write a routine in R to calculate the crosscorelation of to data sets

To calculate the cross-correlation between two time series data sets in R, you can create a function that utilizes the built-in `ccf` (cross-correlation function) function in R. Below is a simple routine that demonstrates how to do this:

1. Load the necessary libraries (if required).
2. Define a function to calculate and plot the cross-correlation.
3. Call the function with your data sets.

Here's a complete example:

```r
# Load necessary package
# Uncomment the next line if you need to install the 'ggplot2' package for enhanced plotting
# install.packages("ggplot2")

library(ggplot2)

# Function to calculate and plot cross-correlation
cross_correlation <- function(time_series1, time_series2, max_lag = 20) {
  # Check if the lengths of both time series are the same
  if (length(time_series1) != length(time_series2)) {
    stop("Time series must have the same length")
  }
  
  # Calculate cross-correlation
  ccf_result <- ccf(time_series1, time_series2, lag.max = max_lag, plot = FALSE)
  
  # Create a data frame for plotting
  ccf_data <- data.frame(lag = ccf_result$lag, ccf = ccf_result$acf)
  
  # Plot the cross-correlation
  ggplot(ccf_data, aes(x = lag, y = ccf)) +
    geom_bar(stat = "identity", fill = "blue") +
    labs(title = "Cross-Correlation Function",
         x = "Lag",
         y = "Cross-Correlation") +
    theme_minimal()
  
  return(ccf_result)
}

# Example of using the function
# Create two example time series
set.seed(123)
data1 <- rnorm(100)
data2 <- rnorm(100, mean = 0.5 * data1)

# Call the cross_correlation function
result <- cross_correlation(data1, data2, max_lag = 20)

# View the summary of the cross-correlation result
print(result)
```

### Explanation:
- **Cross-correlation function**: The `ccf` function computes the cross-correlation of two time series, specifying `lag.max` to determine the maximum lag to consider.
- **Plotting**: The function uses `ggplot2` to visualize the cross-correlation values at various lags.
- **Data check**: Before computing, it checks if the lengths of the two time series are the same.
  
### Example Data:
In this example, two synthetic time series (`data1` and `data2`) are created to demonstrate the procedure. Adjust the two datasets accordingly based on your needs.

You can replace `data1` and `data2` with your actual datasets. Note that these datasets should be numeric vectors of the same length.