dot Mark

library(mosaicr)

The dot mark creates scatter plots by drawing circular dots at specified positions. It’s the most versatile mark for exploring relationships between variables.

Basic Example

# Sample data
data <- data.frame(
  x_val = c(1, 2, 3, 4, 5, 6, 7, 8),
  y_val = c(2, 8, 3, 7, 5, 4, 6, 1)
)

spec <- list(
  plot = list(
    list(
      mark = "dot",
      data = list(from = "sample_data"),
      x = "x_val",
      y = "y_val",
      fill = "steelblue"
    )
  )
)

mosaic(spec, sample_data = data)

Customization Options

Size and Color Mapping

# Enhanced scatter plot data
enhanced_data <- data.frame(
  x = rnorm(50, 10, 2),
  y = rnorm(50, 15, 3),
  size = runif(50, 1, 10),
  category = sample(c("A", "B", "C"), 50, replace = TRUE)
)

spec_enhanced <- list(
  plot = list(
    list(
      mark = "dot",
      data = list(from = "enhanced_data"),
      x = "x",
      y = "y",
      r = list(column = "size"),
      fill = list(column = "category"),
      fillOpacity = 0.7
    )
  )
)

mosaic(spec_enhanced, enhanced_data = enhanced_data)

Using Built-in Datasets

# Using the penguins dataset for a classic example
spec_penguins <- list(
  plot = list(
    list(
      mark = "dot",
      data = list(from = "penguins"),
      x = "body_mass_g",
      y = "flipper_length_mm",
      fill = list(column = "species"),
      r = 3,
      fillOpacity = 0.8
    )
  ),
  xLabel = "Body Mass (g)",
  yLabel = "Flipper Length (mm)"
)

mosaic(spec_penguins, penguins = penguins)

Multiple Series

# Multiple related datasets
set.seed(42)
multi_data <- data.frame(
  x = rep(1:20, times = 3),
  y = c(
    1:20 + rnorm(20, 0, 2),           # Linear trend
    sin((1:20) * 0.5) * 5 + 10 + rnorm(20, 0, 1),  # Sine wave
    20:1 + rnorm(20, 0, 1.5)          # Negative trend
  ),
  series = rep(c("Linear", "Sine", "Negative"), each = 20)
)

spec_multi <- list(
  plot = list(
    list(
      mark = "dot",
      data = list(from = "multi_data"),
      x = "x",
      y = "y",
      fill = list(column = "series"),
      r = 4,
      fillOpacity = 0.8
    )
  )
)

mosaic(spec_multi, multi_data = multi_data)

Use Cases

• Correlation Analysis: Exploring relationships between two continuous variables
• Classification: Visualizing how different groups cluster in 2D space
• Outlier Detection: Identifying unusual data points
• Pattern Recognition: Discovering trends and patterns in data
• Scientific Research: Standard visualization for experimental data