Generating Publication Ready Figures In R

Generating Publication-Ready Figures in R

This skill specializes in transforming ordinary ggplot2 plots into professional, publication-ready figures that meet the strict standards of top-tier journals like Nature, Science, Cell, and others.

Use this skill when the user wants to:

• Convert ggplot plots to journal-style figures
• Apply Nature/Science publication themes to existing plots
• Create multi-panel figures with consistent styling
• Export figures with proper DPI, dimensions, and formats
• Match specific journal submission guidelines
• Create colorblind-safe and publication-quality color schemes

What This Skill Does

When activated, this skill will:

1. Analyze existing ggplot code - Read and understand the current plot structure

2. Apply journal themes - Add publication-quality themes including:

   • Proper font sizes and families
   • Clean axis lines and backgrounds
   • Journal-specific color palettes
   • Legend positioning and styling

3. Optimize for submission - Ensure figures meet:

   • DPI requirements (typically 300-600 DPI)
   • Width/height specifications (single vs double column)
   • File format requirements (TIFF, PDF, EPS)
   • Color space requirements (CMYK vs RGB)

4. Create multi-panel figures - Combine plots using:

   • patchwork for simple layouts
   • cowplot for complex compositions
   • Custom annotation and labeling

5. Export properly - Save with correct:

   • Resolution (DPI)
   • Dimensions (inches/cm)
   • File format
   • Color profile

Example User Requests That Should Trigger This Skill

• "Transform this ggplot to Nature journal style"
• "Make this figure publication-ready for Science"
• "Create a two-column figure matching Cell format"
• "Export these plots at 600 DPI for submission"
• "Apply a colorblind-safe palette to my plots"
• "Combine these four plots into one publication figure"
• "Format my scatter plot for PNAS submission"

Journal Style Guidelines

Nature Style

• Font: Arial or Helvetica
• Font sizes: Axis titles 7-9 pt, axis labels 6-8 pt
• Single column: 89 mm (3.5 in) width
• Double column: 183 mm (7.2 in) width
• Max height: 234 mm (9.2 in)
• Resolution: 300-600 DPI
• Formats: TIFF, PDF, EPS (vector preferred)

Science Style

• Font: Arial
• Font sizes: Title 9 pt, labels 7 pt
• Single column: 57 mm (2.25 in) width
• Double column: 114 mm (4.5 in) width
• Resolution: 300-600 DPI
• Formats: TIFF, PDF, EPS

Cell Press Style

• Font: Arial or Helvetica
• Single column: 85 mm (3.3 in) width
• Double column: 178 mm (7 in) width
• Resolution: 300 DPI minimum
• Formats: TIFF, EPS, PDF

Theme Templates Available

theme_nature()

Clean, minimalist theme matching Nature journals:

• No gray backgrounds
• Minimal grid lines
• Arial font family
• Proper axis sizing

theme_science()

Theme for Science journal submissions:

• Compact layout
• Clean typography
• Optimized for smaller widths

theme_cellpress()

Cell Press journal theme:

• Professional appearance
• Flexible legend placement
• Publication-ready defaults

theme_colorblind()

Colorblind-safe palette with:

• Viridis/Colorbrewer schemes
• High contrast ratios
• Print-friendly colors

Color Palettes

Nature-Approved Colors

# Primary colors
nature_colors <- c(
  blue = "#3B4992",
  red = "#EE0000",
  green = "#008B45",
  purple = "#631879"
)

Colorblind-Safe Scales

• scale_fill_viridis()
• scale_color_okabe_ito() (Okabe-Ito palette)
• scale_color_viridis()

Example Workflow

User: Here's my ggplot code, make it Nature-style.

# Original plot
p <- ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3)

Skill transforms to:

# Publication-ready version
p <- ggplot(mtcars, aes(x = "Weight (tons)", y = "Fuel Efficiency (mpg)",
                        color = "Cylinders")) +
  geom_point(size = 2.5, shape = 16, alpha = 0.8) +
  scale_color_nature() +
  theme_nature(base_size = 8) +
  labs(title = NULL)

# Export at correct size
ggsave("figure1.pdf", p, width = 3.5, height = 3, dpi = 300,
       device = "pdf")

Multi-Panel Figures

# Combine plots with patchwork
library(patchwork)

figure1 <- (panel_a | panel_b) / (panel_c | panel_d)

# Add panel labels
figure1 <- figure1 +
  plot_annotation(tag_levels = "A",
                  tag_suffix = ")")

# Export
ggsave("figure1.pdf", figure1, width = 7, height = 6, dpi = 300)

Tools & Packages Commonly Used

Common Journal Requirements

Quick Reference

Applying a theme

p + theme_nature()           # Nature style
p + theme_science()          # Science style
p + theme_cellpress()        # Cell Press style
p + theme_colorblind()       # Colorblind-safe

Export formats

# Vector (preferred)
ggsave("figure.pdf", ... device = "pdf")
ggsave("figure.eps", ... device = "eps")

# Raster (high DPI)
ggsave("figure.tiff", ... device = "tiff", dpi = 600)
ggsave("figure.png", ... device = "png", dpi = 300)

Common fixes

• Text too small: Increase base_size in theme
• Legend overlap: Use theme(legend.position = "bottom")
• Colors not distinct: Use scale_fill_viridis()
• Fonts not rendering: Use extrafont::font_import()

Notes

• Always check specific journal guidelines before submission
• Vector formats (PDF/EPS) are preferred over raster
• Use consistent styling across all figures in a paper
• Test colorblind accessibility with colorblindr package
• Keep axis labels clear and concise
• Avoid redundant chart junk (backgrounds, grid lines)