Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
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,...
skill openclawclawhub Free
Transform standard ggplot2 figures into publication-quality visualizations matching Nature, Science, and other top journal styles with proper themes, colors,...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md
Validation
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- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
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New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- JackKuo666
- Source page
- View original listing
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Documentation
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: Analyze existing ggplot code - Read and understand the current plot structure 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 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) Create multi-panel figures - Combine plots using: patchwork for simple layouts cowplot for complex compositions Custom annotation and labeling 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"
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_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
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
PurposeR PackagesBase plottingggplot2Themesggplot2, cowplot, hrbrthemesColor palettesviridis, RColorBrewer, scales, ggsciMulti-panelpatchwork, cowplot, ggpubrExportggplot2, raggFontsextrafont, showtextAnnotationsggrepel, ggpp
Common Journal Requirements
JournalWidth (single)Width (double)Max HeightMin DPINature89 mm183 mm234 mm300Science57 mm114 mm229 mm300Cell85 mm178 mm229 mm300PNAS87 mm178 mm227 mm300PLOS ONE170 mm-230 mm300eLife183 mm-244 mm300
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)
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- README.md