID's\n# each row identifies one file in the trash\ntask_info_master <- drive_find(\n pattern = \"task_info.csv\",\n trashed = TRUE\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# set up empty df to store all submissions\norigin <- tibble(\n score = numeric(),\n task = character(),\n team = character(),\n email = character(),\n time = character()\n)\n\n# downloads, reads, and returns one csv file given a file id\nget_file <- function(row) {\n tf <- tempfile()\n row %>%\n as_id() %>%\n drive_download(path = tf)\n new <- read_csv(tf) %>%\n select(score, task, team) %>%\n distinct()\n}\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# quick and dirty for loop to subsequently download each file, extract information\n# merge with previous information and squash it (using distinct()).\nfor (i in 1:nrow(task_info_master)) {\n origin <- rbind(origin, get_file(row = task_info_master[i, ])) %>%\n distinct()\n\n # save progress in a separate file after every 100 downloaded and merged sheets\n if (i %% 100 == 0) {\n print(i)\n write_csv(origin, paste(\"prog_data/task_info_prog_\", i, \".csv\", sep = \"\"))\n # update on progress\n message(i / nrow(task_info_master) * 100)\n }\n}\n```\n:::\n\n\nWhen doing such a time-intensive task, make sure to try it first with only a couple of files to see whether any errors are produced.\nI am not quite sure how long this took but when I returned from my lunch break everything had finished.\n:::\n\nIf you want to stay in the Google framework, I recommend using the `googlesheets4` package for structured data.\n`googlesheets4` allows appending new information to an already existing sheet without the need to download the file first.\nAs both packages follow the same style, going from one to the other is really simple.\n`googlesheets4` requires authentication as well.\nHowever, you can reuse the cached token from the `googledrive` authentication by setting `gs4_auth(token = drive_token())`.\n\n### Security Concerns\n\nConnecting a public shiny app to your Google account introduces a security vulnerability in general.\nEspecially so because we implemented the upload of files to Google drive.\nAnd even more problematic: We run a user generated script and display some of its output.\nA malicious party might be able to extract the authentication token of our Google account or could upload malware to the drive.\n\nTo reduce the risk, I simply created an un-associated Google account to host the drive.\nThere are certainly better options available, but this seemed a reasonable solution achieved with very little effort.\n\n## Register Team\n\nWe wanted to allow users to sign up as teams using the shiny app.\nThe app provides a simple interface where users could input a team name and the number of members.\nThis in turn would open two fields for each user to input their name and email address.\n\nWe do simple checks to make sure at least one valid email address is supplied, and that the group name is acceptable.\nThe group name cannot be empty, already taken, or contain vulgar words.\n\nThe team registration itself was adding the team information to the Google sheets file `event_info` into the sheet `teams` and to create a team folder in which to store the uploaded R files.\n\nThe checks and registration is implemented in the `register_team()` function stored in `interact_with_google.R`.\n\n![Screenshot of the register team interface](register_team.png)\n\nThe challange here was to adapt the number of input fields depending on the number of team members.\nThis means that the team name and email interface must be rendered: \nFirst, we check how many team members are part of the group, this is stored in the `input$n_members` input variable.\nThen we create a `tagList` with as many elements as team members. \nEach element contains two columns, one for the email, one for the member name. \nThis `tagList` is then returned and displayed to the user.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# render email input UI of the register tab\noutput$name_email <- shiny::renderUI({\n # create field names\n N <- input$n_members\n NAME <- sapply(1:N, function(i) {\n paste0(\"name\", i)\n })\n EMAIL <- sapply(1:N, function(i) {\n paste0(\"email\", i)\n })\n\n output <- tagList()\n\n\n firstsecondthird <- c(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth\")\n for (i in 1:N) {\n output[[i]] <- tagList()\n output[[i]] <- fluidRow(\n shiny::h4(paste(firstsecondthird[i], \" Member\")),\n column(6,\n textInput(NAME[i], \"Name\"),\n value = \" \" # displayed default value\n ),\n column(6,\n textInput(EMAIL[i], \"Email\"),\n value = \" \"\n )\n )\n }\n output\n})\n```\n:::\n\nThe team information is then uploaded to Google drive. \nBecause some teams have more members than others, we have to create the respective data-frame with the number of team members in mind. \n\nThe following chunk creates the registration data. \nNoteworthy here the creation of the `NAME` and `EMAIL` variables which depend on the number of members in this team.