README

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

🚀 AutoPlots

AutoPlots is a comprehensive R visualization toolkit built on top of echarts4r, providing a consistent, ultra-customizable API across dozens of chart types — from classical analytics plots to advanced modeling visualizations and 3D charts.

AutoPlots exposes the full power of ECharts.js without requiring users to write JavaScript. If a feature exists in ECharts, AutoPlots aims to make it available.

⭐ Why AutoPlots?

30+ high-quality plot types — all with consistent argument naming

Full access to ECharts options using the xxx.* naming convention

No JavaScript required

Shiny-ready out of the box

Supports multi-plot layouts via display_plots_grid()

3D visualizations, modeling plots, river charts, radar, parallel, contour, density, and more

Designed for data science, EDA, dashboards, and model evaluation

🔍 Philosophy

AutoPlots is built around these principles:

Every plot is a standalone object Modify, tweak, and filter each one independently.

Composition happens outside the plot Use display_plots_grid() to arrange multiple plots into dashboards.

Expose full ECharts power without JS Legend, toolbox, tooltip, axis, series, grid, animation, theme — everything is adjustable.

Beautiful defaults, infinite customization Beginners get great plots immediately; experts can tune everything.

Installation

# Install CRAN dependencies
install.packages(c(
  "combinat", "data.table", "devtools", "dplyr", "e1071", "echarts4r",
  "lubridate", "nortest", "quanteda", "quanteda.textstats", "scales"
))

# Install AutoPlots from GitHub
devtools::install_github("AdrianAntico/AutoPlots", upgrade = FALSE, force = TRUE)

Quick Start

library(AutoPlots)
dt <- data.table::data.table(x = rnorm(1000))

AutoPlots::Density(
  dt,
  XVar = "x",
  legend.show = FALSE
)

Basic Plots

Area
Bar
Box
Copula
Correlogram
Density
Donut
Heatmaps
Histogram
Line
Parallel
Pie
Radar
River
Rosetype
Scatter
Stacked Bar
Step
3D Bar
3D Scatter
3D Copula
Word Cloud

Modeling Plots

Autocorrelation
Calibration
Calibration Scatter
Confusion Matrix Heatmap
Gain
Lift
Partial Autocorrelation
Partial Dependence
Partial Dependence Heatmap
Probability
ROC
Shapely Importance
Variable Importance

📚 Plot Gallery

Below are the full set of examples (kept from your original README), organized using collapsible sections for easier navigation.

📐 Area Plots

Area Plot Examples

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
data <- data[, .(
  IndepVar = mean(Independent_Variable8)
), by = c("DateTime")]

# Build plot
AutoPlots::Area(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar",
  areaStyle.color = c("#80AAFF","#BDD5FF","#CFCFCF"),
  areaStyle.opacity = c(0.9,0.4,0.05),
  legend.show = FALSE)

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
data <- data[, .(
  IndepVar = mean(Independent_Variable8)
), by = c("DateTime", "Factor_1")]

# Build plot
ch <- as.character(sort(unique(data$Factor_1)))
plot_list <- lapply(ch, function(x) {
  AutoPlots::Area(
    dt = data[Factor_1 == x],
    XVar = "DateTime",
    YVar = "IndepVar",
    Height = "300px",
    title.text = paste0("Factor_1: ", x),
    areaStyle.color = c("#80AAFF","#BDD5FF","#CFCFCF"),
    areaStyle.opacity = c(0.9,0.4,0.05),
    legend.show = FALSE)
})

AutoPlots::display_plots_grid(
  plot_list,
  cols = 2
)

📊 Bar Plots

Bar Plot Examples

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
data <- data[, .(
  IndepVar = mean(Independent_Variable8)
), by = c("DateTime")]

# Build plot
AutoPlots::Bar(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar",
  backgroundStyle.color = c("#80AAFF","#BDD5FF","#CFCFCF"),
  backgroundStyle.opacity = c(0.9,0.4,0.05),
  legend.show = FALSE)

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
data <- data[, .(
  IndepVar = mean(Independent_Variable8)
), by = c("DateTime", "Factor_1")]

