Package {ddplot}

Create an animated line chart

Description

Create an animated line chart

Usage

anim_line_chart(
  data,
  x,
  y,
  curve = "curveLinear",
  duration = 5000,
  stroke = "crimson",
  strokeWidth = 1.5,
  xticks = NULL,
  yticks = NULL,
  xtitle = NULL,
  xtitleFontSize = 16,
  ytitle = NULL,
  ytitleFontSize = 16,
  title = NULL,
  titleFontSize = 22,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "#CAD0D3",
  opacity = 1,
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An animated SVG line chart.

Examples

airpassengers <- data.frame(
 passengers = as.matrix(AirPassengers),
 date= zoo::as.Date(time(AirPassengers))
)
anim_line_chart(
 data = airpassengers,
 x = "date",
 y = "passengers",
 duration = 10000 # in milliseconds (10 seconds)
)

Create an animated histogram.

Description

Create an animated histogram.

Usage

animated_histogram(
  x,
  bins = 30,
  duration = 2000,
  delay = 100,
  fill = "crimson",
  xFontSize = 10,
  yFontSize = 10,
  xticks = NULL,
  yticks = NULL,
  xtitle = NULL,
  xtitleFontSize = 16,
  ytitle = NULL,
  ytitleFontSize = 16,
  title = NULL,
  titleFontSize = 22,
  stroke = "crimson",
  strokeWidth = NULL,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "#CAD0D3",
  opacity = 1,
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An animated SVG histogram.

Examples

animated_histogram(
 x = mtcars$mpg,
 duration = 2000,
 delay = 100
)

Create a band chart

Description

Create a band chart

Usage

area_band(
  data,
  x,
  yLower,
  yUpper,
  fill = "crimson",
  stroke = NULL,
  strokeWidth = NULL,
  xticks = NULL,
  yticks = NULL,
  xtitle = NULL,
  xtitleFontSize = 16,
  ytitle = NULL,
  ytitleFontSize = 16,
  title = NULL,
  titleFontSize = 22,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "#CAD0D3",
  opacity = 1,
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

A SVG band chart

Examples

airpassengers <- data.frame(
 passengers_lower = as.matrix(AirPassengers),
 passengers_upper = as.matrix(AirPassengers) + 40,
 date= zoo::as.Date(time(AirPassengers))
)

area_band(
 data = airpassengers,
 x = "date",
 yLower = "passengers_lower",
 yUpper = "passengers_upper",
 fill = "yellow",
 stroke = "black"
)

Create an area chart

Description

Create an area chart

Usage

area_chart(
  data,
  x,
  y,
  fill = "crimson",
  stroke = NULL,
  strokeWidth = NULL,
  xticks = NULL,
  yticks = NULL,
  xtitle = NULL,
  xtitleFontSize = 16,
  ytitle = NULL,
  ytitleFontSize = 16,
  title = NULL,
  titleFontSize = 22,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "#CAD0D3",
  opacity = 1,
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

a SVG area chart

Examples

# 1. converting AirPassengers to a tidy data frame
airpassengers <- data.frame(
  passengers = as.matrix(AirPassengers),
  date= zoo::as.Date(time(AirPassengers))
)

# 2. plotting the area chart
area_chart(
  data = airpassengers,
  x = "date",
  y = "passengers",
  fill = "purple",
  bgcol = "white"
)

Create a bar chart.

Description

Create a bar chart.

Usage

bar_chart(
  data,
  x,
  y,
  fill = "crimson",
  sort = "none",
  paddingWidth = 0.1,
  xticks = NULL,
  xFontSize = 10,
  yFontSize = 10,
  yticks = NULL,
  xtitle = NULL,
  xtitleFontSize = 16,
  ytitle = NULL,
  ytitleFontSize = 16,
  title = NULL,
  titleFontSize = 22,
  stroke = "crimson",
  strokeWidth = NULL,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "#CAD0D3",
  opacity = 1,
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

A SVG bar chart.

Examples

library(ggplot2) #needed for the mpg data frame
library(dplyr) #needed for data wrangling

mpg %>% group_by(manufacturer) %>%
 summarise(mean_cty = mean(cty)) %>%
 bar_chart(
   x = "manufacturer",
   y = "mean_cty",
   sort = "ascending",
   xFontSize = 10,
   yFontSize = 10,
   fill = "orange",
   strokeWidth = 1,
   ytitle = "average cty value",
   title = "Average City Miles per Gallon by manufacturer",
   titleFontSize = 16
 )

Create a bar chart race.

Description

Create a bar chart race.

Usage

bar_chart_race(
  data,
  x,
  y,
  time,
  ease = "Linear",
  frameDur = 500,
  transitionDur = 500,
  colorCategory = "Accent",
  sort = "descending",
  paddingWidth = 0.1,
  xFontSize = 10,
  yFontSize = 10,
  xticks = 10,
  xtitle = NULL,
  xtitleFontSize = 16,
  ytitle = NULL,
  ytitleFontSize = 14,
  title = NULL,
  titleFontSize = 22,
  stroke = "black",
  strokeWidth = NULL,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "#CAD0D3",
  panelcol = "#EBEBEBFF",
  xgridlinecol = "white",
  opacity = 1,
  timeLabel = TRUE,
  timeLabelOpts = list(size = 32, prefix = "", suffix = "", xOffset = 0.5, yOffset = 1),
  width = NULL,
  height = NULL
)

Arguments

Value

An animated SVG bar chart race, wrapped in a div.

Examples

library(gapminder)
library(dplyr)
# let's select a set of countries only
gapminder <- gapminder %>%
filter(
country %in% c("Algeria", "Mexico", "Iceland", "Greece", "Finland")
)

bar_chart_race(
data = gapminder,
x = "lifeExp",
y = "country",
time = "year",
ytitle = "Country",
xtitle = "Life expectancy",
title = "Bar chart race of countries life expectancy"
)

Create a beeswarm plot.

Description

A beeswarm plot displays individual data points along a numeric axis, spreading them out so they do not overlap (like a swarm of bees). It is a transparent alternative to box plots or violin plots because every observation is visible. When a grouping variable is supplied, one swarm is drawn per group along the opposite axis.

The layout is computed with a D3 force simulation that pulls each point toward its true value on the x-axis while resolving collisions so no two circles overlap.

The plot is interactive: hovering a point highlights it while dimming the rest of the swarm, and (when tooltip columns are supplied) shows a tooltip with those columns' values. When a group is supplied, hovering a group label focuses that swarm and clicking a label toggles it on or off. An optional mean/median reference line can be drawn per swarm. The interactive behaviors are controlled by tooltip, hover, animate, and stat.

Usage

beeswarm_plot(
  data,
  x,
  group = NULL,
  tooltip = NULL,
  hover = TRUE,
  animate = TRUE,
  stat = c("none", "mean", "median"),
  statColor = "#d62728",
  col = "steelblue",
  colorPalette = "Tableau10",
  radius = 4,
  opacity = 0.75,
  stroke = "white",
  strokeWidth = 0.5,
  xtitle = NULL,
  xtitleFontSize = 13,
  title = NULL,
  titleFontSize = 16,
  xFontSize = 11,
  yFontSize = 11,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG beeswarm plot.

Examples

library(ggplot2) # for the mpg dataset

# Single swarm — distribution of highway fuel economy
beeswarm_plot(
  data = mpg,
  x    = "hwy",
  col  = "steelblue",
  xtitle = "Highway miles per gallon",
  title  = "Distribution of hwy"
)

# Grouped swarm — hwy by vehicle class
beeswarm_plot(
  data  = mpg,
  x     = "hwy",
  group = "class",
  xtitle = "Highway miles per gallon",
  title  = "Highway fuel economy by vehicle class"
)

# Grouped swarm on the iris dataset
beeswarm_plot(
  data         = iris,
  x            = "Sepal.Length",
  group        = "Species",
  colorPalette = "Set2",
  radius       = 5,
  xtitle       = "Sepal length (cm)",
  title        = "Sepal length by species"
)

# Interactive extras: median line per swarm and a tooltip label column
beeswarm_plot(
  data    = mpg,
  x       = "hwy",
  group   = "class",
  tooltip = "model",
  stat    = "median",
  xtitle  = "Highway miles per gallon",
  title   = "Highway fuel economy by vehicle class"
)

Create a bullet chart.

Description

A bullet chart displays a single quantitative measure (the actual value) against qualitative range bands (e.g. poor / satisfactory / good) and an optional target marker. It was designed by Stephen Few as a compact, information-dense alternative to gauge charts.

Usage

bullet_chart(
  value,
  ranges,
  target = NULL,
  rangeColors = NULL,
  title = "",
  subtitle = "",
  min = 0,
  valueColor = "#1a1a2e",
  targetColor = "#e63946",
  tickCount = 5,
  fontSize = 12,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG bullet chart.

Examples

# Sales revenue vs target
bullet_chart(
  value  = 270,
  target = 300,
  ranges = c(150, 225, 350),
  title  = "Revenue",
  subtitle = "USD thousands"
)

# Customer satisfaction score
bullet_chart(
  value  = 7.4,
  target = 8.0,
  ranges = c(4, 7, 10),
  title  = "Satisfaction",
  subtitle = "out of 10",
  valueColor = "steelblue"
)

# Server response time (lower is better — ranges go poor → good left to right)
bullet_chart(
  value       = 320,
  target      = 250,
  ranges      = c(200, 500, 1000),
  rangeColors = c("#f5f5f5", "#e0e0e0", "#c0c0c0"),
  title       = "Response",
  subtitle    = "ms",
  min         = 0
)

Create a bump chart.

Description

A bump chart shows how the rank of several entities changes across successive time periods. Each entity is drawn as a smooth line that moves up or down as it overtakes, or is overtaken by, the others, with a circle at every period showing its rank. Popularised by the Financial Times, The Economist and FiveThirtyEight (league tables, election polls, country rankings), it is the clearest way to tell a "who is ahead?" story when the order matters more than the exact values.

