In this chapter we discussed wy 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
Answer: Visual Cue - Position and Color Coordinate system- Cartesian Scale - X axis is exponential and Y axis is linear
Answer: There are 4 variables - 1.Menarche(Position and Color visual cues) 2.Psychosocial Maturation(Position and Color visual cues) 3.Age (in terms of years) 4.Time (in terms of years)
Answer: I think the graph is missing Context in regards to having a Title and Subtitle. Also I think positioning of Legend could have been better.
Question #2
Answer the following questions for this graphic World’s top 10 best selling cigarette brands 2004-2007
Answer: Visual Cue - Length and Color Coordinate system- Cartesian Scale - Linear
Answer: There are 2 variables: 1. Cigarette Brand (Color visual cue) 2. Sales in billions (lenth visual cue)
Answer: I think this graph is missing data labels. Adding labels to each bar would have made the numbers standout more.
Question #3
Find two data graphics published in a newspaper on on the internet in the last two years.
Answer: I think the below dual axis graph is a really good example of having color and shape as the visual cue, cartesian coordinate system and linear scale. The overall context and the choice of color are also impressive giving the users useful information at a glance.
knitr::include_graphics("/Users/dhruvishah/Downloads/Revenue_by_customer_cnt.webp")
Answer: I think the below graph is an example where the creator has not done a very good job with Lengends and color (visual cue). Instead showing the data in a cumulative sense (data has been shown for past x months for that particular month), I think showing individual months data would have been more informative and less confusing. I would also have the legend at the top instead of the bottom.
knitr::include_graphics("/Users/dhruvishah/Desktop/HU/Sem2/ANALY 512/Problem Set 2/Figure 2 .webp")
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:
There’s too much descriptive data and I think the report could have been made more succinct by using more visuals and less words. I think “Best source of new science talent” & “biggest obstacle to data science adoption” data visuals would have been better shown by a pie chart.
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:
I like the overall report, however there are a few things which I would have done differently:
I would have used %s (totalling to 100) for the Pie Chart shown in visual # 11.
Legend missing on visual #14
3.Axis labels missing on visual #20