Expressiveness and Effectiveness
library(ggplot2)
data(mtcars)
ggplot(mtcars, aes(x = mpg, y = hp, color = factor(cyl))) +
geom_point(size = 3) +
labs(title = "Proper Use of Marks and Channels",
x = "Miles per Gallon (mpg)",
y = "Horsepower (hp)",
color = "Cylinders")
Caption: Figure 1 shows the relationship between miles per gallon (mpg) and horsepower (hp) using color to distinguish between different numbers of cylinders. This adheres to the expressiveness and effectiveness principles.
library(datasets)
library(ggplot2)
ggplot(mtcars, aes(x = mpg, y = hp, size = factor(cyl), shape = factor(cyl), color = factor(cyl))) +
geom_point() +
labs(title = "Violation of Marks and Channels",
x = "Miles per Gallon (mpg)",
y = "Horsepower (hp)",
size = "Cylinders",
shape = "Cylinders",
color = "Cylinders")Warning: Using size for a discrete variable is not advised.
Caption: Figure 2 uses size, shape, and color to encode the number of cylinders, violating the expressiveness and effectiveness principles. It creates visual clutter and confusion.
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(binwidth = 2, fill = "skyblue", color = "white") +
labs(title = "Proper Discriminability",
x = "Miles per Gallon (mpg)",
y = "Count")
Caption: Figure 3 uses an appropriate number of bins for the histogram, facilitating discriminability of the mpg attribute.
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(binwidth = 0.5, fill = "skyblue", color = "white") +
labs(title = "Violation of Discriminability",
x = "Miles per Gallon (mpg)",
y = "Count")
Caption: Figure 4 uses too many bins, violating the guidelines for discriminability and making it difficult to interpret the distribution of mpg.
ggplot(mtcars, aes(x = mpg, y = hp, color = factor(cyl), shape = factor(gear))) +
geom_point(size = 3) +
labs(title = "Proper Separability",
x = "Miles per Gallon (mpg)",
y = "Horsepower (hp)",
color = "Cylinders",
shape = "Gears")
Caption: Figure 5 uses separate color and shape channels to encode the number of cylinders and gears, maintaining separability.
ggplot(mtcars, aes(x = mpg, y = hp, fill = factor(cyl), alpha = hp)) +
geom_point(size = 5, shape = 21) +
labs(title = "Violation of Separability",
x = "Miles per Gallon (mpg)",
y = "Horsepower (hp)",
fill = "Cylinders",
alpha = "Horsepower")
Caption: Figure 6 uses fill color and transparency, which are less separable, to encode the number of cylinders and horsepower, violating the principle of separability.
ggplot(mtcars, aes(x = mpg, y = hp)) +
geom_point(size = 3, color = "grey") +
geom_point(data = subset(mtcars, cyl == 4), aes(x = mpg, y = hp), color = "red", size = 4) +
labs(title = "Effective Popout",
x = "Miles per Gallon (mpg)",
y = "Horsepower (hp)")
Caption: Figure 7 highlights cars with 4 cylinders in red, effectively using the concept of popout.
ggplot(mtcars, aes(x = mpg, y = hp, color = factor(cyl))) +
geom_point(size = 3) +
labs(title = "Ineffective Popout",
x = "Miles per Gallon (mpg)",
y = "Horsepower (hp)",
color = "Cylinders")
Caption: Figure 8 uses color to distinguish all cylinder counts, making it difficult to identify specific points and failing to achieve the popout effect compared to Figure 7.