\nFurther, the user input of these fields is extracted via `input[[paste0(NAME[i])]]` within a for-loop.\n\nWe also make the data-creation dependent on the press of the `Register Group` button and cache some variables. \n\n::: {.cell}\n\n```{.r .cell-code}\n## registration\nregistrationData <-\n reactive({\n N <- input$n_members\n NAME <- sapply(1:N, function(i) {\n paste0(\"name\", i)\n })\n EMAIL <- sapply(1:N, function(i) {\n paste0(\"email\", i)\n })\n names <- character(0)\n emails <- character(0)\n\n for (i in 1:N) {\n names[i] <- input[[paste0(NAME[i])]]\n emails[i] <- input[[paste0(EMAIL[i])]]\n }\n # create df\n dplyr::tibble(\n team_name = input$team_name,\n n_members = N,\n member_name = names,\n member_email = emails\n )\n }) %>%\n bindCache(input$team_name, input$n_members, input$name1, input$email1) %>%\n bindEvent(input$register) # wait for button press\n```\n:::\n\n\n## Upload & Source Script\n\nTo upload a script, participants had to select their team first.\nThe input options were based on the existing folders on the Google-drive in the `mock_hackathon` folder.\nTo upload a particular script participants had to also select the task to be solved.\nThe uploaded script is then uploaded to the team folder following a standardised script naming convention.\n\nThere are different aspects to be aware of when sourcing scripts on a shiny server.\nFor example, you have to anticipate the packages users will include in their uploaded scripts, as their scripts will load but not install packages.\nFurther, you should keep the global environment of your shiny app separate from the environment in which the script is sourced.\nThis is possible by supplying an environment to the `source()` function, e.g: `source(path_to_script, local = new.env())`\n\nAnother thing we had to consider was to replicate the exact folder-structure on the shiny server that participants were working with when creating the scripts, as they were required to source some scripts and to save their file into a specific folder.\nThis was relatively straight forward as we provided participants with a folder structure in the posit cloud instance they were using.\nThey had access to the `sdtm` folder in which the data was stored, and the `adam` folder into which they saved their solutions.\nThe structure also included a folder with metadata which was also available on the shiny server.\nFor some tasks, participants required some `adam`-datasets stored in the `adam` folder, essentially the output from previous tasks.\n\nThis was achieved by first creating a list mapping tasks to the required adam datasets: \n\n::: {.cell}\n\n```{.r .cell-code}\ndepends_list <- list(\n \"ADADAS\" = c(\"ADSL\"),\n \"ADAE\" = c(\"ADSL\"),\n \"ADLBC\" = c(\"ADSL\"),\n \"ADLBH\" = c(\"ADSL\"),\n \"ADLBHY\" = c(\"ADSL\"),\n \"ADSL\" = NULL,\n \"ADTTE\" = c(\"ADSL\", \"ADAE\"),\n \"ADVS\" = c(\"ADSL\")\n)\n```\n:::\n\nThis list is sourced from the `R/parameters.R` file when initiating the application.\nWe then call the `get_depends()` function sourced from `R/get_depends.R` which copies the required files from the `key` folder (where our solutions to the tasks were stored) to the `adam` folder. \nAfter sourcing the uploaded script the content in the `adam` folder is deleted.\n\n## Compare to Solution File\n\nWe want to compare the file created by participants with our solution (key) file stored in the `key` folder.\nThe `diffdf::diffdf()` function allows for easy comparison of two data-frames and directly provides extensive feedback for the user:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(dplyr)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n\nAttaching package: 'dplyr'\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nThe following objects are masked from 'package:stats':\n\n filter, lag\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nThe following objects are masked from 'package:base':\n\n intersect, setdiff, setequal, union\n```\n:::\n\n```{.r .cell-code}\ndf1 <- tibble(\n numbers = 1:10,\n letters = LETTERS[1:10]\n)\ndf2 <- tibble(\n numbers = 1:10,\n letters = letters[1:10]\n)\n\ndiffdf::diffdf(df1, df2)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning in diffdf::diffdf(df1, df2): \nNot all Values Compared Equal\n```\n:::\n\n::: {.cell-output .cell-output-stdout}\n```\nDifferences found between the objects!