They have a magnificent team. These people are always kind and willing to listen to your concerns or issues. Better yet, your assignment is always ready before the time, they usually send you a draft to double-check before they finalize your paper.
Welcome to the newsroom! You’ve just accepted a data visualization position for a major metro paper. You’re tasked with analyzing the current trends shaping people’s lives, as well as creating charts, graphs, and interactive elements to help readers understand your findings.
The editor wants to run a series of feature stories about the health risks facing particular demographics. She’s counting on you to sniff out the first story idea by sifting through information from the U.S. Census Bureau and the Behavioral Risk Factor Surveillance System.
The data set included with the assignment is based on 2014 ACS 1-year estimates: https://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml, but you are free to investigate a different data set. The current data set incldes data on rates of income, obesity, poverty, etc. by state. MOE stands for “margin of error.”
You need to create a scatter plot between two of the data variables such as
Healthcare vs. Poverty or
Smokers vs. Age.
Using the D3 techniques we taught you in class, create a scatter plot that represents each state with circle elements. You’ll code this graphic in the
app.js file of your homework directory—make sure you pull in the data from
data.csv by using the
d3.csv function. Your scatter plot should ultimately appear like the image at the top of this section.
python -m http.serverto run the visualization. This will host the page at
localhost:8000in your web browser.
Why make a static graphic when D3 lets you interact with your data?
You’re going to include more demographics and more risk factors. Place additional labels in your scatter plot and give them click events so that your users can decide which data to display. Animate the transitions for your circles’ locations as well as the range of your axes. Do this for two risk factors for each axis. Or, for an extreme challenge, create three for each axis.
While the ticks on the axes allow us to infer approximate values for each circle, it’s impossible to determine the true value without adding another layer of data. Enter tooltips: developers can implement these in their D3 graphics to reveal a specific element’s data when the user hovers their cursor over the element. Add tooltips to your circles and display each tooltip with the data that the user has selected. Use the
d3-tip.js plugin developed by Justin Palmer—we’ve already included this plugin in your assignment directory.
Your final product will be assessed on the following metrics:
D3-Challenge(note the kebab-case). Do not add to an already existing repo.
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