ggforge Complete Tutorial

library(ggforge)
library(dplyr)
set.seed(8525)

This tutorial demonstrates every plotting function in ggforge (77 total) with practical examples. Functions requiring optional packages that may not be available use eval=FALSE.

1. Statistics

BoxPlot

treat <- data.frame(
  group = rep(c("Control", "Low Dose", "High Dose"), each = 35),
  response = c(rnorm(35, 5, 1.5), rnorm(35, 7, 2), rnorm(35, 10, 2)),
  sex = sample(c("Male", "Female"), 105, replace = TRUE)
)

BoxPlot(treat, x = "group", y = "response", palette = "lancet",
  add_point = TRUE, pt_alpha = 0.3,
  title = "Basic Box Plot")

BoxPlot(treat, x = "group", y = "response", group_by = "sex",
  palette = "npg", add_point = TRUE, pt_alpha = 0.4,
  comparisons = TRUE, title = "Grouped with Comparisons")

BoxPlot(treat, x = "group", y = "response", palette = "Set2",
  fill_mode = "mean", comparisons = list(c("Control", "High Dose")),
  sig_label = "p.signif", title = "Fill by Mean + Significance Symbols")

ViolinPlot

ViolinPlot(treat, x = "group", y = "response", palette = "npg",
  add_box = TRUE, add_point = TRUE, pt_alpha = 0.3,
  title = "Violin with Box Overlay")

ViolinPlot(treat, x = "group", y = "response", group_by = "sex",
  palette = "jco", add_box = TRUE,
  title = "Grouped Violin")

ViolinPlot(treat, x = "group", y = "response",
  fill_mode = "median", palette = "RdYlBu",
  title = "Fill by Median (Gradient)")

ScatterPlot

sdata <- data.frame(
  expr_A = rnorm(120, 8, 2), expr_B = rnorm(120, 8, 2),
  tissue = sample(c("Normal", "Tumor"), 120, replace = TRUE)
)
sdata$expr_B <- sdata$expr_B + 0.6 * sdata$expr_A + rnorm(120, 0, 1.5)

ScatterPlot(sdata, x = "expr_A", y = "expr_B", group_by = "tissue",
  palette = "jco", add_smooth = TRUE, add_stat = TRUE,
  title = "Grouped Correlation")

ScatterPlot(sdata, x = "expr_A", y = "expr_B",
  color_by = "expr_B", palette = "viridis",
  title = "Continuous Color Mapping")

ScatterPlot(sdata, x = "expr_A", y = "expr_B",
  group_by = "tissue", split_by = "tissue",
  palette = "npg", add_smooth = TRUE,
  combine = TRUE, nrow = 1,
  title = "Split by Tissue")

LinePlot

time_course <- data.frame(
  day = rep(c(0, 3, 7, 14, 21, 28), 2),
  volume = c(100, 120, 180, 320, 500, 780, 100, 110, 130, 150, 170, 200),
  arm = rep(c("Vehicle", "Treatment"), each = 6)
)

LinePlot(time_course, x = "day", y = "volume", group_by = "arm",
  palette = "lancet", title = "Tumor Growth Curve",
  xlab = "Day", ylab = "Volume (mm3)")

LinePlot(time_course, x = "day", y = "volume", group_by = "arm",
  palette = "nejm", add_bg = TRUE, add_smooth = TRUE,
  title = "With Background and Smooth")

BarPlot

cell_freq <- data.frame(
  sample = rep(c("Patient 1", "Patient 2", "Patient 3"), each = 4),
  celltype = rep(c("T cell", "B cell", "Myeloid", "Stromal"), 3),
  fraction = c(0.45, 0.15, 0.25, 0.15, 0.30, 0.25, 0.30, 0.15,
               0.50, 0.10, 0.20, 0.20)
)

BarPlot(cell_freq, x = "sample", y = "fraction", group_by = "celltype",
  position = "stack", palette = "npg", label = TRUE,
  title = "Stacked Bar Plot")

BarPlot(cell_freq, x = "sample", y = "fraction", group_by = "celltype",
  position = "dodge", palette = "Set2",
  title = "Grouped (Dodged) Bar Plot")

BarPlot(treat, x = "group", y = "response", palette = "lancet",
  add_errorbar = TRUE, errorbar_type = "se",
  title = "Mean with Standard Error")

