ggpubr

Version 1.1.0 introduced several new publication-ready plot types and statistical annotation tools:

New Plot Types:

• ggcompare(): One-call group-comparison figures (box/violin/strip plots with points, means, and adjusted-p brackets).
• ggrocplot(): ROC curves with AUC, confidence intervals, and optional cut-point markers.
• ggestimates(): Forest and estimation plots (odds/hazard/risk ratios, regression coefficients).
• ggraincloud(): Raincloud plots (half-violin + box + jittered points).
• ggvolcano(): Volcano plots for differential expression.

Statistical Helpers:

• stat_cld(): Compact letter display.
• add_test_label(): Adds an omnibus test subtitle.
• geom_pwc(pack = "auto"): Automatic bracket packing for p-values.

P-value Formatting:

• New helpers: format_p_value(), get_p_format_style(), and list_p_format_styles().
• Extended parameters in statistical layers: p.format.style, p.digits, p.leading.zero, p.min.threshold, and p.decimal.mark.

Cleveland's dot plot can be created by using ggdotchart() with rotate = TRUE and y.text.col = TRUE to color the y-axis text by groups. You can enhance the plot with theme_cleveland() to add dashed grids.

ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           rotate = TRUE,                                # Rotate vertically
           dot.size = 2,                                 # Large dot size
           y.text.col = TRUE,                            # Color y text by groups
           ggtheme = theme_pubr()                        # ggplot2 theme
           )+
  theme_cleveland()                                      # Add dashed grids

Use ggboxplot() and ggviolin() to create distribution plots. You can add jittered points, boxplots, and statistical significance annotations using stat_compare_means().

Note on P-values: stat_compare_means() with the comparisons argument displays unadjusted p-values. For adjusted p-values, use geom_pwc() or stat_pvalue_manual().

# Box plots with jittered points and pairwise comparisons
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )

p <- ggboxplot(df, x = "dose", y = "len",
                color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
                add = "jitter", shape = "dose")

p + stat_compare_means(comparisons = my_comparisons) +
  stat_compare_means(label.y = 50)

# Violin plots with box plots inside and significance levels
ggviolin(df, x = "dose", y = "len", fill = "dose",
         palette = c("#00AFBB", "#E7B800", "#FC4E07"),
         add = "boxplot", add.params = list(fill = "white"))+
  stat_compare_means(comparisons = my_comparisons, label = "p.signif")+
  stat_compare_means(label.y = 50)

To create a deviation graph, use a standardized variable (e.g., y = "mpg_z") and customize the segment appearance using add.params. You can also add a horizontal reference line using standard ggplot2 functions like geom_hline().

ggdotchart(dfm, x = "name", y = "mpg_z",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           add.params = list(color = "lightgray", size = 2), # Change segment color and size
           group = "cyl",                                # Order by groups
           dot.size = 6,                                 # Large dot size
           label = round(dfm$mpg_z,1),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 9,
                             vjust = 0.5),               # Adjust label parameters
           ggtheme = theme_pubr()                        # ggplot2 theme
           )+
  geom_hline(yintercept = 0, linetype = 2, color = "lightgray")

To create a more advanced Lollipop chart, you can use several parameters in ggdotchart():

• rotate = TRUE: Rotates the plot vertically.
• group: Specifies the variable used to order values inside each group.
• dot.size: Sets the size of the dots.
• label: Adds labels to the dots (e.g., label = "mpg" or label = round(dfm$mpg)).
• font.label: A list to adjust label parameters like color, size, and vjust.
• add = "segments": Adds segments from the baseline to the dots.
• palette: A vector of colors for the groups.
• ggtheme: The ggplot2 theme to apply (e.g., theme_pubr()).

ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = TRUE,                                # Rotate vertically
           group = "cyl",                                # Order by groups
           dot.size = 6,                                 # Large dot size
           label = round(dfm$mpg),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 9,
                             vjust = 0.5),               # Adjust label parameters
           ggtheme = theme_pubr()                        # ggplot2 theme
           )