Generate Publication-Quality Plots

A specialized sub-skill for creating professional, publication-ready figures in R that meet journal standards and scientific visualization best practices.

Overview

This sub-skill generates high-quality figures using ggplot2 and other R visualization libraries. It supports multiple export formats (PDF, PNG, SVG, TIFF) and follows common journal formatting guidelines including resolution, color space, and font specifications.

Use this sub-skill when the user wants to:

• Create publication-quality figures from data
• Export plots in specific formats for journals
• Generate multi-panel composite figures
• Apply journal-specific formatting guidelines
• Create consistent figure styles across publications

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What This Sub-Skill Does

When invoked, this sub-skill will:

1. Analyze data and requirements

   • Examine data structure and variables
   • Determine appropriate plot type
   • Identify journal formatting requirements
   • Check for categorical vs continuous variables

2. Create ggplot2 visualizations

   • Apply theme customization
   • Use color-blind friendly palettes
   • Set appropriate figure dimensions
   • Add proper annotations and labels

3. Export to publication formats

   • PDF (vector format for journals)
   • PNG (raster, 300-600 DPI)
   • SVG (scalable vector graphics)
   • TIFF (high-quality raster)

4. Follow journal guidelines

   • Nature (single column, 89mm or 183mm width)
   • Science (specific dimensions)
   • PLOS ONE (300+ DPI TIFF/PNG)
   • IEEE (vector preferred)

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Supported Plot Types

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Example User Requests

• "Create a publication-quality scatter plot with regression line"
• "Generate a volcano plot for my differential expression results"
• "Make a bar plot with error bars for Nature journal format"
• "Export this plot as 600 DPI PNG and vector PDF"
• "Create a multi-panel figure combining PCA and heatmap"

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Journal Formatting Guidelines

Nature

• Single column: 89mm width
• Double column: 183mm width
• Preferred: PDF, EPS, SVG (vector)
• Minimum: 600 DPI for raster
• Fonts: Arial, Helvetica, or Symbol

Science

• Width: 5.5cm (single) or 11.4cm (double)
• Height: Maximum 22.5cm
• Preferred: TIFF, EPS
• Resolution: 300-600 DPI
• Fonts: Helvetica or Arial

PLOS ONE

• Width: 150-300 pixels per inch
• Preferred: TIFF, PNG, PDF
• Resolution: Minimum 300 DPI
• Color: RGB or CMYK accepted

IEEE

• Column width: 3.5 inches (single column)
• Page width: 7.5 inches (double column)
• Preferred: PDF, EPS
• Fonts: Times New Roman, Helvetica, Arial

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Color Palettes

Color-blind Friendly Palettes

# Okabe-Ito palette (8 colors)
c("#E69F00", "#56B4E9", "#009E73", "#F0E442",
  "#0072B2", "#D55E00", "#CC79A7", "#999999")

# Viridis (perceptually uniform)
viridis::viridis(8)

# ColorBrewer palettes
RColorBrewer::brewer.pal(8, "Set2")

Diverging Palettes

• Red-Blue (for heatmaps, volcano plots)
• RdYlBu, RdBu, BrBG

Sequential Palettes

• Blues, Greens, PuBuGn

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Template Functions

Base Publication Theme

theme_publication <- function(base_size = 12, base_family = "Arial") {
  theme_bw(base_size = base_size, base_family = base_family) +
  theme(
    legend.position = "top",
    panel.border = element_rect(size = 0.5),
    panel.grid.major = element_line(color = "grey90"),
    panel.grid.minor = element_blank(),
    axis.text = element_text(size = rel(0.8)),
    legend.key = element_rect(color = "grey90"),
    legend.key.size = unit(0.4, "cm"),
    legend.margin = margin(0, 0, 0, 0, "cm")
  )
}

Export Function

save_publication_figure <- function(plot, filename,
                                    width = 89, height = 89,
                                    dpi = 600, units = "mm") {
  ggsave(filename, plot = plot,
         width = width, height = height,
         dpi = dpi, units = units,
         device = "pdf")
}

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Parameters

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Example Workflows

Simple Scatter Plot

# Input data: df with columns x, y, group
# Output: scatter_with_regression.pdf

p <- ggplot(df, aes(x = x, y = y, color = group)) +
  geom_point(size = 3, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE) +
  scale_color_okabe_ito() +
  theme_publication() +
  labs(x = "X Variable", y = "Y Variable")

save_publication_figure(p, "scatter_with_regression.pdf",
                       width = 89, height = 89, dpi = 600)

Volcano Plot

# Input data: de_results with log2FC, pvalue
# Output: volcano.pdf

p <- ggplot(de_results, aes(x = log2FC, y = -log10(pvalue))) +
  geom_point(aes(color = significant), alpha = 0.6) +
  scale_color_manual(values = c("grey", "#D55E00", "#0072B2")) +
  theme_publication() +
  geom_vline(xintercept = c(-1, 1), linetype = "dashed") +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
  labs(x = expression(log[2]~Fold~Change),
       y = expression(-log[10]~p~value))

save_publication_figure(p, "volcano.pdf")

Multi-Panel Figure

# Combine multiple plots using patchwork
library(patchwork)

combined <- (p1 | p2) / (p3 | p4)

ggsave("multipanel.pdf", combined,
       width = 183, height = 183,
       dpi = 600)

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Output Specifications

PDF (Vector)

• Recommended for journals
• Scalable without quality loss
• File extension: .pdf

PNG (Raster)

• 300-600 DPI
• Good for web/PPT
• File extension: .png

SVG (Vector)

• Web-friendly vector
• Editable in Inkscape/Illustrator
• File extension: .svg

TIFF (Raster)

• High-quality for publication
• Lossless compression
• File extension: .tiff

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Font Options

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Best Practices

1. Use vector formats when possible - PDF for publication
2. Check color blindness - Use color-blind friendly palettes
3. Maintain aspect ratio - Avoid distorted figures
4. Label clearly - Self-explanatory axis labels and legends
5. Keep it simple - Remove chart junk and unnecessary decorations
6. Use consistent style - Same theme across all figures in paper
7. Export at correct DPI - 600 for most journals

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Notes

• Color-blind friendly palettes used by default
• Font family can be customized per journal requirements
• Multi-panel figures use patchwork package
• All plots use publication-quality theme by default
• Resolution and dimensions match common journal standards

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Related Sub-Skills

• create-project: Set up projects before creating plots
• run-analysis: Generate analysis results for plotting