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
Identify the visual cues, coordinate system, and scale(s) Visual Cues: Color, position, length, direction Coordinate system: Cartesian (x,y) Scale: Logarithmic (x) and linear(y)
How many variables are depicted in the graph? Explicitly link each variable to a visual cue that you listed above.
There are 2 variables Menarche variable is green and pyschosocial maturation is pink. Menarche variable is increasing with time until the last data point. Psychosocial maturation is increasing with time all the way to the end.
Question #2
Answer the following questions for this graphic World’s top 10 best selling cigarette brands 2004-2007
Visual cues:color, length, position Coordinate system: Cartesian (sales on x-axis and brand name on y-axis) Scale: Linear(x) and Categorical(y)
The bars’ length and color stand for the sales in billions and the various cigarette brands, respectively. c. Critique this data graphic using the taxonomy described in the lecture. Different types of colors are not needed. Marlboro and Winston have the same color.
Question #3
Find two data graphics published in a newspaper on on the internet in the last two years.
knitr::include_graphics("C:\\Users\\ChiragShetty\\Downloads\\15869.jpeg")
This infographic is highly
descriptive, making use of bright colors and proper scales. Variables
are neat, and easy to differentiate.
knitr::include_graphics("C:\\Users\\ChiragShetty\\Downloads\\Screenshot 2023-02-14 203839.png")
This
infographic is ambigious, as to what it signifies. There is no title to
indicate the counts on the body.
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.
Answer: Data representing “biggest obstacle to data science adoption in our organization” didn’t need to have fading color codes. The ring graph has long descriptive variables, could have been more concise.
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: Some of the maps could have had less colors to differntiate variables. A lot of colors makes it more convoluted to understand what’s happening. Maybe even categorize the world maps into fewer sections.