A flexible R function to visualize financial returns data in a calendar heatmap format. This visualization helps identify patterns across days of the week, weeks of the month, and months of the year.
- Create calendar heatmaps for any date range
- Customizable colors for positive, negative, and zero returns
- Proper visualization of weekdays and calendar structure
- Automatic handling of missing data
- Clear month and year labeling
# Install required packages if you don't have them
install.packages(c("ggplot2", "lubridate", "dplyr"))The main function is create_calendar_heatmap(). Here's a basic example:
# Load the script
source("calender_heatmap.R")
# Create sample data
set.seed(123)
returns <- rnorm(365, mean = 0, sd = 0.02)
start_date <- "2023-01-01"
end_date <- "2023-12-31"
# Create the visualization
heatmap <- create_calendar_heatmap(
returns = returns,
start_date = start_date,
end_date = end_date,
title = "Stock Returns Calendar Heatmap"
)
# Display the plot
print(heatmap)
# Save the plot
ggsave("calendar_heatmap.png", heatmap, width = 12, height = 8, dpi = 300)| Parameter | Description | Default |
|---|---|---|
returns |
Numeric vector of daily returns | (required) |
start_date |
Start date (Date object or "YYYY-MM-DD" string) | Previous year from today |
end_date |
End date (Date object or "YYYY-MM-DD" string) | Today |
title |
Plot title | "Calendar Heatmap of Returns" |
low_color |
Color for negative returns | "red" |
mid_color |
Color for zero returns | "white" |
high_color |
Color for positive returns | "green" |
The script includes a demo_calendar_heatmap() function that demonstrates two different use cases:
- A full year of returns with simulated April trend and Monday effect
- A quarter of returns with alternative color scheme
To run the demo:
source("calender_heatmap.R")
demo_calendar_heatmap()This project is licensed under the MIT License - see the LICENSE file for details.