Github Actions with D3

My prediction is that Automation and DevOps will get used more and more by non-software companies. You can already see this with data analytics pipelines where your SQL models are checked into your repository. If you run an e-commerce company, why not manage parts of your operations and customer support in Github and benefit from version control, issue tracking, team collaboration and workflow automation?

The use cases are endless, and while there are many workflow automation tools available, I suspect that you'll want to manage your workflows where you store your source code.

If you add a new product take screenshots and run Lighthouse of your published site and make sure it looks good on various devices.

count_targeted count_injected count_rejected count_sent to from
178 178 0 174 2020-12-22T10:00 2020-12-22T16:00
294 294 0 290 2020-12-22T12:00 2020-12-22T18:00
447 447 0 442 2020-12-22T18:00 2020-12-23T00:00
373 373 0 368 2020-12-23T00:00 2020-12-23T06:00

This workflow can be described in a simple yaml file:

name: Run D3 stats manually

workflow_dispatch: # Launch manually and provide default values
description: 'csv file to analyze'
default: 'billybob.csv'
required: true
description: 'SVG'
default: 'output.svg'
required: true
schedule: # Also run once every day at 8am
- cron: '0 8 * * *'

runs-on: ubuntu-latest

- name: checkout # Check out the files in the repo
- uses: actions/checkout@v2

- name: Get Sparkpost
run: |
TSFROM="$(date -u -d '6 hour ago' '+%Y-%m-%dT%H:00')"
TSTO="$(date -u '+%Y-%m-%dT%H:00')"
echo $TSFROM
echo $TSTO
curl \
-H "Accept: application/json" \
-H "Authorization: $" \
"${TSFROM}&to=${TSTO}&metrics=count_targeted,count_injected,count_rejected,count_sent" | jq -r --arg from "${TSFROM}" --arg to "$TSTO" '.results[] + {from: $from, to: $to} | [.[]] | @csv' >> billybob.csv

- name: Analyze csv
DEFAULT_FILE: billybob.csv
id: analyze-csv
uses: mwhitaker/d3-line-chart-action@main
csv-file: $NaN
date-column: from # Specify date column in csv
value-column: count_sent # Specify value column in csv
- name: Get the summary stats
if: steps.analyze-csv.outputs.summary-alert == 'false' # only run this step if there are outliers and create a new issue.
id: summary-stats
uses: actions/github-script@v3
github-token: $
script: |
const result = $NaN
const comment = [
`⚡️ Summary Sparkpost Stats ⚡️`,
'| Category | Value |',
'| --- | --- |',
`| Q1 | ${result.quartiles[0]} |`,
`| Q2 | ${result.quartiles[1]} |`,
`| Q3 | ${result.quartiles[2]} |`,
`| Range lower | ${result.range[0]} |`,
`| Range upper | ${result.range[1]} |`,
`| Outliers | ${result.outliers.join(" ")} |`,
' ',
`See the [Sparkpost graph](../blob/main/screenshot-output/sparkpost_emails_docker.png)`
owner: context.repo.owner,
repo: context.repo.repo,
title: `Sparkpost alert`,
body: comment

- name: Commit and push if it changed # commit new line graph if it has changed
run: |-
git config "Automated"
git config ""
git add -A
timestamp=$(date -u)
git commit -m "Latest data: ${timestamp}" || exit 0
git push