# Load required libraries
library(tidyverse)

# Load the data
inbound <- read.csv("/path/to/inbound.csv", stringsAsFactors = FALSE)
outbound <- read.csv("/path/to/outbound.csv", stringsAsFactors = FALSE)

# Compute speed differences
compute_speed_difference <- function(data) {
  data <- data %>%
    arrange(X..Timestamp) %>%
    group_by(Name) %>%
    mutate(speed_difference = c(0, diff(SOG)))

  return(data)
}

inbound <- compute_speed_difference(inbound)
outbound <- compute_speed_difference(outbound)

# Combine the datasets and differentiate direction
inbound$Direction <- "Inbound"
outbound$Direction <- "Outbound"
combined_data <- rbind(inbound, outbound)

# Filter for the ship of interest
combined_data <- combined_data %>% filter(Name == "NEWNEW POLAR BEAR")

# Categorize the data into the two bins
#combined_data$New_Speed_Bins <- ifelse(abs(combined_data$speed_difference) <= 0.1, "Speed delta: -0.1 to 0.1", "> -0.1 to 0.1")
combined_data$New_Speed_Bins <- ifelse(abs(combined_data$speed_difference) <= 0.2, "-Speed delta: 0.2 to 0.2", "> -0.2 to 0.2")

# Create a contingency table of counts using the new bins
new_contingency_table <- table(combined_data$Direction, combined_data$New_Speed_Bins)

# Conduct the Chi-Squared Test for Independence
new_chi_squared_test <- chisq.test(new_contingency_table)

# Print the new contingency table for inspection
print(new_contingency_table)

# Print the Chi-Squared test results
new_chi_squared_test