# Build plot
ch <- as.character(sort(unique(data$Factor_1)))
plot_list <- lapply(ch, function(x) {
  AutoPlots::Bar(
    dt = data[Factor_1 == x],
    XVar = "DateTime",
    YVar = "IndepVar",
    Height = "300px",
    title.text = paste0("Factor_1: ", x),
    backgroundStyle.color = c("#80AAFF","#BDD5FF","#CFCFCF"),
    backgroundStyle.opacity = c(0.9,0.4,0.05),
    legend.show = FALSE)
})

AutoPlots::display_plots_grid(
  plot_list,
  cols = 2
)

📦 Box Plots

Box Plot Examples

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
data[1, Independent_Variable8 := 0.6]
data[2, Independent_Variable8 := 0.7]
data[3, Independent_Variable8 := 0.8]
data[4, Independent_Variable8 := 0.9]

# Build plot
AutoPlots::Box(
  dt = data,
  XVar = "Factor_1",
  YVar = "Independent_Variable8",
  yAxis.title = "IndepVar",
  itemStyle.color = c("red","#BDD5FF","blue"),
  itemStyle.opacity = c(0.9,0.4,0.05),
  legend.show = FALSE)

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
data[1, Independent_Variable8 := 0.6]
data[2, Independent_Variable8 := 0.7]
data[3, Independent_Variable8 := 0.8]
data[4, Independent_Variable8 := 0.9]

# Build plot
ch <- c("Factor_1", "Factor_2")
plot_list <- lapply(ch, function(x) {
  AutoPlots::Box(
    dt = data,
    XVar = x,
    YVar = "Independent_Variable8",
    Height = "300px",
    title.text = paste0("Factor_1: ", x),
    itemStyle.color = c("red","#BDD5FF","blue"),
    itemStyle.opacity = c(0.9,0.4,0.05),
    legend.show = FALSE)
})

AutoPlots::display_plots_grid(
  plot_list,
  cols = 1
)

🍥 Copula

Copula Plot Examples

# Create correlated data
n   <- 1000        # rows
p   <- 4           # number of variables
rho <- 0.8         # correlation between neighbors (AR(1))
mu  <- c(50, 100, 0, 10)     # means (length p)
sds <- c(10, 5, 3, 1)        # standard deviations (length p)
Sigma_cor <- outer(1:p, 1:p, \(i, j) rho^abs(i - j))   # AR(1) correlation matrix
L <- chol(Sigma_cor)                                   # Cholesky factor (upper-tri)
Z <- matrix(rnorm(n * p), nrow = n, ncol = p)          # iid N(0,1)
X_cor <- Z %*% L                                       # correlated, unit variance
X <- X_cor %*% diag(sds)                               # set std devs
X <- sweep(X, 2, mu, `+`)                              # set means
dt <- data.table::as.data.table(X)
data.table::setnames(dt, paste0("x", seq_len(p)))

# Build plot
p1 <- AutoPlots::Copula(
  dt = dt,
  XVar = "x1",
  YVar = "x2",
  legend.show = FALSE
)

# Create correlated data
n   <- 1000        # rows
p   <- 4           # number of variables
rho <- 0.8         # correlation between neighbors (AR(1))
mu  <- c(50, 100, 0, 10)     # means (length p)
sds <- c(10, 5, 3, 1)        # standard deviations (length p)
Sigma_cor <- outer(1:p, 1:p, \(i, j) rho^abs(i - j))   # AR(1) correlation matrix
L <- chol(Sigma_cor)                                   # Cholesky factor (upper-tri)
Z <- matrix(rnorm(n * p), nrow = n, ncol = p)          # iid N(0,1)
X_cor <- Z %*% L                                       # correlated, unit variance
X <- X_cor %*% diag(sds)                               # set std devs
X <- sweep(X, 2, mu, `+`)                              # set means
dt <- data.table::as.data.table(X)
data.table::setnames(dt, paste0("x", seq_len(p)))

# Build plot
p1 <- AutoPlots::Copula(
  dt = dt,
  XVar = "x1",
  YVar = "x2",
  title.text = "x1 vs. x2",
  legend.show = FALSE
)

# Build plot
p2 <- AutoPlots::Copula(
  dt = dt,
  XVar = "x1",
  YVar = "x3",
  title.text = "x1 vs. x3",
  legend.show = FALSE
)

