How to Plot Time Series and Line Graphs in R with ggplot2
Line plots are the primary tool to show trends over continuous variables, especially time. In R's ggplot2, line plots are built with the geom_line() geometry, often paired with geom_point() to highlight discrete sampling events.
In This Tutorial
0.Live Code: Multiple Series Line Plot
1.Basic Line Plot (geom_line)
2.Handling Date and Time Variables
3.Mapping Colors and Line Types
4.Adding Area Fills
0. Live Code: Multiple Series Line Plot
Time-series rendering. Customize parameters using Python below, or upload your data to run R directly.
1. Basic Line Plot with geom_line
A simple line graph connects discrete coordinate observations chronologically:
R / ggplot2
ggplot(df, aes(x = time_step, y = measurement)) +
geom_line(color = "#4f46e5", size = 1)2. Handling Date and Time Variables
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Ensure date columns are parsed correctly using R's as.Date(). Format scale labels using the `scales` library:
R / ggplot2
library(scales)
df$date <- as.Date(df$date)
ggplot(df, aes(x = date, y = measurement)) +
geom_line() +
scale_x_date(date_labels = "%b %Y", date_breaks = "2 months")3. Mapping Colors and Line Types
Distinguish multiple treatments using combinations of colors and dashed line patterns for accessibility:
R / ggplot2
ggplot(df, aes(x = time_step, y = measurement, color = treatment, linetype = treatment)) +
geom_line(size = 1.2) +
scale_color_manual(values = c("#4f46e5", "#059669"))4. Adding Area Fills (geom_ribbon)
Overlay a shaded margin representation for errors or confidence boundaries:
R / ggplot2
ggplot(df, aes(x = time_step, y = measurement)) +
geom_ribbon(aes(ymin = measurement - standard_error, ymax = measurement + standard_error), fill = "gray80", alpha = 0.4) +
geom_line(color = "black", size = 1)Chart gallery
Explore related formats
Review linear trends.
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 styling
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) - 50Build This Time Series Plot Online
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