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10 changes: 10 additions & 0 deletions 404.qmd
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---
title: "404 - Page Not Found"
format: html
---

Sorry, but the page you are looking for does not exist.

R you sure that's the correct address?

Please double-check the URL or go back to our [homepage](/).
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121 changes: 121 additions & 0 deletions README.md
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# Development Server for R Consortium Website

This is a new Quarto-based website which updates and supersedes the original website [here:](https://www.r-consortium.org/)

R Consortium Blog - [How to upload your blog post to a quarto-based website](https://oppkeyquarto.netlify.app/blog/how_to_post.html)

## Contributing to this website

This website is being built members of the R Consortium, R-Ladies Gaborone and other volunteer contributors. Thank you! If you want to help contribute to the site, please use the following workflow.

### Workflow for making contributions

#### 1\. Clone this website

#### 2\. Make a branch

Make a branch and edit your branch locally.

To preview the website locally you can execute this quarto command in your terminal:

#### 3\. Commit your changes

Make your changes locally save them and commit them. Be sure to make your commit message descriptive of the work you did.

#### 4\. Do a Pull Request

Do a pull request from your local copy to make sure branch is in sync with the website branch. We will review your Pull Request!

## What is the R Consortium?

The R Consortium, Inc. is a group organized under an open source governance and foundation model to support the worldwide community of users, maintainers and developers of R software. Its [members](https://oppkeyquarto.netlify.app/members) include leading institutions and companies dedicated to the use, development and growth of R.

The R language is an open source environment for statistical computing and graphics. The R community has enjoyed significant growth, with more than 2 million users worldwide. A broad range of organizations have adopted the R language as a data science platform, including biotech, finance, research and high technology industries. The R language is often integrated into third-party analysis, visualization and reporting applications, and runs on a wide variety of computing platforms.

The R Consortium’s mission is to promote the R language and to develop the technical and social infrastructure required to support the R ecosystem and the R Community. Its activities and programs include:

Promoting the growth and development of R as a leading platform for data science and statistical computing.
[Members of the R Consortium](https://oppkeyquarto.netlify.app/members) are recognized as supporters of the R Project and the R community, and the R Consortium represents its members to the R community and to the media.
Supporting and collaborating with the [R Foundation](https://www.r-project.org/foundation/), the governing body of the R Project.

The R Foundation maintains a permanent seat on the board of the R Consortium, as an open communication channel for R Consortium members.

Funding projects to enhance R and support its users.

Projects are proposed by the R community at large, and selected for funding by the Infrastructure Steering Committee. R Consortium members nominate the selection committee and provide funds for project grants with their membership dues. (Here is a list of [projects funded by the R Consortium](https://oppkeyquarto.netlify.app/all-projects/funded-projects) to date.)

Fostering the continued growth of R community and the data science ecosystem.

The R Consortium sponsors R-related conferences (including useR!), meetings (including SatRDays and RLadies), and local user groups worldwide.

Enabling the use of R in commercial environments, and fostering collaboration between companies investing in R.
R Consortium committees are developing programs for R language certification and training, consulting, and employment.

The mission of the R Consortium is formally defined in the [R Consortium bylaws](rc-docs/Bylaws-GU-Draft-7-9-2024.docx.pdf) (PDF) and the [Infrastructure Steering Committee charter](rc-docs/ISC-Charter-08-11-23.pdf) (PDF).

## Main Links

Main Site: [https://www.r-consortium.org/](https://www.r-consortium.org/)
News: [https://www.r-consortium.org/news](https://www.r-consortium.org/news)
Blog: [https://www.r-consortium.org/news/blog](https://www.r-consortium.org/news/blog)
Join: [https://www.r-consortium.org/about/join](https://www.r-consortium.org/about/join)
Twitter: [https://twitter.com/rconsortium?lang=en](https://twitter.com/rconsortium?lang=en)
LinkedIn: [https://www.linkedin.com/company/r-consortium/](https://www.linkedin.com/company/r-consortium/)
Mastodon: [https://fosstodon.org/@RConsortium](https://fosstodon.org/@RConsortium)

---

## Development Section

## Quarto Project hosted with Netlify

## Live site here: [https://oppkeyquarto.netlify.app/](https://oppkeyquarto.netlify.app/)

## Running locally

Clone repo

Use `quarto preview` to run locally. Ensure you are in the root directory of the Quarto project where the _quarto.yml file is located.

