The Complete ggplot2 Scatter Plot Guide: Custom Themes & Fit Lines
Scatter plots represent the most common layout to check correlations, distributions, and trends in scientific analysis. In R's ggplot2 system, scatter plots are built using the geom_point() geometry layer.
In This Guide
0.Live Code: Scatter Plot & Trendline
1.Basic Scatter Plot (geom_point)
2.Mapping Colors and Shapes
3.Adding Fit Lines (geom_smooth)
4.Customizing Point Aesthetics
0. Live Code: Scatter Plot & Trendline
Visualize numeric correlations. Modify the Python code below for standard plotting, or upload your data to run R directly.
1. Basic Scatter Plot with geom_point
Use `geom_point()` to draw data coordinates as points:
R / ggplot2
ggplot(df, aes(x = temperature, y = activity)) +
geom_point()2. Mapping Colors and Shapes to Groups
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Map groups to aesthetic markers (`color`, `shape`) inside `aes()`:
R / ggplot2
ggplot(df, aes(x = temperature, y = activity, color = genotype, shape = genotype)) +
geom_point(size = 2.5) +
scale_color_brewer(palette = "Set1")3. Adding Fit Lines with geom_smooth
Add regression lines (`method = "lm"`) or local LOESS curves (`method = "loess"`):
R / ggplot2
ggplot(df, aes(x = temperature, y = activity)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE, color = "red", fill = "gray80")4. Customizing Point Aesthetics
Specify shape codes (pch) such as filled circles with custom borders (pch = 21) to enhance scientific legibility:
R / ggplot2
ggplot(df, aes(x = temperature, y = activity)) +
geom_point(shape = 21, fill = "royalblue", color = "white", size = 3, stroke = 0.5)Chart gallery
Explore related formats
Review scatter and line formats.
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]
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