# Create example data (no FakeDataGenerator) data <- data.table::data.table( DateTime = seq.Date( from = Sys.Date() - 89, to = Sys.Date(), by = "day" ) ) # Smooth wave + a bit of noise for a nice area shape data[, IndepVar := 50 + 30 * sin(seq(0, 2 * pi, length.out = .N)) + stats::rnorm(.N, mean = 0, sd = 3)] # Optional: ensure values stay positive for cleaner visuals data[IndepVar < 0, IndepVar := 0] # Build plot AutoPlots::Area( dt = data, XVar = "DateTime", YVar = "IndepVar", areaStyle.color = c("#80AAFF","#BDD5FF","#8B008B"), areaStyle.opacity = c(1,0.6,0.05), legend.show = FALSE, title.textStyle.textShadowColor = "purple", title.textStyle.textShadowBlur = 4, title.textStyle.textShadowOffsetX = 2, title.textStyle.textShadowOffsetY = 1, yAxis.nameTextStyle.fontSize = 20, yAxis.nameTextStyle.padding = 60, xAxis.nameTextStyle.fontSize = 20, tooltip.backgroundColor = "#80AAFFAA", tooltip.textStyle.color = "black" ) 

# 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 )

# Create example data (no FakeDataGenerator) data <- data.table::data.table( DateTime = seq.Date( from = Sys.Date() - 89, to = Sys.Date(), by = "day" ) ) # Smooth wave + a bit of noise for a nice area shape data[, IndepVar := 50 + 30 * sin(seq(0, 2 * pi, length.out = .N)) + stats::rnorm(.N, mean = 0, sd = 3)] # Optional: ensure values stay positive for cleaner visuals data[IndepVar < 0, IndepVar := 0] # Build plot AutoPlots::Bar( dt = data, XVar = "DateTime", YVar = "IndepVar", backgroundStyle.color = c("#80AAFF","#BDD5FF","#8B008B"), backgroundStyle.opacity = c(1,0.6,0.05), legend.show = FALSE, title.textStyle.textShadowColor = "purple", title.textStyle.textShadowBlur = 4, title.textStyle.textShadowOffsetX = 2, title.textStyle.textShadowOffsetY = 1, yAxis.nameTextStyle.fontSize = 20, yAxis.nameTextStyle.padding = 60, xAxis.nameTextStyle.fontSize = 20, tooltip.backgroundColor = "#80AAFFAA", tooltip.textStyle.color = "black", title.text = "Bar Plot" )

# 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 ) 

# Create boxplot demo data with intentional outliers set.seed(123) n_per_group <- 60 data <- data.table::data.table( Factor_1 = factor(rep(c("Group A", "Group B", "Group C", "Group D"), each = n_per_group)) ) # Base distributions data[, Independent_Variable8 := c( rnorm(n_per_group, mean = 40, sd = 4), rnorm(n_per_group, mean = 55, sd = 5), rnorm(n_per_group, mean = 70, sd = 6), rnorm(n_per_group, mean = 85, sd = 7) )] # Add controlled outliers per group outliers <- data.table::data.table( Factor_1 = factor(c( rep("Group A", 6), rep("Group B", 7), rep("Group C", 8), rep("Group D", 6) ), levels = levels(data$Factor_1)), Independent_Variable8 = c( rnorm(6, mean = 25, sd = 2), # A: low outliers rnorm(7, mean = 45, sd = 3), # B: low-ish outliers rnorm(8, mean = 100, sd = 3), # C: high outliers rnorm(6, mean = 115, sd = 4) # D: high outliers ) ) data <- rbind(data, outliers) # Safety: no negatives data[Independent_Variable8 < 0, Independent_Variable8 := 0] # Final box plot AutoPlots::Box( dt = data, XVar = "Factor_1", YVar = "Independent_Variable8", yAxis.title = "IndepVar", itemStyle.color = c("#80AAFF", "#BDD5FF", "#8B008B"), itemStyle.opacity = c(0.98, 0.85, 0.55), legend.show = FALSE, tooltip.backgroundColor = "#80AAFFDD", tooltip.textStyle.color = "black", yAxis.nameTextStyle.fontSize = 20, yAxis.nameTextStyle.padding = 60, yAxis.axisLabel.color = "#CCCCCC", xAxis.nameTextStyle.fontSize = 20, xAxis.axisLabel.color = "#CCCCCC", xAxis.axisLabel.fontSize = 14, title.textStyle.textShadowColor = "purple", title.textStyle.textShadowBlur = 4, title.textStyle.textShadowOffsetX = 2, title.textStyle.textShadowOffsetY = 1 ) 

# 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 ) 

# 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 )

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 )

# 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 )

# 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 )

# 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 ) 

# 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 )

# 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 )

# 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 )

# 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 )

# 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 )

# 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 )

# 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 )

# 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 )

library(data.table) set.seed(123) # Nice, small, structured dataset data <- data.table::CJ( Factor_1 = factor( paste0("Segment ", LETTERS[1:6]), levels = paste0("Segment ", LETTERS[1:6]) ), Factor_2 = factor( c("Search", "Social", "Display", "Email"), levels = c("Search", "Social", "Display", "Email") ) ) data[, IndepVar := round( 600 + # base level 150 * as.integer(Factor_1) + # larger segments toward the right c(500, 350, 250, 150)[as.integer(Factor_2)] + # channel strength rnorm(.N, 0, 80) # a bit of noise )] # No negative values data[IndepVar < 0, IndepVar := 0] # Stacked bar example AutoPlots::StackedBar( dt = data, XVar = "Factor_1", YVar = "IndepVar", GroupVar = "Factor_2", legend.right = 35, legend.top = 85, yAxis.nameTextStyle.padding = 60, yAxis.nameTextStyle.fontSize = 20, xAxis.nameTextStyle.fontSize = 20, legend.left = 200, legend.orient = "horizontal" ) 