# Build plot
p3 <- AutoPlots::Copula(
  dt = dt,
  XVar = "x2",
  YVar = "x3",
  title.text = "x2 vs. x3",
  legend.show = FALSE
)

# Build plot
p4 <- AutoPlots::Copula(
  dt = dt,
  XVar = "x3",
  YVar = "x4",
  title.text = "x3 vs. x4",
  legend.show = FALSE
)

AutoPlots::display_plots_grid(
  list(p1,p2,p3,p4),
  cols = 2
)

📈 Correlogram

Correlogram Examples

n   <- 1000        # rows
p   <- 4           # number of variables
rho <- 0.8         # correlation between neighbors (AR(1))
mu  <- c(50, 100, 0, 10)     # means (length p)
sds <- c(10, 5, 3, 1)        # standard deviations (length p)
Sigma_cor <- outer(1:p, 1:p, \(i, j) rho^abs(i - j))   # AR(1) correlation matrix
L <- chol(Sigma_cor)                                   # Cholesky factor (upper-tri)
Z <- matrix(rnorm(n * p), nrow = n, ncol = p)          # iid N(0,1)
X_cor <- Z %*% L                                       # correlated, unit variance
X <- X_cor %*% diag(sds)                               # set std devs
X <- sweep(X, 2, mu, `+`)                              # set means
dt <- data.table::as.data.table(X)
data.table::setnames(dt, paste0("x", seq_len(p)))

# Build Plot
AutoPlots::CorrMatrix(
  dt = dt,
  PreAgg = FALSE,
  Method = "Spearman",
  CorrVars = c("x1","x2","x3","x4"),
  ShowLabels = TRUE,
  visualMap.InRange.color = c("white", "gray", "darkblue")
)

n   <- 1000        # rows
p   <- 4           # number of variables
rho <- 0.8         # correlation between neighbors (AR(1))
mu  <- c(50, 100, 0, 10)     # means (length p)
sds <- c(10, 5, 3, 1)        # standard deviations (length p)
Sigma_cor <- outer(1:p, 1:p, \(i, j) rho^abs(i - j))   # AR(1) correlation matrix
L <- chol(Sigma_cor)                                   # Cholesky factor (upper-tri)
Z <- matrix(rnorm(n * p), nrow = n, ncol = p)          # iid N(0,1)
X_cor <- Z %*% L                                       # correlated, unit variance
X <- X_cor %*% diag(sds)                               # set std devs
X <- sweep(X, 2, mu, `+`)                              # set means
dt <- data.table::as.data.table(X)
data.table::setnames(dt, paste0("x", seq_len(p)))

# Build Plot
p1 <- AutoPlots::CorrMatrix(
  dt = dt,
  PreAgg = FALSE,
  Method = "Spearman",
  CorrVars = c("x1","x2","x3","x4"),
  ShowLabels = TRUE,
  visualMap.InRange.color = c("white", "gray", "darkblue"),
  title.text = "Spearman Correlation"
)

p2 <- AutoPlots::CorrMatrix(
  dt = dt,
  PreAgg = FALSE,
  Method = "Pearson",
  CorrVars = c("x1","x2","x3","x4"),
  ShowLabels = TRUE,
  visualMap.InRange.color = c("white", "gray", "darkgreen"),
  title.text = "Pearson Correlation"
)

p3 <- AutoPlots::CorrMatrix(
  dt = dt,
  PreAgg = FALSE,
  Method = "Kendall",
  CorrVars = c("x1","x2","x3","x4"),
  ShowLabels = TRUE,
  visualMap.InRange.color = c("white", "gray", "darkred"),
  title.text = "Kendall's Tau Correlation"
)

AutoPlots::display_plots_grid(
  list(p1,p2,p3),
  cols = 2
)

🌡 Density

Density Plot Examples

# Create fake data
data <- data.table::data.table(IndepVar = rnorm(1000, mean = 0, sd = 1))

# Build plot
AutoPlots::Density(
  dt = data,
  XVar = "IndepVar",
  areaStyle.color = c("#697AFF","#A1ADFF","#D9DEFF"),
  areaStyle.opacity = c(0.9,0.4,0.05),
  legend.show = FALSE
)