DensityPlot

DensityPlot(treat, x = "response", group_by = "group",
  palette = "npg", add_rug = TRUE,
  title = "Density with Rug")

DensityPlot(treat, x = "response", group_by = "group",
  facet_by = "sex", palette = "Set1",
  title = "Faceted Density")

JitterPlot

JitterPlot(treat, x = "group", y = "response", palette = "Set2",
  title = "Basic Jitter Plot")

JitterPlot(treat, x = "group", y = "response", group_by = "sex",
  palette = "npg", title = "Grouped Jitter")

BeeswarmPlot

BeeswarmPlot(treat, x = "group", y = "response", palette = "npg",
  title = "Beeswarm Layout")

Histogram

Histogram(data.frame(score = c(rnorm(200, 5, 2), rnorm(200, 10, 2)),
  group = rep(c("A", "B"), each = 200)),
  x = "score", group_by = "group", palette = "Set1",
  title = "Histogram")

RidgePlot

ridge_df <- data.frame(
  value = c(rnorm(100, 3), rnorm(100, 5), rnorm(100, 7), rnorm(100, 9)),
  celltype = rep(c("T cell", "B cell", "Myeloid", "NK"), each = 100)
)

RidgePlot(ridge_df, x = "value", group_by = "celltype",
  palette = "npg", title = "Ridge Plot")

RidgePlot(ridge_df, x = "value", group_by = "celltype",
  palette = "Set2", add_vline = TRUE, vline_color = TRUE,
  title = "Ridge with Median Lines")

AreaPlot

comp <- data.frame(
  time = rep(paste0("T", 1:6), 3),
  fraction = c(0.50, 0.45, 0.40, 0.35, 0.30, 0.25,
               0.30, 0.30, 0.35, 0.35, 0.40, 0.40,
               0.20, 0.25, 0.25, 0.30, 0.30, 0.35),
  celltype = rep(c("Epithelial", "Immune", "Stromal"), each = 6)
)

AreaPlot(comp, x = "time", y = "fraction", group_by = "celltype",
  palette = "nejm", title = "Stacked Area")

AreaPlot(comp, x = "time", y = "fraction", group_by = "celltype",
  palette = "Set2", scale_y = TRUE,
  title = "100% Stacked Area")

TrendPlot

trend_df <- data.frame(
  timepoint = rep(LETTERS[1:5], 2),
  value = c(2, 5, 8, 6, 3, 4, 7, 11, 9, 5),
  cohort = rep(c("Cohort 1", "Cohort 2"), each = 5)
)

TrendPlot(trend_df, x = "timepoint", y = "value", group_by = "cohort",
  palette = "Set1", title = "Trend Comparison")

TrendPlot(trend_df, x = "timepoint", y = "value", group_by = "cohort",
  palette = "nejm", scale_y = TRUE,
  title = "Scaled Trend")

QQPlot

QQPlot(data.frame(values = rnorm(200)), val = "values",
  band = TRUE, title = "Normal Q-Q Plot")

DotPlot

dot_df <- data.frame(
  gene = rep(c("CD3D", "CD8A", "MS4A1", "CD68", "NKG7"), 4),
  cluster = rep(paste0("C", 1:4), each = 5),
  avg_expr = runif(20, -1, 2),
  pct_expr = runif(20, 0.1, 0.9)
)

DotPlot(dot_df, x = "gene", y = "cluster",
  size_by = "pct_expr", fill_by = "avg_expr",
  palette = "RdBu", title = "Marker Expression Dot Plot")

DotPlot(dot_df, x = "gene", y = "cluster",
  size_by = "pct_expr", fill_by = "avg_expr",
  fill_cutoff = 0, palette = "RdBu", add_bg = TRUE,
  title = "With Background and Cutoff")

CorPlot

cor_df <- data.frame(
  BRCA1 = rnorm(80), TP53 = rnorm(80),
  tissue = sample(c("Normal", "Tumor"), 80, replace = TRUE)
)
cor_df$TP53 <- cor_df$BRCA1 * 0.7 + rnorm(80, sd = 0.5)

CorPlot(cor_df, x = "BRCA1", y = "TP53", group_by = "tissue",
  palette = "jco", add_smooth = TRUE, title = "Gene Correlation")

CorPlot(cor_df, x = "BRCA1", y = "TP53",
  palette = "lancet", add_smooth = TRUE,
  anno_items = c("n", "eq", "r2", "pearson"),
  title = "With Full Annotations")

