r-plotter

name	r-plotter
description	Skill for modeling and rendering mathematical functions and data visualizations using R. Generates R scripts to create high-quality, appropriately scaled images for LaTeX documents. Handles directory management for image assets.

R Plotter Skill

When to use this skill

ALWAYS use this skill when the user requests:

• To graph mathematical functions (contours, surfaces, 2D plots).
• To visualize data distributions or statistical models.
• To create high-quality images for LaTeX or other documents.
• To replicate existing graphs from screenshots using R.

1. Environment Setup

Ensure R is installed. If not, request installation:

sudo apt install r-base-core

Verify installation with Rscript --version.

2. Script Generation Protocol

1. Create a dedicated R script for the specific visualization (e.g., plot_q9.R).
2. Define the Output Path:
   • Identify the target directory for images (e.g., images/ or figures/ within the LaTeX project).
   • Ensure the directory exists or create it in the script.
3. Configure the Device:
   • Use png() or pdf() devices.
   • CRITICAL: Set appropriate dimensions and resolution for LaTeX inclusion.
   • Recommended for LaTeX: width = 6, height = 6, units = "in", res = 300 (for PNG).
4. Plotting:
   • Use ggplot2 for complex, layered plots or base graphics for simple mathematical functions if dependencies are limited.
   • For Contour Plots (as in Q9): Use filled.contour() or levelplot() from lattice, or geom_contour_filled() from ggplot2.
5. Export:
   • Close the device with dev.off().

3. Image Placement Strategy

• Standard Location: /material-recopilado/guia_antigua/images/ (or relative to the .tex file).
• Naming Convention: [question_number]_[description].png (e.g., q9_contour_plot.png).
• LaTeX Integration:

  \begin{center}
  \includegraphics[width=0.6\textwidth]{images/q9_contour_plot.png}
  \end{center}
  

4. Replicating Specific Graphs (Reverse Engineering)

• Analyze the Screenshot: Identify the function type (hyperbolas, parabolas, etc.), domains, and ranges.
• Estimate Parameters: Guess coefficients to match the visual curves.
• Iterate: Run the script, check the output, adjust parameters until it matches the screenshot.

5. Example Script (Template)

# Load necessary libraries (if any)
# library(ggplot2)

# Define output file
output_file <- "images/example_plot.png"
dir.create(dirname(output_file), showWarnings = FALSE, recursive = TRUE)

# Open device
png(output_file, width = 6, height = 5, units = "in", res = 300)

# Define data
x <- seq(-10, 10, length.out = 100)
y <- seq(-10, 10, length.out = 100)
grid <- expand.grid(x = x, y = y)
z <- with(grid, x * y) # Example function z = xy

# Plot
# Use a custom color palette similar to the screenshot (e.g., heat.colors)
filled.contour(x, y, matrix(z, nrow = 100),
               color.palette = function(n) hcl.colors(n, "YlOrRd", rev = TRUE),
               plot.title = title(main = "Question 9: Level Curves"),
               xlab = "x", ylab = "y")

# Close device
dev.off()
print(paste("Image saved to:", output_file))