BoxPlot
# Create sample data
set.seed(123)
data <- data.frame(
group = rep(LETTERS[1:4], each = 25),
value = c(
rnorm(25, 10, 2), rnorm(25, 12, 2),
rnorm(25, 11, 3), rnorm(25, 14, 2)
),
treatment = rep(c("Control", "Treatment"), 50),
gender = sample(c("Male", "Female"), 100, replace = TRUE)
)
# Simple box plot
BoxPlot(data, x = "group", y = "value")
# Box plot with custom labels
BoxPlot(data,
x = "group", y = "value",
title = "Distribution by Group",
xlab = "Experimental Group",
ylab = "Measurement Value"
)
Grouped Box Plots
# Side-by-side (dodged) box plots by treatment
BoxPlot(data, x = "group", y = "value", group_by = "treatment")
# With custom group legend name
BoxPlot(data,
x = "group", y = "value",
group_by = "treatment",
group_name = "Treatment Group"
)
# With custom color palette
BoxPlot(data,
x = "group", y = "value",
group_by = "treatment",
palette = "Set1"
)
Adding Data Points
# Add jittered points to show individual observations
BoxPlot(data, x = "group", y = "value", add_point = TRUE)
# Customize point appearance
BoxPlot(data,
x = "group", y = "value",
add_point = TRUE,
pt_color = "darkblue",
pt_size = 1.5,
pt_alpha = 0.6
)
# With grouped data
BoxPlot(data,
x = "group", y = "value",
group_by = "treatment",
add_point = TRUE,
pt_alpha = 0.5
)
Highlighting Specific Points
# Highlight outliers or specific observations
BoxPlot(data,
x = "group", y = "value",
add_point = TRUE,
highlight = "value > 15",
highlight_color = "red",
highlight_size = 3
)
# Highlight by row indices
BoxPlot(data,
x = "group", y = "value",
add_point = TRUE,
highlight = c(1, 5, 10, 15),
highlight_color = "orange"
)
Statistical Comparisons
# Compare specific pairs
BoxPlot(data,
x = "group", y = "value",
comparisons = list(c("A", "B"), c("A", "D"))
)
# Compare all pairs
BoxPlot(data,
x = "group", y = "value",
comparisons = TRUE
)
# Use t-test instead of Wilcoxon
BoxPlot(data,
x = "group", y = "value",
comparisons = list(c("A", "D")),
pairwise_method = "t.test"
)
# Show significance symbols instead of p-values
BoxPlot(data,
x = "group", y = "value",
comparisons = TRUE,
sig_label = "p.signif"
)
# Hide non-significant comparisons
BoxPlot(data,
x = "group", y = "value",
comparisons = TRUE,
hide_ns = TRUE
)
# With grouped data - compare groups within each x category
BoxPlot(data,
x = "group", y = "value",
group_by = "treatment",
comparisons = TRUE
)
# Multiple group comparison (Kruskal-Wallis)
BoxPlot(data,
x = "group", y = "value",
multiplegroup_comparisons = TRUE
)
Paired Data Analysis
# Create paired data (before/after measurements)
paired_data <- data.frame(
time = factor(rep(c("Before", "After"), each = 20)),
subject = factor(rep(1:20, 2)),
score = c(rnorm(20, 50, 10), rnorm(20, 55, 10))
)
# Paired box plot with connecting lines
BoxPlot(paired_data,
x = "time", y = "score",
paired_by = "subject",
add_point = TRUE
)
# With paired statistical test
BoxPlot(paired_data,
x = "time", y = "score",
paired_by = "subject",
comparisons = list(c("Before", "After")),
pairwise_method = "t.test"
)
Sorting and Orientation
# Sort by mean value (ascending)
BoxPlot(data, x = "group", y = "value", sort_x = "mean_asc")
# Sort by median value (descending)
BoxPlot(data, x = "group", y = "value", sort_x = "median_desc")
# Horizontal box plot
BoxPlot(data, x = "group", y = "value", flip = TRUE)
Visual Enhancements
# Add trend line connecting medians
BoxPlot(data, x = "group", y = "value", add_trend = TRUE)
# Add reference line
BoxPlot(data,
x = "group", y = "value",
add_line = 12,
line_color = "red",
line_type = 2
)