CorPairsPlot

CorPairsPlot(iris[, 1:4], palette = "Spectral",
  title = "Pairwise Correlations")

LollipopPlot

x is the numeric value axis, y is the category axis.

lollipop_df <- data.frame(
  pathway = paste0("Pathway ", LETTERS[1:8]),
  score = c(3.2, 2.8, 2.5, 2.1, -1.5, -1.9, -2.3, -2.7)
)

LollipopPlot(lollipop_df, x = "score", y = "pathway",
  palette = "Set2", title = "Lollipop Plot")

LollipopPlot(lollipop_df, x = "score", y = "pathway",
  fill_by = "score", palette = "RdBu",
  title = "Color by Score")

SplitBarPlot

x is the numeric value, y is the category label. Positive and negative values are split automatically.

split_bar <- data.frame(
  term = paste0("GO:", 1:10),
  nes = c(2.1, 1.8, 1.5, 1.3, 1.1, -1.2, -1.4, -1.7, -1.9, -2.3)
)

SplitBarPlot(split_bar, x = "nes", y = "term",
  palette = "RdBu", title = "NES Split Bar")

SplitBarPlot(split_bar, x = "nes", y = "term",
  palette = "RdBu", flip = TRUE,
  title = "Horizontal Split Bar")

WaterfallPlot

Alias for SplitBarPlot. x = numeric, y = category.

wf_df <- data.frame(
  patient = paste0("Pt", 1:20),
  change = sort(runif(20, -80, 40), decreasing = TRUE)
)

WaterfallPlot(wf_df, x = "change", y = "patient",
  palette = "RdBu", title = "Tumor Size Change (%)")

PieChart

cell_comp <- data.frame(
  type = c("T cell", "B cell", "NK", "Monocyte", "Other"),
  count = c(35, 20, 15, 20, 10)
)

PieChart(cell_comp, x = "type", y = "count", palette = "Set2",
  title = "Cell Composition")

RingPlot

RingPlot(cell_comp, x = "type", y = "count", group_by = "type",
  palette = "Set2", title = "Ring Chart")

RadarPlot

radar_df <- data.frame(
  metric = rep(c("Proliferation", "Apoptosis", "Migration",
                 "Invasion", "Angiogenesis"), 2),
  value = c(8, 4, 7, 3, 6, 5, 8, 4, 9, 3),
  group = rep(c("Wild Type", "Mutant"), each = 5)
)

RadarPlot(radar_df, x = "metric", y = "value", group_by = "group",
  scale_y = "none", palette = "Set1", title = "Radar Plot")

SpiderPlot

SpiderPlot(radar_df, x = "metric", y = "value", group_by = "group",
  scale_y = "none", palette = "lancet", title = "Spider Plot")

2. Enrichment & Pathway

EnrichMap

data(enrich_example)

EnrichMap(enrich_example, top_term = 20, layout = "fr",
  palette = "Spectral", title = "GO Enrichment Map")

EnrichMap(enrich_example, top_term = 15, show_keyword = TRUE,
  label = "term", title = "With Keyword Clustering")

EnrichNetwork

data(enrich_multidb_example)

EnrichNetwork(enrich_multidb_example, top_term = 15, layout = "fr",
  palette = "Set3", title = "Enrichment Network")

EnrichNetwork(enrich_multidb_example, top_term = 20, layout = "circle",
  palette = "Paired", title = "Circular Layout")

GSEASummaryPlot

data(gsea_example)

GSEASummaryPlot(gsea_example, top_term = 15, palette = "RdBu",
  title = "GSEA Summary")

GSEASummaryPlot(gsea_example, top_term = 10, palette = "PiYG",
  title = "Top 10 Pathways")

GSEAPlot

GSEAPlot(gsea_example, gs = gsea_example$ID[1],
  title = gsea_example$Description[1])

GSEAPlot(gsea_example, gs = gsea_example$ID[1:3],
  title = "Multiple Gene Sets")

3. Single-Cell & Spatial

DimPlot

data(dim_example)

DimPlot(dim_example, dims = c("basis_1", "basis_2"),
  group_by = "clusters", palette = "igv",
  pt_size = 1.2, label = TRUE, label_insitu = TRUE,
  title = "UMAP Clustering")

DimPlot(dim_example, dims = c("basis_1", "basis_2"),
  group_by = "clusters", palette = "Set3",
  add_density = TRUE, title = "With Density Contours")

