This repository is your starting point for the assignment and includes the instructions below.

Link to your GitHub pages website: [insert your *clickable* hyperlink here]

This assignment will help you get started with brushing and linking which you will need for your project. You will be creating a new table that is connected to the existing scatterplot and line chart using brushing and linking.

Please look through all the materials below so you understand the setup instructions; how to run, organize, and submit your code; and our requirements for the visualization.

Under no circumstances should you be editing files via the GitHub website user interface. Do all your edits locally after cloning the repository. Commit major versions to your git repository.

1. Clone this repository to your local machine. E.g., in your terminal / command prompt CD to where you want this the folder for this activity to be. Then run git clone <YOUR_REPO_URL>

2. CD or open a terminal / command prompt window into the cloned folder.

3. Start a simple python webserver. E.g., python -m http.server, python3 -m http.server, or py -m http.server. If you are using python 2 you will need to use python -m SimpleHTTPServer instead, but please switch to python 3 as Python 2 was sunset on 2020-01-01.

4. Wait for the output: Serving HTTP on 0.0.0.0 port 8000 (http://0.0.0.0:8000/).

5. Now open your web browser (Firefox or Chrome) and navigate to the URL: http://localhost:8000

1. Edit near the top of this README.md file to include a clickable hyperlink to the GitHub pages website for your repo., replacing the `[insert your *clickable* hyperlink here]` code with your markdown. (Detailed instructions for GitHub pages here.)

2. In index.html update the GitHub repo URL with the URL of your repository. It is in the span with id='forkongithub'.

Here is an overview of the files and folders we provide for you in your repo.

• README.md is this explanatory file for the repo.

• index.html contains the main website content.

• style.css contains the CSS.

• LICENCE is the source code license for the template. You can add your name or leave it as is.

Each folder has an explanatory README.md file.

• data holds the data file for the visualization, texas.json.

• favicons contains the favicons for the web page. You shouldn't change anything here.

• .github contains GitHub Actions (docs) which will automatically validate your HTML, CSS, and hyperlinks when you push (see the validation last step below). Do not edit files here except to create new .yml files for any additional actions you choose to add (you are not required to make any).

• img contains a descriptive image for the README.md.

• js will contain all JavaScript files you write. E.g.,

  • visualization.js is the main code that builds all your visualizations.

  • scatterplot.js is the code for creating a scatterplot using a Reusable Chart model.

  • linechart.js is the code for creating a line chart using a Reusable Chart model.

• lib will contain any JavaScript library you use. It currently includes D3. To ensure long-term survivability, use the included D3 here rather than linking to d3js.org or some other CDN. Likewise, put your other libraries here rather than loading them from elsewhere.

You will be creating a new table that is connected to the existing scatterplot and line chart using brushing and linking. Make your edits and commit major versions to your git repository. Under no circumstances should you be editing files via the GitHub user interface.

1. In README.md (this file) update the URL above with your actual GitHub pages URL.

2. In index.html update the GitHub repo URL with the URL of your repository. It is in the span with id='forkongithub'.

3. Create a new JavaScript file js/table.js.

4. Following the Reusable Chart model set out in scatterplot.js and linechart.js, write the code in js/table.js necessary for creating a table of all the data in data/texas.json. E.g., a header with the four columns for year, poverty, unemployment, and murder followed by 22 rows of data like so:

   year	poverty	unemployment	murder
   1996	16.6	7.7	5.7
   1997	16.7	6.8	5.3
   ...	...	...	...

5. Add the necessary <div> code to index.html to place your table directly below the existing visualizations.

6. Add any necessary CSS to style.css for displaying your table.

7. Add code to js/visualization.js to create your table in the appropriate <div>. It should be of a set height. E.g., it should not resize as you select elements. You may need a scroll bar.

8. Add brushing and highlighting functionality to your table to match both function and style of the scatterplot and line chart.

9. Add code to js/visualization.js to create the three-way brushing and linking between the table, line chart, and scatterplot. E.g., selections in any of the three should highlight the associated data marks in all three visualizations.

10. Ensure your code passes the 'Validate HTML, CSS, and Links' checks we run when you push to GitHub. I.e., you want to see a green check next to your commit () and not a red X (). You can also see the results in the Actions tab of your repo:

The final interaction should look like this (without the concentric red rings on click):

You are welcome to use D3 tutorials or resources as a starting point for your code. However, you must cite and reference the resources or sample code you use and explain how you use them. This includes anything from bl.ocks.org, Observable, or Stack Overflow! Failure to properly cite and attribute code is a breach of academic honesty. Also, per our syllabus, homework is an individual assessment and should be completed by you alone. Simply copying existing examples without performing thoughtful edits is a breach of academic honesty. You are welcome to ask fellow classmates and students for help and discuss the assignment, but the submission should be your own work. See the syllabus for much more detail on our academic integrity policy and expectations.

1. Ensure you updated (1) the GitHub Pages URL at the top of this README.md file and (2) the GitHub repository URL in index.html in the span with id='forkongithub'.

2. Commit all your local files and push them to the remote repository on GitHub which was generated by GitHub Classroom. We will grade based on what is visible on the GitHub Page.

3. Submit the URL of your GitHub Classroom-generated repository (not your GitHub Page) to the associated assignment on Canvas. Do not submit a link to a personal repository. It must be within our class GitHub organization.

See https://github.com/NEU-DS-4200-F20-Staff/D3_Tips_and_Tricks.

D3 is not just an SVG data binding and manipulation library, though that is what most of the examples show. It is actually a general-purpose DOM data binding and manipulation library. You can thus use it to manipulate arbitrary HTML tags too, e.g., the tags for tables: <table>, <thead>, <tbody>, <tr>, <th>, <td>.

You can search for at examples online, e.g., Jonah Williams' Interactive HTML Table I. Note that this is using D3 version 3 while you are using D3 version 5, so some changes may be necessary. Make sure to cite any materials you use.

d3.brush would be hard to use directly atop an HTML table. Instead, think about how you can re-create similar functionality by listening for DOM events. D3's d3-selection module can listen for any of the standard DOM events using the selection.on(typenames[, listener[, options]]) function.

E.g., to provide row highlighting on mouseover a la Jonah Williams' Interactive HTML Table I you can listen for Mouse Events like so (with D3v6):

d3.selectAll('tr')
    .on('mouseover', (event, d) => {
      const e = selection.nodes(),
            i = e.indexOf(this);
      d3.select(e[i]).classed('highlighted', true);
    })
    .on('mouseout', (event, d) => {
      const e = selection.nodes(),
      i = e.indexOf(this);
      d3.select(e[i]).classed('highlighted', false);
    });

The d3-dispatch module d3-dispatch is made for emiting and listening for events, which we use to coordinate selection updates between our linked views.

E.g.,

let dispatcher = d3.dispatch('selectionUpdated');
dispatcher.on('selectionUpdated', callback1);

where callback1 is a callback function for when the event happens. However, to have multiple listeners for that same event you would need to have unique suffixes for the same string beginning with '.'. E.g., to have both the line chart and table listening to scatterplot updates we could have

dispatcher.on('selectionUpdated.sp-to-lc', callback1);
dispatcher.on('selectionUpdated.sp-to-tab', callback1);

where 'sp-to-lc' and 'sp-to-tab' are arbitrary but written here to be informative.

See https://github.com/NEU-DS-4200-F20-Staff/Assignment_Setup_Instructions.