# Add alternating background
BoxPlot(data,
x = "group", y = "value",
add_bg = TRUE,
bg_alpha = 0.1
)
# Add mean indicator
BoxPlot(data,
x = "group", y = "value",
add_stat = mean,
stat_name = "Mean",
stat_color = "red",
stat_shape = 18
)
Faceting
# Facet by another variable
BoxPlot(data,
x = "group", y = "value",
facet_by = "gender"
)
# Control facet layout
BoxPlot(data,
x = "group", y = "value",
facet_by = "gender",
facet_ncol = 2
)
# Free y-axis scales per facet
BoxPlot(data,
x = "group", y = "value",
facet_by = "gender",
facet_scales = "free_y"
)
Fill Modes
# Fill by x-axis category (default when no group_by)
BoxPlot(data, x = "group", y = "value", fill_mode = "x")
# Fill by mean value (gradient)
BoxPlot(data,
x = "group", y = "value",
fill_mode = "mean",
palette = "RdYlBu"
)
# Fill by median value (gradient)
BoxPlot(data,
x = "group", y = "value",
fill_mode = "median",
palette = "viridis"
)
Wide Format Data
# Wide format: each column is a group
wide_data <- data.frame(
GroupA = rnorm(30, 10, 2),
GroupB = rnorm(30, 12, 2),
GroupC = rnorm(30, 11, 3)
)
BoxPlot(wide_data,
x = c("GroupA", "GroupB", "GroupC"),
in_form = "wide"
)
Splitting into Multiple Plots
# Create separate plots by a variable
BoxPlot(data,
x = "group", y = "value",
split_by = "gender",
combine = TRUE,
ncol = 2
)
ViolinPlot
# Create sample data with different distributions
set.seed(456)
vdata <- data.frame(
category = rep(c("Normal", "Bimodal", "Skewed", "Uniform"), each = 100),
value = c(
rnorm(100, 50, 10), # Normal
c(rnorm(50, 30, 5), rnorm(50, 70, 5)), # Bimodal
rexp(100, 0.1), # Skewed
runif(100, 20, 80) # Uniform
),
group = rep(c("A", "B"), 200)
)
# Simple violin plot
ViolinPlot(vdata, x = "category", y = "value")
# With title and labels
ViolinPlot(vdata,
x = "category", y = "value",
title = "Distribution Shapes Comparison",
xlab = "Distribution Type",
ylab = "Value"
)
Violin with Box Plot Overlay
# Add box plot inside violin (shows quartiles)
ViolinPlot(vdata,
x = "category", y = "value",
add_box = TRUE
)
# Customize box overlay
ViolinPlot(vdata,
x = "category", y = "value",
add_box = TRUE,
box_color = "white",
box_width = 0.15,
box_ptsize = 3
)
Adding Data Points
# Add jittered points to show individual observations
ViolinPlot(vdata,
x = "category", y = "value",
add_point = TRUE
)
# Combine box overlay with points
ViolinPlot(vdata,
x = "category", y = "value",
add_box = TRUE,
add_point = TRUE,
pt_alpha = 0.3
)
# Customize point appearance
ViolinPlot(vdata,
x = "category", y = "value",
add_point = TRUE,
pt_color = "navy",
pt_size = 0.8,
pt_alpha = 0.5,
jitter_width = 0.3
)
Grouped Violin Plots
# Side-by-side violins by group
ViolinPlot(vdata,
x = "category", y = "value",
group_by = "group"
)
# With box overlay
ViolinPlot(vdata,
x = "category", y = "value",
group_by = "group",
add_box = TRUE
)
# Custom palette
ViolinPlot(vdata,
x = "category", y = "value",
group_by = "group",
palette = "Set2",
add_box = TRUE
)
Statistical Comparisons
# Compare specific distributions
ViolinPlot(vdata,
x = "category", y = "value",
comparisons = list(
c("Normal", "Bimodal"),
c("Normal", "Skewed")
)
)
# All pairwise comparisons
ViolinPlot(vdata,
x = "category", y = "value",
comparisons = TRUE,
sig_label = "p.signif"
)
# Grouped comparisons
ViolinPlot(vdata,
x = "category", y = "value",
group_by = "group",
comparisons = TRUE
)
Paired Data Analysis
# Create paired data
paired_data2 <- data.frame(
condition = factor(rep(c("Baseline", "Treatment"), each = 30)),
patient = factor(rep(1:30, 2)),
response = c(rnorm(30, 100, 20), rnorm(30, 120, 20))