# Create fake data
data <- data.table::data.table(IndepVar = rnorm(1000, mean = 5, sd = 2))
data[, IndepVar2 := rnorm(1000, mean = 10, sd = 5)]
data[, IndepVar3 := rnorm(1000, mean = 10, sd = 15)]
data[, IndepVar4 := rnorm(1000, mean = 10, sd = 20)]

# Build Plots
p1 <- AutoPlots::Density(
  dt = data,
  XVar = "IndepVar",
  title.text = "IndepVar",
  areaStyle.color = c("#697AFF","#A1ADFF","#D9DEFF"),
  areaStyle.opacity = c(0.9,0.4,0.05),
  legend.show = FALSE
)

p2 <- AutoPlots::Density(
  dt = data,
  XVar = "IndepVar2",
  title.text = "IndepVar2",
  areaStyle.color = c("#697AFF","#A1ADFF","#D9DEFF"),
  areaStyle.opacity = c(0.9,0.4,0.05),
  legend.show = FALSE
)

p3 <- AutoPlots::Density(
  dt = data,
  XVar = "IndepVar3",
  title.text = "IndepVar3",
  areaStyle.color = c("#697AFF","#A1ADFF","#D9DEFF"),
  areaStyle.opacity = c(0.9,0.4,0.05),
  legend.show = FALSE
)

p4 <- AutoPlots::Density(
  dt = data,
  XVar = "IndepVar4",
  title.text = "IndepVar4",
  areaStyle.color = c("#697AFF","#A1ADFF","#D9DEFF"),
  areaStyle.opacity = c(0.9,0.4,0.05),
  legend.show = FALSE
)

AutoPlots::display_plots_grid(
  list(p1,p2,p3,p4),
  cols = 2
)

🍩 Donut

Donut Plot Examples

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
data <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_1")]

# Build Plot
AutoPlots::Donut(
  dt = data,
  XVar = "Factor_1",
  YVar = "IndepVar"
)

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
dt1 <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_1")]
dt2 <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_2")]

# Build Plots
p1 <- AutoPlots::Donut(
  dt = dt1,
  XVar = "Factor_1",
  YVar = "IndepVar",
  title.text = "Factor_1"
)

p2 <- AutoPlots::Donut(
  dt = dt2,
  XVar = "Factor_2",
  YVar = "IndepVar",
  title.text = "Factor_2"
)

AutoPlots::display_plots_grid(
  list(p1, p2),
  cols = 1
)

🔥 Heatmap

Heatmap Plot Examples

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 100000, Correlation = 0.1)
data <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_1", "Factor_2")]

# Build Plots
AutoPlots::HeatMap(
  dt = data,
  XVar = "Factor_1",
  YVar = "Factor_2",
  ZVar = "IndepVar",
  title.text = "V1",
  label.show = TRUE,
  label.fontWeight = "bolder",
  emphasis.shadowColor = "white",
  emphasis.shadowBlur = 10, 
  visualMap.InRange.color = c("blue", "white", "red")
)

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 100000, Correlation = 0.1)
data <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_1", "Factor_2")]

# Build Plots
p1 <- AutoPlots::HeatMap(
  dt = data,
  XVar = "Factor_1",
  YVar = "Factor_2",
  ZVar = "IndepVar",
  title.text = "V1",
  label.show = TRUE,
  label.fontWeight = "bolder",
  emphasis.shadowColor = "white",
  emphasis.shadowBlur = 10, 
  visualMap.InRange.color = c("blue", "white", "red")
)

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 100000, Correlation = 0.1)
data <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_1", "Factor_2")]

# Build Plots
p2 <- AutoPlots::HeatMap(
  dt = data,
  XVar = "Factor_1",
  YVar = "Factor_2",
  ZVar = "IndepVar",
  title.text = "V2",
  label.show = TRUE,
  label.fontWeight = "bolder",
  emphasis.shadowColor = "white",
  emphasis.shadowBlur = 10, 
  visualMap.InRange.color = c("green", "white", "orange")
)

AutoPlots::display_plots_grid(
  list(p1, p2),
  cols = 1
)

