Understanding Level Plots and Adding Mean and Median

In the field of data visualization, level plots are a type of plot that displays the relationship between two variables on a 3D surface. This is particularly useful for visualizing complex relationships between large datasets.

Prerequisites: Level Plot Basics

Before we dive into adding mean and median to a level plot, it’s essential to understand how level plots work. A level plot typically consists of three main components:

• The x-axis represents the values of one variable.
• The y-axis represents the values of another variable.
• The z-axis represents the relationship between the two variables on a 3D surface.

In this specific case, we’re working with a raster-based level plot using the mapTheme and my.brks functions in R. We have a dataset called Gm, which contains information about R_land.

Calculating Mean and Median

To calculate the mean and median of a dataset, we use various mathematical formulas.

• Mean: The mean is calculated by adding up all values in the dataset and dividing by the total number of values.

mean_value = (sum_values / total_numbers)

*   **Median**: The median is calculated by arranging the data in ascending order. If there's an odd number of observations, the middle value is the median. For even numbers, the average of the two middle values is used.

Here's how we can calculate the mean and median for our `Gm` dataset using R:

```markdown
# Calculate mean and median for Gm
mean_Gm <- mean(Gm)
median_Gm <- median(Gm)

print(paste("Mean: ", mean_Gm))
print(paste("Median: ", median_Gm))

Adding Mean and Median to a Level Plot

Adding the mean and median values to our level plot manually might seem tedious, but there’s an easier way using R’s levelplot function with some additional arguments.

Using the levelplot Function

To add the mean and median values directly to the level plot, we can use the col.keys argument. This argument allows us to display custom text on a color key legend.

# Create a new figure for plotting
new_p1 <- levelplot(R_land, par.settings = mapTheme,
                     at=my.brks, 
                     colorkey = myColorkey, 
                     margin = F,
                     main=list('IMD'),
                     xlab = 'Mean=0.728 Median=0.368',
                     col.keys = list(title="Level Plot Keys",
                                   key.text = c("Mean", "Median"),
                                   key.width = 2))

# Display the new level plot
print(new_p1)

Using a Custom Color Key

The col.keys argument allows us to specify custom text for each color in our color key.

# Create a new figure for plotting
new_p1 <- levelplot(R_land, par.settings = mapTheme,
                     at=my.brks, 
                     colorkey = myColorkey, 
                     margin = F,
                     main=list('IMD'),
                     xlab = 'Mean=0.728 Median=0.368',
                     col.keys = list(title="Level Plot Keys",
                                   key.text = c("Low", "Medium", "High"),
                                   key.width = 2))

# Display the new level plot
print(new_p1)

In this example, we’re using a simple text-based color key with labels for “Low,” “Medium,” and “High.”

Real-World Example: Visualizing R Land Data

Let’s take our R_land dataset and create a real-world example of adding mean and median to the level plot.

# Load required libraries
library(RColorBrewer)

# Create a new figure for plotting
new_p1 <- levelplot(R_land, par.settings = mapTheme,
                     at=my.brks, 
                     colorkey = myColorkey, 
                     margin = F,
                     main=list('R Land Data'),
                     xlab = 'Value',
                     col.keys = list(title="Level Plot Keys",
                                   key.text = c("Low", "Medium", "High"),
                                   key.width = 2))

# Display the new level plot
print(new_p1)

In this example, we’re using a real-world dataset called R_land and adding mean and median values to our level plot. The resulting figure displays the relationship between two variables on a 3D surface with added labels for easy interpretation.

Conclusion

Adding mean and median values to your level plots can be tedious but is easier when using R’s built-in functions, such as levelplot’s custom color key feature. By leveraging this feature, you can create informative visualizations that help you understand complex relationships between large datasets.

In conclusion, adding mean and median values to a level plot involves the following steps:

1. Calculate the mean and median values for your dataset.
2. Use R’s levelplot function with custom color key arguments.
3. Display the resulting figure on your graph.

By following these steps, you can create clear and informative visualizations that help you better understand complex relationships between large datasets.

Additional Tips

Here are some additional tips to keep in mind when working with level plots:

• Choose a suitable colormap: The color map should be chosen based on the type of data being displayed. For example, using a grayscale colormap for binary data can make it easier to distinguish between different categories.
• Use meaningful labels: Use clear and concise labels for your axes to ensure that your viewers understand what they’re looking at.
• Experiment with customization options: Don’t be afraid to experiment with different visualization parameters, such as color maps, markers, or line styles.

By following these tips, you can create visually appealing level plots that effectively communicate complex information.

Last modified on 2024-03-09