)
# Paired violin with connecting lines
ViolinPlot(paired_data2,
x = "condition", y = "response",
paired_by = "patient",
add_box = TRUE
)
# With paired t-test
ViolinPlot(paired_data2,
x = "condition", y = "response",
paired_by = "patient",
comparisons = list(c("Baseline", "Treatment")),
pairwise_method = "t.test"
)
Visual Enhancements
# Highlight extreme values
ViolinPlot(vdata,
x = "category", y = "value",
add_point = TRUE,
highlight = "value > 80 | value < 10",
highlight_color = "red",
highlight_size = 2
)
# Add trend line
ViolinPlot(vdata,
x = "category", y = "value",
add_trend = TRUE,
trend_linewidth = 1.5
)
# Add reference line
ViolinPlot(vdata,
x = "category", y = "value",
add_line = 50,
line_color = "darkgreen",
line_type = 1
)
# Add mean indicator
ViolinPlot(vdata,
x = "category", y = "value",
add_stat = mean,
stat_name = "Mean",
stat_color = "red",
stat_shape = 18,
stat_size = 3
)
Fill Modes
# Fill by x category
ViolinPlot(vdata,
x = "category", y = "value",
fill_mode = "x",
palette = "Pastel1"
)
# Fill by mean (gradient coloring)
ViolinPlot(vdata,
x = "category", y = "value",
fill_mode = "mean",
palette = "RdYlGn"
)
# Fill by median with reversed gradient
ViolinPlot(vdata,
x = "category", y = "value",
fill_mode = "median",
palette = "Blues",
fill_reverse = TRUE
)
Sorting and Orientation
# Sort by mean value
ViolinPlot(vdata,
x = "category", y = "value",
sort_x = "mean_desc",
add_box = TRUE
)
# Horizontal violin plot
ViolinPlot(vdata,
x = "category", y = "value",
flip = TRUE,
add_box = TRUE
)
Faceting
# Add faceting variable
vdata$experiment <- sample(c("Exp1", "Exp2"), nrow(vdata), replace = TRUE)
# Facet by experiment
ViolinPlot(vdata,
x = "category", y = "value",
facet_by = "experiment",
add_box = TRUE
)
# Free scales
ViolinPlot(vdata,
x = "category", y = "value",
facet_by = "experiment",
facet_scales = "free_y"
)
Wide Format Data
# Wide format input
wide_data2 <- data.frame(
Control = rnorm(50, 100, 15),
LowDose = rnorm(50, 110, 15),
HighDose = rnorm(50, 130, 20)
)
ViolinPlot(wide_data2,
x = c("Control", "LowDose", "HighDose"),
in_form = "wide",
add_box = TRUE,
xlab = "Treatment",
ylab = "Response"
)
ScatterPlot
# Create sample data
set.seed(8525)
sdata <- data.frame(
x = rnorm(100),
y = rnorm(100),
size_val = abs(rnorm(100)),
category = sample(c("A", "B", "C"), 100, replace = TRUE),
group = sample(c("Group1", "Group2"), 100, replace = TRUE)
)
# Basic scatter plot
ScatterPlot(sdata, x = "x", y = "y")
# Highlight specific points
ScatterPlot(sdata, x = "x", y = "y", highlight = "x > 1 & y > 0")
# Size by numeric column
ScatterPlot(sdata, x = "x", y = "y", size_by = "size_val")
# Color by numeric column with continuous gradient
ScatterPlot(sdata, x = "x", y = "y", color_by = "size_val", palette = "RdYlBu")
# Color by categorical column
ScatterPlot(sdata, x = "x", y = "y", color_by = "category")
# Combine size and color mapping with custom border
ScatterPlot(sdata,
x = "x", y = "y", size_by = "size_val", color_by = "category",
border_color = "black"
)
# Border color matches fill color (for cohesive look)
ScatterPlot(sdata,
x = "x", y = "y", color_by = "category",
border_color = TRUE
)
# Use shape without fill (solid points)
ScatterPlot(sdata,
x = "x", y = "y", color_by = "category",
shape = 16, palette = "Set1"
)
# Split by group to create separate panels
ScatterPlot(sdata,
x = "x", y = "y", color_by = "category",
split_by = "group", combine = TRUE
)
# Facet by group (alternative to split_by)
ScatterPlot(sdata, x = "x", y = "y", color_by = "category", facet_by = "group")