If you use `quarto serve` you may get the error:

ERROR: No input passed to serve.
If you are attempting to preview a website or book use the quarto preview command instead.

## Setup for Contributors

Install R using sudo apt-get install r-base and sudo apt-get install r-base-dev

Install R packages on Linux; type R in console and then install.packages('rmarkdown')

GGPLOT2 installation: install.packages("ggplot2")

dygraphs installation : install.packages("dygraphs")

## Project Setup

The .gitignore of this project is setup to ignore `_site/` and `.quarto/`

![gitignore](image.png)

- `_site/` is also known as `docs/` in other quarto projects

- `_site` was specified as netlify publish directory on the website

## Notable findings

- Only the freeze directory is needed when hosting on netlify using the [plugin](https://github.com/quarto-dev/netlify-plugin-quarto)

- Can use the "local only" ignoring files strategy. Each person adds `_freeze/` to the exclude file and only have one person who is reponsible for rendering and uploading to netlify

![exclude](image-1.png)

- When adding a new post these are the files that are added and the ones that are modified, I *manually* modified README.md and _quarto.yml

![modified](image-2.png)

- Second time after initial setup these are the files that need to be added, my observation from this is that it's possible that only during initial setup merge conflicts are a problem due to `site_libs/` directory. I think `site_libs/` is modified only when someone sets up quarto. I can try git_ignoring that directory and see if it causes a build failure or not on netlify. Maybe when someone is setting up R and the libraries on their computer it downloads and modifies this directory.

![sitelibs](image-3.png)

![sitelib2](image-4.png)
18 changes: 18 additions & 0 deletions _freeze/posts/Slides-Post/index/execute-results/html.json
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"markdown": "---\ntitle: \"Regulatory Repositories\"\nauthor:\n - name: Coline Zeballos\n affiliations: \n - name: Roche\n - name: Yann Féat\n affiliations:\n - name: mainanalytics\nformat: revealjs\n---\n\n\n## Introduction\n\n- R has a meticulously maintained ecosystem, which is immensely valuable in a regulatory space\n- In a regulatory setting, the trust of any software needs to be thoroughly documented (NOTE: replace with more precise wording based on HA documents)\n\n::: {.notes}\nColine\n:::\n\n## Goals\n\n- Provide a community-maintained catalog of package quality indicators (\"risk metrics\")\n- Serve quality indicators in a standard format\n- Thoroughly document the system used to perform quality assessment\n- Demonstrate how regulatory-ready risk assessments can be provided using public quality indicators\n- Serve subsets of packages that conform to a specified risk tolerance\n- Improve transparency of industry R package adoption, endorsement and regulator interaction\n\n::: {.notes}\nColine\n:::\n\n## An evolving R ecosystem\n\n- (NOTE: show interaction between CRAN, RVH Reg R Repo (us), RC Submissions WG, RC Repositories WG, pharmaverse, other?)\n\n# Interacting with the repo\n\n## Packages risk filters\n\n::: columns\n::: {.column width=\"50%\"}\n- Helper package for system administrators\n- Restricts packages available for installation to those fitting in a policy\n- Uses packages metadata in the repo\n- May be use together with manual checks (e.g. read a statistical review)\n:::\n::: {.column width=\"50%\"}\n- Example of automated filter for safety-critical activities:\n - Acceptance Criteria 1: “Code Coverage > 95% ”\n - Acceptance Criteria 2: “Package documentation available”\n:::\n:::\n\n::: {.notes}\nYann\n:::\n\n## Usage\n\n\n::: {.cell}\n\n:::\n\n\n::: columns\n::: {.column width=\"40%\"}\n### Unfiltered\n\n::: {.cell}\n\n```{.r .cell-code}\navailable.packages()\n```\n:::\n\n:::\n\n::: {.column width=\"60%\"}\n### Filtered\n\n::: {.cell}\n\n```{.r .cell-code}\nfltr <- risk_filter(\n ReverseDependencies >= 0.99)\noptions(available_packages_filters = fltr)\navailable.packages()\n```\n:::\n\n:::\n:::\n\n::: {.notes}\nYann\n:::\n\n# Repository ‘back-end’\n\n## Repository forked from `r-hub/repos`\n\n![](resources/repos_pkgs.png)\n\n::: {.notes}\nYann\n:::\n\n## Packages cohort validation\n\n* Risk metrics calculated on packages with new versions and on their reverse dependencies\n* Uses the GitHub API to fetch new release assets\n\n\n::: {.notes}\nYann\n:::\n\n# Our roadmap\n\n## What's next\n\n* (NOTE: STAGE 1: Pipeline Integration)\n\n# Closing\n\n## Join us\n\n* (NOTE: link to GH join us issue, add R Consortium info)\n\n## Why Join the R Consortium\n* Community Leadership and Support\n* Funding Key Infrastructure and Projects\n* Influence Over Project Development\n![Elephant](elephant.png){width=300}\n\n## Thank you\n\n* (NOTE: List of Core team members)\n",
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20 changes: 20 additions & 0 deletions _freeze/posts/eriks-cool-post/index/execute-results/html.json

