## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)

## ----load---------------------------------------------------------------------
library(rainerosr)

## ----example_data-------------------------------------------------------------
storm_data <- data.frame(
  datetime = as.POSIXct(c(
    "2024-01-15 14:00:00",
    "2024-01-15 14:10:00",
    "2024-01-15 14:20:00",
    "2024-01-15 14:30:00",
    "2024-01-15 14:40:00",
    "2024-01-15 14:50:00",
    "2024-01-15 15:00:00"
  )),
  rainfall_mm = c(2.5, 5.8, 8.2, 6.1, 3.4, 2.0, 1.5)
)

print(storm_data)

## ----calc_i30-----------------------------------------------------------------
i30_result <- calculate_i30(storm_data, "datetime", "rainfall_mm")

print(i30_result)

## ----calc_ei30----------------------------------------------------------------
ei30_result <- calculate_ei30(storm_data, "datetime", "rainfall_mm")

print(ei30_result)

## ----ke_equations-------------------------------------------------------------
# Brown & Foster (default)
ei30_bf <- calculate_ei30(storm_data, "datetime", "rainfall_mm", 
                         ke_equation = "brown_foster")

# Wischmeier & Smith
ei30_ws <- calculate_ei30(storm_data, "datetime", "rainfall_mm", 
                         ke_equation = "wischmeier")

# McGregor & Mutchler
ei30_mm <- calculate_ei30(storm_data, "datetime", "rainfall_mm", 
                         ke_equation = "mcgregor_mutchler")

# Compare results
comparison <- data.frame(
  Equation = c("Brown & Foster", "Wischmeier", "McGregor & Mutchler"),
  EI30 = c(ei30_bf$ei30, ei30_ws$ei30, ei30_mm$ei30),
  Energy = c(ei30_bf$total_energy, ei30_ws$total_energy, ei30_mm$total_energy)
)

print(comparison)

## ----multi_events-------------------------------------------------------------
# Create data with two storms
multi_storm <- data.frame(
  datetime = as.POSIXct(c(
    # Storm 1
    "2024-03-10 09:00:00", "2024-03-10 09:15:00", 
    "2024-03-10 09:30:00", "2024-03-10 09:45:00",
    # Storm 2 (10 hours later)
    "2024-03-10 19:00:00", "2024-03-10 19:20:00", 
    "2024-03-10 19:40:00", "2024-03-10 20:00:00"
  )),
  rainfall_mm = c(
    4.5, 7.2, 9.1, 5.3,  # Storm 1
    6.8, 10.2, 8.5, 4.9  # Storm 2
  )
)

# Process all events
events_summary <- process_storm_events(
  multi_storm,
  "datetime",
  "rainfall_mm",
  min_gap_hours = 6,
  min_rainfall_mm = 10,
  calculate_ei30 = TRUE
)

print(events_summary)

## ----validation---------------------------------------------------------------
validation <- validate_rainfall_data(storm_data, "datetime", "rainfall_mm")

if (validation$valid) {
  cat("✓ Data validation passed\n")
} else {
  cat("✗ Data validation failed:\n")
  print(validation$issues)
}

if (length(validation$warnings) > 0) {
  cat("\nWarnings:\n")
  print(validation$warnings)
}

## ----plot_pattern, fig.width=7, fig.height=5----------------------------------
plot_rainfall_pattern(storm_data, "datetime", "rainfall_mm", plot_type = "both",
                     title = "Storm Event - January 15, 2024")

## ----plot_intensity, fig.width=7, fig.height=5--------------------------------
plot_intensity_profile(storm_data, "datetime", "rainfall_mm", 
                      highlight_i30 = TRUE,
                      title = "Rainfall Intensity Profile")

## ----explicit_intervals-------------------------------------------------------
data_with_intervals <- data.frame(
  time = as.POSIXct(c(
    "2024-01-01 10:00:00",
    "2024-01-01 10:05:00",
    "2024-01-01 10:15:00",
    "2024-01-01 10:30:00"
  )),
  depth = c(3.2, 5.1, 4.8, 2.9),
  interval = c(5, 5, 10, 15)  # minutes
)

result <- calculate_i30(data_with_intervals, "time", "depth", "interval")
print(result)

## ----custom_events------------------------------------------------------------
data_with_event_id <- data.frame(
  datetime = as.POSIXct(c(
    "2024-01-01 10:00", "2024-01-01 10:15",
    "2024-01-01 11:00", "2024-01-01 11:15"
  )),
  rainfall_mm = c(6, 8, 7, 9),
  event_id = c(1, 1, 2, 2)
)

events <- process_storm_events(
  data_with_event_id,
  "datetime",
  "rainfall_mm",
  event_col = "event_id"
)

print(events)

## ----workflow, eval=FALSE-----------------------------------------------------
#  # 1. Load your data
#  my_data <- read.csv("rainfall_data.csv")
#  my_data$datetime <- as.POSIXct(my_data$datetime)
#  
#  # 2. Validate the data
#  validation <- validate_rainfall_data(my_data, "datetime", "rainfall_mm")
#  if (!validation$valid) {
#    stop("Data validation failed")
#  }
#  
#  # 3. Process multiple events
#  events <- process_storm_events(
#    my_data,
#    "datetime",
#    "rainfall_mm",
#    min_gap_hours = 6,
#    min_rainfall_mm = 12.7,
#    calculate_ei30 = TRUE,
#    ke_equation = "brown_foster"
#  )
#  
#  # 4. Analyze results
#  summary(events$ei30)
#  mean_erosivity <- mean(events$ei30)
#  
#  # 5. Visualize individual storms
#  for (evt_id in unique(events$event_id)) {
#    evt_data <- my_data[my_data$event_id == evt_id, ]
#    p <- plot_intensity_profile(evt_data, "datetime", "rainfall_mm")
#    print(p)
#  }
#  
#  # 6. Export results
#  write.csv(events, "erosivity_results.csv", row.names = FALSE)