How to Get Multiple Years Y-Axis Data from a Single File on the Same Plot using R

In time-series data analysis, it is common to work with data that spans multiple years. Displaying data from multiple years on the same plot can provide valuable insights by allowing us to visually compare trends over time. In this article, we will walk through the process of plotting data from multiple years on the same y-axis using R's ggplot2 package.

Understanding the Problem

When dealing with time-series data, especially with multiple years of data, you often need to plot them on the same plot. This can be useful for comparing trends year-over-year. We will demonstrate how to plot multiple years of data from a single file on the same plot while keeping the y-axis values aligned.

Now we will discuss the step-by-step implementation of How to Get Multiple Years of Y-Axis Data from a Single File on the Same Plot using R Programming Language.

Step 1: Preparing the Data

Let’s assume you have a dataset that includes daily or monthly values for multiple years. Here’s an example dataset with temperature data for three years.

# Sample dataset
data <- data.frame(
  Date = as.Date(c('2020-01-01', '2020-02-01', '2020-03-01', '2021-01-01', '2021-02-01', 
                               '2021-03-01', '2022-01-01', '2022-02-01', '2022-03-01')),
  Value = c(10, 12, 15, 9, 13, 16, 8, 14, 18),
  Year = c(2020, 2020, 2020, 2021, 2021, 2021, 2022, 2022, 2022)
)

# View the data
print(data)

Output:

        Date Value Year
1 2020-01-01    10 2020
2 2020-02-01    12 2020
3 2020-03-01    15 2020
4 2021-01-01     9 2021
5 2021-02-01    13 2021
6 2021-03-01    16 2021
7 2022-01-01     8 2022
8 2022-02-01    14 2022
9 2022-03-01    18 2022

This dataset contains values for the months of January, February, and March for the years 2020, 2021, and 2022.

Step 2: Plotting the Data Using ggplot2

We can use the ggplot2 package to create a line chart that displays data for multiple years on the same plot. The ggplot2 package allows us to plot data by mapping the color aesthetic to the Year variable, which helps differentiate between years.

# Install ggplot2 if not already installed
install.packages("ggplot2")

# Load the ggplot2 package
library(ggplot2)

# Plot the data using ggplot2
ggplot(data, aes(x = Date, y = Value, color = as.factor(Year))) +
  geom_line(size = 1.2) +
  labs(title = "Multiple Years Data on the Same Y-Axis",
       x = "Date",
       y = "Value",
       color = "Year") +
  scale_x_date(date_labels = "%b", date_breaks = "1 month") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Output:

• aes(x = Date, y = Value, color = as.factor(Year)): This aesthetic mapping defines the x-axis (Date), y-axis (Value), and color (Year). We use as.factor(Year) to treat the Year variable as a categorical variable for coloring.
• geom_line(size = 1.2): Adds the lines to the plot with a specified line thickness.
• labs(): Adds labels for the plot title, x-axis, y-axis, and color legend.
• scale_x_date(date_labels = "%b", date_breaks = "1 month"): Formats the x-axis to display month names and adds breaks every month.
• theme_minimal(): Applies a minimalist theme to the plot.
• theme(axis.text.x = element_text(angle = 45, hjust = 1)): Rotates the x-axis labels 45 degrees for better readability.

Step 3: Customizing the Plot

You can further customize the plot by adding additional aesthetics, changing themes, or adjusting line properties. Here’s an example where we customize the plot by adding point markers and changing the line colors.

# Customize the plot with additional aesthetics
ggplot(data, aes(x = Date, y = Value, color = as.factor(Year))) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +  # Add points to the plot
  labs(title = "Customized Multiple Years Data on the Same Y-Axis",
       x = "Date",
       y = "Value",
       color = "Year") +
  scale_x_date(date_labels = "%b", date_breaks = "1 month") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_color_manual(values = c("2020" = "red", "2021" = "blue", "2022" = "green"))

Output:

• Added point markers with geom_point().
• Customized the line colors using scale_color_manual(), where each year is assigned a specific color.

Conclusion

In this article, we demonstrated how to plot multiple years of data on the same y-axis using R and the ggplot2 package. This approach is useful for comparing trends year-over-year. By using aesthetics such as color to represent different years and formatting the x-axis for better readability, you can create clear and insightful time-series plots.