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Merge branch 'dev' into 01-reading-canlang
2 parents 83ca834 + b0f86fc commit ad52a6d

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name: Publish Docker image on DockerHub
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on:
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push:
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paths:
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- 'Dockerfile'
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jobs:
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build:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@master
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- uses: actions/checkout@master
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with:
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fetch-depth: '0'
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- name: Bump version and push tag
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uses: anothrNick/[email protected]
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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WITH_V: true
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id: bump
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- name: Create Release
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id: create_release
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uses: actions/create-release@v1
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # This token is provided by Actions, you do not need to create your own token
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with:
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tag_name: ${{ steps.bump.outputs.new_tag }}
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release_name: ${{ steps.bump.outputs.new_tag }}
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body: |
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Changes in this Release
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- Rebuilt Docker image and published to DockerHub with new tag
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draft: false
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prerelease: false
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- name: Publish to Registry
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uses: elgohr/Publish-Docker-Github-Action@master
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with:
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name: ubcdsci/intro-to-ds
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username: ${{ secrets.DOCKER_USERNAME }}
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password: ${{ secrets.DOCKER_PASSWORD }}
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tags: "latest,${{ steps.bump.outputs.new_tag }}"
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03-viz.Rmd

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- Use `ggsave` to save visualizations in `.png` and `.svg` format
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## Choosing the visualization
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#### *Ask a question, and answer it* {-#my-section}
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#### *Ask a question, and answer it*
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The purpose of a visualization is to answer a question about a data set of interest. So naturally, the first thing to do **before** creating
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a visualization is to formulate the question about the data that you are trying to answer.
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## Refining the visualization
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#### *Convey the message, minimize noise* {-#my-section}
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#### *Convey the message, minimize noise*
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Just being able to make a visualization in R with `ggplot2` (or any other tool for that matter) doesn't mean that it is effective at communicating
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your message to others. Once you have selected a broad type of visualization to use, you will have to refine it to suit your particular need.
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## Creating visualizations with `ggplot2`
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#### *Build the visualization iteratively* {-#my-section}
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#### *Build the visualization iteratively*
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This section will cover examples of how to choose and refine a visualization given a data set and a question that you want to answer,
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and then how to create the visualization in R using `ggplot2`. To use the `ggplot2` library, we need to load the `tidyverse` metapackage.
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## Explaining the visualization
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#### *Tell a story* {-#my-section}
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#### *Tell a story*
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Typically, your visualization will not be shown completely on its own, but rather it will be part of a larger presentation.
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Further, visualizations can provide supporting information for any part of a presentation, from opening to conclusion.
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## Saving the visualization
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#### *Choose the right output format for your needs* {-#my-section}
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#### *Choose the right output format for your needs*
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Just as there are many ways to store data sets, there are many ways to store visualizations and images.
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Which one you choose can depend on a number of factors, such as file size/type limitations

Dockerfile

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# Copyright (c) UBC-DSCI Development Team.
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FROM rocker/verse:4.0.0
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RUN apt-get update --fix-missing \

data/can_lang.csv

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Aboriginal languages,Woods Cree,1840,800,75,2665
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Non-Official & Non-Aboriginal languages,Wu (Shanghainese),12915,7650,105,16530
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Non-Official & Non-Aboriginal languages,Yiddish,13555,7085,895,20985
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Non-Official & Non-Aboriginal languages,Yoruba,9080,2615,15,22415
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Non-Official & Non-Aboriginal languages,Yoruba,9080,2615,15,22415

docs/GitHub.html

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<meta name="author" content="Melissa Lee" />
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<meta name="date" content="2020-11-03" />
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<meta name="date" content="2020-11-24" />
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<meta name="apple-mobile-web-app-capable" content="yes" />
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