The data must be in long format: one row per entity and period. By default the ranks are computed per period from the numeric y column (rank 1 = highest value). Set ranked = TRUE if y already contains the ranks.

Usage

bump_chart(
  data,
  x,
  y,
  group,
  ranked = FALSE,
  rankOrder = "desc",
  colors = NULL,
  highlight = NULL,
  greyCol = "#d3d3d3",
  lineWidth = 3,
  circleRadius = 11,
  showRank = TRUE,
  title = NULL,
  titleFontSize = 16,
  xFontSize = 11,
  labelFontSize = 11,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG bump chart.

Examples

# Points scored by five teams over four seasons
league <- data.frame(
  season = rep(c("2023", "2024", "2025", "2026"), each = 5),
  team   = rep(c("Lions", "Tigers", "Bears", "Wolves", "Eagles"), 4),
  points = c(80, 75, 60, 55, 50,
             70, 82, 58, 66, 49,
             65, 78, 72, 69, 52,
             60, 74, 81, 70, 77)
)

bump_chart(
  data  = league,
  x     = "season",
  y     = "points",
  group = "team",
  title = "League ranking by season"
)

# GDP per capita rank of the Asian "tiger" economies, highlighting two
library(gapminder)

countries <- c("Japan", "Singapore", "Hong Kong, China", "Taiwan",
               "Korea, Rep.", "Malaysia", "Thailand", "China")
gdp <- gapminder[gapminder$country %in% countries &
                 gapminder$year >= 1967, ]
gdp$country <- as.character(gdp$country)

bump_chart(
  data      = gdp,
  x         = "year",
  y         = "gdpPercap",
  group     = "country",
  highlight = c("Singapore", "Korea, Rep."),
  title     = "GDP per capita rank, 1967-2007"
)

Create a calendar heatmap.

Description

A calendar heatmap lays out one rounded square per day on a grid where each column is a week and each row is a day of the week, so a whole year fits in a single compact band. Each square is colored by the value recorded on that day using a sequential palette, which makes weekly rhythms, seasonal patterns, streaks and gaps easy to spot. The layout was popularised by GitHub's contribution graph and is the signature chart of the Frappe Charts library.

Month labels are drawn above the grid, weekday labels on its left, and a "Less ... More" legend underneath. When the data span several years, one panel is stacked per year. Days without data (and, when all values are non-negative, days with a value of zero) are drawn in emptyCol. Hovering over a day shows a tooltip with its value and date, and hovering over a legend square highlights the days that fall in that color bucket.

Usage

calendar_heatmap(
  data,
  date,
  value = NULL,
  aggregate = c("sum", "mean", "max", "min", "count"),
  weekStart = c("sunday", "monday"),
  colors = c("#c0ddf9", "#73b3f3", "#3886e1", "#17459e"),
  emptyCol = "#ebedf0",
  cellRadius = 2,
  cellGap = 3,
  valueLabel = NULL,
  valueFormat = ",.2~f",
  dateFormat = "%a %b %-d, %Y",
  showYearLabel = TRUE,
  showMonthLabels = TRUE,
  showWeekdayLabels = TRUE,
  labelFontSize = 10,
  legend = TRUE,
  legendLabels = c("Less", "More"),
  hover = TRUE,
  animate = TRUE,
  title = NULL,
  titleFontSize = 16,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "#555555",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG calendar heatmap.

Examples

# One year of simulated daily activity, busier on weekdays
set.seed(2026)
days <- seq(as.Date("2026-01-01"), as.Date("2026-12-31"), by = "day")
weekday <- !(format(days, "%u") %in% c("6", "7"))
activity <- data.frame(
  day     = days,
  commits = rpois(length(days), lambda = ifelse(weekday, 6, 1.5)) *
            rbinom(length(days), 1, 0.8)
)

calendar_heatmap(
  data       = activity,
  date       = "day",
  value      = "commits",
  title      = "Commits in 2026"
)

# Several years stacked, weeks starting on Monday, GitHub-like greens
set.seed(1)
days <- seq(as.Date("2021-01-01"), as.Date("2023-12-31"), by = "day")
season <- 10 + 8 * sin(2 * pi * as.numeric(format(days, "%j")) / 365)
rainfall <- data.frame(day = days, rain = pmax(0, rnorm(length(days), season, 6)))

calendar_heatmap(
  data        = rainfall,
  date        = "day",
  value       = "rain",
  weekStart   = "monday",
  colors      = c("#9be9a8", "#40c463", "#30a14e", "#216e39"),
  valueLabel  = "mm of rain",
  valueFormat = ",.1f",
  title       = "Daily rainfall, 2021-2023"
)

# Event log: one row per event, counted per day
set.seed(7)
events <- data.frame(
  when = as.Date("2025-01-01") + sample(0:364, 900, replace = TRUE)
)

calendar_heatmap(
  data       = events,
  date       = "when",
  valueLabel = "orders",
  colors     = c("#fde0c5", "#facba6", "#f59e72", "#e8603c", "#c0392b"),
  emptyCol   = "#f2f2f2",
  cellRadius = 3,
  title      = "Orders per day"
)

Create a chord diagram.

Description

A chord diagram shows the flows (or connections) between a set of entities arranged around a circle. Each entity is drawn as an outer arc whose length is proportional to the total flow it is involved in, and each pair of entities is linked by a ribbon whose width at either end is proportional to the flow in that direction. Chord diagrams are a compact way to display migration, trade, transitions between states, or any other square matrix of flows.

The look and feel are inspired by the chord chart of the Nivo visualization library, reimplemented in plain D3. The chart is interactive: hovering an arc (or its label) keeps the ribbons connected to that entity and fades out all the others, while a tooltip lists the entity's totals and its largest flows. Hovering a ribbon isolates it and shows the flow in both directions. Clicking an arc pins its highlight; clicking it again, or clicking the background, releases it.

Usage

chord_diagram(
  data,
  from = "from",
  to = "to",
  value = "value",
  directed = TRUE,
  colors = NULL,
  colorPalette = "Tableau10",
  ribbonColor = c("source", "target", "gradient"),
  sortGroups = c("none", "descending", "ascending"),
  padAngle = 0.03,
  innerRadiusRatio = 0.92,
  arcOpacity = 1,
  ribbonOpacity = 0.6,
  fadeOpacity = 0.1,
  strokeWidth = 0.5,
  showLabels = TRUE,
  rotateLabels = FALSE,
  labelFontSize = 11,
  labelOffset = 10,
  valueFormat = ",~f",
  hover = TRUE,
  animate = TRUE,
  title = NULL,
  titleFontSize = 16,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  textCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG chord diagram.

Examples

# Edge list: hypothetical migration flows between regions (thousands)
migration <- data.frame(
  from  = c("Africa", "Africa", "Africa", "Asia", "Asia", "Asia",
            "Europe", "Europe", "Latin America", "Latin America",
            "North America", "Oceania", "Asia", "Europe"),
  to    = c("Europe", "North America", "Asia", "Europe", "North America",
            "Oceania", "North America", "Asia", "North America", "Europe",
            "Europe", "Asia", "Africa", "Oceania"),
  value = c(420, 150, 90, 610, 520, 230, 180, 120, 870, 260,
            95, 40, 60, 75)
)

chord_diagram(
  data        = migration,
  from        = "from",
  to          = "to",
  value       = "value",
  valueFormat = ",.0f",
  title       = "Migration flows between regions (thousands)"
)

# Square matrix with dimnames: the classic hair-color transition data
hair <- matrix(
  c(11975,  5871, 8916, 2868,
     1951, 10048, 2060, 6171,
     8010, 16145, 8090, 8045,
     1013,   990,  940, 6907),
  nrow = 4, byrow = TRUE,
  dimnames = list(c("Black", "Blond", "Brown", "Red"),
                  c("Black", "Blond", "Brown", "Red"))
)

chord_diagram(
  data        = hair,
  colors      = c("#3b3b3b", "#f2c45a", "#8c5a3c", "#d0533a"),
  ribbonColor = "gradient",
  padAngle    = 0.05,
  valueFormat = ",.0f",
  title       = "Hair color flows"
)

# Undirected co-authorship links between departments, on a dark background
coauthors <- data.frame(
  from  = c("Biology", "Biology", "Biology", "Chemistry", "Chemistry",
            "Physics", "Physics", "Mathematics", "Computer Science",
            "Computer Science", "Medicine", "Medicine"),
  to    = c("Chemistry", "Medicine", "Computer Science", "Physics",
            "Medicine", "Mathematics", "Computer Science",
            "Computer Science", "Medicine", "Biology", "Chemistry",
            "Physics"),
  value = c(34, 51, 18, 27, 22, 30, 25, 41, 16, 12, 9, 6)
)

chord_diagram(
  data         = coauthors,
  directed     = FALSE,
  sortGroups   = "descending",
  colorPalette = "Set2",
  ribbonOpacity = 0.7,
  bgcol        = "#1e1e2e",
  textCol      = "#f0f0f0",
  title        = "Joint publications between departments"
)

Create a dumbbell chart.

Description

A dumbbell chart (also called a connected dot plot) displays two values per category as dots connected by a horizontal line. It is ideal for showing the change or gap between two groups, time points, or conditions across multiple categories — a clean alternative to grouped bar charts favoured by outlets such as the New York Times and The Economist.

Usage

dumbbell_chart(
  data,
  x1,
  x2,
  y,
  x1Label = NULL,
  x2Label = NULL,
  col1 = "steelblue",
  col2 = "crimson",
  lineCol = "#aaaaaa",
  lineWidth = 2,
  circleRadius = 6,
  sort = "none",
  xtitle = NULL,
  xtitleFontSize = 13,
  title = NULL,
  titleFontSize = 16,
  xFontSize = 11,
  yFontSize = 11,
  legend = TRUE,
  legendFontSize = 11,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG dumbbell chart.

