Assignment 3

Goals

Use different visualization tools, including Tableau, Observable Plot, and Data-Driven Documents (D3), to create a stacked bar chart.

Instructions

You will need to complete this assignment in multiple parts. All the results can be inserted into a single Observable Notebook, but you may submit a web page containing the results as well. You must use Tableau Public (or the Desktop version), Observable Plot, and D3 for this assignment. All D3 visualization should be done using the 7.x version. You may use other libraries (e.g. lodash) as long as they are not used to construct or modify SVGs, but you must credit them in the HTML file you turn in. Tableau offers video tutorials, and Observable Plot has documentation and examples For D3, there is extensive documentation available as well as examples, and Vadim Ogievetsky’s example-based introduction and the bar chart examples that we went through in class are also useful references.

Due Date

The assignment is due at 11:59pm on Friday, October 11.

Submission

You should submit any files required for this assignment on Blackboard. For Observable, do not publish your notebook; instead, (1) share it with me (@dakoop) and (2) use the “Export -> Download Code” option and turn in that file renamed to a3.tar.gz (or a3.tgz) file to Blackboard. Please do both of these steps as (1) is easier for me to grade, but (2) makes it possible to persist the state of the submission. If you complete the assignment outside of Observable, you may complete the assignment in a single HTML file or use multiple files (e.g. one for HTML and one for CSS). Note that the files should be linked to the main HTML document accordingly in a relative manner (style.css not C:\My Documents\Jane\NIU\CSCI627\assignment3\styles.css). If you submit multiple files, you may need to zip them in order for Blackboard to accept the submission. The filename of the main HTML document should be a3.html.

Details

In this assignment, we will again be working with data about the computer science graduates according to data collected by the Integrated Postsecondary Education Data System (IPEDS). This dataset details college and university graduates including the type of degree awarded and the area of study. Rather than using this dataset directly, I have extracted the computer science majors data as a subset that is available as a csv file. This data is available here:

https://gist.githubusercontent.com/dakoop/b9d1161ee63e323bf49b64ec0ecd8749/raw/7c5f43816834b389f83c91538bd16dc856a4daa0/cs-grads-with-subarea.csv

Each data item has the following attributes:

• YEAR: the year
• CIP_SUBAREA: the subarea of computer science that the student’s degree is in. (See this listing for what this code means.)
• AWLEVEL: the type of degree awarded (5 for Bachelor’s, 7 for Master’s, and 9 for Ph.D.)
• CTOTALT: the total number of graduates (missing values have been filled)
• CTOTALM: the total number of graduates who were allocated as men
• CTOTALW: the total number of graduates who were allocated as women

We will be using Tableau (Public), Observable Plot, and D3 to create a vertically stacked bar chart. The visualizations should show each year along the x-axis and a bar with the total number of computer science Bachelor’s graduates each year. That bar should be split into subunits for the different subareas, where each subarea receives its own color. Order the bar fragments appropriately. Provide a legend describing which colors map to which result.

As with Assignment 1, make sure the beginning of your main web page (or notebook) contains the following text:

• Your name
• Your student id
• The course title (“Data Visualization (CS 627/490)”), and
• The assignment title (“Assignment 3”)

If you used any additional JavaScript libraries, please append a note indicating their usage to the text above (e.g. “I used the jQuery library to write callback functions.”) Include links to the libraries used. You do not need to adhere to any particular style for this text, but I would suggest using headings to separate the sections of the assignment.

2. Observable Plot (30 points)

Observable notebooks automatically import the d3 and Plot libraries so you can use them directly. However, if you are using another tool for this assignment, make sure to include these two libraries. See the documentation about how to include them, specifically about how to use them in vanilla HTML or as a UMD bundle. To load the data, use d3.csv which can load the data from the URL above. Note that converting the data types after reading the csv or using D3’s autoType functionality when loading will help avoid parseInt calls later on. You do not need to match the colors in the example solution, but you should make sure that your colors are in the same order for each year. Do not put commas in the year values. Check the margins and make sure you have a legend.

Hints

• Make sure to filter the data so only Bachelor’s degrees are shown.
• Investigate the sort option to order the individual bars in a stack.
• Examine the options for the x-axis, including formatting and rotating the tick labels
• Look at the layout options for margins
• There are also many options for the legend

3. D3 (30/45 points)

In this part of the assignment, use D3 to create the same (stacked) bar chart. CS 490 students need only create a bar chart showing the totals per year with appropriate axes and labels, but CS 627 students should create the stacked version with a legend. For both parts, we will need the data to be organized by year. You can use the following code which assumes your original data is stored in grads:

gradsByYear = [...d3.group(csGrads, d => d.YEAR).entries()].map(d => ({
  YEAR: d[0], 
  CTOTALT: d[1].reduce((s,dd) => s + dd.CTOTALT,0),
  ...Object.fromEntries(d[1].map(dd => [dd["CIP_SUBAREA"], dd["CTOTALT"]]))
}))

This groups the grads by a numeric year, and for each year creates a new object with the year, the total number of grads (CTOTALT), and entries for each subarea where the key is the subarea number, and the value is the number of graduates in that subarea. CS 490 students can then access the values from the CTOTALT attribute.

For CS 627 students, it may be easier to first create a non-stacked bar chart (use the provided method above), and then try the stacked version. The axes and labels should be similar to Part 2. The stacked version should have a legend that indicates the relationship between the bar components and the colors. This legend can be created using the Swatches code in this notebook. In Observable, you can do

import {Swatches} from "@d3/color-legend"

in one cell and then pass your color scale (color) to Swatches, appending to another d3 selection divElt as follows:

const swatches = Swatches(color);
divElt.append(() => swatches)

Note that you’ll want to create a div that holds the SVG and the swatches if you take this approach.

Hints

• d3.scaleBand is useful for bar charts.
• D3 has routines (e.g. d3.axisLeft) to build an axis given a scale
• A group element with a transform can help shift the entire visualization so that labels or axes have space (margin convention)
• To obtain the maximum yearly total, you can use d3.max with a proper accessor
• d3.stack may be useful here. Check how the keys function works with the stack.
• If you use d3.stack, you will need nested selections (note that these groups may be different than you expect)