How to Use ggsave for High-Resolution Figures (PNG, PDF, SVG, TIFF)
Exporting blurry figures with overlapping labels is a common cause of journal peer-review delay. In R, ggsave() provides a convenient utility to export publication-ready plots.
In This Guide
0.Live Code: Plot and Export
1.The ggsave Formula
2.Exporting PNG vs Vector (PDF/SVG)
3.Sizing for Journal Columns
4.TIFF Export for Medical Journals
0. Live Code: Plot and Export
Sizing and resolution testing. Customize parameters using Python below, or upload your data to run R directly.
1. The ggsave Formula
By default, `ggsave()` saves the last displayed plot. We recommend explicitly binding the plot object to prevent saving errors:
R / ggplot2
p1 <- ggplot(df, aes(x, y)) + geom_point()
# Explicit export formula
ggsave(
filename = "figure1.png",
plot = p1,
device = "png",
width = 6,
height = 4.5,
units = "in",
dpi = 300
)2. Exporting PNG vs Vector (PDF/SVG)
Try it
Try it now: turn this method into your next figure
Apply the same approach to your own dataset and generate clean, publication-ready code and plots in minutes.
Open in Plotivy Analyze →Newsletter
Get a weekly Python plotting tip
One concise tip each week for cleaner, faster scientific figures. Built for researchers who publish.
Use vector formats (`.pdf`, `.svg`) for publication figures because they scale infinitely without raster pixelation. Use raster formats (`.png`, `.tiff`) for web or embedded presentations:
R / ggplot2
# Vector export (no DPI required)
ggsave("figure1.pdf", plot = p1, width = 150, height = 110, units = "mm")
ggsave("figure1.svg", plot = p1, width = 150, height = 110, units = "mm")3. Sizing for Journal Columns
Most journals use a two-column layout. Size your figures matching their column configurations to avoid scaling text distortions:
R / ggplot2
# Single-column target: ~80 to 90 mm
ggsave("fig_single.pdf", plot = p1, width = 85, height = 65, units = "mm")
# Double-column target: ~170 to 180 mm
ggsave("fig_double.pdf", plot = p1, width = 175, height = 130, units = "mm")4. TIFF Export for Medical Journals
Medical journals often demand LZW-compressed TIFF formats. Specify this using `compression = "lzw"`:
R / ggplot2
ggsave("figure1.tiff", plot = p1, width = 120, height = 90, units = "mm",
dpi = 600, compression = "lzw")Chart gallery
Explore related formats
Review chart standards.
Scatterplot
Displays values for two variables as points on a Cartesian coordinate system.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import pandas as pd
# Generate sample data
np.random.seed(42)
n_samples = 200
height = np.random.normal(170, 8, n_samples)
weight = height * 0.6 + np.random.normal(0, 8, n_samples) - 50
Line Graph
Displays data points connected by straight line segments to show trends over time.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
# Generate temperature data for 3 major US cities over 12 months
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
nyc = [30, 32, 40, 52, 65, 75, 82, 81, 74, 63, 50, 38]
miami = [65, 66, 70, 76, 82, 87, 90, 90, 87, 80, 72, 66]
chicago = [25, 27, 35, 48, 62, 72, 80, 79, 71, 60, 45, 32]
# Create figure with enhanced stylingExport ggplot2 Figures Online
Upload your data and describe the design. Plotivy writes the ggplot2 code and executes it instantly.
Technique guides scientists read next
scipy.signal.find_peaks guide
Tune prominence and width parameters for robust peak extraction.
Savitzky-Golay smoothing
Reduce noise while preserving peak shape and position.
PCA visualization workflow
Move from high-dimensional measurements to interpretable components.
ANOVA with post-hoc brackets
Add statistically correct pairwise significance annotations.
Found this helpful? Share it with your network.
Experimental Physicist & Photonics Researcher
Hands-on experience in silicon photonics, semiconductor fabrication (DRIE/ICP-RIE), optical simulation, and data-driven analysis. Built Plotivy to help researchers focus on discoveries instead of data struggles.
More about the authorVisualize your own data
Apply the techniques from this article to your own datasets. Upload CSV, Excel, or paste data directly.