DimPlot(dim_example, dims = c("basis_1", "basis_2"),
  group_by = "clusters", palette = "igv",
  split_by = "group", combine = TRUE, nrow = 1,
  title = "Split by Group")

FeatureDimPlot

dim_example$CD3D <- rnorm(nrow(dim_example))

FeatureDimPlot(dim_example, dims = c("basis_1", "basis_2"),
  features = "CD3D", palette = "viridis", pt_size = 1.2,
  title = "Feature Expression")

FeatureDimPlot(dim_example, dims = c("basis_1", "basis_2"),
  features = "CD3D", palette = "RdBu", pt_size = 1.2,
  title = "Diverging Palette")

VelocityPlot

embedding <- as.matrix(dim_example[, c("basis_1", "basis_2")])
v_embedding <- as.matrix(dim_example[, c("stochasticbasis_1", "stochasticbasis_2")])

VelocityPlot(embedding = embedding, v_embedding = v_embedding,
  plot_type = "grid", title = "RNA Velocity Field")

TrajectoryPlot

dim_example$pseudotime <- abs(rnorm(nrow(dim_example)))

TrajectoryPlot(dim_example, dims = c("basis_1", "basis_2"),
  pseudotime = "pseudotime", group_by = "clusters",
  palette = "viridis", title = "Pseudotime Trajectory")

StackedViolinPlot

marker_df <- data.frame(
  cluster = sample(c("T cell", "B cell", "NK", "Mono"), 200, replace = TRUE),
  CD3D = rnorm(200, 3), CD8A = rnorm(200, 2),
  MS4A1 = rnorm(200, 4), NKG7 = rnorm(200, 1)
)

StackedViolinPlot(marker_df,
  features = c("CD3D", "CD8A", "MS4A1", "NKG7"),
  group_by = "cluster", palette = "npg",
  title = "Marker Gene Expression")

ClustreePlot

Requires clustree and a Seurat object or data frame with resolution columns.

ClustreePlot(seurat_obj, prefix = "RNA_snn_res.",
  title = "Clustering Resolution Tree")

SpatImagePlot / SpatPointsPlot / SpatShapesPlot / SpatMasksPlot

These require spatial data objects (SpatRaster, SpatVector). See ?SpatImagePlot for details.

SpatImagePlot(spe, feature = "gene_of_interest",
  palette = "viridis", title = "Spatial Expression")

SpatPointsPlot(spe, group_by = "celltype",
  palette = "igv", title = "Spatial Cell Types")

SpatShapesPlot(spe, group_by = "region",
  palette = "Set3", title = "Spatial Regions")

SpatMasksPlot(spe, group_by = "mask_type",
  palette = "Set2", title = "Spatial Masks")

4. Genomics

VolcanoPlot

real_genes <- c("TP53","EGFR","MYC","KRAS","BRCA1","CDK4","PTEN","RB1",
                "PIK3CA","AKT1","CD274","CTLA4","PDCD1","IDO1","VEGFA",
                "BRAF","JAK2","STAT3","NRAS","HAVCR2")
deg <- data.frame(
  gene = c(real_genes, paste0("Gene", 1:480)),
  log2FC = c(rnorm(10, 2.5, 0.8), rnorm(10, -2.5, 0.8), rnorm(480, 0, 0.8)),
  padj = c(runif(20, 1e-10, 1e-3), runif(480, 0.01, 1))
)

VolcanoPlot(deg, x = "log2FC", y = "padj", label_by = "gene",
  x_cutoff = 1, y_cutoff = 0.05, nlabel = 10,
  title = "Volcano Plot")

VolcanoPlot(deg, x = "log2FC", y = "padj", label_by = "gene",
  x_cutoff = 1, y_cutoff = 0.05, nlabel = 5,
  flip_negatives = TRUE, title = "Mirrored Volcano")

deg$group <- sample(c("Responder", "NonResponder"), 500, replace = TRUE)
VolcanoPlot(deg, x = "log2FC", y = "padj", label_by = "gene",
  x_cutoff = 1, y_cutoff = 0.05, nlabel = 5,
  facet_by = "group", title = "Faceted Volcano")

ManhattanPlot

gwas <- data.frame(
  chr = rep(paste0("chr", 1:22), each = 500),
  pos = rep(1:500, 22) * 1e5,
  pvalue = runif(11000, 0, 1)
)
gwas$pvalue[sample(11000, 30)] <- runif(30, 0, 1e-8)

