inst/replication_code/figure_code/fig7_1.r

In psre: Presenting Statistical Results Effectively

## Load packages
library(ggplot2)

## Make anscombe data
anscombe <- data.frame(
  x1 = c(10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5),
  x2 = c(10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5),
  x3 = c(10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5),
  x4 = c(8, 8, 8, 8, 8, 8, 8, 19, 8, 8, 8),
  y1 = c(8.04, 6.95,  7.58, 8.81, 8.33, 9.96, 7.24, 4.26,10.84, 4.82, 5.68),
  y2 = c(9.14, 8.14,  8.74, 8.77, 9.26, 8.1,  6.13, 3.1,  9.13, 7.26, 4.74),
  y3 = c(7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73),
  y4 = c(6.58, 5.76,  7.71, 8.84, 8.47, 7.04, 5.25, 12.5, 5.56, 7.91, 6.89)
)

## A. Linear

ggplot(anscombe, aes(x=x1, y=y1)) + 
  geom_point(shape=1) + 
  geom_smooth(method="lm", se=FALSE, col="black") + 
  theme_classic() + 
  labs(x="x", y="y")
# ggssave("output/f7_1a.png", height=4.5, width=4.5, dpi=300)

## B. Curvilinear

ggplot(anscombe, aes(x=x2, y=y2)) + 
  geom_point(shape=1) + 
  geom_smooth(method="lm", se=FALSE, col="black") + 
  theme_classic() + 
  labs(x="x", y="y")
# ggssave("output/f7_1b.png", height=4.5, width=4.5, dpi=300)

## C. Weakly Influential Outlier

ggplot(anscombe, aes(x=x3, y=y3)) + 
  geom_point(shape=1) + 
  geom_smooth(method="lm", se=FALSE, col="black") + 
  theme_classic() + 
  labs(x="x", y="y")
# ggssave("output/f7_1c.png", height=4.5, width=4.5, dpi=300)

## D. Strongly Influential Outlier

ggplot(anscombe, aes(x=x4, y=y4)) + 
  geom_point(shape=1) + 
  geom_smooth(method="lm", se=FALSE, col="black") + 
  theme_classic() + 
  labs(x="x", y="y")
# ggssave("output/f7_1d.png", height=4.5, width=4.5, dpi=300)