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14 changes: 14 additions & 0 deletions _freeze/posts/eriks-table-flavors/index/execute-results/html.json
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"markdown": "---\ntitle: \"Table of Flavors\"\ndescription: \"R Code for Pharma\"\nauthor: \"Erik Rodriguez\"\ndate: \"11/08/2023\"\nimage: \"image.jpg\"\n---\n\n\n# Tables\n\nYou can use the `kable()` function from the **knitr** package to format tables that integrate well with the rest of the Tufte handout style. The table captions are placed in the margin like figures in the HTML output. Testing for Quarto Preview Save Awesome.\n\n\n::: {.cell .tbl-cap-location-margin}\n\n```{.r .cell-code}\nknitr::kable(\n mtcars[1:6, 1:6], caption = 'A subset of flavors.'\n)\n```\n\n::: {.cell-output-display}\nTable: A subset of flavors.\n\n| | mpg| cyl| disp| hp| drat| wt|\n|:-----------------|----:|---:|----:|---:|----:|-----:|\n|Mazda RX4 | 21.0| 6| 160| 110| 3.90| 2.620|\n|Mazda RX4 Wag | 21.0| 6| 160| 110| 3.90| 2.875|\n|Datsun 710 | 22.8| 4| 108| 93| 3.85| 2.320|\n|Hornet 4 Drive | 21.4| 6| 258| 110| 3.08| 3.215|\n|Hornet Sportabout | 18.7| 8| 360| 175| 3.15| 3.440|\n|Valiant | 18.1| 6| 225| 105| 2.76| 3.460|\n:::\n:::\n",
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"markdown": "---\ntitle: \"Table Post Test\"\ndescription: \"R Code for Pharma\"\nauthor: \"Erik Rodriguez\"\ndate: \"11/08/2023\"\nimage: \"image.jpg\"\n---\n\n\n# Tables\n\nYou can use the `kable()` function from the **knitr** package to format tables that integrate well with the rest of the Tufte handout style. The table captions are placed in the margin like figures in the HTML output.\n\n\n::: {.cell .tbl-cap-location-margin}\n\n```{.r .cell-code}\nknitr::kable(\n mtcars[1:6, 1:6], caption = 'A subset of mtcars.'\n)\n```\n\n::: {.cell-output-display}\nTable: A subset of mtcars.\n\n| | mpg| cyl| disp| hp| drat| wt|\n|:-----------------|----:|---:|----:|---:|----:|-----:|\n|Mazda RX4 | 21.0| 6| 160| 110| 3.90| 2.620|\n|Mazda RX4 Wag | 21.0| 6| 160| 110| 3.90| 2.875|\n|Datsun 710 | 22.8| 4| 108| 93| 3.85| 2.320|\n|Hornet 4 Drive | 21.4| 6| 258| 110| 3.08| 3.215|\n|Hornet Sportabout | 18.7| 8| 360| 175| 3.15| 3.440|\n|Valiant | 18.1| 6| 225| 105| 2.76| 3.460|\n:::\n:::\n",
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16 changes: 16 additions & 0 deletions _freeze/posts/graph-food/index/execute-results/html.json