🧱 Histogram

Histogram Examples

# Create fake data
data <- data.table::data.table(Variable = rnorm(n = 1000, mean = 5, sd = 2))

# Build Plots
AutoPlots::Histogram(
  dt = data,
  XVar = "Variable",
  backgroundStyle.color = c("lightblue","darkblue","blue","blue"),
  backgroundStyle.opacity = c(0.9,0.7,0.6,0.05),
  legend.show = FALSE
)

# Create fake data
data <- data.table::data.table(Variable = rnorm(n = 1000, mean = 5, sd = 2))
data[, Variable2 := rnorm(n = 1000, mean = 5, sd = 5)]
data[, Variable3 := rnorm(n = 1000, mean = 5, sd = 10)]
data[, Variable4 := rnorm(n = 1000, mean = 5, sd = 20)]

# Build Plots
p1 <- AutoPlots::Histogram(
  dt = data,
  XVar = "Variable",
  title.text = "Var",
  backgroundStyle.color = c("lightblue","darkblue","blue","blue"),
  backgroundStyle.opacity = c(0.9,0.7,0.6,0.05),
  legend.show = FALSE
)

p2 <- AutoPlots::Histogram(
  dt = data,
  XVar = "Variable2",
  title.text = "Var2",
  backgroundStyle.color = c("lightblue","darkblue","blue","blue"),
  backgroundStyle.opacity = c(0.9,0.7,0.6,0.05),
  legend.show = FALSE
)

p3 <- AutoPlots::Histogram(
  dt = data,
  XVar = "Variable3",
  title.text = "Var3",
  backgroundStyle.color = c("lightblue","darkblue","blue","blue"),
  backgroundStyle.opacity = c(0.9,0.7,0.6,0.05),
  legend.show = FALSE
)

p4 <- AutoPlots::Histogram(
  dt = data,
  XVar = "Variable4",
  title.text = "Var4",
  backgroundStyle.color = c("lightblue","darkblue","blue","blue"),
  backgroundStyle.opacity = c(0.9,0.7,0.6,0.05),
  legend.show = FALSE
)
  
AutoPlots::display_plots_grid(
  list(p1,p2,p3,p4),
  cols = 2
)

💡 Line

Line Plot Examples

# Create data
data <- AutoPlots::FakeDataGenerator(N = 1000, AddDate = T)
data <- data[, .(
  IndepVar = mean(Independent_Variable8)
), by = c("DateTime")]

# Build plot
AutoPlots::Line(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar",
  title.text = "IndepVar",
  lineStyle.color = c("red","#3848FF","blue"),
  legend.show = FALSE
)

# Create data
data <- AutoPlots::FakeDataGenerator(N = 1000, AddDate = T)
data <- data[, .(
  IndepVar = mean(Independent_Variable8),
  IndepVar2 = mean(Independent_Variable7),
  IndepVar3 = mean(Independent_Variable6),
  IndepVar4 = mean(Independent_Variable5)
), by = c("DateTime")]

# Build plot
p1 <- AutoPlots::Line(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar",
  title.text = "IndepVar",
  lineStyle.color = c("red","#3848FF","blue"),
  legend.show = FALSE
)

p2 <- AutoPlots::Line(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar2",
  title.text = "IndepVar2",
  lineStyle.color = c("red","#3848FF","blue"),
  legend.show = FALSE
)

p3 <- AutoPlots::Line(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar3",
  title.text = "IndepVar3",
  lineStyle.color = c("red","#3848FF","blue"),
  legend.show = FALSE
)

p4 <- AutoPlots::Line(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar4",
  title.text = "IndepVar4",
  lineStyle.color = c("red","#3848FF","blue"),
  legend.show = FALSE
)

AutoPlots::display_plots_grid(
  list(p1,p2,p3,p4),
  cols = 2
)