# Log transformation of axes
data_pos <- data.frame(
x = 10^rnorm(100, 2, 1),
y = 10^rnorm(100, 3, 0.5)
)
ScatterPlot(data_pos, x = "x", y = "y", xtrans = "log10", ytrans = "log10")
BarPlot
# Simple bar plot (counts)
bar_data1 <- data.frame(
category = c("A", "B", "C", "D", "A", "B")
)
BarPlot(bar_data1, x = "category")
# Bar plot with values
bar_data2 <- data.frame(
category = c("A", "B", "C", "D"),
value = c(10, 15, 8, 12)
)
BarPlot(bar_data2, x = "category", y = "value")
# Grouped bar plot (dodged)
bar_data3 <- data.frame(
category = rep(c("A", "B", "C"), 2),
value = c(10, 15, 8, 12, 18, 14),
group = rep(c("G1", "G2"), each = 3)
)
BarPlot(bar_data3, x = "category", y = "value", group_by = "group")
# Stacked bar plot
BarPlot(bar_data3, x = "category", y = "value", group_by = "group", position = "stack")
# With labels and customization
BarPlot(
bar_data3,
x = "category", y = "value", group_by = "group",
label = TRUE, palette = "Set2", flip = TRUE
)
# With splits (multiple plots)
bar_data3$experiment <- rep(c("Exp1", "Exp2"), 3)
BarPlot(bar_data3, x = "category", y = "value", split_by = "experiment")
# With faceting (single plot, multiple panels)
BarPlot(bar_data3, x = "category", y = "value", facet_by = "experiment")
LinePlot
# Basic line plot
line_data <- data.frame(
time = rep(c("T1", "T2", "T3", "T4"), 2),
value = c(10, 15, 13, 20, 8, 12, 11, 18),
group = rep(c("Control", "Treatment"), each = 4)
)
LinePlot(line_data, x = "time", y = "value")
# Grouped line plot
LinePlot(line_data, x = "time", y = "value", group_by = "group")
# With background stripes
LinePlot(line_data, x = "time", y = "value", group_by = "group", add_bg = TRUE)
# With error bars
line_data$sd <- c(2, 2.5, 2, 3, 1.5, 2, 1.8, 2.5)
LinePlot(
line_data,
x = "time",
y = "value",
group_by = "group",
add_errorbars = TRUE,
errorbar_sd = "sd"
)
# With horizontal reference line
LinePlot(
line_data,
x = "time",
y = "value",
group_by = "group",
add_hline = 15,
hline_type = "dashed"
)
# Split by another variable
line_data$batch <- rep(c("Batch1", "Batch2"), each = 4)
LinePlot(line_data, x = "time", y = "value", group_by = "group", split_by = "batch")
# Faceted plot
LinePlot(line_data, x = "time", y = "value", group_by = "group", facet_by = "batch")
DensityPlot
set.seed(8525)
dens_data <- data.frame(
x = c(rnorm(500, -1), rnorm(500, 1)),
group = rep(c("A", "B"), each = 500),
facet = sample(c("F1", "F2"), 1000, replace = TRUE)
)
DensityPlot(dens_data, x = "x")
DensityPlot(dens_data, x = "x", group_by = "group")
DensityPlot(dens_data, x = "x", group_by = "group", facet_by = "facet")
DensityPlot(dens_data, x = "x", split_by = "facet", add_bars = TRUE)
DotPlot
mtcars_data <- datasets::mtcars
mtcars_data$carb <- factor(mtcars_data$carb)
mtcars_data$gear <- factor(mtcars_data$gear)
DotPlot(mtcars_data,
x = "carb", y = "gear", size_by = "wt",
fill_by = "mpg", fill_cutoff = 18
)
DotPlot(mtcars_data,
x = "carb", y = "gear", size_by = "wt",
fill_by = "mpg", fill_cutoff = 18, add_bg = TRUE
)
# Scatter plot (both axes numeric)
DotPlot(mtcars_data,
x = "qsec", y = "drat", size_by = "wt",
fill_by = "mpg", fill_cutoff = 18
)
JitterPlot
set.seed(8525)
n <- 200
x_jit <- sample(LETTERS[1:5], n, replace = TRUE)
group_jit <- sample(c("G1", "G2"), n, replace = TRUE)
size_jit <- rexp(n, rate = 1)
id_jit <- paste0("pt", seq_len(n))
y_jit <- rnorm(n, mean = ifelse(group_jit == "G1", 0.5, -0.5)) +
as.numeric(factor(x_jit, levels = LETTERS[1:5])) / 10
jit_df <- data.frame(
x = factor(x_jit, levels = LETTERS[1:5]),
y = y_jit,
group = group_jit,
