Data Lab Part 2

Using charts for effective visualization

In my current BCA 332 class, my professor assigned a lab for us to experiment with creating data visualization posts through different types of charts. All charts for this assignment were created and are linked to datawrapper.de.

For this portion of the assignment, students were asked to compare the charts in our textbook to a similar example. This example asked us to answer the question of: Which proportion is larger, 25.6 of 32 or 160 of 200? We were then asked to create pie charts out of these variables in data wrapper, as shown below.

As you can see, these charts show the exact same percentages for values that are mathematically equal. But before looking at these pie charts, we must ask ourselves what our first thought was when we were asked which proportion is larger. Our chapter tells us that vision is the most developed sense in the human species. A huge chunk of our brains is devoted to gathering, filtering, processing, organizing, and interpreting data collected from the retinas at the back of our eyes. We’ve evolved to be really fast at detecting visual patterns and exceptions to those patterns. It is only natural, then, that a set of methods consisting of mapping data into visual properties — spatial and otherwise — would prove to be so powerful.

For the next portion of this assignment, students were asked to create a bar chart and a range chart, representing a few different topics.

First up, we have a chart which represents the best and worst quarterbacks in the NFL 2018:

This chart was created based off of the information we were provided with for this portion of the assignment. This chart represents the best and the worst quarterbacks in the NFL in the year 2018, based on the players amounts of touchdowns, interceptions, sacks, comp % and QBR.

The final portion of this assignment asks students to practice their skills in annotating graphs via datawrapper, emphasizing on periods of recessions for the U.S. The US economies went through recession periods: July 1990 — March 1991; March 2001 — November 2001; and December 2007 — July 2009.

This chart visualizes the recessions that occurred in the U.S. from 1990–2017. From looking at this chart, we can gather how many jobs were lost or gained throughout these recessions, as well as when the economy seemed to be looking up again in the Boom Years.