LIS 4317 Module #10 Assignment

Color Gradient Based on a Continuous Variable

Style:
Scatter Plot with a color gradient (based on a continuous variable qsec).

Do you like the visualization? Why?
Yes. The color gradient from yellow to dark blue effectively shows the variation in quarter-mile time across points. It’s visually appealing and functionally informative.

Is the Headline Accurate?
Yes. The title "Quarter-Mile Time Gradient on MPG vs Weight" is accurate, clearly describing the visual mapping.

Does the visualization support the title narrative?
Yes. The scatter plot shows mpg vs wt and color encodes qsec, aligning with the title.

Does the visualization lie?
No. It presents data truthfully and avoids distortions.

Unnecessary Graphic Elements?
No unnecessary elements. Clean and focused on the data.

Like or Dislike (Design, Color, Layout)?
Like. The yellow-to-dark blue gradient is effective for continuous data, and the layout is clean.

CODE:

ggplot(mtcars, aes(x = wt, y = mpg, color = qsec)) +

  geom_point(size = 4) +

  scale_color_gradient(low = "yellow", high = "darkblue") +

  labs(title = "Quarter-Mile Time Gradient on MPG vs Weight",

       color = "1/4 Mile Time (sec)")

Scatter Plot with Customize Point Shapes, Colors, and Sizes

Style:

Scatter Plot with multiple aesthetic mappings (color = cyl shape = am, size = hp).

Do you like the visualization? Why?
Yes. It layers multiple variables in a single plot while remaining readable due to thoughtful color and shape distinctions.

Is the Headline Accurate?
Yes. "MPG vs Weight with Cylinder & Transmission Info" accurately summarizes what’s encoded.

Does the visualization support the title narrative?
Yes. The mapping to cylinder, transmission, and horsepower enhances the title's promise.

Does the visualization lie?
No misleading elements are present.

Unnecessary Graphic Elements?
No. All encoded aesthetics serve a purpose.

Like or Dislike (Design, Color, Layout)?
Like. Use of Set2 palette ensures visual clarity. Smart use of shape and size keeps it readable.

CODE:

ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl), shape = factor(am), size = hp)) +

  geom_point(alpha = 0.8) +
  scale_color_brewer(palette = "Set2") +
  labs(title = "MPG vs Weight with Cylinder & Transmission Info",
       color = "Cylinders",
       shape = "Transmission",
       size = "Horsepower")

Scatter Plot: MPG vs Horsepower with Regression Line

Style:
Scatter Plot with linear regression line (geom_smooth(method = "lm")).

Do you like the visualization? Why?
Yes. The regression line helps show the trend (negative correlation) between hp and mpg.

Is the Headline Accurate?
Yes. Both "MPG vs Horsepower" and "with Linear Fit" titles are clear and descriptive.

Does the visualization support the title narrative?
Yes. The plot directly displays what the title promises.

Does the visualization lie?
No. The regression line is statistically generated and displayed transparently.

Unnecessary Graphic Elements?
No clutter. Clean minimal theme.

Like or Dislike (Design, Color, Layout)?
Like. Steel blue color for points is soothing, and the minimal theme adds professionalism.

CODE:

library(ggplot2)
data(mtcars)
mtcars$car <- rownames(mtcars)  # Add car names as a column
ggplot(mtcars, aes(x = hp, y = mpg)) +
  geom_point(color = "steelblue", size = 3) +
  labs(title = "MPG vs Horsepower",
       x = "Horsepower (hp)",
       y = "Miles per Gallon (mpg)") +
  theme_minimal()
ggplot(mtcars, aes(x = hp, y = mpg)) +

  geom_point() +
  geom_smooth(method = "lm", color = "red", se = TRUE) +
  labs(title = "MPG vs Horsepower with Linear Fit")

Faceted Scatter Plot: MPG vs Weight by Cylinder

Style:
Faceted Scatter Plot (small multiples by cyl).

Do you like the visualization? Why?
Yes. Breaking the scatter plot into facets by cylinder count makes comparison easier and more intuitive.

Is the Headline Accurate?
Yes. "MPG vs Weight by Cylinder Count" clearly reflects the content.

Does the visualization support the title narrative?
Yes. Each facet displays how the relationship between mpg and weight differs across cylinder groups.

Does the visualization lie?
No. Data is accurately shown in each facet.

Unnecessary Graphic Elements?
None. Clean use of theme minimal() helps maintain focus.

Like or Dislike (Design, Color, Layout)?
Like. Dark red points contrast well with the background, and the layout is clean.

CODE:

ggplot(mtcars, aes(x = wt, y = mpg)) +

  geom_point(color = "darkred") +

  facet_wrap(~cyl) +

  labs(title = "MPG vs Weight by Cylinder Count",

       x = "Weight (1000 lbs)",

       y = "Miles per Gallon") +

  theme_minimal()

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