In this chapter we discussed why well-designed data graphics are important and we described a taxonomy for understanding their composition.
The objective of this assignment is for you to understand what characteristics you can use to develop a great data graphic.
Each question is worth 5 points.
To submit this homework you will create the document in Rstudio, using the knitr package (button included in Rstudio) and then submit the document to your Rpubs account. Once uploaded you will submit the link to that document on Canvas. Please make sure that this link is hyper linked and that I can see the visualization and the code required to create it.
Question #1
Answer the following questions for this graphic Relationship between ages and psychosocial maturity
url <-"http://ars.els-cdn.com/content/image/1-s2.0-S1043276005002602-gr2.jpg"
knitr::include_graphics(url)
a. Identify the visual cues, coordinate system, and scale(s)
Visual cues are color, position, length, and direction. Coordinate system is Cartesian. Scale is Linear.
How many variables are depicted in the graph? Explicitly link each variable to a visual cue that you listed above. Age and time period are depicted by position cue Age spans are depicted by length cue Categorical variables i.e. Menarche Maturation and Psychosocial Maturation are depicted by color.
Critique this data graphic using the taxonomy described in the lecture.
This data graphic has position, length, and color as visual cues to depict the changing pattern across period for different age span regarding Menarche and Psychosocial Maturation.
Question #2
Answer the following questions for this graphic World’s top 10 best selling cigarette brands 2004-2007
url <-"https://farm3.static.flickr.com/2695/4149541331_482fbb0aaf_o.png"
knitr::include_graphics(url)
a. Identify the visual cues, coordinate system, and scale(s)
Visual cues are color and length Coordinate system is Cartesian Scale is Linear
Cigarette brand is depicted with color visual cue
Sales in billions is depicted with length visual cue.
The graphic has length and color as visual cues to depict sales in billions for different Cigarette brands. The title and label improve the visualization and gives us a clear context of the purpose of the data graphinc i.e. to make meaning full comparison.
Question #3
Find two data graphics published in a newspaper on on the internet in the last two years.
url <-"https://www.maptive.com/wp-content/uploads/2022/03/bubble-chart-1024x1024.png"
knitr::include_graphics(url)
Answer I find the graphics in this bubble chart of the ‘50 most visited
websites in the world’ very understandable. It is easy to read, with
different bubble size conveying different site’s audiences relatively to
others.The color cue used distinguishes between the range of audiences a
group of websites fall under. The addition of a smaller bar chart aids
in understanding the industries that the sites mentioned are part
of.
url <-"https://equitablegrowth.org/wp-content/uploads/2022/04/The-share-of-workers-who-are-unemployed-due-to-job-loss-is-much-smaller-now-than-at-the-onset-of-the-coronavirus-recession.png"
knitr::include_graphics(url)
Answer The graph of ‘U.S. labor market during a month’ does not look
compelling.It is hard to read. It is not clear from the graph that what
is the exact percentage of unemployed workers for any specific reason
for unemployment. The data could have been visualized better with bar
graphs shaded with different colors for each reason.
Question #4
Briefly (one paragraph) critique the designer’s choices. Would you have made different choices? Why or why not? Note: Link contains a collection of many data graphics, and I don’t expect (or want) you to write a full report on each individual graphic. But each collection shares some common stylistic elements. You should comment on a few things that you notice about the design of the collection.
url <-"https://static.guim.co.uk/sys-images/Guardian/Pix/pictures/2012/3/2/1330695817953/EMC2-graphic-on-data-scie-008.jpg"
knitr::include_graphics(url)
Answer:
The visual cue used is quiet straight forward with the usage of colors, numbers, length etc.The color palettes used are making it easier to understand the variations among various attributes.It is clear and consistent. There is a lot of context provided,that is helping us understand the visualization.Although, the use of coordinate system can help in improving the visualization. The way graphics are used to conclude how “Data scientist are significantly more likely to have advanced degrees than BI professional”, it is harder to identify the meaning of Y axis.
Question #5
Briefly (one paragraph) critique the designer’s choices. Would you have made different choices? Why or why not? Note: Link contains a collection of many data graphics, and I don’t expect (or want) you to write a full report on each individual graphic. But each collection shares some common stylistic elements. You should comment on a few things that you notice about the design of the collection.
Charts that explain food in America
Answer:
Using geographic coordinate system to show how food industry has been changed throughout the years. Of course, some of the 40 data visualizations have different coordinate system, such as Cartesian coordinate system to convey the food changes. That said, almost all data visualizations have a clear visual cue using color to distinguish different data points in the visualizations. To make it even better, I would suggest using less visualization to convey where our food comes from and how we eat it. In other words, what the visualizations need is a focus. And they should give a clear context of what each visualization is try to emphasize. Most of graphics are using Geographic coordinate system and color visual cues. They gave enough context to help reader understanding the visualization. I will made same choice as the designer made since it’s a great way to visualize geographic related infomration. The designer has mostly used Geographic Coordinate System to capture how the American food industry has changed over the years. The color visual cues used to identify various data points has made the data visualization very clear to read and understand. The usage of other coordinate systems in some scenarios, like Cartesian Coordinate System to explain the food changes, gave enough context to the reader to understand the visualization.