size = size_jit,
id = id_jit
)
# Basic
JitterPlot(jit_df, x = "x", y = "y")
# Map size with transform; legend shows original values
JitterPlot(jit_df,
x = "x", y = "y", size_by = "size", size_name = "Abundance",
size_trans = sqrt, order_by = "-y^2"
)
# Dodge by group and add a horizontal line
JitterPlot(jit_df,
x = "x", y = "y", group_by = "group",
add_hline = 0, hline_type = "dashed", hline_color = "red2"
)
# Label top points by distance (y^2 + size^2)
JitterPlot(jit_df, x = "x", y = "y", size_by = "size", label_by = "id", nlabel = 3)
# Flip axes
JitterPlot(jit_df, x = "x", y = "y", flip = TRUE)
AreaPlot
area_data <- data.frame(
x = rep(c("A", "B", "C", "D"), 2),
y = c(1, 3, 6, 4, 2, 5, 7, 8),
group = rep(c("F1", "F2"), each = 4),
split = rep(c("X", "Y"), 4)
)
AreaPlot(area_data, x = "x", y = "y", group_by = "group")
AreaPlot(area_data,
x = "x", y = "y", group_by = "group",
scale_y = TRUE
)
AreaPlot(area_data, x = "x", y = "y", split_by = "group")
AreaPlot(area_data,
x = "x", y = "y", group_by = "group", split_by = "split",
legend.direction = c(X = "horizontal", Y = "vertical"),
legend.position = c(X = "top", Y = "right")
)
RidgePlot
set.seed(8525)
ridge_data <- data.frame(
x = c(rnorm(250, -1), rnorm(250, 1)),
group = rep(LETTERS[1:5], each = 100)
)
RidgePlot(ridge_data, x = "x") # fallback to density plot
RidgePlot(ridge_data, x = "x", add_vline = 0, vline_color = "black")
RidgePlot(ridge_data, x = "x", group_by = "group")
RidgePlot(ridge_data, x = "x", group_by = "group", reverse = TRUE)
RidgePlot(ridge_data, x = "x", group_by = "group", add_vline = TRUE, vline_color = TRUE)
# wide form
ridge_wide <- data.frame(
A = rnorm(100),
B = rnorm(100),
C = rnorm(100),
D = rnorm(100),
E = rnorm(100),
group = sample(letters[1:4], 100, replace = TRUE)
)
RidgePlot(ridge_wide, group_by = LETTERS[1:5], in_form = "wide")
RidgePlot(ridge_wide, group_by = LETTERS[1:5], in_form = "wide", facet_by = "group")
EnrichMap
EnrichMap(enrich_example, label = "feature")
EnrichMap(enrich_example, show_keyword = TRUE, label = "term")
EnrichMap(enrich_example, show_keyword = TRUE, label = "feature")
EnrichMap(enrich_multidb_example,
split_by = "Database",
palette = list(DB1 = "Paired", DB2 = "Set1")
)
GSEAPlot
data(gsea_example)
# Plot single gene set
GSEAPlot(gsea_example, gene_sets = attr(gsea_example, "gene_sets")[1])
# Plot multiple gene sets
GSEAPlot(gsea_example, gene_sets = attr(gsea_example, "gene_sets")[1:4])
# Label core genes
GSEAPlot(
gsea_example,
gene_sets = attr(gsea_example, "gene_sets")[1],
n_coregenes = 5
)
# Customize line appearance
GSEAPlot(
gsea_example,
gene_sets = attr(gsea_example, "gene_sets")[1],
line_width = 2,
line_color = "#FF6B6B"
)
# Return separate plots instead of combined
plots <- GSEAPlot(
gsea_example,
gene_sets = attr(gsea_example, "gene_sets")[1:3],
combine = FALSE
)VolcanoPlot
set.seed(8525)
deg_data <- data.frame(
avg_log2FC = c(
-3.69, -4.10, -2.68, -3.51, -3.09, -2.52, -3.53, -3.35, -2.82, -2.71,
3.67, 4.79, 10.14, 5.36, 4.56, 4.62, 3.31, 4.72, 3.01, 3.86
),
p_val_adj = c(
3.82e-09, 1.52e-07, 1.79e-07, 4.68e-07, 4.83e-07, 6.26e-07, 2.61e-06,
1.33e-05, 1.79e-05, 3.71e-05, 8.93e-04, 9.61e-04, 1.47e-03, 4.35e-03,
4.85e-03, 5.12e-03, 1.90e-02, 2.13e-02, 3.80e-02, 6.72e-02
),
gene = c(
"HLA-DPB1", "LYZ", "HLA-DRA", "TYMP", "HLA-DPA1", "HLA-DRB1", "CST3",
"HLA-DQB1", "HLA-DRB5", "LST1", "CCL5", "LCK", "MS4A6A", "CD3D", "CD7",
"CD3E", "CTSW", "GZMM", "GZMA", "IL32"
),
group = sample(LETTERS[1:2], 20, replace = TRUE)
)
VolcanoPlot(deg_data, x = "avg_log2FC", y = "p_val_adj", y_cutoff_name = "-log10(0.05)")