\n\nA summary is given below.\n\nNot all Values Compared Equal\nAll rows are shown in table below\n\n =============================\n Variable No of Differences \n -----------------------------\n letters 10 \n -----------------------------\n\n\nAll rows are shown in table below\n\n ========================================\n VARIABLE ..ROWNUMBER.. BASE COMPARE \n ----------------------------------------\n letters 1 A a \n letters 2 B b \n letters 3 C c \n letters 4 D d \n letters 5 E e \n letters 6 F f \n letters 7 G g \n letters 8 H h \n letters 9 I i \n letters 10 J j \n ----------------------------------------\n```\n:::\n:::\n\n\n## Score\n\nTo compare submissions between participants we implemented a simple scoring function (`score_f()`) based on the table comparison by `diffdf()`.\nThe function can be found in the `compare_dfs.R` file:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nscore_f <- function(df_user, df_key, keys) {\n score <- 10\n diff <- diffdf::diffdf(df_user, df_key, keys = keys)\n if (!diffdf::diffdf_has_issues(diff)) {\n return(score)\n }\n\n # check if there are any differences if the comparison is not strict:\n if (!diffdf::diffdf_has_issues(diffdf::diffdf(df_user,\n df_key,\n keys = keys,\n strict_numeric = FALSE,\n strict_factor = FALSE\n ))) {\n # if differences are not strict, return score - 1\n return(score - 1)\n }\n\n return(round(min(max(score - length(diff) / 3, 1), 9), 2))\n}\n```\n:::\n\n\nEvery comparison starts with a score of `10`.\nWe then subtract the length of the comparison object divided by a factor of `3`.\nThe length of the comparison object is a simplified way to represent the difference between the two data-frames by one value.\nFinally, the score is bounded by `1` using `max(score, 1)`.\n\nThe score is not a perfect capture of the quality of the script uploaded but: 1.\nhelped participants get an idea of how close their data-frame is to the solution file 2.\nallowed us to raffle prizes based on the merit of submitted r-scripts\n\n## Reactiveness\n\nSome of the app functions can take quite some time to execute, e.g. running the uploaded script. \nOther tasks, e.g. registering a team, do not intrinsically generate user facing outputs. \nThis would make the app using really frustrating, as users would not know whether the app is correctly working or whether it froze. \n\nWe implemented two small features that made the app more responsive. \nOne is simple loading icons that integrate into the user interface and show that output is being computed -- that something is working. \nThe other is a pop up window which communicates whether team registration was successful, and if not, why not. \nWe further aimed to forward errors generated by the uploaded scripts to the user interface, but errors generated by the application itself should be concealed.\n\n## Conclusion\n\nAlthough the application was continuously improved during the hackathon it proved to be a useful resource for participants from day one as it allowed groups to set their own pace. \nIt further allowed `admiral` developers to gain insights on package usage of a relatively large sample of potential end users.\nFrom our perspective, the application provided a great added value to the hackathon and eased the workload of guiding the participants through all the tasks. \n\n\n\n\n\n::: {.cell}\n\n:::\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/posts/2023-03-14_hackathon_writeup/index/execute-results/html.json b/_freeze/posts/2023-03-14_hackathon_writeup/index/execute-results/html.json index b260f9dd..06616706 100644 --- a/_freeze/posts/2023-03-14_hackathon_writeup/index/execute-results/html.json +++ b/_freeze/posts/2023-03-14_hackathon_writeup/index/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "7d73c9951785b7152e13566e46bfc5e4", + "hash": "488a3ed5aa8c82345d937bdaa67cbaf1", "result": { - "markdown": "---\ntitle: \"Admiral Hackathon 2023 Revisited\"\nauthor:\n - name: Stefan Thoma\n\ndescription: \"Let's have a look at the Admiral Hackathon 2023 together.\"\ndate: \"2023-04-24\"\ncategories: [community]\nimage: \"community.png\"\n\n---\n\n\n\n\n\n\n\n\n\n\n\n\n## What was the intention\n\n\n### where you can find the materials\n\n\n## describe the participants using the polls\n\n\n## How did the workshop go?\n(link to hackathon app blogpost)\n\n## conclusion / future workshops\n\n\n\n\n\n\n\n\n\n::: {.cell}\n\n:::", + "markdown": "---\ntitle: \"Admiral Hackathon 2023 Revisited\"\nauthor:\n - name: Stefan Thoma\n\ndescription: \"Let's have a look at the Admiral Hackathon 2023 together.