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Publication-Ready Figure Export

Python (matplotlib)

python
import matplotlib.pyplot as plt

# Set publication defaults
plt.rcParams.update({
    'font.size': 8,
    'font.family': 'Arial',
    'axes.linewidth': 0.5,
    'lines.linewidth': 1,
    'figure.dpi': 300
})

fig, ax = plt.subplots(figsize=(3.5, 3))  # Single column width
# ... create plot ...

# Save in multiple formats
fig.savefig('figure1.pdf', bbox_inches='tight', dpi=300)
fig.savefig('figure1.png', bbox_inches='tight', dpi=300)
fig.savefig('figure1.svg', bbox_inches='tight')

R (ggplot2)

r
library(ggplot2)

p <- ggplot(data, aes(x, y)) + geom_point() +
  theme_classic(base_size = 8) +
  theme(text = element_text(family = 'Arial'))

# PDF for vector graphics
ggsave('figure1.pdf', p, width = 3.5, height = 3, units = 'in')

# High-res PNG
ggsave('figure1.png', p, width = 3.5, height = 3, units = 'in', dpi = 300)

# TIFF (some journals require)
ggsave('figure1.tiff', p, width = 3.5, height = 3, units = 'in',
       dpi = 300, compression = 'lzw')

Journal Requirements

| Journal Type | Format | Resolution | Width | |--------------|--------|------------|-------| | Most journals | PDF/EPS | Vector | 3.5" (1-col), 7" (2-col) | | Online-only | PNG | 300 DPI | Variable | | Print | TIFF | 300-600 DPI | Column width |

Multi-panel Figures

python
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

fig = plt.figure(figsize=(7, 5))  # Two-column width
gs = GridSpec(2, 3, figure=fig)

ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1:])
ax3 = fig.add_subplot(gs[1, :])

# Add panel labels
for ax, label in zip([ax1, ax2, ax3], ['A', 'B', 'C']):
    ax.text(-0.1, 1.1, label, transform=ax.transAxes,
            fontsize=10, fontweight='bold')

fig.savefig('figure_multipanel.pdf', bbox_inches='tight')

Color Considerations

• Use colorblind-friendly palettes (viridis, cividis)
• Ensure sufficient contrast for grayscale printing
• Maintain consistency across all figures

Related Skills

• data-visualization/ggplot2-fundamentals - Creating plots in R
• data-visualization/heatmaps-clustering - Complex visualizations
• data-visualization/multipanel-figures - Figure composition

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