VolcanoPlot(deg_data,
x = "avg_log2FC", y = "p_val_adj",
label_by = "gene", y_cutoff_name = "-log10(0.05)"
)
VolcanoPlot(deg_data,
x = "avg_log2FC", y = "p_val_adj", y_cutoff_name = "none",
flip_negatives = TRUE, label_by = "gene"
)
VolcanoPlot(deg_data,
x = "avg_log2FC", y = "p_val_adj", y_cutoff_name = "none",
flip_negatives = TRUE, facet_by = "group", label_by = "gene"
)
VolcanoPlot(deg_data,
x = "avg_log2FC", y = "p_val_adj", y_cutoff_name = "none",
flip_negatives = TRUE, split_by = "group", label_by = "gene"
)
KMPlot
library(survival)
# Basic Kaplan-Meier plot
KMPlot(data = lung, time = "time", status = "status")
# Multiple groups with p-value
KMPlot(
data = lung,
time = "time",
status = "status",
group_by = "sex",
show_pval = TRUE
)
# With risk table
KMPlot(
data = lung,
time = "time",
status = "status",
group_by = "sex",
show_risk_table = TRUE,
show_pval = TRUE
)
# With confidence intervals and median lines
KMPlot(
data = lung,
time = "time",
status = "status",
group_by = "sex",
show_conf_int = TRUE,
show_median_line = "hv",
palette = "Set1"
)
# Publication-ready plot
KMPlot(
data = lung,
time = "time",
status = "status",
group_by = "sex",
show_risk_table = TRUE,
show_pval = TRUE,
show_conf_int = TRUE,
show_median_line = "hv",
palette = "jco",
title = "Overall Survival by Sex",
xlab = "Time (days)",
ylab = "Survival Probability",
theme_args = list(base_size = 14)
)Heatmap
set.seed(8525)
matrix_data <- matrix(rnorm(60), nrow = 6, ncol = 10)
rownames(matrix_data) <- paste0("R", 1:6)
colnames(matrix_data) <- paste0("C", 1:10)
if (requireNamespace("cluster", quietly = TRUE)) {
Heatmap(matrix_data)
}
if (requireNamespace("cluster", quietly = TRUE)) {
# use a different color palette
# change the main legend title
# show row names (legend will be hidden)
# show column names
# change the row name annotation name and side
# change the column name annotation name
Heatmap(matrix_data,
palette = "viridis", values_by = "z-score",
show_row_names = TRUE, show_column_names = TRUE,
rows_name = "Features", row_names_side = "left",
columns_name = "Samples"
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
# flip the heatmap
Heatmap(matrix_data,
palette = "viridis", values_by = "z-score",
show_row_names = TRUE, show_column_names = TRUE,
rows_name = "Features", row_names_side = "left",
columns_name = "Samples", flip = TRUE
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
# add annotations to the heatmap
rows_data <- data.frame(
rows = paste0("R", 1:6),
group = sample(c("X", "Y", "Z"), 6, replace = TRUE)
)
Heatmap(matrix_data,
rows_data = rows_data,
row_annotation = list(Group = "group"),
row_annotation_type = list(Group = "simple"),
row_annotation_palette = list(Group = "Spectral")
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
Heatmap(matrix_data,
rows_data = rows_data,
rows_split_by = "group"
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
# add labels to the heatmap
Heatmap(matrix_data,
rows_data = rows_data,
rows_split_by = "group", cell_type = "label",
label = function(x) {
ifelse(
x > 0, scales::number(x, accuracy = 0.01), NA
)
}
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
# add labels based on an external data
pvalues <- matrix(runif(60, 0, 0.5), nrow = 6, ncol = 10)
Heatmap(matrix_data,
rows_data = rows_data,
rows_split_by = "group", cell_type = "label",
label = function(x, i, j) {
pv <- ComplexHeatmap::pindex(pvalues, i, j)
ifelse(pv < 0.01, "***",
ifelse(pv < 0.05, "**",
ifelse(pv < 0.1, "*", NA)
)
)
}