\"\ndate: \"2023-04-24\"\ncategories: [community, WIP]\nimage: \"community.png\"\n\n---\n\n\n\n\n\n\n\n\n\n\n\n\n## What was the intention\n\n\n### where you can find the materials\n\n\n## describe the participants using the polls\n\n\n## How did the workshop go?\n(link to hackathon app blogpost)\n\n## conclusion / future workshops\n\n\n\n\n\n\n\n\n\n::: {.cell}\n\n:::\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/posts/2023-05-16_check_links/index/execute-results/html.json b/_freeze/posts/2023-05-16_check_links/index/execute-results/html.json deleted file mode 100644 index e8efb393..00000000 --- a/_freeze/posts/2023-05-16_check_links/index/execute-results/html.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "hash": "1da02eaa39ffa13f8e1a6a9c1deb5db3", - "result": { - "markdown": "---\ntitle: \"Check Links\"\nauthor:\n - name: Stefan Thoma\n\ndescription: \"Just here to verify whether CICD runs properly\"\ndate: \"2024-05-16\"\ncategories: [CICD]\nimage: \"admiral.png\"\n\n---\n\n\n\n\n\n\n\n\n\n\n\nLets write some links down: \n\n\ndoes [this](www.google.com) work? It should.\n\nwhat about [that](https://gobledigook.com/blablabla)? this link does not work.\n\nLet's see what happens [with 404 errors](https://www.apple.com/iphony):\n\n\n\n\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n## Last updated {.appendix}\n \n2023-05-16 05:40:41.796726\n \n## Details {.appendix}\n \n[source code](https://github.com/StefanThoma/quarto-blog/tree/main/posts/2023-05-16_link/index.qmd), [R environment](https://github.com/StefanThoma/quarto-blog/tree/main/posts/2023-05-16_link/renv.lock)\n:::\n:::\n", - "supporting": [], - "filters": [ - "rmarkdown/pagebreak.lua" - ], - "includes": {}, - "engineDependencies": {}, - "preserve": {}, - "postProcess": true - } -} \ No newline at end of file diff --git a/_freeze/posts/2023-07-20_cross_company_dev/index/execute-results/html.json b/_freeze/posts/2023-07-20_cross_company_dev/index/execute-results/html.json new file mode 100644 index 00000000..8fee9923 --- /dev/null +++ b/_freeze/posts/2023-07-20_cross_company_dev/index/execute-results/html.json @@ -0,0 +1,14 @@ +{ + "hash": "31a77b8106e2a20bc0d23cd71a83e1fe", + "result": { + "markdown": "---\ntitle: \"Collaborative Open Source Development: Revolutionizing Data Standards in the Pharmaceutical Industry\"\nauthor:\n - name: Stefan Thoma\ndescription: \"\"\ndate: \"2023-07-20\"\ncategories: [WIP, admiral]\nimage: \"shiny_klee.png\"\neditor: \n markdown: \n wrap: sentence\n---\n\n\n\n\n\n\n\n\n\n\n\n# Introduction:\n\nIn the fast-paced world of pharmaceutical research and development, the need for efficient and standardized data management solutions has never been more crucial. \nThis blog post delves into the journey of an ambitious team working towards an end-to-end solution in R, particularly focusing on the transformation from SDTM to ADaM, while advocating for collaborative open-source development across companies. \nWe will explore the challenges we faced, the strategies we employed, and the significant impact this approach has had on both Roche and the industry as a whole.\n\n## Who am I:\nI joined Roche as an intern in November 2022, and since April, I have become a permanent member of the Admiral team, dedicating 20% of my time to this project.\n\n# Problem to be solved:\nThe lack of a comprehensive, standardized solution in R for end-to-end clinical data analysis presented a significant challenge in the industry. Many companies developed their own custom solutions, resulting in a fragmented landscape. Even individuals proficient in SAS had to relearn company-specific solutions, leading to inefficiencies and delays. The team recognized the need for an end-to-end framework. The admiral package covers the transformation from SDTM to ADaM, addressing a specific part of this pipeline.\n\n# Proposed Solution:\nEnter Admiral! Admiral is not a Roche product, but a collaborative effort that aims to create an open-source package to solve the data standards challenge. The vision was to develop a solution that would be widely accessible, fostering collaboration and standardization across the pharmaceutical industry.\n\n# Challenges at Startup:\nLaunching a collaborative open-source project is no small feat. The team faced the task of convincing higher-ups and peers about the value and relevance of the project. By demonstrating the real problem that needed solving, they managed to gain support from key stakeholders. Furthermore, they sought to expand their influence beyond Roche and reached out to other companies, forming a team consisting of Thomas Neitmann from Roche and Marco Rimmler from GSK, right from the start.