ManhattanPlot(gwas, chr_by = "chr", pos_by = "pos", pval_by = "pvalue",
  signif = 5e-8, title = "GWAS Results")

VennDiagram

gene_sets <- list(
  "RNA-seq" = paste0("Gene", 1:100),
  "Proteomics" = paste0("Gene", 50:150),
  "Metabolomics" = paste0("Gene", 80:160)
)

VennDiagram(gene_sets, palette = "Set2", title = "Multi-Omics Overlap")

VennDiagram(gene_sets, palette = "material-indigo",
  label = "both", alpha = 0.6,
  title = "With Count + Percentage")

UpsetPlot

pathways <- list(
  PI3K = paste0("Gene", sample(1:200, 50)),
  MAPK = paste0("Gene", sample(1:200, 60)),
  JAK_STAT = paste0("Gene", sample(1:200, 45)),
  Wnt = paste0("Gene", sample(1:200, 55))
)

UpsetPlot(pathways, palette = "Set2", title = "Pathway Intersection")

5. Clinical & Prediction

KMPlot

surv <- data.frame(
  time = c(rexp(80, 0.008), rexp(70, 0.015)),
  status = sample(0:1, 150, replace = TRUE, prob = c(0.35, 0.65)),
  risk = rep(c("Low", "High"), c(80, 70))
)

KMPlot(surv, time = "time", status = "status", group_by = "risk",
  palette = "jco", show_risk_table = TRUE, show_pval = TRUE,
  title = "Kaplan-Meier Curve")

KMPlot(surv, time = "time", status = "status", group_by = "risk",
  palette = "lancet", show_conf_int = TRUE,
  show_median_line = "hv", title = "With CI and Median")

KMPlot(surv, time = "time", status = "status", group_by = "risk",
  palette = "nejm", show_risk_table = TRUE, show_pval = TRUE,
  show_conf_int = TRUE, title = "Publication-Ready KM")

CoxPlot

cox_df <- data.frame(
  time = rexp(200, 0.01),
  event = sample(0:1, 200, replace = TRUE, prob = c(0.3, 0.7)),
  age = rnorm(200, 60, 10),
  gender = sample(c("Male", "Female"), 200, replace = TRUE),
  stage = sample(c("I-II", "III-IV"), 200, replace = TRUE)
)

CoxPlot(cox_df, time = "time", event = "event",
  vars = c("age", "gender", "stage"),
  plot_type = "forest", palette = "nejm",
  title = "Cox Forest Plot")

CoxPlot(cox_df, time = "time", event = "event",
  vars = c("age", "gender", "stage"),
  plot_type = "forest2", palette = "lancet", digits = 2,
  title = "Detailed Forest with Stats Table")

ROCCurve

roc_df <- data.frame(
  truth = sample(0:1, 200, replace = TRUE),
  score = rnorm(200)
)
roc_df$score <- roc_df$score + roc_df$truth * 1.5

ROCCurve(roc_df, truth_by = "truth", score_by = "score",
  palette = "lancet", title = "ROC Analysis")

NomogramPlot

Requires a pre-fitted rms model object.

library(rms)
dd <- datadist(df)
options(datadist = "dd")
fit <- cph(Surv(time, event) ~ age + grade + size, data = df, surv = TRUE)

NomogramPlot(fit, fun = function(x) 1 / (1 + exp(-x)),
  funlabel = "3-Year Survival", title = "Clinical Nomogram")

CalibrationPlot

predicted = predicted probabilities, observed = binary outcome.

cal_df <- data.frame(
  outcome = sample(0:1, 500, replace = TRUE),
  pred = runif(500)
)
cal_df$pred <- ifelse(cal_df$outcome == 1,
  pmin(cal_df$pred + 0.2, 1), pmax(cal_df$pred - 0.2, 0))

CalibrationPlot(cal_df, predicted = "pred", observed = "outcome",
  title = "Calibration (LOESS)")

CalibrationPlot(cal_df, predicted = "pred", observed = "outcome",
  method = "bins", title = "Calibration (Bins)")

DecisionCurvePlot

outcome = binary outcome column, predictors = predicted probability column(s).