🧭 Parallel

Parallel Plot Examples

# Create data
n   <- 1000        # rows
p   <- 8           # number of variables
rho <- 0.8         # correlation between neighbors (AR(1))
mu  <- c(50, 100, 0, 10,1,2,3,4)     # means (length p)
sds <- c(10, 5, 3, 1, 1,2,3,4)        # standard deviations (length p)
Sigma_cor <- outer(1:p, 1:p, \(i, j) rho^abs(i - j))   # AR(1) correlation matrix
L <- chol(Sigma_cor)                                   # Cholesky factor (upper-tri)
Z <- matrix(rnorm(n * p), nrow = n, ncol = p)          # iid N(0,1)
X_cor <- Z %*% L                                       # correlated, unit variance
X <- X_cor %*% diag(sds)                               # set std devs
X <- sweep(X, 2, mu, `+`)                              # set means
dt <- data.table::as.data.table(X)
data.table::setnames(dt, paste0("x", seq_len(p)))
data.table::setnames(dt, "x1", "bl bl bl")

# Build Plot
AutoPlots::Parallel(
  dt = dt,
  CorrVars = names(dt),
  ShowLabels = T,
  lineStyle.color = "#00C7FF",
  lineStyle.width = 0.2
)

# Create data
data <- AutoPlots::FakeDataGenerator(N = 1000, ID = 0)
data.table::setnames(
  data,
  paste0("Independent_Variable", 1:8),
  paste0("x", 1:8)
)

# Build Plots
group_vars <- sort(as.character(unique(data$Factor_1)))[1:4]
plot_list <- lapply(group_vars, function(x) {
  AutoPlots::Parallel(
    dt = data[Factor_1 == x],
    CorrVars = paste0("x", 1:8),
    ShowLabels = T,
    lineStyle.color = "#00C7FF",
    lineStyle.width = 0.2,
    title.text = x
  )
})

AutoPlots::display_plots_grid(
  plot_list,
  cols = 2
)

🥧 Pie

Pie Plot Examples

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
data <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_1")]

# Build Plot
AutoPlots::Pie(
  dt = data,
  XVar = "Factor_1",
  YVar = "IndepVar",
  ShowLabels = T)

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
dt1 <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_1")]
dt2 <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_2")]

# Build Plots
p1 <- AutoPlots::Pie(
  dt = dt1,
  XVar = "Factor_1",
  YVar = "IndepVar",
  title.text = "Factor_1",
  ShowLabels = T)

p2 <- AutoPlots::Pie(
  dt = dt2,
  XVar = "Factor_2",
  YVar = "IndepVar",
  title.text = "Factor_2",
  ShowLabels = T)

AutoPlots::display_plots_grid(
  list(p1, p2),
  cols = 1
)

🎯 Radar

Radar Plot Examples

# Create Data
dt <- data.table::data.table(
  Y1 = pnorm(q = runif(10)),
  Y2 = pnorm(q = runif(10)),
  Y3 = pnorm(q = runif(10)),
  GV = sample(LETTERS[1:10], 10, TRUE))

# Create plot
AutoPlots::Radar(
  dt = dt,
  AggMethod = "mean",
  ShowLabels = TRUE,
  PreAgg = FALSE,
  YVar = c("Y1","Y2","Y3"),
  GroupVar = "GV",
  lineStyle.color = c("#00BFFF", "#FF69B4", "#32CD32"))

# Create Data
dt <- data.table::data.table(
  Y1 = pnorm(q = runif(10)),
  Y2 = pnorm(q = runif(10)),
  Y3 = pnorm(q = runif(10)),
  GV = sample(LETTERS[1:10], 10, TRUE))

dt2 <- data.table::data.table(
  Y1 = pnorm(q = runif(10)),
  Y2 = pnorm(q = runif(10)),
  Y3 = pnorm(q = runif(10)),
  GV = sample(LETTERS[11:20], 10, TRUE))

# Create plot
p1 <- AutoPlots::Radar(
  dt = dt,
  AggMethod = "mean",
  ShowLabels = TRUE,
  PreAgg = FALSE,
  YVar = c("Y1","Y2","Y3"),
  GroupVar = "GV",
  title.text = "Data 1",
  lineStyle.color = c("#00BFFF", "#FF69B4", "#32CD32"))

p2 <- AutoPlots::Radar(
  dt = dt2,
  AggMethod = "mean",
  ShowLabels = TRUE,
  PreAgg = FALSE,
  YVar = c("Y1","Y2","Y3"),
  GroupVar = "GV",
  title.text = "Data 2",
  lineStyle.color = c("#00BFFF", "#FF69B4", "#32CD32"))

