## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE
)

## ----setup--------------------------------------------------------------------
library(nhscancerwaits)

## -----------------------------------------------------------------------------
set.seed(123)

example_data <- expand.grid(
  provider_code = paste0("P", 1:12),
  cancer_type = c("Breast", "Lung", "Skin", "Lower GI"),
  month_index = 1:12,
  KEEP.OUT.ATTRS = FALSE
)

example_data$provider_name <- paste(
  "Provider",
  example_data$provider_code
)

example_data$standard <- "62-day"

example_data$reporting_date <- seq.Date(
  from = as.Date("2026-01-01"),
  by = "month",
  length.out = 12
)[example_data$month_index]

example_data$total_treated <- sample(
  30:120,
  nrow(example_data),
  replace = TRUE
)

example_data$performance_percent <- round(
  runif(
    nrow(example_data),
    min = 60,
    max = 92
  ),
  1
)

head(example_data)

## -----------------------------------------------------------------------------
kpi_summary <- summarise_kpis(
  example_data,
  group_var = "standard",
  performance_var = "performance_percent"
)

kpi_summary

## -----------------------------------------------------------------------------
filtered_data <- filter_providers(
  example_data,
  provider_var = "provider_code",
  activity_var = "total_treated",
  performance_var = "performance_percent",
  min_mean_activity = 20,
  min_observations = 5,
  max_cv = 0.5
)

nrow(filtered_data)

## -----------------------------------------------------------------------------
provider_summary <- summarise_providers(
  filtered_data,
  provider_var = "provider_code",
  performance_var = "performance_percent",
  activity_var = "total_treated"
)

head(provider_summary)

## -----------------------------------------------------------------------------
pathway_summary <- summarise_pathways(
  filtered_data,
  pathway_var = "cancer_type",
  performance_var = "performance_percent"
)

pathway_summary

## -----------------------------------------------------------------------------
model <- fit_cwt_mixed_model(
  filtered_data,
  performance_var = "performance_percent",
  month_var = "month_index",
  pathway_var = "cancer_type",
  provider_var = "provider_code"
)

model

## -----------------------------------------------------------------------------
icc_results <- calculate_icc(model)

icc_results

## -----------------------------------------------------------------------------
model_effects <- extract_model_effects(model)

model_effects

## -----------------------------------------------------------------------------
provider_effects <- extract_provider_effects(
  model,
  provider_name = "provider_code"
)

head(provider_effects)

## -----------------------------------------------------------------------------
pathway_predictions <- predict_pathway_performance(
  model,
  filtered_data,
  pathway_var = "cancer_type",
  month_var = "month_index",
  provider_var = "provider_code"
)

pathway_predictions

## -----------------------------------------------------------------------------
provider_clusters <- cluster_providers(
  filtered_data,
  provider_var = "provider_code",
  performance_var = "performance_percent",
  activity_var = "total_treated",
  k = 3
)

head(provider_clusters)

## -----------------------------------------------------------------------------
sensitivity_results <- run_sensitivity_analysis(
  filtered_data,
  provider_var = "provider_code",
  activity_var = "total_treated",
  performance_var = "performance_percent",
  month_var = "month_index",
  pathway_var = "cancer_type"
)

sensitivity_results

## -----------------------------------------------------------------------------
wide_table <- pivot_provider_months(
  filtered_data,
  provider_var = "provider_code",
  month_var = "reporting_date",
  performance_var = "performance_percent"
)

head(wide_table)

## -----------------------------------------------------------------------------
silhouette_score <- calculate_silhouette_score(
  provider_clusters
)

silhouette_score

## ----fig.width=7, fig.height=5------------------------------------------------
plot_national_trends(
  filtered_data,
  month_var = "reporting_date",
  performance_var = "performance_percent",
  group_var = "standard"
)

## ----fig.width=7, fig.height=5------------------------------------------------
plot_provider_effects(
  provider_effects,
  provider_var = "provider_code",
  effect_var = "adjusted_effect"
)

## ----fig.width=7, fig.height=5------------------------------------------------
plot_pathway_predictions(
  pathway_predictions,
  pathway_var = "cancer_type",
  prediction_var = "predicted_performance"
)

## ----fig.width=7, fig.height=5------------------------------------------------
plot_provider_clusters(
  provider_clusters
)

## ----eval = FALSE-------------------------------------------------------------
# export_excel_tables(
#   tables = list(
#     kpi_summary = kpi_summary,
#     provider_summary = provider_summary,
#     pathway_summary = pathway_summary,
#     icc_results = icc_results,
#     model_effects = model_effects,
#     provider_effects = provider_effects,
#     pathway_predictions = pathway_predictions,
#     provider_clusters = provider_clusters,
#     sensitivity_results = sensitivity_results
#   ),
#   path = "nhscancerwaits_results.xlsx"
# )

## ----eval = FALSE-------------------------------------------------------------
# library(nhscancerwaits)
# 
# data <- load_cwt_excel(
#   "your_nhs_cancer_waiting_times_file.xlsx"
# )
# 
# data <- clean_cwt_data(data)
# 
# kpis <- summarise_kpis(data)
# 
# filtered <- filter_providers(data)
# 
# model <- fit_cwt_mixed_model(filtered)
# 
# icc <- calculate_icc(model)
# 
# provider_effects <- extract_provider_effects(model)
# 
# pathway_predictions <- predict_pathway_performance(
#   model,
#   filtered
# )
# 
# provider_clusters <- cluster_providers(filtered)
# 
# sensitivity <- run_sensitivity_analysis(filtered)