Examples

# Life expectancy change between 1952 and 2007 for selected countries
life_exp <- data.frame(
  country = c("Brazil", "China", "Egypt", "India",
              "Japan", "Mexico", "Nigeria", "Turkey"),
  year_1952 = c(50.9, 44.0, 41.9, 37.4, 63.0, 50.8, 36.3, 43.6),
  year_2007 = c(72.4, 72.9, 71.3, 64.7, 82.6, 76.2, 46.9, 71.8)
)

dumbbell_chart(
  data     = life_exp,
  x1       = "year_1952",
  x2       = "year_2007",
  y        = "country",
  x1Label  = "1952",
  x2Label  = "2007",
  title    = "Life expectancy: 1952 vs 2007",
  xtitle   = "Life expectancy (years)",
  sort     = "ascending"
)

# Comparing city vs highway fuel economy by car class
library(ggplot2)
library(dplyr)

mpg_summary <- summarise(group_by(mpg, class),
  city = mean(cty), highway = mean(hwy))

dumbbell_chart(
  data    = mpg_summary,
  x1      = "city",
  x2      = "highway",
  y       = "class",
  x1Label = "City",
  x2Label = "Highway",
  col1    = "steelblue",
  col2    = "darkorange",
  title   = "City vs highway fuel economy by class",
  xtitle  = "Miles per gallon",
  sort    = "ascending"
)

Display an Animated Flame Visualization

Description

Creates an animated flame SVG visualization whose size and color gradient can be customized. The flame grows or shrinks based on the intensity parameter, with smooth pulsing and wobbling animation.

Usage

flame(
  intensity = 50,
  flameGradientColors = c("white", "yellow", "darkred"),
  flameOutline = "darkred",
  bgcol = "white",
  width = NULL,
  height = NULL
)

Arguments

Value

An r2d3 object displaying the animated flame visualization.

Examples

flame(intensity = 5)
flame(
  intensity = 50,
  flameGradientColors = c("lightblue", "blue", "darkblue")
)

Display a Rotating Flower Visualization

Description

Display a Rotating Flower Visualization

Usage

flower(
  petalCount = 6,
  petalLength = 100,
  petalWidth = 60,
  petalColor = "lightpink",
  petalStroke = "deeppink",
  centerRadius = 20,
  centerColor = "gold",
  centerStroke = "darkorange",
  centerText = NULL,
  centerTextSize = 16,
  centerTextColor = "black",
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  rotationSpeed = 2,
  width = NULL,
  height = NULL
)

Arguments

Value

An animated rotating flower SVG.

Examples

flower(
  petalCount = 5,
  petalColor = "plum",
  rotationSpeed = 1.5
)

Create a gauge (speedometer) chart.

Description

A gauge (speedometer) chart displays a single quantitative measure against colored zones (warning and danger) with a needle indicator. By default the needle animates on load, sweeping up from the minimum to the value and settling with a small wobble, like a car dashboard.

Usage

gauge_chart(
  value,
  min = 0,
  max = 100,
  title = "",
  warningZone = NULL,
  warningColor = "orange",
  dangerZone = NULL,
  dangerColor = "red",
  arcBgColor = "#ddd",
  needleColor = "crimson",
  pivotColor = "steelblue",
  titleFontSize = 16,
  valueFontSize = 14,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  animate = TRUE,
  animationDuration = 1500,
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG gauge chart.

Examples

# Basic usage
gauge_chart(value = 8, title = "Memory")

# Custom warning and danger thresholds
gauge_chart(
  value = 72,
  min = 0,
  max = 100,
  title = "CPU Load",
  warningZone  = 60,
  warningColor = "orange",
  dangerZone   = 80,
  dangerColor  = "red"
)

# Non-percentage range (response time in ms)
gauge_chart(
  value = 530,
  min = 0,
  max = 1000,
  title = "Response (ms)",
  warningZone  = 400,
  dangerZone   = 700
)

Visualize a Glass Filling with Water using D3

Description

This function generates an SVG visualization of a glass filled with water to a specified level, rendered via D3 using the r2d3 package. The fill level, appearance of the glass, and label settings can be customized.

Usage

glass_fill(
  fill_level = 0.65,
  glassWidth = 80,
  glassHeight = 200,
  strokeColor = "#555",
  strokeWidth = 3,
  fillColor = "skyblue",
  renderFillLabel = TRUE,
  labelFontSize = "16px",
  titleText = "Fill level",
  labelColor = "#333",
  titleColor = "#333",
  titleFontSize = "14px",
  font = "Verdana, Geneva, Tahoma, sans-serif"
)

Arguments

Value

An interactive D3 visualization rendered in the RStudio Viewer or web browser.

Examples

glass_fill(fill_level = 0.75)
glass_fill(fill_level = 0.3, fillColor = "lightblue", titleText = "Water Intake")

Visualize a Heart Filling with Color using D3

Description

This function renders a heart-shaped SVG graphic that fills from the bottom up based on the provided level. The appearance of the heart and the optional label can be fully customized. It uses the 'r2d3' package to render the visualization with D3.js.

Usage

heart_fill(
  fill_level = 0.65,
  heartSize = 150,
  strokeColor = "#C00",
  strokeWidth = 4,
  fillColor = "red",
  renderFillLabel = TRUE,
  labelColor = "#333",
  labelFontSize = "16px",
  titleText = "Fill level",
  titleColor = "#333",
  titleFontSize = "14px",
  font = "Verdana, Geneva, Tahoma, sans-serif"
)

Arguments

Value

An interactive D3 heart fill visualization rendered in the RStudio Viewer or web browser.

Examples

heart_fill(fill_level = 0.9)
heart_fill(
  fill_level = 0.4,
  fillColor = "pink",
  labelColor = "#C00",
  strokeColor = "#900"
)

Create a hexbin plot.

Description

A hexbin plot is a two-dimensional histogram: the x/y plane is tiled with a grid of regular hexagons, every observation is assigned to the hexagon it falls into, and each hexagon is colored by the number of observations it contains. It is a readable alternative to a scatter plot when there are too many points to see individually (overplotting), because dense regions remain distinguishable instead of turning into a solid blob.

The look is inspired by the hexbin transform of the Observable Plot library: light gridlines, a sequential color scale with a continuous legend, and an optional mode where the hexagon size (area) also encodes the count. Binning is done in pixel space, so the hexagons are always regular regardless of the units of x and y.

The plot is interactive: hovering a hexagon outlines it, marks its count on the color legend and (when tooltip = TRUE) shows a tooltip with the bin count, its share of all observations, and the x/y range covered by the hexagon.

Usage

hexbin_plot(
  data,
  x,
  y,
  binWidth = 12,
  sizeByCount = FALSE,
  colorPalette = "YlGnBu",
  reverseColors = FALSE,
  colorScale = c("linear", "sqrt", "log"),
  legend = TRUE,
  legendTitle = "Count",
  tooltip = TRUE,
  hover = TRUE,
  animate = TRUE,
  opacity = 1,
  stroke = "white",
  strokeWidth = 0.5,
  grid = TRUE,
  gridCol = "#e5e5e5",
  xtitle = NULL,
  ytitle = NULL,
  xtitleFontSize = 13,
  ytitleFontSize = 13,
  title = NULL,
  titleFontSize = 16,
  xFontSize = 11,
  yFontSize = 11,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG hexbin plot.

Examples

# Old Faithful geyser: eruption duration vs waiting time
hexbin_plot(
  data     = faithful,
  x        = "waiting",
  y        = "eruptions",
  binWidth = 14,
  xtitle   = "Waiting time to next eruption (min)",
  ytitle   = "Eruption duration (min)",
  title    = "Old Faithful eruptions"
)

# 10,000 diamonds: carat vs price, with a log color scale
library(ggplot2) # for the diamonds dataset
set.seed(1)
dsample <- diamonds[sample(nrow(diamonds), 10000), ]

hexbin_plot(
  data         = dsample,
  x            = "carat",
  y            = "price",
  binWidth     = 8,
  colorPalette = "Viridis",
  colorScale   = "log",
  xtitle       = "Carat",
  ytitle       = "Price (USD)",
  title        = "Diamond price by carat"
)

# Hexagon size encodes the count as well as the color
set.seed(42)
pts <- data.frame(
  a = c(rnorm(1500, 0, 1), rnorm(800, 3, 0.6)),
  b = c(rnorm(1500, 0, 1), rnorm(800, 2, 0.6))
)

hexbin_plot(
  data         = pts,
  x            = "a",
  y            = "b",
  binWidth     = 13,
  sizeByCount  = TRUE,
  colorPalette = "Magma",
  reverseColors = TRUE,
  title        = "Two bivariate normal clusters"
)

Create a histogram.

Description

Create a histogram.

Usage

histogram(
  x,
  bins = 30,
  fill = "crimson",
  xFontSize = 10,
  yFontSize = 10,
  xticks = NULL,
  yticks = NULL,
  xtitle = NULL,
  xtitleFontSize = 16,
  ytitle = NULL,
  ytitleFontSize = 16,
  title = NULL,
  titleFontSize = 22,
  stroke = "crimson",
  strokeWidth = NULL,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "#CAD0D3",
  opacity = 1,
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

A SVG histogram.

Examples

histogram(
  x = mtcars$mpg,
  bins = 20,
  fill = "crimson",
  stroke = "white",
  strokeWidth = 1,
  title = "Distribution of the hwy variable",
  width = "20",
  height = "10"
)

Create a horizontal bar chart

Description

Create a horizontal bar chart

Usage

horz_bar_chart(
  data,
  label,
  value,
  fill = "crimson",
  sort = "none",
  paddingWidth = 0.1,
  stroke = NULL,
  strokeWidth = 1,
  bgcol = "#CAD0D3",
  valueTicks = NULL,
  valueFontSize = 10,
  labelFontSize = 10,
  valueTitle = NULL,
  valueTitleFontSize = 14,
  labelTitle = NULL,
  labelTitleFontSize = 14,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  title = NULL,
  titleFontSize = 20,
  opacity = 1,
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

A SVG horizontal bar chart.