DecisionCurvePlot(cal_df, outcome = "outcome", predictors = "pred",
  title = "Decision Curve Analysis")

6. Networks & Relationships

Heatmap

genes <- c("CD3D","CD3E","CD8A","FOXP3","CD19","MS4A1",
           "CD68","CSF1R","NCAM1","NKG7")
mat <- matrix(rnorm(120, 0, 1.5), nrow = 10,
  dimnames = list(genes, paste0("S", 1:12)))
mat[1:4, 1:4] <- mat[1:4, 1:4] + 2
mat[5:6, 5:8] <- mat[5:6, 5:8] + 2
mat[9:10, 9:12] <- mat[9:10, 9:12] + 2

Heatmap(mat, palette = "RdBu",
  show_row_names = TRUE, show_column_names = TRUE,
  title = "Basic Heatmap")

Heatmap(mat, palette = "viridis", values_by = "z-score",
  show_row_names = TRUE, show_column_names = TRUE,
  rows_name = "Genes", columns_name = "Samples",
  title = "Z-score Normalized")

rows_data <- data.frame(
  rows = genes,
  module = c(rep("T cell", 4), rep("B cell", 2),
             rep("Myeloid", 2), rep("NK", 2))
)

Heatmap(mat, palette = "RdBu",
  rows_data = rows_data,
  rows_annotation = list(Module = "module"),
  rows_annotation_palette = list(Module = "Set2"),
  show_row_names = TRUE, show_column_names = TRUE,
  title = "With Row Annotation")

ChordPlot

interactions <- data.frame(
  from = c("CD4 T", "CD8 T", "B cell", "NK", "Monocyte"),
  to = c("Fibroblast", "Endothelial", "Fibroblast", "Tumor", "Tumor"),
  weight = c(15, 20, 10, 25, 18)
)

ChordPlot(interactions, from = "from", to = "to", y = "weight",
  palette = "Set3", title = "Chord Diagram")

ChordPlot(interactions, from = "from", to = "to", y = "weight",
  links_color = "to", labels_rot = TRUE,
  palette = "Paired", title = "Colored by Target")

CircosPlot

Alias for ChordPlot.

CircosPlot(interactions, from = "from", to = "to", y = "weight",
  palette = "Set3", title = "Circos Plot")

SankeyPlot

flow <- data.frame(
  diagnosis = rep(c("Early", "Advanced", "Metastatic"), each = 3),
  outcome = rep(c("CR", "PR", "PD"), 3),
  n = c(40, 8, 2, 10, 20, 10, 2, 8, 20)
)

SankeyPlot(flow, x = c("diagnosis", "outcome"), y = "n",
  links_fill_by = "diagnosis", palette = "Set3",
  title = "Treatment Flow")

SankeyPlot(flow, x = c("diagnosis", "outcome"), y = "n",
  links_fill_by = "outcome", palette = "npg",
  title = "Colored by Outcome")

AlluvialPlot

Alias for SankeyPlot.

AlluvialPlot(flow, x = c("diagnosis", "outcome"), y = "n",
  links_fill_by = "diagnosis", palette = "Paired",
  title = "Alluvial Plot")

Network

edges <- data.frame(
  from = c("TP53", "EGFR", "MYC", "KRAS", "BRCA1", "PTEN"),
  to = c("MDM2", "ERBB2", "MAX", "BRAF", "RAD51", "AKT1"),
  score = c(0.9, 0.85, 0.8, 0.75, 0.7, 0.65)
)
nodes <- data.frame(
  name = unique(c(edges$from, edges$to)),
  type = c("Oncogene","Oncogene","Oncogene","Oncogene",
           "TSG","TSG","TSG","Oncogene","Oncogene",
           "Oncogene","TSG","Oncogene")
)

Network(edges, nodes, link_weight_by = "score",
  node_fill_by = "type", layout = "fr",
  title = "Protein Interaction Network")

7. Specialized

DumbbellPlot

x_start and x_end are the numeric value columns, y is the category.

dumb <- data.frame(
  gene = paste0("Gene", LETTERS[1:8]),
  before = rnorm(8, 5, 1.5), after = rnorm(8, 8, 1.5)
)

DumbbellPlot(dumb, x_start = "before", x_end = "after", y = "gene",
  palette = "Set1", title = "Before vs After Treatment")

DumbbellPlot(dumb, x_start = "before", x_end = "after", y = "gene",
  palette = "lancet", sort_y = "desc",
  title = "Sorted by Difference")