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
# Make the row/column name annotation thicker
Heatmap(matrix_data,
# Use the "name." prefix
column_annotation_params = list(name.columns = list(height = 5)),
row_annotation_params = list(name.rows = list(width = 5))
)
}
Long Format Data
# Use long form data
N <- 500
heatmap_long <- data.frame(
value = rnorm(N),
c = sample(letters[1:8], N, replace = TRUE),
r = sample(LETTERS[1:5], N, replace = TRUE),
p = sample(c("x", "y"), N, replace = TRUE),
q = sample(c("X", "Y", "Z"), N, replace = TRUE),
a = as.character(sample(1:5, N, replace = TRUE)),
p1 = runif(N),
p2 = runif(N)
)
if (requireNamespace("cluster", quietly = TRUE)) {
Heatmap(heatmap_long,
rows_by = "r", columns_by = "c", values_by = "value",
rows_split_by = "p", columns_split_by = "q", show_column_names = TRUE
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
# split into multiple heatmaps
Heatmap(heatmap_long,
values_by = "value", columns_by = "c", rows_by = "r", split_by = "p",
upper_cutoff = 2, lower_cutoff = -2, legend.position = c("none", "right"),
design = "AAAAAA#BBBBBBB"
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
# cell_type = "bars" (default is "tile")
Heatmap(heatmap_long,
values_by = "value", rows_by = "r", columns_by = "c",
cell_type = "bars"
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
p <- Heatmap(heatmap_long,
values_by = "value", rows_by = "r", columns_by = "c",
cell_type = "dot", dot_size = length, dot_size_name = "data points",
add_bg = TRUE, add_reticle = TRUE
)
p
}
if (requireNamespace("cluster", quietly = TRUE)) {
Heatmap(heatmap_long,
values_by = "value", rows_by = "r", columns_by = "c",
cell_type = "pie", pie_group_by = "q", pie_size = sqrt,
add_bg = TRUE, add_reticle = TRUE
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
Heatmap(heatmap_long,
values_by = "value", rows_by = "r", columns_by = "c",
cell_type = "violin", add_bg = TRUE, add_reticle = TRUE
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
Heatmap(heatmap_long,
values_by = "value", rows_by = "r", columns_by = "c",
cell_type = "boxplot", add_bg = TRUE, add_reticle = TRUE
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
Heatmap(heatmap_long,
values_by = "value", rows_by = "r", columns_by = "c",
column_annotation = list(r1 = "p", r2 = "q", r3 = "p1"),
column_annotation_type = list(r1 = "ring", r2 = "bar", r3 = "violin"),
column_annotation_params = list(
r1 = list(height = grid::unit(10, "mm"), show_legend = FALSE),
r3 = list(height = grid::unit(18, "mm"))
),
row_annotation = c("q", "p2", "a"),
row_annotation_side = "right",
row_annotation_type = list(q = "pie", p2 = "density", a = "simple"),
row_annotation_params = list(q = list(width = grid::unit(12, "mm"))),
show_row_names = TRUE, show_column_names = TRUE
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
Heatmap(heatmap_long,
values_by = "value", rows_by = "r", columns_by = "c",
split_by = "p", palette = list(x = "Reds", y = "Blues")
)
}
if (requireNamespace("cluster", quietly = TRUE)) {
# implies in_form = "wide-rows"
Heatmap(heatmap_long, rows_by = c("p1", "p2"), columns_by = "c")
}
if (requireNamespace("cluster", quietly = TRUE)) {
# implies wide-columns
Heatmap(heatmap_long, rows_by = "r", columns_by = c("p1", "p2"))
}
ChordPlot
set.seed(8525)
chord_data <- data.frame(
nodes1 = sample(c("Source1", "Source2", "Source3"), 10, replace = TRUE),
nodes2 = sample(letters[1:3], 10, replace = TRUE),
y = sample(1:5, 10, replace = TRUE)
)
# Basic chord plot
ChordPlot(chord_data, from = "nodes1", to = "nodes2")