Examples

library(ggplot2) # needed for the mpg data frame
library(dplyr) # needed for the data wrangling process

mpg %>% group_by(manufacturer) %>%
 summarise(median_hwy = median(hwy)) %>%
 horz_bar_chart(
   label = "manufacturer",
   value = "median_hwy",
   sort = "ascending"
 )

Create a horizontal lollipop chart

Description

Create a horizontal lollipop chart

Usage

horz_lollipop(
  data,
  label,
  value,
  sort = "none",
  bgcol = "white",
  valueTicks = NULL,
  labelTicks = NULL,
  valueFontSize = 12,
  labelFontSize = 12,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  valueTitle = NULL,
  valueTitleFontSize = 14,
  labelTitle = NULL,
  labelTitleFontSize = 14,
  title = NULL,
  titleFontSize = 20,
  lineStroke = "maroon",
  lineStrokeWidth = 4,
  circleFill = "lime",
  circleStroke = "lime",
  circleStrokeWidth = 1,
  circleRadius = 5,
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

A SVG horizontal lollipop chart.

Create an interactive time series line chart

Description

Draws one or several time series as lines, with the interactions that people who work with time series expect: a crosshair tooltip that shows every series at the hovered date, a clickable legend, direct labels at the end of each line, range buttons (1M, 6M, YTD, 1Y, ...), a navigator with a brush under the chart, drag-to-zoom and double-click to reset. The y axis rescales to the data in view whenever you zoom.

Usage

line_chart(
  data,
  x = NULL,
  y = NULL,
  group = NULL,
  lower = NULL,
  upper = NULL,
  colors = NULL,
  stroke = NULL,
  strokeWidth = 2,
  curve = "curveLinear",
  opacity = 1,
  area = FALSE,
  points = FALSE,
  yScale = c("linear", "log"),
  yZero = NULL,
  transform = c("none", "index", "percent"),
  indexBase = 100,
  rollmean = NULL,
  showRaw = TRUE,
  events = NULL,
  bands = NULL,
  hlines = NULL,
  legend = NULL,
  endLabels = NULL,
  tooltip = TRUE,
  navigator = TRUE,
  rangeButtons = TRUE,
  zoom = TRUE,
  animate = TRUE,
  valueFormat = NULL,
  valuePrefix = "",
  valueSuffix = "",
  dateFormat = NULL,
  xticks = NULL,
  yticks = NULL,
  xtitle = NULL,
  xtitleFontSize = 13,
  ytitle = NULL,
  ytitleFontSize = 13,
  title = NULL,
  titleFontSize = 20,
  subtitle = NULL,
  subtitleFontSize = 13,
  xFontSize = 11,
  yFontSize = 11,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  gridCol = "#e9e9e9",
  axisCol = "#333333",
  width = NULL,
  height = NULL
)

Arguments

Details

Series can be given in three ways:

• one column: y = "value";

• wide data: several columns, y = c("a", "b", "c");

• long data: one value column and a group column that names the series.

A ts or mts object (for example AirPassengers or EuStockMarkets) can also be passed directly as data, in which case x and y are not needed. Monthly, quarterly and yearly series get exact calendar dates; for other frequencies (for example the 260 trading days a year of EuStockMarkets) the dates are interpolated from the decimal time index, so pass a data frame with a real date column when exact dates matter.

Time-series extras: transform = "index" rebases every series to 100 at the start of the visible range (or "percent" for the change since then), rollmean overlays a moving average, lower / upper draw a confidence or forecast ribbon, bands shade periods such as recessions, events mark dates with a vertical line and hlines draw reference values.

Missing values (NA) leave a gap in the line.

Value

An interactive SVG line chart.

Examples

# A ts object can be passed directly
line_chart(AirPassengers, y = "passengers", title = "Airline passengers")

# The classic data frame interface still works
airpassengers <- data.frame(
  passengers = as.matrix(AirPassengers),
  date = zoo::as.Date(time(AirPassengers))
)
line_chart(data = airpassengers, x = "date", y = "passengers")

# Several series from an mts object, rebased to 100 at the start of the
# visible range: zoom with the range buttons or the navigator and the
# comparison is recomputed
line_chart(
  EuStockMarkets,
  transform = "index",
  title = "European stock indices",
  subtitle = "Daily closing prices, rebased to 100"
)

# Long data with a group column, area fill and shaded recessions
library(ggplot2) # for the economics data set

recessions <- data.frame(
  start = as.Date(c("1969-12-01", "1973-11-01", "1980-01-01", "1981-07-01",
                    "1990-07-01", "2001-03-01", "2007-12-01")),
  end   = as.Date(c("1970-11-01", "1975-03-01", "1980-07-01", "1982-11-01",
                    "1991-03-01", "2001-11-01", "2009-06-01"))
)

line_chart(
  economics,
  x = "date",
  y = "unemploy",
  area = TRUE,
  bands = recessions,
  valueFormat = ",.0f",
  valueSuffix = "k",
  title = "US unemployment",
  subtitle = "Thousands of persons, recessions shaded"
)

# A forecast with its prediction interval
fit <- HoltWinters(log(AirPassengers))
fc <- predict(fit, n.ahead = 24, prediction.interval = TRUE)
fc_dates <- zoo::as.Date(time(fc))

passengers <- rbind(
  data.frame(date = zoo::as.Date(time(AirPassengers)),
             value = as.numeric(AirPassengers), lo = NA, hi = NA,
             series = "Observed"),
  data.frame(date = fc_dates, value = exp(fc[, "fit"]),
             lo = exp(fc[, "lwr"]), hi = exp(fc[, "upr"]),
             series = "Forecast")
)

line_chart(
  passengers,
  x = "date", y = "value", group = "series",
  lower = "lo", upper = "hi",
  colors = c(Observed = "#4269d0", Forecast = "#ff725c"),
  events = c("Forecast starts" = as.Date("1961-01-01")),
  valueFormat = ",.0f",
  title = "Airline passengers: Holt-Winters forecast"
)

Create a liquid fill gauge chart.

Description

A liquid fill gauge represents a single value as a circle filled with an animated liquid up to the corresponding level. The fill rises smoothly on load, two wave layers give the water a natural depth, and the label text changes color at the water surface — appearing in textColor above the water and in waveTextColor below it.

Usage

liquid_chart(
  value,
  label = NULL,
  fillColor = "steelblue",
  circleColor = NULL,
  circleThickness = 0.05,
  textColor = NULL,
  waveTextColor = "white",
  textSize = 0.38,
  waveAmplitude = 0.09,
  waveCount = 2,
  waveSpeed = 1,
  title = NULL,
  titleFontSize = 14,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  width = NULL,
  height = NULL
)

Arguments

Value

An animated SVG liquid fill gauge.

Examples

# Basic usage — 55% fill
liquid_chart(value = 0.55)

# Custom color and label
liquid_chart(
  value     = 0.72,
  label     = "72%",
  fillColor = "#e67e22",
  title     = "Disk usage"
)

# Low fill with a raw value label
liquid_chart(
  value         = 0.28,
  label         = "28%",
  fillColor     = "tomato",
  circleColor   = "tomato",
  textColor     = "tomato",
  waveTextColor = "white"
)

Create a lollipop chart

Description

Create a lollipop chart

Usage

lollipop_chart(
  data,
  x,
  y,
  sort = "none",
  bgcol = "white",
  xticks = NULL,
  yticks = NULL,
  xFontSize = 12,
  yFontSize = 12,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  xtitle = NULL,
  xtitleFontSize = 14,
  ytitle = NULL,
  ytitleFontSize = 14,
  title = NULL,
  titleFontSize = 20,
  lineStroke = "maroon",
  lineStrokeWidth = 4,
  circleFill = "lime",
  circleStroke = "lime",
  circleStrokeWidth = 1,
  circleRadius = 10,
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

A SVG lollipop chart.

Examples

library(ggplot2) # needed for the mpg data frame
library(dplyr) # needed for data wrangling

mpg %>% group_by(drv) %>%
  summarise(median_cty = median(cty)) %>%
  lollipop_chart(
    x = "drv",
    y = "median_cty",
    sort = "ascending",
    xtitle = "drv variable",
    ytitle = "median cty",
    title = "Median cty per drv"
  )

Create a mosaic (Marimekko) chart.

Description

A mosaic chart, also called a Marimekko or Mekko chart, shows how two categorical variables relate to each other and to the whole. Each column stands for one category of x, and its width is that category's share of the grand total. Each column is then split into stacked segments, one per category of fill, normalised to 100% of the column. So the area of every cell is proportional to its count (or summed value), and you can read both marginal and conditional shares in a single picture. The look follows the mosaic example from the Vega-Lite gallery: white gaps between cells, percentage axes, column labels across the top and in-cell labels wherever they fit.

Hovering a cell highlights it and shows a tooltip with its count, the share of its column in the total and its share within the column. Hovering a legend entry highlights every cell of that fill category, and hovering a column label highlights the whole column.

Column and segment order follow the factor levels of x and fill. Character columns are ordered alphabetically and numeric columns in increasing order.

Usage

mosaic_chart(
  data,
  x,
  fill,
  value = NULL,
  colors = NULL,
  gap = 2,
  labelType = c("percent", "count", "both", "none"),
  valueFormat = ",.0f",
  labelFontSize = 11,
  labelCol = NULL,
  columnFontSize = 12,
  hover = TRUE,
  animate = TRUE,
  legend = TRUE,
  legendTitle = NULL,
  legendFontSize = 11,
  xtitle = NULL,
  ytitle = NULL,
  axisTitleFontSize = 12,
  xFontSize = 10,
  yFontSize = 10,
  title = NULL,
  titleFontSize = 16,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG mosaic chart.