\n\n# Strategies:\nTo ensure success, the team adopted several key strategies. Acting quickly was imperative, as they open-sourced the project at an early stage and communicated their intentions widely. By involving multiple companies, they fostered a collaborative community, where users could actively participate in the development process, providing feedback and creating issues. The team also embraced a modular approach, developing a core package alongside therapeutic area-specific packages and internal company packages. This modularity was complemented by consistent interfaces and design guidelines across all packages, facilitated by a developer's guide and continuous integration and continuous deployment (CICD). Furthermore, they created templates for common and more complex tasks, enhancing productivity and efficiency.\n\n# Impact for Roche:\nFor Roche, the adoption of the Admiral project yielded remarkable benefits. Instead of relying on just two developers, they gained the power of a full team consisting of ten developers. This amplified workforce not only accelerated progress but also provided Roche with a seat at the table. By collaborating with other companies, Roche ensured that they were part of the conversation, driving industry-wide standards.\n\n# Attractive Workplace:\nEmbracing open-source development and utilizing R as a primary tool for data management also made Roche an attractive workplace for talented individuals. Open-source development inherently promotes transparency and ethics, appealing to those seeking a more collaborative and inclusive work environment. The collaboration across companies dissolved traditional hierarchies, encouraging autonomous work and empowering individuals to choose the issues they wished to tackle. Additionally, this approach expanded networks and facilitated knowledge transfer, making talent more adaptable and valuable across various positions.\n\n# Conclusion:\nThe benefits of developing open-source packages across companies, as exemplified by the Admiral project, are truly transformative. By pooling resources, knowledge, and expertise, companies in the pharmaceutical industry can overcome the challenges of data management and foster a culture of collaboration and standardization. The impact extends beyond immediate gains in efficiency and productivity; it attracts new hires, encourages transparency, and cultivates a vibrant community of professionals dedicated to advancing pharmaceutical research and development. The Admiral project serves as a testament to the power of collaborative open-source development and the potential it holds for the future of the industry.\n\n\n\n\n::: {.cell}\n\n:::\n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/posts/2023-07-20_cross_company_dev/index.qmd b/posts/2023-07-20_cross_company_dev/index.qmd new file mode 100644 index 00000000..92367049 --- /dev/null +++ b/posts/2023-07-20_cross_company_dev/index.qmd @@ -0,0 +1,63 @@ +--- +title: "Collaborative Open Source Development: Revolutionizing Data Standards in the Pharmaceutical Industry" +author: + - name: Stefan Thoma +description: "" +date: "2023-07-20" +categories: [WIP, admiral] +image: "shiny_klee.png" +editor: + markdown: + wrap: sentence +--- + + + +```{r setup, include=FALSE} +long_slug <- "2023-07-20_shiny_run_script" +# renv::use(lockfile = "renv.lock") +``` + + + + +# Introduction: + +In the fast-paced world of pharmaceutical research and development, the need for efficient and standardized data management solutions has never been more crucial. +This blog post delves into the journey of an ambitious team working towards an end-to-end solution in R, particularly focusing on the transformation from SDTM to ADaM, while advocating for collaborative open-source development across companies. +We will explore the challenges we faced, the strategies we employed, and the significant impact this approach has had on both Roche and the industry as a whole. + +## Who am I: +I joined Roche as an intern in November 2022, and since April, I have become a permanent member of the Admiral team, dedicating 20% of my time to this project. + +# Problem to be solved: +The lack of a comprehensive, standardized solution in R for end-to-end clinical data analysis presented a significant challenge in the industry. Many companies developed their own custom solutions, resulting in a fragmented landscape. Even