How to Create a geom Line Plot with Single geom Point at the End with Legend in R

The combination of a geom_line plot with a single geom_point at the end is a highly effective visualization technique. It highlights the endpoint of each series in a plot, making it easier to compare trends across categories or groups in time-series data or other continuous datasets. This article will walk you through the entire process of creating such plots using R’s ggplot2 package, providing complete theory, detailed steps, and multiple examples using R Programming Language.

What Are Line Plots and Why Use geom_point at the End?

• Line Plots (geom_line): These plots are ideal for visualizing trends over time or across an ordered sequence of values. Each line represents a category, showing how its values change across a continuous variable (such as time).
• Single geom_point at the End: Adding a single point at the end of each line emphasizes the final value. This helps to highlight the most recent data point, making the visualization more informative, especially when comparing multiple lines.

Prerequisites

We will use the ggplot2 package for visualization. Make sure you have it installed:

install.packages("ggplot2") 
library(ggplot2)           

Step 1: Create the Sample Data

Let's create a sample dataset that includes multiple categories and a time variable:

# Sample data creation
data <- data.frame(
  Year = rep(2010:2020, 3),  # Years from 2010 to 2020 for three categories
  Value = c(10, 12, 15, 18, 22, 27, 30, 34, 38, 42, 45,   # Values for Category A
            8, 10, 13, 17, 19, 23, 26, 29, 31, 35, 39,    # Values for Category B
            15, 14, 18, 19, 22, 28, 30, 32, 36, 40, 43),  # Values for Category C
  Category = rep(c("A", "B", "C"), each = 11)  # Category labels
)
head(data)

Output:

  Year Value Category
1 2010    10        A
2 2011    12        A
3 2012    15        A
4 2013    18        A
5 2014    22        A
6 2015    27        A

This dataset represents three categories ("A," "B," and "C") with their respective values over the years from 2010 to 2020.

Step 2: Plot the Basic Line Graph

Next, create a basic line plot using ggplot2:

# Create the basic line plot
line_plot <- ggplot(data, aes(x = Year, y = Value, color = Category)) +
  geom_line(size = 1.2) +  # Create the lines
  theme_minimal() +        # Apply a minimal theme
  labs(title = "Basic Line Plot", x = "Year", y = "Value")
  
# Display the plot
print(line_plot)

Output:

This code produces a simple line plot showing trends across different categories over time.

Step 3: Adding geom_point at the End of Each Line

Now, we will add a point only at the endpoint of each line using geom_point().

# Add a single geom_point at the end of each line
end_points <- data %>%
  group_by(Category) %>%
  filter(Year == max(Year))  # Extract the last data point for each category

final_plot <- ggplot(data, aes(x = Year, y = Value, color = Category)) +
  geom_line(size = 1.2) +  # Create the lines
  geom_point(data = end_points, aes(x = Year, y = Value), size = 4) +  # Add end points
  theme_minimal() +
  labs(title = "Line Plot with Endpoints", x = "Year", y = "Value") +
  scale_color_manual(values = c("A" = "blue", "B" = "green", "C" = "red"))  # Custom color for lines

# Display the plot
print(final_plot)

Output:

• geom_line(size = 1.2): Draws thicker lines for better visibility.
• group_by(Category): Groups data by Category to handle each line separately.
• filter(Year == max(Year)): Selects the endpoint (last value) for each category.
• geom_point(size = 4): Adds a larger point at the end.

Example 2: Handling Multiple Lines with Different Data Types

Let’s consider another example with different groups represented by different line types (linetype).

# New dataset with an additional column for line types
data2 <- data.frame(
  Year = rep(2010:2020, 3),
  Value = c(12, 15, 18, 22, 25, 30, 35, 40, 43, 48, 50,
            10, 13, 17, 20, 24, 28, 32, 37, 41, 44, 49,
            17, 20, 22, 27, 29, 34, 39, 42, 47, 51, 55),
  Category = rep(c("X", "Y", "Z"), each = 11),
  LineType = rep(c("solid", "dashed", "dotted"), each = 11)
)
plot2 <- ggplot(data2, aes(x = Year, y = Value, color = Category, linetype = LineType)) +
  geom_line(size = 1.2) +
  geom_point(data = data2 %>% group_by(Category) %>% filter(Year == max(Year)), size = 4) +
  theme_minimal() +
  labs(title = "Line Plot with Different Line Types and End Points", x = "Year", y = "Value") +
  scale_color_manual(values = c("X" = "purple", "Y" = "orange", "Z" = "brown"))

print(plot2)

Output:

• linetype = LineType: Differentiates the lines by their type (e.g., solid, dashed).
• scale_color_manual(): Allows us to customize colors.

Common Errors and Troubleshooting

1. Mismatch in Data Types: Ensure your x and y variables are numeric or factors.
2. Missing Points: Make sure the filter function correctly identifies the last observation for each group.
3. Legend Customization: If your legend does not display correctly, check the aes() mappings.

Conclusion

Adding a geom_point at the end of geom_line plots provides an insightful way to highlight and emphasize the final data points of each series in your dataset. It’s a versatile technique that works across various scenarios, especially in time-series analysis, comparison studies, or trend visualization.