Examples

# Survival on the Titanic by passenger class (pre-aggregated counts)
titanic <- as.data.frame(Titanic)

mosaic_chart(
  data   = titanic,
  x      = "Class",
  fill   = "Survived",
  value  = "Freq",
  colors = c("#e45756", "#4c78a8"),
  xtitle = "Share of passengers and crew",
  ytitle = "Share within class",
  title  = "Who survived the Titanic?"
)

# Counting rows: gears by number of cylinders. Factor levels set the order
# of both the columns and the stacked segments.
cars <- mtcars
cars$cyl  <- factor(cars$cyl,  levels = c(8, 6, 4),
                    labels = c("8 cyl", "6 cyl", "4 cyl"))
cars$gear <- factor(cars$gear, levels = c(3, 4, 5),
                    labels = c("3 gears", "4 gears", "5 gears"))

mosaic_chart(
  data        = cars,
  x           = "cyl",
  fill        = "gear",
  labelType   = "both",
  legendTitle = "Gears",
  title       = "Motor Trend cars: gears by cylinders"
)

# Hair and eye color of statistics students (counts are summed over Sex)
hair_eye <- as.data.frame(HairEyeColor)
hair_eye$Eye <- factor(hair_eye$Eye,
                       levels = c("Brown", "Hazel", "Green", "Blue"))

mosaic_chart(
  data   = hair_eye,
  x      = "Hair",
  fill   = "Eye",
  value  = "Freq",
  colors = c("#8c564b", "#bcbd22", "#2ca02c", "#1f77b4"),
  gap    = 3,
  xtitle = "Hair color",
  title  = "Hair and eye color of 592 students"
)

Display a Parliament Chart

Description

Display a Parliament Chart

Usage

parliament_chart(
  data,
  categorical_column,
  numerical_column,
  seatSize = 6,
  padding = 2,
  maxRows = NULL,
  title = NULL,
  titleFontSize = 22,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "#CAD0D3",
  width = NULL,
  height = NULL
)

Arguments

Value

A D3-rendered Parliament chart

Examples

political_results_example <- data.frame(
    political_party = c("SDP", "CDU", "Linke"),
    number_of_seats = c(200, 40, 30)
)
parliament_chart(
  data = political_results_example,
  categorical_column = "political_party",
  numerical_column = "number_of_seats",
  title = "German Bundestag (results not real, just an example)",
  seatSize = 10,
  bgcol = "#fefefe"
)

Create a pie chart

Description

Create a pie chart

Usage

pie_chart(
  data,
  value,
  label,
  colorCategory = "Paired",
  innerRadius = 0,
  outerRadius = "auto",
  padRadius = 0,
  padAngle = NULL,
  cornerRadius = 0,
  labelFont = "sans-serif",
  title = NULL,
  titleFontSize = 22,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  opacity = 1,
  labelHeight = 18,
  width = NULL,
  height = NULL
)

Arguments

Value

A SVG pie chart

Examples

library(dplyr) # needed for the starwars data frame

# starwars is part of the dplyr data frame
mini_starwars <- starwars %>% tidyr::drop_na(mass) %>%
  sample_n(size = 5) # getting 5 random values

pie_chart(
  data = mini_starwars,
  value = "mass",
  label = "name"
)

Visualize Plant Growth Using D3

Description

This function creates a plant growth meter visualization.

Usage

plant_growth(
  fill_level = 0.5,
  potWidth = 100,
  potHeight = 40,
  plantMaxHeight = 150,
  stemColor = "#228B22",
  potColor = "#8B4513",
  flowerColor = "#FF69B4",
  strokeColor = "#333",
  strokeWidth = 2,
  renderFillLabel = TRUE,
  titleText = "Plant Growth",
  titleColor = "#333",
  titleFontSize = "14px",
  font = "sans-serif"
)

Arguments

Create a pulse grid.

Description

'pulse_grid()' creates a tiled grid where each cell color reflects a numeric value and the cells gently pulse according to their intensity.

Usage

pulse_grid(
  data,
  x,
  y,
  value,
  low = "#E0FBFC",
  high = "#0B525B",
  stroke = "white",
  strokeWidth = 1,
  cellPadding = 0.12,
  cornerRadius = 10,
  pulse = TRUE,
  pulseStrength = 0.18,
  showValues = TRUE,
  digits = 1,
  valueFontSize = 12,
  xFontSize = 12,
  yFontSize = 12,
  title = NULL,
  titleFontSize = 22,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "#0F172A",
  labelColor = "#F8FAFC",
  titleColor = "#F8FAFC",
  width = NULL,
  height = NULL
)

Arguments

Value

A SVG pulse grid.

Examples

pulse_data <- expand.grid(
  hour = c("09:00", "12:00", "15:00", "18:00"),
  team = c("North", "South", "East")
)
pulse_data$load <- c(0.35, 0.72, 0.56, 0.91,
                     0.41, 0.67, 0.83, 0.58,
                     0.28, 0.49, 0.77, 0.63)

pulse_grid(
  data = pulse_data,
  x = "hour",
  y = "team",
  value = "load",
  title = "Team Activity Pulse Grid",
  low = "#D9F0FF",
  high = "#EE6C4D",
  bgcol = "#0B132B"
)

Create a radar chart.

Description

A radar chart (also called a spider or web chart) compares several quantitative variables at once by placing each one on its own spoke radiating from a common center. Each series (a row of the data) is drawn as a closed, translucent polygon joining its values on every spoke, so series can be compared by their overall shape and footprint. Concentric grid rings mark the scale. The look and feel is inspired by the radar chart of the Chart.js JavaScript library, reimplemented here in plain D3.

The chart is interactive: the polygons grow out from the center on first render, hovering a point shows a tooltip with its value while the other series fade, hovering a polygon focuses that series, and clicking a legend entry toggles the corresponding series on or off.

Usage

radar_chart(
  data,
  group,
  axes = NULL,
  axisLabels = NULL,
  maxValue = NULL,
  scaleEachAxis = FALSE,
  levels = 5,
  gridShape = c("polygon", "circle"),
  colors = NULL,
  fillOpacity = 0.2,
  lineWidth = 2,
  pointRadius = 3,
  showTicks = TRUE,
  valueFormat = ",",
  gridCol = "#dddddd",
  hover = TRUE,
  animate = TRUE,
  legend = TRUE,
  legendFontSize = 12,
  labelFontSize = 12,
  tickFontSize = 10,
  title = NULL,
  titleFontSize = 16,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG radar chart.

Examples

# Skill ratings of three football players (shared 0-100 scale)
players <- data.frame(
  player    = c("Ana", "Ben", "Chloe"),
  speed     = c(88, 72, 65),
  power     = c(60, 91, 70),
  passing   = c(79, 58, 93),
  dribbling = c(92, 64, 81),
  defending = c(45, 83, 74),
  stamina   = c(70, 77, 88)
)

radar_chart(
  data     = players,
  group    = "player",
  axes     = c("speed", "power", "passing", "dribbling",
               "defending", "stamina"),
  maxValue = 100,
  title    = "Player skill ratings"
)

# Variables measured in different units: give every axis its own scale
cyl_means <- aggregate(cbind(mpg, hp, wt, qsec, disp) ~ cyl, data = mtcars,
                  FUN = mean)
cyl_means$cyl <- paste(cyl_means$cyl, "cylinders")

radar_chart(
  data          = cyl_means,
  group         = "cyl",
  axisLabels    = c("Miles/gallon", "Horsepower", "Weight (1000 lbs)",
                    "1/4 mile time (s)", "Displacement (cu.in.)"),
  scaleEachAxis = TRUE,
  levels        = 4,
  gridShape     = "circle",
  valueFormat   = ".1f",
  title         = "mtcars averages by number of cylinders"
)

Create a radial bar chart.

Description

A radial bar chart (sometimes called a multi-ring progress chart or "activity rings") draws each category as a concentric circular track. A faint background track shows the full scale, and a colored arc with rounded ends sweeps along it in proportion to the category's value relative to max. It is a compact way to compare a handful of progress or completion rates. The look is inspired by the radialBar chart of the ApexCharts JavaScript library, reimplemented here in plain D3.

The first row of data is drawn as the outermost ring. The arcs sweep in on load, each ring is labelled at the start of its track, and the center shows the average (or total) of the values. Hovering a ring highlights it, replaces the center text with that ring's name and value, and shows a tooltip.

By default the rings cover 270 degrees (startAngle = 0, endAngle = 270), leaving the top-left quadrant free for the ring labels. Angles are in degrees, measured clockwise from 12 o'clock. Use endAngle = 360 for full circles (labels then follow the curve of each track), or for example startAngle = -90, endAngle = 90 for a semicircle gauge.

Usage

radial_bar_chart(
  data,
  label,
  value,
  max = 100,
  startAngle = 0,
  endAngle = 270,
  colors = NULL,
  trackCol = "#ececec",
  hollow = 0.35,
  ringGap = 0.3,
  roundedCaps = TRUE,
  showLabels = TRUE,
  showValues = TRUE,
  labelFontSize = 12,
  centerValue = c("average", "total", "none"),
  centerLabel = NULL,
  centerFontSize = NULL,
  valueFormat = ",.0f",
  suffix = NULL,
  hover = TRUE,
  animate = TRUE,
  duration = 1200,
  title = NULL,
  titleFontSize = 16,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  textCol = "#333333",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG radial bar chart.

