R ggplot2 Tutorial: Create Publication-Ready Figures in Minutes
Creating publication-ready figures is a critical step in communicating scientific findings. R and the ggplot2 package provide one of the most powerful declarative systems for building statistical graphics. This guide walks you through the fundamentals of constructing a high-quality figure from scratch.
When to use this R ggplot2 tutorial
Use this workflow when you need a clean scientific figure with deliberate theme, colour, and export settings. It is especially useful after you have chosen a chart type and are ready to turn a draft plot into a consistent final figure.
In This Tutorial
0.Live Code: Publication-Ready Plot
1.The ggplot2 Structure
2.Customizing Themes (theme_minimal)
3.Adding Custom Colors
4.Exporting to High-Resolution
0. Live Code: Publication-Ready Classic Style
Explore the clean, minimalist classic style equivalent to ggplot2's classic theme. Use the interactive editor below to customize parameters, or upload your data to run R directly.
1. The ggplot2 Grammar of Graphics Structure
The grammar of graphics separates data, aesthetics, and geometric layers. Here is the absolute baseline formula for any ggplot2 figure in R:
R / ggplot2
library(ggplot2)
# Baseline structure
ggplot(data = df, mapping = aes(x = variable_x, y = variable_y)) +
geom_point() +
geom_smooth(method = "lm")Try it
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2. Customizing Themes for Journals
Default grey backgrounds are not suitable for journal submission. Utilize theme_classic() or theme_minimal() to establish a clean base:
R / ggplot2
ggplot(df, aes(x, y)) +
geom_point() +
theme_classic(base_size = 11, base_family = "Arial") +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, color = "black")
)3. Adding Custom Colors
Map qualitative variables using discrete scales with high-contrast, professional colors. Avoid generic primary colors:
R / ggplot2
ggplot(df, aes(x, y, color = group)) +
geom_point(size = 2, alpha = 0.8) +
scale_color_manual(values = c("#4f46e5", "#d97706", "#059669"))4. Exporting to High-Resolution
Use ggsave() to export your plot. Standard journals require 300 DPI or higher:
R / ggplot2
p <- ggplot(df, aes(x, y)) + geom_point()
ggsave("figure1.png", plot = p, width = 120, height = 90, units = "mm", dpi = 300)Chart gallery
Explore related formats
Review different plot formats where themes are crucial.
Scatterplot
A scatter plot displays two quantitative variables as points, helping you examine relationships, clusters, outliers, and regression trends.
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
Create a line graph in Python to connect ordered data points, reveal time-series trends, and compare how multiple values change across a continuous scale.
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 styling
Bar Chart
Create a bar chart in Python to compare categorical values with proportional bars, error bars, clear labels, and grouped comparisons.
Sample code / prompt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# Generate performance scores for 5 treatment groups
np.random.seed(42)
groups = ['Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Treatment D']
n_samples = 30.png&w=1280&q=70)
Box and Whisker Plot
Create a box plot in Python to compare distributions using quartiles, medians, whiskers, and outliers.
Sample code / prompt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# Generate gene expression data for 4 genotypes
np.random.seed(42)
genotypes = ['WT', 'KO1', 'KO2', 'Mutant']
n_per_group = 20Build This Figure Online Instantly
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ANOVA with post-hoc brackets
Add statistically correct pairwise significance annotations.
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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.
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