AutoPlots::display_plots_grid(
  list(p1,p2),
  cols = 1
)

🌊 River

River Plot Examples

# Create fake data
dates <- seq.Date(Sys.Date() - 30, Sys.Date(), by = "day")
grps <- lapply(LETTERS[1:5], rep, 31) |> unlist()
dt <- data.table::data.table(
  dates = rep(dates, 5),
  groups = grps,
  values = runif(length(grps), 1, 50)
)

# Build Plot
AutoPlots::River(
  dt = dt,
  PreAgg = TRUE,
  XVar = "dates",
  YVar = "values",
  GroupVar = "groups",
  legend.orient = "horizontal",
  itemStyle.color = c("#FF4C4C", "#00BFFF", "#FFD700", "#32CD32", "#FF69B4")
  )

# Create fake data
dates <- seq.Date(Sys.Date() - 30, Sys.Date(), by = "day")
grps <- lapply(LETTERS[1:5], rep, 31) |> unlist()
dt1 <- data.table::data.table(
  dates = rep(dates, 5),
  groups = grps,
  values = runif(length(grps), 1, 50)
)

dates <- seq.Date(Sys.Date() - 30, Sys.Date(), by = "day")
grps <- lapply(LETTERS[6:10], rep, 31) |> unlist()
dt2 <- data.table::data.table(
  dates = rep(dates, 5),
  groups = grps,
  values = runif(length(grps), 1, 50)
)
+-
# Build Plot
p1 <- AutoPlots::River(
  dt = dt1,
  PreAgg = TRUE,
  XVar = "dates",
  YVar = "values",
  GroupVar = "groups",
  title.text = "Data 1",
  legend.orient = "horizontal",
  itemStyle.color = c("#FF4C4C", "#00BFFF", "#FFD700", "#32CD32", "#FF69B4")
  )

p2 <- AutoPlots::River(
  dt = dt2,
  PreAgg = TRUE,
  XVar = "dates",
  YVar = "values",
  GroupVar = "groups"
  title.text = "Data 2",
  legend.orient = "horizontal",
  itemStyle.color = c("#00FFFF", "#FF7F50", "#8A2BE2", "#00FF7F", "#FF4500"))

AutoPlots::display_plots_grid(
  list(p1, p2),
  cols = 1
)

🌹 Rosetype

Rosetype Plot Examples

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
data <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_1")]

# Build Plot
AutoPlots::Rosetype(
  dt = data,
  XVar = "Factor_1",
  YVar = "IndepVar"
)

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 1000)
dt1 <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_1")]
dt2 <- data[, .(
  IndepVar = round(mean(Independent_Variable8), 3)
), by = c("Factor_2")]

# Build Plots
p1 <- AutoPlots::Rosetype(
  dt = dt1,
  XVar = "Factor_1",
  YVar = "IndepVar",
  title.text = "Factor_1"
)

p2 <- AutoPlots::Rosetype(
  dt = dt2,
  XVar = "Factor_2",
  YVar = "IndepVar",
  title.text = "Factor_2"
)

AutoPlots::display_plots_grid(
  list(p1, p2),
  cols = 1
)

☁ Scatter

Scatter Plot Examples

# Create data
n   <- 1000        # rows
p   <- 4           # number of variables
rho <- 0.8         # correlation between neighbors (AR(1))
mu  <- c(50, 100, 0, 10)     # means (length p)
sds <- c(10, 5, 3, 1)        # standard deviations (length p)
Sigma_cor <- outer(1:p, 1:p, \(i, j) rho^abs(i - j))   # AR(1) correlation matrix
L <- chol(Sigma_cor)                                   # Cholesky factor (upper-tri)
Z <- matrix(rnorm(n * p), nrow = n, ncol = p)          # iid N(0,1)
X_cor <- Z %*% L                                       # correlated, unit variance
X <- X_cor %*% diag(sds)                               # set std devs
X <- sweep(X, 2, mu, `+`)                              # set means
dt <- data.table::as.data.table(X)
data.table::setnames(dt, paste0("x", seq_len(p)))