Examples

# Share of users active on each platform (percentages)
platforms <- data.frame(
  platform = c("Vimeo", "Messenger", "Facebook", "LinkedIn"),
  share    = c(76, 67, 61, 90)
)

radial_bar_chart(
  data  = platforms,
  label = "platform",
  value = "share",
  title = "Weekly active users (share of total)"
)

# Full circles, total in the center, custom colors and a custom maximum
sales <- data.frame(
  region = c("North", "South", "East", "West", "Central"),
  units  = c(420, 310, 275, 190, 120)
)

radial_bar_chart(
  data        = sales,
  label       = "region",
  value       = "units",
  max         = 500,
  endAngle    = 360,
  centerValue = "total",
  centerLabel = "Units sold",
  colors      = c("#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e"),
  title       = "Units sold by region (target: 500)"
)

# Gauge layout open at the bottom, without values in the ring labels
radial_bar_chart(
  data       = platforms,
  label      = "platform",
  value      = "share",
  startAngle = -135,
  endAngle   = 135,
  hollow     = 0.45,
  showValues = FALSE,
  trackCol   = "#f2f2f2"
)

Create a ridgeline plot (joy plot).

Description

A ridgeline plot (also known as a "joy plot") shows the distribution of a numeric variable for several groups as a stack of partially overlapping density curves that share a common x-axis. The nickname comes from the cover of Joy Division's 1979 album Unknown Pleasures, itself a reproduction of a 1970 plot of successive radio pulses from the first discovered pulsar. The format was popularised by Claus Wilke's ggridges package and by FiveThirtyEight and The Washington Post graphics desks.

Ridgeline plots are ideal for comparing the shape of many distributions at once (e.g. across months, categories, or experimental conditions) while using far less vertical space than a set of separate histograms.

The kernel density estimates are computed in the browser. Ridges are drawn from the back (top) to the front (bottom), so lower ridges overlap the ones above them. Hovering a ridge (or its label) brings it to the front, dims the others, and shows a small label with the group's number of observations and mean.

Usage

ridgeline_plot(
  data,
  x,
  group,
  bandwidth = NULL,
  kernel = c("gaussian", "epanechnikov"),
  overlap = 1.5,
  fill = "steelblue",
  fillOpacity = 0.8,
  lineCol = "#333333",
  lineWidth = 1,
  sort = c("none", "mean", "median"),
  stat = c("none", "mean", "median"),
  statColor = "#d62728",
  hover = TRUE,
  xtitle = NULL,
  xtitleFontSize = 13,
  title = NULL,
  titleFontSize = 16,
  xFontSize = 11,
  yFontSize = 11,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG ridgeline plot.

Examples

# Simulated reaction times for five experimental conditions
set.seed(2026)
reaction <- data.frame(
  time = c(rnorm(200, 300, 40), rnorm(200, 340, 50),
           rnorm(200, 380, 35), c(rnorm(100, 360, 25), rnorm(100, 460, 30)),
           rnorm(200, 420, 60)),
  condition = rep(c("Baseline", "Noise", "Fatigue", "Dual task", "Night"),
                  each = 200)
)

ridgeline_plot(
  data   = reaction,
  x      = "time",
  group  = "condition",
  sort   = "mean",
  stat   = "median",
  title  = "Reaction time by condition",
  xtitle = "Reaction time (ms)"
)

# Highway fuel economy by vehicle class
library(ggplot2) # for the mpg dataset

ridgeline_plot(
  data        = mpg,
  x           = "hwy",
  group       = "class",
  sort        = "median",
  fill        = "#2c7fb8",
  lineCol     = "white",
  fillOpacity = 0.9,
  overlap     = 2,
  title       = "Highway fuel economy by vehicle class",
  xtitle      = "Highway miles per gallon"
)

# One color per group with a named vector, Epanechnikov kernel
ridgeline_plot(
  data    = iris,
  x       = "Petal.Length",
  group   = "Species",
  kernel  = "epanechnikov",
  fill    = c(setosa = "#66c2a5", versicolor = "#fc8d62",
              virginica = "#8da0cb"),
  overlap = 1,
  stat    = "mean",
  xtitle  = "Petal length (cm)"
)

Create a Nightingale rose chart.

Description

A Nightingale rose (also called a coxcomb or polar area chart) divides a circle into equal-angle wedges, one per category, and encodes each value by how far its wedge extends from the center. The form is credited to Florence Nightingale, who used it in 1858 to show that far more British soldiers in the Crimean War died of preventable disease than of wounds.

Radii follow a square-root scale so that the area of each wedge, not its length, is proportional to the value, keeping the visual honest. When a group column is supplied, each wedge is split into stacked segments whose areas are again proportional to their values. Dashed radial grid circles carry value labels, and category labels sit outside the wedges. The look is inspired by the polar-interval "rose" chart of the AntV G2 library, reimplemented in plain D3.

The chart is interactive: wedges grow out from the center on first render, hovering a wedge pops it outward and shows a tooltip (with a per-group breakdown when stacked), and, when stacked, hovering a legend entry focuses that group while clicking it toggles the group on or off.

Usage

rose_chart(
  data,
  category,
  value,
  group = NULL,
  colors = NULL,
  sort = c("none", "desc", "asc"),
  innerRadius = 0,
  startAngle = 0,
  padAngle = 0,
  cornerRadius = 0,
  opacity = 0.9,
  strokeCol = "white",
  strokeWidth = 1,
  gridLines = 4,
  gridCol = "#e0e0e0",
  gridFontSize = 10,
  showLabels = TRUE,
  labelFontSize = 11,
  valueFormat = ",.0f",
  hover = TRUE,
  animate = TRUE,
  legend = TRUE,
  legendFontSize = 11,
  title = NULL,
  titleFontSize = 16,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG Nightingale rose chart.

Examples

# Florence Nightingale's data: deaths in the British army in the East,
# April 1854 to March 1855, by cause (as in the HistData package)
mon <- c("Apr 1854", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov",
            "Dec", "Jan 1855", "Feb", "Mar")
crimea <- data.frame(
  month = factor(rep(mon, times = 3), levels = mon),
  cause = factor(rep(c("Preventable disease", "Wounds", "Other causes"),
                     each = 12),
                 levels = c("Preventable disease", "Wounds", "Other causes")),
  deaths = c(1, 12, 11, 359, 828, 788, 503, 844, 1725, 2761, 2120, 1205,
             0, 0, 0, 0, 1, 81, 132, 287, 114, 83, 42, 32,
             5, 9, 6, 23, 30, 70, 128, 106, 131, 324, 361, 172)
)

rose_chart(
  data     = crimea,
  category = "month",
  value    = "deaths",
  group    = "cause",
  colors   = c("#7fa7c9", "#e0645c", "#4d4d4d"),
  title    = "Causes of death in the army in the East, 1854-55",
  height   = 560
)

# A simple rose: one wedge per category, sorted by value
rose_chart(
  data     = data.frame(state = rownames(USArrests)[1:12],
                        murder = USArrests$Murder[1:12]),
  category = "state",
  value    = "murder",
  sort     = "desc",
  valueFormat = ".1f",
  title    = "Murder arrests per 100,000 residents (1973)"
)

# Donut-style rose with a hole, gaps and rounded corners
rose_chart(
  data         = as.data.frame(table(cyl = mtcars$cyl, gear = mtcars$gear)),
  category     = "gear",
  value        = "Freq",
  group        = "cyl",
  innerRadius  = 0.25,
  padAngle     = 0.03,
  cornerRadius = 4,
  title        = "Cars by number of gears and cylinders"
)

Create a scatter plot.

Description

Create a scatter plot.

Usage

scatter_plot(
  data,
  x,
  y,
  col = "crimson",
  size = 2,
  xticks = NULL,
  yticks = NULL,
  xtitle = NULL,
  xtitleFontSize = 16,
  ytitle = NULL,
  ytitleFontSize = 16,
  title = NULL,
  titleFontSize = 22,
  stroke = NULL,
  strokeWidth = NULL,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "#CAD0D3",
  opacity = 1,
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

A SVG scatter plot.

Examples

scatter_plot(
  data = mtcars,
  x = "mpg",
  y = "wt"
  )

Create a slope chart.

Description

A slope chart (or slopegraph) compares two values per category by placing them on two vertical axes, typically "before" and "after" or two years, and connecting them with a straight line. The steepness and direction of each line show at a glance which categories rose, which fell, and how their ranking changed. The form was popularised by Edward Tufte in The Visual Display of Quantitative Information (1983) and is a staple of the New York Times graphics desk.

Each line is labelled with its category name and value at both ends; labels that would overlap are nudged apart vertically. Lines can be colored by direction (increase, decrease, no change), a subset of categories can be emphasised with highlight, and hovering over a line fades the others.

Usage

slope_chart(
  data,
  start,
  end,
  group,
  startLabel = NULL,
  endLabel = NULL,
  colorByDirection = TRUE,
  upCol = "#1a9850",
  downCol = "#d73027",
  flatCol = "#888888",
  lineCol = "steelblue",
  highlight = NULL,
  mutedCol = "#cccccc",
  lineWidth = 2,
  circleRadius = 4,
  showValues = TRUE,
  digits = NULL,
  labelFontSize = 11,
  headerFontSize = 13,
  title = NULL,
  titleFontSize = 16,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG slope chart.

