DSCI 531 Quiz 1

Time: 30 minutes

Introduction to visualization, marks, and channels, select and justify a data abstraction to use for a given task, and table data part

Part 1 - Analyze plots

Provide an analysis of the following plots:

1(a)

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1ai. State which mark type and which visual channels are being used to visually encode which attributes.

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1aii. State if any attributes are redundantly coded with morethan one channel.

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1aiii. Specify two abstract tasks for which this visual encoding would be effective.

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1aiv. Describe the maximum scale for which a plot of this type would be effective (in terms of the number of items, the number of attributes, and the number of distinct levels for the attributes).

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1(b)

Source: https://www.ncsu.edu/labwrite/res/gh/gh-linegraph.html

1bi. State which mark type and which visual channels are being used to visually encode which attributes.

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1bii. State if any attributes are redundantly coded with morethan one channel.

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1biii. Specify two abstract tasks for which this visual encoding would be effective.

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1biv. Describe the maximum scale for which a plot of this type would be effective (in terms of the number of items, the number of attributes, and the number of distinct levels for the attributes).

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Part 2 - Redesign plots to address shortcomings

Review the plot below and answer the questions that follow:

# some set-up code to load libraries and data
import pandas as pd
import matplotlib.pyplot as plt
% matplotlib inline

chopstick = pd.read_csv("http://blog.yhat.com/static/misc/data/chopstick-effectiveness.csv")
chopstick.head()

chop_scatter = chopstick.plot.scatter(x = 'Chopstick.Length', y='Food.Pinching.Effeciency', color = "black" )
plt.xlabel('Chopstick length (mm)')
plt.ylabel('Food pinching efficiency')
chop_scatter.set_ylim(25, 50)
chop_scatter.set_xlim(0, 400)
plt.show(chop_scatter)

2a. identify at least one shortcoming of this plot and propose a solution in words that would make it more effective

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2b. Re-implement the plot below, fixing the problem you identified

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# answer goes here

Part 3 - Critique channel usage

Review the plot below and answer the questions that follow:

mtcars = pd.read_csv("mtcars.csv")
mtcars.head()

import matplotlib.patches as patches

fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.set_xlim(25,300)
ax1.set_ylim(0,45)

for i in range(0,mtcars.shape[0]):
    ax1.add_patch(
        patches.Ellipse(
            (mtcars.hp.iloc[i], mtcars.mpg.iloc[i] ), # (x,y) position
            mtcars.disp.iloc[i]/10, # width
            mtcars.gear.iloc[i]**2, # height
        )
    )
  
plt.xlabel("horsepower")
plt.ylabel("miles per gallon")
ax1.annotate('bubble width = diplacement/10', xy=(150, 40),xytext=(150, 40))
ax1.annotate('bubble heigth = $gear^{2}$', xy=(150, 38),xytext=(150, 38))
plt.show(fig1)

3a. In 2-3 sentences, critique this plot in terms of integral vs separable channels.

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3b. Propose an alternate visual encoding that is more effective.

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3c. Re-implement the plot below to make the visual encoding more effective (as you suggested in 3b).

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# answer goes here

Part 4 -Visual popout

Which of these visual encodings supports popout? Answer True or False for each:

4a. position and color

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4b. orientation and color

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4c. width and height

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4d. color and shape

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4e. position and color

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4f. parallelism

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4g. color

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4h. orientation

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