# Build Plot
AutoPlots::Scatter(
  dt = dt,
  XVar = "x2",
  YVar = "x1",
  legend.show = FALSE
)

# Create data
n   <- 1000        # rows
p   <- 4           # number of variables
rho <- 0.8         # correlation between neighbors (AR(1))
mu  <- c(50, 100, 0, 10)     # means (length p)
sds <- c(10, 5, 3, 1)        # standard deviations (length p)
Sigma_cor <- outer(1:p, 1:p, \(i, j) rho^abs(i - j))   # AR(1) correlation matrix
L <- chol(Sigma_cor)                                   # Cholesky factor (upper-tri)
Z <- matrix(rnorm(n * p), nrow = n, ncol = p)          # iid N(0,1)
X_cor <- Z %*% L                                       # correlated, unit variance
X <- X_cor %*% diag(sds)                               # set std devs
X <- sweep(X, 2, mu, `+`)                              # set means
dt <- data.table::as.data.table(X)
data.table::setnames(dt, paste0("x", seq_len(p)))

# Build Plots
p1 <- AutoPlots::Scatter(
  dt = dt,
  XVar = "x2",
  YVar = "x1",
  legend.show = FALSE,
  title.text = "x1 vs x2"
)

p2 <- AutoPlots::Scatter(
  dt = dt,
  XVar = "x3",
  YVar = "x1",
  legend.show = FALSE,
  title.text = "x1 vs x3"
)

p3 <- AutoPlots::Scatter(
  dt = dt,
  XVar = "x3",
  YVar = "x2",
  legend.show = FALSE,
  title.text = "x2 vs x3"
)

p4 <- AutoPlots::Scatter(
  dt = dt,
  XVar = "x4",
  YVar = "x3",
  legend.show = FALSE,
  title.text = "x3 vs x4"
)

AutoPlots::display_plots_grid(
  list(p1,p2,p3,p4),
  cols = 2
)

🧱 Stacked Bar

Stacked Bar Plot Examples

# Create fake data
data <- AutoPlots::FakeDataGenerator(N = 100000)
data <- data[, .(
  IndepVar = sum(Independent_Variable1)
), by = c("Factor_1","Factor_2")]


# Echarts Stacked Bar Chart
AutoPlots::StackedBar(
  dt = data,
  XVar = "Factor_1",
  YVar = "IndepVar",
  GroupVar = "Factor_2",
  legend.right = 35,
  legend.top = 45,
  yAxis.nameTextStyle.padding = 40
)

📉 Step

Step Plot Examples

# Create data
data <- AutoPlots::FakeDataGenerator(N = 1000, AddDate = T)
data <- data[, .(
  IndepVar = mean(Independent_Variable8)
), by = c("DateTime")]

# Build plot
AutoPlots::Step(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar",
  title.text = "IndepVar",
  lineStyle.color = c("red","#3848FF","blue"),
  legend.show = FALSE
)

# Create data
data <- AutoPlots::FakeDataGenerator(N = 1000, AddDate = T)
data <- data[, .(
  IndepVar = mean(Independent_Variable8),
  IndepVar2 = mean(Independent_Variable7),
  IndepVar3 = mean(Independent_Variable6),
  IndepVar4 = mean(Independent_Variable5)
), by = c("DateTime")]

# Build plot
p1 <- AutoPlots::Step(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar",
  title.text = "IndepVar",
  lineStyle.color = c("red","#3848FF","blue"),
  legend.show = FALSE
)

p2 <- AutoPlots::Step(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar2",
  title.text = "IndepVar2",
  lineStyle.color = c("red","#3848FF","blue"),
  legend.show = FALSE
)

p3 <- AutoPlots::Step(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar3",
  title.text = "IndepVar3",
  lineStyle.color = c("red","#3848FF","blue"),
  legend.show = FALSE
)

p4 <- AutoPlots::Step(
  dt = data,
  XVar = "DateTime",
  YVar = "IndepVar4",
  title.text = "IndepVar4",
  lineStyle.color = c("red","#3848FF","blue"),
  legend.show = FALSE
)

AutoPlots::display_plots_grid(
  list(p1,p2,p3,p4),
  cols = 2
)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.