Examples

# Current receipts of government as a percentage of GDP, 1970 vs 1979
# (adapted from Edward Tufte's classic slopegraph)
receipts <- data.frame(
  country = c("Sweden", "Netherlands", "Norway", "Britain", "France",
              "Germany", "Belgium", "Canada", "Finland", "Italy",
              "United States", "Greece", "Switzerland", "Spain", "Japan"),
  y1970 = c(46.9, 44.0, 43.5, 40.7, 39.0, 37.5, 35.2, 35.2, 34.9, 30.4,
            30.3, 26.8, 26.5, 22.5, 20.7),
  y1979 = c(57.4, 55.8, 52.2, 39.0, 43.4, 42.9, 43.2, 35.8, 38.2, 35.7,
            32.5, 30.6, 33.2, 27.1, 26.6)
)

slope_chart(
  data       = receipts,
  start      = "y1970",
  end        = "y1979",
  group      = "country",
  startLabel = "1970",
  endLabel   = "1979",
  digits     = 1,
  title      = "Government receipts as % of GDP",
  height     = 520
)

# Life expectancy in 1952 vs 2007, highlighting two countries
library(gapminder)
library(dplyr)
library(tidyr)

countries <- c("China", "India", "Japan", "Brazil", "Nigeria",
               "Egypt", "Germany", "United States", "South Africa")

life <- filter(gapminder, year %in% c(1952, 2007), country %in% countries)
life <- select(life, country, year, lifeExp)
life <- pivot_wider(life, names_from = year, values_from = lifeExp,
                    names_prefix = "y")

slope_chart(
  data       = life,
  start      = "y1952",
  end        = "y2007",
  group      = "country",
  startLabel = "1952",
  endLabel   = "2007",
  highlight  = c("China", "South Africa"),
  digits     = 1,
  title      = "Life expectancy: 1952 vs 2007"
)

Create a stacked area chart

Description

Create a stacked area chart

Usage

stacked_area_chart(
  data,
  x,
  colorCategory = "Category10",
  curve = "curveLinear",
  stroke = NULL,
  strokeWidth = 1.5,
  xticks = NULL,
  yticks = NULL,
  xtitle = NULL,
  xtitleFontSize = 16,
  ytitle = NULL,
  ytitleFontSize = 16,
  title = NULL,
  titleFontSize = 22,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "#CAD0D3",
  opacity = 1,
  axisCol = "black",
  legendBoxSize = 18,
  legendTextSize = 18,
  width = NULL,
  height = NULL
)

Arguments

Value

a SVG stacked area chart

Examples

data <- data.frame(
  date = c(
    "2000-01-01", "2000-02-01", "2000-03-01", "2000-04-01",
    "2000-05-01", "2000-06-01", "2000-07-01",
    "2000-08-01", "2000-09-01", "2000-10-01"
  ),
  Trade = c(
    2000,1023, 983, 2793, 1821, 1837, 1792, 1853, 791, 739
  ),
  Manufacturing = c(
    734, 694, 739, 736, 685, 621, 708, 685, 667, 693
  ),
  Leisure = c(
    1782, 1779, 1789, 658, 675, 833, 786, 675, 636, 691
  ),
  Agriculture = c(
    655, 587,623, 517, 561, 2545, 636, 584, 559, 2504
  )
)

stacked_area_chart(
  data = data,
  x = "date",
  legendTextSize = 14,
  curve = "curveCardinal",
  colorCategory = "Accent",
  bgcol = "white",
  stroke = "black",
  strokeWidth = 1
)

Create a streamgraph.

Description

A streamgraph is a stacked area chart whose baseline is not fixed at zero but displaced around a central axis, so the layers flow like a river. It shows how the composition of a total changes over time while keeping attention on the shape of each layer rather than on exact values. The form was popularised by Lee Byron and Martin Wattenberg's "Stacked Graphs: Geometry & Aesthetics" (2008); this implementation reproduces the look and feel of the Highcharts streamgraph in plain D3.

Unlike stacked_area_chart(), which takes wide data and stacks areas on a zero baseline, streamgraph() takes long (tidy) data, uses a centred baseline (offset), orders layers inside out so the largest layers sit in the middle, and labels each layer inline at its thickest point instead of relying on a separate legend.

The chart is interactive: hovering a layer highlights it while the others dim, a vertical crosshair snaps to the nearest x value and shows a tooltip with the value of every layer at that x, and (when legend = TRUE) hovering a legend item focuses its layer while clicking it hides or shows the layer, re-stacking the stream with an animation. Missing x/group combinations are treated as zero.

Usage

streamgraph(
  data,
  x,
  group,
  value,
  offset = c("wiggle", "silhouette", "expand"),
  order = c("insideOut", "none", "reverse", "ascending", "descending"),
  curve = c("basis", "catmullRom", "monotoneX", "linear", "step"),
  colors = NULL,
  colorPalette = "Tableau10",
  opacity = 0.9,
  strokeCol = "white",
  strokeWidth = 0.5,
  labels = TRUE,
  labelFontSize = 11,
  labelCol = NULL,
  tooltip = TRUE,
  hover = TRUE,
  animate = TRUE,
  legend = TRUE,
  legendFontSize = 11,
  valueFormat = ",.2~f",
  xFormat = NULL,
  xticks = NULL,
  xFontSize = 11,
  gridCol = "#e6e6e6",
  xtitle = NULL,
  xtitleFontSize = 13,
  title = NULL,
  titleFontSize = 16,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG streamgraph.

Examples

# Popularity of music genres over four decades (simulated)
set.seed(2026)
years  <- 1980:2020
genres <- c("Rock", "Pop", "Hip hop", "Electronic", "Country",
            "R&B", "Jazz", "Metal", "Reggae", "Latin")
peaks  <- c(1984, 1999, 2012, 2008, 1993, 2003, 1981, 1990, 1986, 2018)
sizes  <- c(60, 55, 70, 40, 30, 35, 18, 25, 15, 45)
music  <- expand.grid(year = years, genre = genres)
idx    <- match(music$genre, genres)
music$listeners <- round(
  sizes[idx] * exp(-((music$year - peaks[idx]) / 9)^2) + runif(nrow(music), 1, 6)
)

streamgraph(
  data   = music,
  x      = "year",
  group  = "genre",
  value  = "listeners",
  valueFormat = ",.0f",
  xtitle = "Year",
  title  = "Music genres, 1980-2020 (millions of listeners)"
)

# Share of the world's telephones by region (base R data), 100% stream
phones <- data.frame(
  year   = rep(as.numeric(rownames(WorldPhones)), ncol(WorldPhones)),
  region = factor(rep(colnames(WorldPhones), each = nrow(WorldPhones)),
                  levels = colnames(WorldPhones)),
  phones = as.vector(WorldPhones)
)

streamgraph(
  data         = phones,
  x            = "year",
  group        = "region",
  value        = "phones",
  offset       = "expand",
  order        = "descending",
  curve        = "monotoneX",
  colorPalette = "Set2",
  valueFormat  = ",.0f",
  title        = "Telephones by region, 1951-1961 (thousands)"
)

# Date x-axis with a silhouette baseline: weekly sign-ups per channel
set.seed(7)
weeks    <- seq(as.Date("2026-01-01"), by = "week", length.out = 52)
channels <- c("Organic", "Paid search", "Social", "Referral", "Email")
signups  <- expand.grid(week = weeks, channel = channels)
t        <- as.numeric(signups$week - min(weeks)) / 7
k        <- as.integer(signups$channel)
signups$n <- round(
  (60 + 25 * sin(t / 6 + k)) * c(1.4, 1, 0.8, 0.5, 0.6)[k] + rpois(length(t), 6)
)

streamgraph(
  data    = signups,
  x       = "week",
  group   = "channel",
  value   = "n",
  offset  = "silhouette",
  xFormat = "%b %Y",
  colors  = c("#2caffe", "#544fc5", "#00e272", "#fe6a35", "#6b8abc"),
  legend  = FALSE,
  title   = "Weekly sign-ups by channel, 2026"
)

Create a zoomable sunburst chart.

Description

A sunburst chart displays hierarchical data as concentric rings. The centre represents the whole, each ring is one level of the hierarchy, and every sector's angle is proportional to its value. The look and feel is inspired by the sunburst series of Apache ECharts, reimplemented here in plain D3.

The chart is interactive: hovering a sector highlights it together with its ancestors and shows a tooltip with its full path, value and shares; clicking a sector zooms into it (its children then fill the whole circle with a smooth arc transition) and clicking the centre zooms back out one level.

Usage

sunburst_chart(
  data,
  levels,
  value = NULL,
  colors = NULL,
  valueFormat = ",.0f",
  innerRadius = 0.22,
  cornerRadius = 0,
  padAngle = 0,
  strokeCol = "white",
  strokeWidth = 1,
  showLabels = TRUE,
  labelRotate = c("radial", "tangential"),
  labelFontSize = 11,
  labelCol = NULL,
  rootLabel = "Total",
  centerFontSize = 13,
  zoom = TRUE,
  hover = TRUE,
  animate = TRUE,
  duration = 750,
  title = NULL,
  titleFontSize = 16,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  textCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG sunburst chart.

Examples

# World population in 2007 by continent and country
library(gapminder)

pop_2007 <- gapminder[gapminder$year == 2007, ]

sunburst_chart(
  data        = pop_2007,
  levels      = c("continent", "country"),
  value       = "pop",
  valueFormat = ".3~s",
  rootLabel   = "World",
  title       = "World population in 2007"
)

# A three-level hierarchy: a toy company's headcount
staff <- data.frame(
  division = c(rep("Engineering", 6), rep("Sales", 4), rep("Operations", 4)),
  team     = c("Platform", "Platform", "Data", "Data", "Mobile", "Mobile",
               "EMEA", "EMEA", "Americas", "APAC",
               "Finance", "Finance", "HR", "Legal"),
  role     = c("Backend", "SRE", "Engineers", "Analysts", "iOS", "Android",
               "Account execs", "Sales engineers", "Account execs",
               "Account execs", "Accounting", "FP&A", "Recruiting", NA),
  people   = c(42, 12, 18, 9, 11, 13, 25, 8, 31, 14, 10, 6, 9, 5)
)

sunburst_chart(
  data        = staff,
  levels      = c("division", "team", "role"),
  value       = "people",
  rootLabel   = "Company",
  labelRotate = "tangential",
  padAngle    = 0.006,
  cornerRadius = 3,
  title       = "Headcount by division, team and role"
)

# Without a value column, sectors are sized by row counts
library(ggplot2) # for the mpg dataset

sunburst_chart(
  data      = mpg,
  levels    = c("class", "manufacturer"),
  rootLabel = "Cars",
  title     = "Number of car models by class and manufacturer"
)

Create a treemap chart.