StreamGraph

stream_df <- data.frame(
  year = rep(2015:2024, 4),
  count = abs(rnorm(40, 50, 20)),
  category = rep(c("Immunotherapy", "Targeted", "Chemo", "Radiation"), each = 10)
)

StreamGraph(stream_df, x = "year", y = "count", group_by = "category",
  palette = "Set2", title = "Treatment Trends")

TreemapPlot

area = numeric size column, label = text label, fill = fill group.

treemap_df <- data.frame(
  pathway = c("PI3K-Akt", "MAPK", "Wnt", "p53", "Cell Cycle",
              "Apoptosis", "JAK-STAT", "NF-kB"),
  gene_count = c(45, 38, 32, 28, 25, 22, 18, 15),
  category = c("Signaling","Signaling","Signaling","TSG",
               "Cell Cycle","Death","Signaling","Signaling")
)

TreemapPlot(treemap_df, area = "gene_count", label = "pathway",
  fill = "category", palette = "Set2",
  title = "Pathway Gene Counts")

ParallelCoordPlot

ParallelCoordPlot(iris,
  columns = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"),
  group_by = "Species", palette = "Set1",
  title = "Iris Features")

WafflePlot

WafflePlot(cell_comp, x = "type", y = "count",
  palette = "Set2", title = "Waffle Chart")

TimelinePlot

events <- data.frame(
  event = c("Diagnosis", "Surgery", "Chemo", "Radiation", "Follow-up"),
  start = as.Date(c("2024-01-15", "2024-02-10", "2024-03-01",
                     "2024-06-01", "2024-09-01")),
  end = as.Date(c("2024-01-15", "2024-02-12", "2024-05-15",
                   "2024-07-15", "2024-12-01"))
)

TimelinePlot(events, start = "start", end = "end", label = "event",
  palette = "Set2", title = "Treatment Timeline")

DendrogramPlot

DendrogramPlot(mtcars[1:15, ],
  columns = c("mpg", "disp", "hp", "wt", "qsec"),
  k = 3, palette = "Set1",
  title = "Hierarchical Clustering")

SunburstPlot

Requires plotly. Returns interactive output.

SunburstPlot(sunburst_data,
  labels = "label", parents = "parent", values = "count",
  title = "Hierarchical Composition")

WordCloudPlot

terms <- data.frame(
  word = c("apoptosis", "proliferation", "migration", "invasion",
           "angiogenesis", "metastasis", "differentiation",
           "inflammation", "autophagy", "senescence"),
  score = c(8, 7, 6, 5, 9, 4, 7, 8, 3, 4)
)

WordCloudPlot(terms, word_by = "word", score_by = "score",
  palette = "Spectral", title = "Biological Processes")

RarefactionPlot

RarefactionPlot(inext_result, palette = "Set1",
  title = "Species Rarefaction")

8. Earth & Environmental

ContourPlot

grid_data <- expand.grid(
  x = seq(-3, 3, length.out = 50),
  y = seq(-3, 3, length.out = 50)
)
grid_data$z <- with(grid_data, sin(x) * cos(y) * exp(-(x^2 + y^2) / 8))

ContourPlot(grid_data, x = "x", y = "y", z = "z",
  type = "filled", palette = "Spectral",
  title = "Filled Contour")

ContourPlot(grid_data, x = "x", y = "y", z = "z",
  type = "lines", palette = "viridis",
  title = "Contour Lines")

ContourPlot(grid_data, x = "x", y = "y", z = "z",
  type = "both", palette = "RdBu",
  title = "Lines on Filled")

TernaryPlot

Requires ggtern (namespace may conflict with ggplot2).

ternary_df <- data.frame(
  sand = c(0.4, 0.2, 0.6), silt = c(0.3, 0.5, 0.2),
  clay = c(0.3, 0.3, 0.2), type = c("Loam", "Silty", "Sandy")
)

TernaryPlot(ternary_df, x = "sand", y = "silt", z = "clay",
  group_by = "type", palette = "Set1",
  title = "Soil Composition")

PolarPlot

theta = angle variable, r = radius variable.

wind <- data.frame(
  direction = rep(seq(0, 350, by = 10), 3),
  speed = abs(rnorm(108, 10, 5)),
  season = rep(c("Spring", "Summer", "Autumn"), each = 36)
)

PolarPlot(wind, theta = "direction", r = "speed",
  group_by = "season", palette = "Set2",
  title = "Wind Pattern")

PolarPlot(wind, theta = "direction", r = "speed",
  group_by = "season", palette = "npg", type = "scatter",
  title = "Polar Scatter")

WindRosePlot

Alias for PolarPlot.

