contour Mark

library(mosaicr)

The contour mark creates contour lines that show levels of equal values across a 2D surface. It’s excellent for visualizing density distributions, topographic data, or any continuous surface.

Basic Example

# Generate 2D density data
set.seed(123)
n <- 1000
data <- data.frame(
  x = rnorm(n, 50, 15),
  y = rnorm(n, 50, 15)
)

spec <- list(
  plot = list(
    list(
      mark = "contour",
      data = list(from = "sample_data"),
      x = "x",
      y = "y",
      stroke = "steelblue"
    )
  )
)

mosaic(spec, sample_data = data)

Customization Options

Multiple Contour Levels

# Bivariate normal with correlation
set.seed(456)
n <- 2000

# Create correlated variables
x1 <- rnorm(n)
x2 <- 0.7 * x1 + sqrt(1 - 0.7^2) * rnorm(n)

correlated_data <- data.frame(
  x = x1,
  y = x2
)

spec_levels <- list(
  plot = list(
    list(
      mark = "contour",
      data = list(from = "correlated_data"),
      x = "x",
      y = "y",
      stroke = "darkblue",
      strokeWidth = 1.5
    )
  )
)

mosaic(spec_levels, correlated_data = correlated_data)

Multiple Distributions

# Create data with multiple peaks
set.seed(789)
n_per_group <- 400

multi_peak_data <- data.frame(
  x = c(
    rnorm(n_per_group, -1, 0.5),   # Peak 1
    rnorm(n_per_group, 1, 0.5),    # Peak 2
    rnorm(n_per_group, 0, 0.3)     # Peak 3 (center)
  ),
  y = c(
    rnorm(n_per_group, 1, 0.4),    # Peak 1
    rnorm(n_per_group, -1, 0.4),   # Peak 2
    rnorm(n_per_group, 0, 0.3)     # Peak 3 (center)
  )
)

spec_multi <- list(
  plot = list(
    list(
      mark = "contour",
      data = list(from = "multi_peak_data"),
      x = "x",
      y = "y",
      stroke = "purple",
      strokeWidth = 2
    )
  )
)

mosaic(spec_multi, multi_peak_data = multi_peak_data)

Combined with Points

# Show both the data points and contours
spec_combined <- list(
  plot = list(
    list(
      mark = "dot",
      data = list(from = "correlated_data"),
      x = "x",
      y = "y",
      fill = "lightblue",
      fillOpacity = 0.3,
      r = 2
    ),
    list(
      mark = "contour",
      data = list(from = "correlated_data"),
      x = "x",
      y = "y",
      stroke = "navy",
      strokeWidth = 2
    )
  )
)

mosaic(spec_combined, correlated_data = correlated_data)

Filled Contours

# Using filled contours for better visualization
spec_filled <- list(
  plot = list(
    list(
      mark = "contour",
      data = list(from = "multi_peak_data"),
      x = "x",
      y = "y",
      fill = "density",
      stroke = "white",
      strokeWidth = 1
    )
  ),
  colorScheme = "viridis"
)

mosaic(spec_filled, multi_peak_data = multi_peak_data)

Advanced Example

# Simulate a more complex surface (e.g., a mathematical function)
create_surface_data <- function() {
  x_seq <- seq(-3, 3, length.out = 50)
  y_seq <- seq(-3, 3, length.out = 50)
  grid <- expand.grid(x = x_seq, y = y_seq)

  # Create a complex surface
  grid$z <- with(grid, sin(sqrt(x^2 + y^2)) * exp(-0.1 * (x^2 + y^2)))

  return(grid)
}

surface_data <- create_surface_data()

spec_surface <- list(
  plot = list(
    list(
      mark = "contour",
      data = list(from = "surface_data"),
      x = "x",
      y = "y",
      fill = "z",
      stroke = "black",
      strokeWidth = 0.5,
      strokeOpacity = 0.7
    )
  ),
  colorScheme = "spectral",
  xLabel = "X coordinate",
  yLabel = "Y coordinate"
)

mosaic(spec_surface, surface_data = surface_data)

Use Cases

• Density Visualization: Showing probability density for 2D distributions
• Topographic Maps: Representing elevation or other geographic data
• Scientific Data: Visualizing continuous fields in physics, chemistry, or biology
• Statistical Analysis: Exploring bivariate distributions and their properties
• Function Visualization: Displaying mathematical functions of two variables