Description

A treemap displays hierarchical (or flat) part-to-whole data as a set of nested rectangles whose areas are proportional to a value. The technique was invented by Ben Shneiderman in the early 1990s and was made famous by SmartMoney's "Map of the Market" and by the New York Times' graphics of the US federal budget and spending. When a group column is supplied the chart becomes a two-level hierarchy: tiles are coloured by group and each group gets its own header and border. Hovering over a tile highlights it and shows a tooltip with its label, group, value and share of the total.

Usage

treemap_chart(
  data,
  value,
  label,
  group = NULL,
  tile = "squarify",
  padding = 2,
  colors = NULL,
  valueFormat = ",.0f",
  showValues = TRUE,
  labelFontSize = 11,
  labelCol = NULL,
  groupFontSize = 12,
  strokeCol = "white",
  strokeWidth = 1,
  hover = TRUE,
  legend = TRUE,
  legendFontSize = 11,
  title = NULL,
  titleFontSize = 16,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  textCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG treemap chart.

Examples

# A toy government budget (in millions) by department and ministry
budget <- data.frame(
  department = c("Hospitals", "Primary care", "Public health",
                 "Schools", "Universities", "Vocational training",
                 "Army", "Navy", "Air force",
                 "Roads", "Railways", "Airports",
                 "Pensions", "Unemployment", "Family benefits"),
  ministry   = c("Health", "Health", "Health",
                 "Education", "Education", "Education",
                 "Defence", "Defence", "Defence",
                 "Transport", "Transport", "Transport",
                 "Social security", "Social security", "Social security"),
  spending   = c(5200, 2100, 800,
                 3900, 1800, 600,
                 2300, 1400, 1700,
                 1500, 1100, 400,
                 7400, 1900, 1300)
)

treemap_chart(
  data        = budget,
  value       = "spending",
  label       = "department",
  group       = "ministry",
  valueFormat = "$,.0f",
  title       = "Where the money goes (millions)"
)

# World population in 2007 by country, grouped by continent
library(gapminder)

pop_2007 <- gapminder[gapminder$year == 2007, ]

treemap_chart(
  data        = pop_2007,
  value       = "pop",
  label       = "country",
  group       = "continent",
  valueFormat = ".3~s",
  padding     = 1,
  title       = "World population in 2007"
)

Create a violin plot.

Description

A violin plot shows the distribution of a numeric variable for one or several groups. Each group is drawn as a mirrored kernel density estimate (the "violin"), so the width of the shape at a given value is proportional to how many observations lie around it. Violin plots reveal features that a box plot hides, such as multi-modality or skewness, while an inner box plot keeps the familiar summary statistics in view. The look and feel is inspired by the violin traces of the Plotly.js library.

The kernel density estimates are computed in the browser with a Gaussian kernel. By default each violin gets its own bandwidth, computed with Silverman's rule of thumb (equivalent to stats::bw.nrd0()).

The plot is interactive: hovering a violin dims the others, draws a horizontal spike line at the cursor (with its value tagged on the y-axis) and shows a tooltip with the group's summary statistics (n, min, q1, median, q3, max and mean). Hovering a raw point shows its value.

Usage

violin_plot(
  data,
  y,
  group = NULL,
  bandwidth = NULL,
  span = c("soft", "hard"),
  scale = c("width", "count"),
  box = TRUE,
  meanline = FALSE,
  points = c("outliers", "all", "none"),
  jitter = 0.6,
  pointRadius = 2.5,
  pointOpacity = 0.7,
  sort = c("none", "mean", "median"),
  colorPalette = "Category10",
  fillOpacity = 0.5,
  lineWidth = 1.5,
  violinWidth = 0.8,
  hover = TRUE,
  animate = TRUE,
  ytitle = NULL,
  ytitleFontSize = 13,
  xtitle = NULL,
  xtitleFontSize = 13,
  title = NULL,
  titleFontSize = 16,
  xFontSize = 11,
  yFontSize = 11,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  gridCol = "#e6e6e6",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG violin plot.

Examples

# Sepal length by species, with the inner box plot and outliers
violin_plot(
  data   = iris,
  y      = "Sepal.Length",
  group  = "Species",
  title  = "Sepal length by species",
  ytitle = "Sepal length (cm)"
)

# Every observation jittered inside its violin, plus a mean line
library(ggplot2) # for the mpg dataset

violin_plot(
  data         = mpg,
  y            = "hwy",
  group        = "class",
  points       = "all",
  meanline     = TRUE,
  sort         = "median",
  colorPalette = "Tableau10",
  title        = "Highway fuel economy by vehicle class",
  ytitle       = "Highway miles per gallon"
)

# A bimodal distribution, width proportional to group size, hard span
set.seed(2026)
waiting <- data.frame(
  minutes = c(rnorm(300, 55, 6), rnorm(200, 80, 5),
              rnorm(150, 70, 10), rnorm(60, 62, 4)),
  station = rep(c("North", "Central", "South"), c(500, 150, 60))
)

violin_plot(
  data         = waiting,
  y            = "minutes",
  group        = "station",
  scale        = "count",
  span         = "hard",
  colorPalette = c(North = "#636efa", Central = "#ef553b",
                   South = "#00cc96"),
  title        = "Waiting time by station",
  ytitle       = "Minutes",
  xtitle       = "Station"
)

# A single violin with a custom bandwidth
violin_plot(
  data         = faithful,
  y            = "eruptions",
  bandwidth    = 0.2,
  colorPalette = "#9467bd",
  points       = "all",
  ytitle       = "Eruption time (min)"
)

Create a waffle chart.

Description

A waffle chart is a grid of squares (by default 10 x 10 = 100 cells) in which each category fills a number of cells proportional to its share of the whole. Each cell therefore reads as "one percent" (or one unit of the total), which makes part-to-whole comparisons far easier to read than a pie chart. The format is a staple of Pew Research Center, FiveThirtyEight, The Economist and the Washington Post, and was popularised in R by Bob Rudis's waffle package.

Cells are allocated with the largest-remainder method, so the cell counts always add up exactly to rows * cols. Unlike parliament_chart, which draws individual seats along a hemicycle, a waffle chart uses a rectangular grid to show proportions of a whole rather than absolute counts.

Usage

waffle_chart(
  data,
  category,
  value,
  rows = 10,
  cols = 10,
  total = NULL,
  colors = NULL,
  emptyCol = "#e5e5e5",
  cellPadding = 2,
  cellRadius = 2,
  direction = "row",
  legend = TRUE,
  legendFontSize = 12,
  hover = TRUE,
  animate = TRUE,
  animationDuration = 1200,
  title = NULL,
  titleFontSize = 16,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  textCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An SVG waffle chart.

Examples

# How respondents answered a survey question
survey <- data.frame(
  response = c("Strongly agree", "Agree", "Neutral",
               "Disagree", "Strongly disagree"),
  n = c(312, 405, 158, 87, 38)
)

waffle_chart(
  data     = survey,
  category = "response",
  value    = "n",
  colors   = c("#1a9641", "#a6d96a", "#bdbdbd", "#fdae61", "#d7191c"),
  title    = "Do you support the new policy?"
)

# Share of the world population by continent in 2007, filled by column
library(gapminder)

pop_2007 <- gapminder[gapminder$year == 2007, ]
pop_continent <- aggregate(pop ~ continent, data = pop_2007, FUN = sum)

waffle_chart(
  data      = pop_continent,
  category  = "continent",
  value     = "pop",
  direction = "col",
  rows      = 10,
  cols      = 20,
  title     = "World population by continent, 2007"
)

Create an interactive waterfall chart.

Description

A waterfall chart shows how an initial value is built up or eroded by a series of positive and negative contributions to reach a final total. Each bar starts exactly where the previous one ended, making cumulative effects immediately visible. It is the standard chart for P&L decomposition, budget variance analysis, and any "what changed and why" narrative.

Hovering over a bar shows a tooltip with the label, the delta value, and the running total at that point.

Usage

waterfall_chart(
  data,
  x,
  y,
  measure = NULL,
  positiveColor = "#4CAF50",
  negativeColor = "#F44336",
  totalColor = "#5C85D6",
  connector = TRUE,
  connectorColor = "#aaaaaa",
  showLabels = TRUE,
  labelFontSize = 10,
  title = NULL,
  titleFontSize = 16,
  xtitle = NULL,
  xtitleFontSize = 13,
  ytitle = NULL,
  ytitleFontSize = 13,
  xFontSize = 11,
  yFontSize = 11,
  opacity = 1,
  font = "Verdana, Geneva, Tahoma, sans-serif",
  bgcol = "white",
  axisCol = "black",
  width = NULL,
  height = NULL
)

Arguments

Value

An interactive SVG waterfall chart.

Examples

# P&L bridge: from revenue down to net income
pnl <- data.frame(
  label   = c("Revenue", "COGS", "Gross Profit",
              "R&D", "S&M", "G&A", "Operating Income"),
  value   = c(1200, -450, 750, -120, -90, -60, 480),
  measure = c("relative", "relative", "total",
              "relative", "relative", "relative", "total")
)

waterfall_chart(
  data    = pnl,
  x       = "label",
  y       = "value",
  measure = "measure",
  title   = "P&L bridge",
  ytitle  = "USD thousands"
)

# Monthly cash flow
cashflow <- data.frame(
  month = c("Jan", "Feb", "Mar", "Apr", "May", "Jun"),
  delta = c(120, -30, 80, -60, 95, -20)
)

waterfall_chart(
  data           = cashflow,
  x              = "month",
  y              = "delta",
  title          = "Monthly cash flow",
  ytitle         = "USD thousands",
  positiveColor  = "steelblue",
  negativeColor  = "tomato"
)