individuals proficient in SAS had to relearn company-specific solutions, leading to inefficiencies and delays. The team recognized the need for an end-to-end framework. The admiral package covers the transformation from SDTM to ADaM, addressing a specific part of this pipeline. + +# Proposed Solution: +Enter Admiral! Admiral is not a Roche product, but a collaborative effort that aims to create an open-source package to solve the data standards challenge. The vision was to develop a solution that would be widely accessible, fostering collaboration and standardization across the pharmaceutical industry. + +# Challenges at Startup: +Launching a collaborative open-source project is no small feat. The team faced the task of convincing higher-ups and peers about the value and relevance of the project. By demonstrating the real problem that needed solving, they managed to gain support from key stakeholders. Furthermore, they sought to expand their influence beyond Roche and reached out to other companies, forming a team consisting of Thomas Neitmann from Roche and Marco Rimmler from GSK, right from the start. + +# Strategies: +To ensure success, the team adopted several key strategies. Acting quickly was imperative, as they open-sourced the project at an early stage and communicated their intentions widely. By involving multiple companies, they fostered a collaborative community, where users could actively participate in the development process, providing feedback and creating issues. The team also embraced a modular approach, developing a core package alongside therapeutic area-specific packages and internal company packages. This modularity was complemented by consistent interfaces and design guidelines across all packages, facilitated by a developer's guide and continuous integration and continuous deployment (CICD). Furthermore, they created templates for common and more complex tasks, enhancing productivity and efficiency. + +# Impact for Roche: +For Roche, the adoption of the Admiral project yielded remarkable benefits. Instead of relying on just two developers, they gained the power of a full team consisting of ten developers. This amplified workforce not only accelerated progress but also provided Roche with a seat at the table. By collaborating with other companies, Roche ensured that they were part of the conversation, driving industry-wide standards. + +# Attractive Workplace: +Embracing open-source development and utilizing R as a primary tool for data management also made Roche an attractive workplace for talented individuals. Open-source development inherently promotes transparency and ethics, appealing to those seeking a more collaborative and inclusive work environment. The collaboration across companies dissolved traditional hierarchies, encouraging autonomous work and empowering individuals to choose the issues they wished to tackle. Additionally, this approach expanded networks and facilitated knowledge transfer, making talent more adaptable and valuable across various positions. + +# Conclusion: +The benefits of developing open-source packages across companies, as exemplified by the Admiral project, are truly transformative. By pooling resources, knowledge, and expertise, companies in the pharmaceutical industry can overcome the challenges of data management and foster a culture of collaboration and standardization. The impact extends beyond immediate gains in efficiency and productivity; it attracts new hires, encourages transparency, and cultivates a vibrant community of professionals dedicated to advancing pharmaceutical research and development. The Admiral project serves as a testament to the power of collaborative open-source development and the potential it holds for the future of the industry. + + + +```{r, echo=FALSE} +#| eval: false +source("appendix.R") +insert_appendix( + repo_spec = "pharmaverse/comms_wg", + name = long_slug +) +``` diff --git a/posts/2023-07-20_cross_company_dev/shiny_klee.png b/posts/2023-07-20_cross_company_dev/shiny_klee.png new file mode 100644 index 0000000000000000000000000000000000000000..333730fcc9809058adc96ac6b3d259d5c8047d86 GIT binary patch literal 2467874 zcmV)OK(@b$P)1^@s6D%*rW00001b5ch_0Itp) z=>Px#1ZP1_K>z@;j|==^1poj532;bRa{vGi!~g&e!~vBn4jTXf07PY2NoGw=04e|g z00;mAl6(LH000070000cl7;{R0000M0000k00000PjF>!K}i4rO<`nZAa`kWXdq8; zWo|)9AWUIma{zC6BANgI|5ZsuK~#8NME%FFt!Z|riLLFuJNCLTnHiCWOnO$a$ttqC zS?p>wR6Wr|nrR@Qfrd~L8l^#NqyY3ksWlP6?v|-ykx8bD$jC4^;`-ya_eRgNb|w_b zVBCAo-rx7W>s{uz>|6NifBWwbQE5f(q#LVUF?O+u^>!D{PBUtiS}Z5CC>#ng+AZRj zgh 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