# 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 )

library(data.table) set.seed(123) # Define hierarchy continents <- c("Americas", "Europe", "Asia") countries <- list( Americas = c("USA", "Canada", "Mexico"), Europe = c("UK", "Germany", "Spain"), Asia = c("China", "Japan", "India") ) cities <- list( USA = c("New York", "Los Angeles", "Chicago"), Canada = c("Toronto", "Vancouver"), Mexico = c("Mexico City", "Guadalajara"), UK = c("London", "Manchester"), Germany = c("Berlin", "Hamburg"), Spain = c("Madrid", "Barcelona"), China = c("Beijing", "Shanghai"), Japan = c("Tokyo", "Osaka"), India = c("Mumbai", "Delhi") ) neighborhoods <- list( "New York" = c("Manhattan", "Brooklyn"), "Los Angeles" = c("Hollywood", "Venice"), "Chicago" = c("Loop", "Hyde Park"), "Toronto" = c("North York", "Scarborough"), "Vancouver" = c("Downtown", "Kitsilano"), "Mexico City" = c("Centro", "Roma"), "Guadalajara" = c("Zapopan", "Tlaquepaque"), "London" = c("Camden", "Chelsea"), "Manchester" = c("Salford", "Didsbury"), "Berlin" = c("Mitte", "Kreuzberg"), "Hamburg" = c("Altona", "Eimsbüttel"), "Madrid" = c("Centro", "Chamartín"), "Barcelona" = c("Gràcia", "Eixample"), "Beijing" = c("Chaoyang", "Haidian"), "Shanghai" = c("Pudong", "Xuhui"), "Tokyo" = c("Shinjuku", "Shibuya"), "Osaka" = c("Kita", "Namba"), "Mumbai" = c("Bandra", "Andheri"), "Delhi" = c("Karol Bagh", "Saket") ) # Build the full dataset dt <- rbindlist(lapply(names(countries), function(cont) { rbindlist(lapply(countries[[cont]], function(ctry) { rbindlist(lapply(cities[[ctry]], function(city) { data.table( Continent = cont, Country = ctry, City = city, Neighborhood = neighborhoods[[city]], Value = sample(50:500, length(neighborhoods[[city]]), replace = TRUE) ) })) })) }), use.names = TRUE) # Build plot AutoPlots::Treemap( dt = dt, YVar = "Value", GroupVar = c("Continent","Country","City","Neighborhood") )

library(data.table) set.seed(123) # ----- Define hierarchy ------------------------------------------------------- continents <- c("Americas", "Europe", "Asia") countries <- list( Americas = c("USA", "Canada", "Mexico"), Europe = c("UK", "Germany", "Spain"), Asia = c("China", "Japan", "India") ) cities <- list( USA = c("New York", "Los Angeles", "Chicago"), Canada = c("Toronto", "Vancouver"), Mexico = c("Mexico City", "Guadalajara"), UK = c("London", "Manchester"), Germany = c("Berlin", "Hamburg"), Spain = c("Madrid", "Barcelona"), China = c("Beijing", "Shanghai"), Japan = c("Tokyo", "Osaka"), India = c("Mumbai", "Delhi") ) neighborhoods <- list( "New York" = c("Manhattan", "Brooklyn"), "Los Angeles" = c("Hollywood", "Venice"), "Chicago" = c("Loop", "Hyde Park"), "Toronto" = c("North York", "Scarborough"), "Vancouver" = c("Downtown", "Kitsilano"), "Mexico City" = c("Centro", "Roma"), "Guadalajara" = c("Zapopan", "Tlaquepaque"), "London" = c("Camden", "Chelsea"), "Manchester" = c("Salford", "Didsbury"), "Berlin" = c("Mitte", "Kreuzberg"), "Hamburg" = c("Altona", "Eimsbüttel"), "Madrid" = c("Centro", "Chamartín"), "Barcelona" = c("Gràcia", "Eixample"), "Beijing" = c("Chaoyang", "Haidian"), "Shanghai" = c("Pudong", "Xuhui"), "Tokyo" = c("Shinjuku", "Shibuya"), "Osaka" = c("Kita", "Namba"), "Mumbai" = c("Bandra", "Andheri"), "Delhi" = c("Karol Bagh", "Saket") ) # ----- Build the full dataset ------------------------------------------------- dt <- rbindlist( lapply(names(countries), function(cont) { rbindlist(lapply(countries[[cont]], function(ctry) { rbindlist(lapply(cities[[ctry]], function(city) { n_nbhd <- length(neighborhoods[[city]]) # base volume metric (e.g. revenue, signups) value <- sample(50:500, n_nbhd, replace = TRUE) # impressions & clicks for rate example imps <- sample(1e3:1e4, n_nbhd, replace = TRUE) ctr <- runif(n_nbhd, 0.01, 0.20) # 1%–20% click-through clicks <- pmin(round(imps * ctr), imps) data.table( Continent = cont, Country = ctry, City = city, Neighborhood = neighborhoods[[city]], Value = value, Impressions = imps, Clicks = clicks ) })) })) }), use.names = TRUE ) # -------------------------------------------------------------------- # 2) Rate-based treemap – area by Click-through-rate (Clicks / Impressions) # (uses your new RateNumer / RateDenom / AreaMetric wiring) # -------------------------------------------------------------------- AutoPlots::Treemap( dt = dt, YVar = c("Value", "Clicks", "Impressions"), GroupVar = c("Continent", "Country", "City", "Neighborhood"), RateNumer = "Clicks", RateDenom = "Impressions", AreaMetric = "rate" )