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"markdown": "---\ntitle: \"Food Graph\"\nauthor: \"Erik Rodriguez\"\nsubtitle: \"Just an example of working with a .qmd file\"\ndate: \"2023-11-08\"\n---\n\n\n# MPG vs Horsepower\n\nThis is a graph about food:\n\n\n::: {.cell .column-margin}\n\n```{.r .cell-code}\nlibrary(ggplot2)\nmtcars2 <- mtcars\nmtcars2$am <- factor(\n mtcars$am, labels = c('chocolate', 'vanilla')\n)\nggplot(mtcars2, aes(hp, mpg, color = am)) +\n geom_point() + geom_smooth() +\n theme(legend.position = 'bottom')\n```\n\n::: {.cell-output-display}\n![Different kinds of food.](index_files/figure-html/food-margin-1.png){width=672}\n:::\n:::\n\n\nShowing it bigger:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(ggplot2)\nmtcars2 <- mtcars\nmtcars2$am <- factor(\n mtcars$am, labels = c('chocolate', 'vanilla')\n)\nggplot(mtcars2, aes(hp, mpg, color = am)) +\n geom_point() + geom_smooth() +\n theme(legend.position = 'bottom')\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using method = 'loess' and formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-2-1.png){width=672}\n:::\n:::",
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16 changes: 16 additions & 0 deletions _freeze/posts/mpg-v-horsepower/index/execute-results/html.json
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"markdown": "---\ntitle: \"MPG v Horsepower EXAMPLE\"\nauthor: \"Jesse Casman\"\nsubtitle: \"Just an example of working with a .qmd file\"\ndate: \"2023-11-06\"\n---\n\n\n# MPG vs Horsepower\n\nI'm guessing MPG trends downward as the horsepower of engines goes up. Let's take a look:\n\n\n::: {.cell .column-margin}\n\n```{.r .cell-code}\nlibrary(ggplot2)\nmtcars2 <- mtcars\nmtcars2$am <- factor(\n mtcars$am, labels = c('automatic', 'manual')\n)\nggplot(mtcars2, aes(hp, mpg, color = am)) +\n geom_point() + geom_smooth() +\n theme(legend.position = 'bottom')\n```\n\n::: {.cell-output-display}\n![MPG vs horsepower, colored by transmission.](index_files/figure-html/fig-margin-1.png){#fig-margin width=672}\n:::\n:::\n\n\nShowing it bigger:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(ggplot2)\nmtcars2 <- mtcars\nmtcars2$am <- factor(\n mtcars$am, labels = c('automatic', 'manual')\n)\nggplot(mtcars2, aes(hp, mpg, color = am)) +\n geom_point() + geom_smooth() +\n theme(legend.position = 'bottom')\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n`geom_smooth()` using method = 'loess' and formula = 'y ~ x'\n```\n:::\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-2-1.png){width=672}\n:::\n:::",
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20 changes: 20 additions & 0 deletions _freeze/posts/second-cool-post/index/execute-results/html.json

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