Directions

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

#[Relationship between ages and psychosocial maturity]
knitr::include_graphics("http://ars.els-cdn.com/content/image/1-s2.0-S1043276005002602-gr2.jpg")

  1. Identify the visual cues, coordinate system, and scale(s) Answer to 1a:Visual cues are shape, color, positions and directions; Coordinate system:The Relationship between ages and psychosocial maturity graph has Cartesian (x,y) coordinate system; Scale:the graph’s scales are linear numeric for y axis, and it is also initiated from the y scale of 0 age year till the y axis scale of 20 years of age, and graph further has logarithmic backward numeric and initiated from 20,000 years of x-axis scale till present year.
  2. How many variables are depicted in the graph? Explicitly link each variable to a visual cue that you listed above. Answer to 1b: The graph has 4 variables - menarche, psychosocial matureation, age and years. It tries to explain the age difference between 2 groups - menarche and psychosocial matureation on the different years on the timeline (X-axis).
  3. Critique this data graphic using the taxonomy described in the lecture. Answer to 1c: The lables and x-axis name are missing. The lables next to the different groups (bright pink and green ones) are very hard to read against a light background - would be better to just include a legend instead.

Question #2

#[World's top 10 best selling cigarette brands 2004-2007]
knitr::include_graphics("https://farm3.static.flickr.com/2695/4149541331_482fbb0aaf_o.png")

a. Identify the visual cues, coordinate system, and scale(s) Answer to 2a:Visual cues:color and shape length; Coordinate system: it is cartesian (x,y) coordinate system.Scales: the graph’s scales is linear numeric for x axis and it is also initiated from the x scale of 0 dollar till 500 dollar and the graph further has categorical for depicting different categories of cigarette brands. b. How many variables are depicted in the graph? Explicitly link each variable to a visual cue that you listed above. Answer to 2b:2 variables:Cigarette brands on the y axis and sales(in billions) in the x axis c. Critique this data graphic using the taxonomy described in the lecture. Answer to 2c:Marlbono’s sales is very large so the ranked bar chart’s length is sololy based on its sales. we can transform (log) the x-axis to make this chart more well presented.

Question #3

Find two data graphics published in a newspaper on on the internet in the last two years.

#Q3-A Most Frequently Used Visuals
knitr::include_graphics("https://visme.co/blog/wp-content/uploads/2017/07/Pie-Charts-1024x626.jpg")

a. Identify a graphical display that you find compelling. What aspects of the display work well, and how do these relate to the principles that we have just gone over in lecture. Include a screenshot of the display along with your solution (Hint:use the following in a code chunk: knitr::include_graphics(“your_graphic”). Answer to 3a:The pie chart displays the distribution of different visuals used by markerters in their content based on frequency. The he visual clues are color, number and size. I found this chart very compelling because it contains all the elements user needs to understand a chart - title, subtitle, legend lines, labels of category names, percentages and even with the logos to represent each category.

#Q3 b - Sales Order by brand by location
knitr::include_graphics("https://ppcexpo.com/blog/wp-content/uploads/2022/03/what-is-the-purpose-of-a-graph-23.jpg")

b. Identify a graphical display that you find less compelling. What aspects of the display don’t work well? Are there ways that the display might be improved? Include a screenshot of the display along with your solution (Hint:use the following in a code chunk: knitr::include_graphics(“your_graphic”). Answer to 3b: This bar chart is very messy. It tries to display each fashion brand’s sales orders by locations. The issue is putting each location bar under each fashion brand and it is difficult to read and interpret. There is no lables and the bar chart is not rankee.

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.

#[What is a Data Scientist]
knitr::include_graphics("https://static.guim.co.uk/sys-images/Guardian/Pix/pictures/2012/3/2/1330695817953/EMC2-graphic-on-data-scie-008.jpg")

Answer: On the first glance, without diving in too deep, I think this collection of charts contains way too many words. It is more of a report than a data visualization. For example, the very last charts on who does a data scientist work with? There is no explaination on what the data (percentage) above each group means and there is no differentiate among each groups other than colors. I would do a ranking bar chart in this one.

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 this collection of data visualization better than the one in question 4. Each chart has clear visual cues. With foot notes and a simple paragraph to elaborate on the charts, it is very easy to understand what the designer tries to display.