# With rotated labels and colored by target
ChordPlot(chord_data,
from = "nodes1", to = "nodes2",
links_color = "to", labels_rot = TRUE
)
# With values
ChordPlot(chord_data, from = "nodes1", to = "nodes2", y = "y")
# Split by variable
ChordPlot(chord_data, from = "nodes1", to = "nodes2", split_by = "y")
# With custom palettes per split
ChordPlot(chord_data,
from = "nodes1", to = "nodes2", split_by = "y",
palette = c("1" = "Reds", "2" = "Blues", "3" = "Greens")
)
# Flip source and target
ChordPlot(chord_data, from = "nodes1", to = "nodes2", flip = TRUE)VennDiagram
set.seed(8525)
venn_sets <- list(
A = sort(sample(letters, 8)),
B = sort(sample(letters, 8)),
C = sort(sample(letters, 8)),
D = sort(sample(letters, 8))
)
VennDiagram(venn_sets)
VennDiagram(venn_sets, fill_mode = "set")
VennDiagram(venn_sets, label = "both")
VennDiagram(venn_sets, palette = "material-indigo", alpha = 0.6)CorPlot
data(iris)
# Basic correlation plot with grouping
CorPlot(iris, x = "Sepal.Length", y = "Sepal.Width", group_by = "Species")
# With custom annotations and positioning
CorPlot(iris,
x = "Sepal.Length", y = "Sepal.Width",
group_by = "Species",
anno_items = c("n", "eq", "r2", "pearson"),
anno_position = "bottomright"
)
# With highlighting specific points
CorPlot(iris,
x = "Sepal.Length", y = "Sepal.Width",
group_by = "Species",
highlight = 'Species == "setosa"',
highlight_color = "red",
highlight_size = 3,
highlight_stroke = 1.5
)
# With faceting by groups
CorPlot(iris,
x = "Sepal.Length", y = "Sepal.Width",
facet_by = "Species",
facet_scales = "free",
add_smooth = TRUE,
smooth_color = "blue"
)
# With splitting and custom palettes
CorPlot(iris,
x = "Sepal.Length", y = "Sepal.Width",
split_by = "Species",
palette = c(setosa = "Set1", versicolor = "Dark2", virginica = "Paired"),
combine = TRUE
)
Session Info
sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] dplyr_1.2.0 ggplot2_4.0.2 ggforge_1.0.1
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.1 farver_2.1.2 S7_0.2.1
#> [4] fastmap_1.2.0 tweenr_2.0.3 digest_0.6.39
#> [7] lifecycle_1.0.5 cluster_2.1.8.1 Cairo_1.7-0
#> [10] survival_3.8-3 magrittr_2.0.4 compiler_4.5.2
#> [13] rlang_1.1.7 sass_0.4.10 tools_4.5.2
#> [16] igraph_2.2.2 yaml_2.3.12 knitr_1.51
#> [19] ggsignif_0.6.4 labeling_0.4.3 RColorBrewer_1.1-3
#> [22] abind_1.4-8 withr_3.0.2 purrr_1.2.1
#> [25] BiocGenerics_0.56.0 desc_1.4.3 stats4_4.5.2
#> [28] grid_4.5.2 polyclip_1.10-7 ggpubr_0.6.3
#> [31] colorspace_2.1-2 scales_1.4.0 iterators_1.0.14
#> [34] MASS_7.3-65 ggridges_0.5.7 dichromat_2.0-0.1
#> [37] cli_3.6.5 rmarkdown_2.30 crayon_1.5.3
#> [40] ragg_1.5.0 generics_0.1.4 otel_0.2.0
#> [43] rjson_0.2.23 cachem_1.1.0 ggforce_0.5.0
#> [46] stringr_1.6.0 splines_4.5.2 parallel_4.5.2
#> [49] matrixStats_1.5.0 vctrs_0.7.1 Matrix_1.7-4
#> [52] jsonlite_2.0.0 carData_3.0-6 car_3.1-5
#> [55] IRanges_2.44.0 GetoptLong_1.1.0 patchwork_1.3.2
#> [58] S4Vectors_0.48.0 rstatix_0.7.3 ggrepel_0.9.7
#> [61] clue_0.3-67 Formula_1.2-5 magick_2.9.1
#> [64] systemfonts_1.3.1 foreach_1.5.2 ggnewscale_0.5.2
#> [67] tidyr_1.3.2 jquerylib_0.1.4 glue_1.8.0
#> [70] pkgdown_2.2.0 codetools_0.2-20 shape_1.4.6.1
#> [73] stringi_1.8.7 gtable_0.3.6 ComplexHeatmap_2.26.1
#> [76] tibble_3.3.1 pillar_1.11.1 htmltools_0.5.9
#> [79] circlize_0.4.17 R6_2.6.1 textshaping_1.0.4
#> [82] doParallel_1.0.17 evaluate_1.0.5 lattice_0.22-7
#> [85] png_0.1-8 backports_1.5.0 broom_1.0.12
#> [88] bslib_0.10.0 Rcpp_1.1.1 nlme_3.1-168
#> [91] mgcv_1.9-3 xfun_0.56 fs_1.6.6
#> [94] zoo_1.8-15 forcats_1.0.1 pkgconfig_2.0.3
#> [97] GlobalOptions_0.1.3