Generating Publication Ready Figures In R

Transform standard ggplot2 figures into publication-quality visualizations matching Nature, Science, and other top journal styles with proper themes, colors,...

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

Purpose	R Packages
Base plotting	ggplot2
Themes	ggplot2, cowplot, hrbrthemes
Color palettes	viridis, RColorBrewer, scales, ggsci
Multi-panel	patchwork, cowplot, ggpubr
Export	ggplot2, ragg
Fonts	extrafont, showtext
Annotations	ggrepel, ggpp

---

Common Journal Requirements

Journal	Width (single)	Width (double)	Max Height	Min DPI
Nature	89 mm	183 mm	234 mm	300
Science	57 mm	114 mm	229 mm	300
Cell	85 mm	178 mm	229 mm	300
PNAS	87 mm	178 mm	227 mm	300
PLOS ONE	170 mm	-	230 mm	300
eLife	183 mm	-	244 mm	300

---

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)