WindRosePlot(wind, theta = "direction", r = "speed",
  group_by = "season", palette = "Set1",
  title = "Wind Rose")

MapPlot

Requires rnaturalearth and rnaturalearthdata.

cities <- data.frame(
  lon = c(-73.9, -87.6, -118.2, -122.4),
  lat = c(40.7, 41.9, 34.1, 37.8),
  pop = c(8.3, 2.7, 3.9, 0.87)
)

MapPlot(cities, type = "point", point_x = "lon", point_y = "lat",
  value = "pop", color_name = "Population (M)")

9. Meta-Analysis & Agreement

ForestPlot

estimate, ci_lower, ci_upper are the effect size columns. is_summary marks diamond rows.

meta <- data.frame(
  study = c("Smith 2018", "Chen 2019", "Garcia 2020",
            "Kim 2021", "Patel 2022", "Overall"),
  or = c(1.32, 0.85, 1.51, 1.08, 0.92, 1.12),
  lower = c(0.95, 0.60, 1.05, 0.78, 0.65, 0.93),
  upper = c(1.84, 1.20, 2.17, 1.50, 1.30, 1.35),
  weight = c(22, 18, 15, 25, 20, NA),
  is_summary = c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE)
)

ForestPlot(meta, estimate = "or", ci_lower = "lower", ci_upper = "upper",
  label = "study", weight = "weight", is_summary = "is_summary",
  null_value = 1, log_scale = TRUE,
  title = "Meta-Analysis Forest Plot")

ForestPlot(meta, estimate = "or", ci_lower = "lower", ci_upper = "upper",
  label = "study", weight = "weight", is_summary = "is_summary",
  null_value = 1, show_values = TRUE,
  title = "With Value Labels")

FunnelPlot

estimate = effect sizes, se = standard errors.

funnel_df <- data.frame(
  effect = rnorm(20, 0.5, 0.3),
  se = runif(20, 0.05, 0.4)
)

FunnelPlot(funnel_df, estimate = "effect", se = "se",
  title = "Publication Bias Assessment")

BlandAltmanPlot

method1 and method2 are the two measurement columns.

ba_df <- data.frame(
  method_A = rnorm(50, 100, 15),
  method_B = rnorm(50, 100, 15)
)
ba_df$method_B <- ba_df$method_A + rnorm(50, 2, 5)

BlandAltmanPlot(ba_df, method1 = "method_A", method2 = "method_B",
  title = "Method Agreement")

10. Ecology & Evolution

OrdinationPlot

Requires pre-computed ordination coordinates (e.g., from vegan::metaMDS).

library(vegan)
ord <- metaMDS(species_mat, k = 2, trace = 0)
ord_df <- as.data.frame(scores(ord, display = "sites"))
ord_df$habitat <- env$habitat

OrdinationPlot(ord_df, x = "NMDS1", y = "NMDS2",
  group_by = "habitat", palette = "Set1",
  title = "NMDS Ordination")

PhyloTreePlot

tree <- ape::rtree(12, tip.label = paste0("Species_", LETTERS[1:12]))

PhyloTreePlot(tree, palette = "Set2",
  title = "Phylogenetic Tree")

RankAbundancePlot

species = species names, abundance = abundance values.

rank_df <- data.frame(
  species = paste0("Sp", 1:30),
  abundance = sort(rpois(30, 20), decreasing = TRUE),
  community = sample(c("Site A", "Site B"), 30, replace = TRUE)
)

RankAbundancePlot(rank_df, species = "species", abundance = "abundance",
  group_by = "community", palette = "Set1",
  title = "Rank-Abundance Curve")

11. Physics & Engineering

QuiverPlot

field <- expand.grid(x = seq(-2, 2, 0.4), y = seq(-2, 2, 0.4))
field$u <- -field$y
field$v <- field$x

QuiverPlot(field, x = "x", y = "y", u = "u", v = "v",
  title = "Vector Field")

StreamlinePlot

StreamlinePlot(field, x = "x", y = "y", u = "u", v = "v",
  title = "Flow Streamlines")

12. 3D & Interactive

Scatter3D