ggpubr : Pair wise comparison

Data preparation

df <- ToothGrowth
df$dose <- factor(df$dose)

# Transform `dose` into factor variable
df <- ToothGrowth
df$dose <- as.factor(df$dose)
# Add a random grouping variable
df$group <- factor(rep(c("grp1", "grp2"), 30))
head(df, 3)

##    len supp dose group
## 1  4.2   VC  0.5  grp1
## 2 11.5   VC  0.5  grp2
## 3  7.3   VC  0.5  grp1

Two groups by x position

# Create a box plot
# Add 10% spaces between the p-value labels and the plot border
bxp <- ggboxplot(
  df, x = "dose", y = "len",
  color = "supp", palette = c("#00AFBB", "#E7B800")
) + scale_y_continuous(expand = expansion(mult = c(0.05, 0.10)))
bxp

canvasXpress(bxp)

Add p-values onto the box plots

# label can be "p.format"  or "p.adj.format"
bxp1 <- bxp + geom_pwc(
  aes(group = supp), tip.length = 0,
  method = "t_test", label = "p.format"
)
bxp1

canvasXpress(bxp1)

Show adjusted p-values and significance levels

# Hide ns (non-significant)
bxp2 <- bxp + geom_pwc(
  aes(group = supp), tip.length = 0,
  method = "t_test", label = "{p.adj.format}{p.adj.signif}",
  p.adjust.method = "bonferroni", p.adjust.by = "panel",
  hide.ns = TRUE
)
bxp2

canvasXpress(bxp2)

Complex cases

# 1. Add p-values of OJ vs VC at each dose group
bxp.complex <- bxp +
  geom_pwc(
    aes(group = supp), tip.length = 0,
    method = "t_test", label = "p.adj.format",
    p.adjust.method = "bonferroni", p.adjust.by = "panel"
  )
# 2. Add pairwise comparisons between dose levels
# Nudge up the brackets by 20% of the total height
bxp.complex <- bxp.complex +
  geom_pwc(
    method = "t_test", label = "p.adj.format",
    p.adjust.method = "bonferroni",
    bracket.nudge.y = 0.2
  )
# 3. Display the plot
bxp.complex

canvasXpress(bxp.complex)

Three groups by x position

# Simple plots
# Box plots with p-values
bxp4 <- ggboxplot(
  df, x = "supp", y = "len", fill = "dose",
  palette = "npg"
)
bxp4 <- bxp4 +
  geom_pwc(
    aes(group = dose), tip.length = 0,
    method = "t_test", label = "p.adj.format",
    bracket.nudge.y = -0.08
  ) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
bxp4

canvasXpress(bxp4)

Bar plots with p-values

bp <- ggbarplot(
  df, x = "supp", y = "len", fill = "dose",
  palette = "npg", add = "mean_sd",
  position = position_dodge(0.8)
)
bp <- bp +
  geom_pwc(
    aes(group = dose), tip.length = 0,
    method = "t_test", label = "p.adj.format",
    bracket.nudge.y = -0.08
  ) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
bp

canvasXpress(bp)

Data preparation 2

df <- ToothGrowth
df$dose <- as.factor(df$dose)
# Add a random grouping variable
df$group <- factor(rep(c("grp1", "grp2"), 30))
head(df, 3)

##    len supp dose group
## 1  4.2   VC  0.5  grp1
## 2 11.5   VC  0.5  grp2
## 3  7.3   VC  0.5  grp1

Boxplot: Two groups by panel

# Create a box plot
bxp5 <- ggboxplot(
  df, x = "supp", y = "len", fill = "#00AFBB",
  facet.by = "dose"
)
# Make facet and add p-values
bxp5a <- bxp5 + geom_pwc(method = "t_test")
bxp5a

canvasXpress(bxp5a)

Adjust all p-values together after

bxp5b <- ggadjust_pvalue(
  bxp5a, p.adjust.method = "bonferroni",
  label = "{p.adj.format}{p.adj.signif}", hide.ns = TRUE
)
bxp5b

#canvasXpress(bxp5b)

Boxplot: Three groups by panel

# Create a box plot
bxp6 <- ggboxplot(
  df, x = "dose", y = "len", fill = "#00AFBB",
  facet.by = "supp"
)
# Make facet and add p-values
bxp6a <- bxp6 + geom_pwc(method = "t_test")
bxp6a

canvasXpress(bxp6a)