harmonized.R

### Fichier harmonized.R - Module d'indicateurs harmonisés pour l'analyse du paludisme
### à la frontière Brésil-Colombie

# Fonctions réactives pour la gestion des dates
date1 <- reactive({
  as.Date(paste0(input$dates[1],"-01-01"))
})

date2 <- reactive({
  as.Date(paste0(input$dates[2],"-12-31"))
})

# Filtrage principal des données
HAR_sub <- reactive({
  date1 <- as.Date(paste0(input$dates[1],"-01-01"))
  date2 <- as.Date(paste0(input$dates[2],"-12-31"))
  
  consultation_har_filter(JSON_cons_har,
                          new_attack = input$new_attack,
                          diagn = input$diagn,
                          sex = input$sex,
                          minAge = input$age[1], 
                          maxAge = input$age[2]
  ) %>%
    filter(consultation_date >= date1 & consultation_date <= date2)
})

# Séparation des données par pays
HAR_br <- reactive({
  HAR_sub() %>% filter(source == "BR")
})

HAR_co <- reactive({
  # Vérification au niveau du filtrage initial
  initial_data <- HAR_sub() %>% filter(source == "CO")
  print("Données colombiennes après filtrage initial :")
  print(paste("Nombre de cas :", nrow(initial_data)))
  print("Exemple de dates :")
  print(head(initial_data$consultation_date))
  
  return(initial_data)
})

# Création des tableaux temporels pour le Brésil
HAR_br_tab <- reactive({
  agg <- as.numeric(input$agg)
  date1 <- as.Date(paste0(input$dates[1],"-01-01"))
  date2 <- as.Date(paste0(input$dates[2],"-12-31"))
  
  if(agg == 1){
    # Agrégation journalière
    HAR_br() %>% 
      group_by(dates = floor_date(consultation_date, "day")) %>%
      summarize(counts=n()) %>%
      complete(dates = seq.Date(date1, date2, by = "day"), 
               fill = list(counts = 0))
  } else if(agg == 6){
    # Agrégation hebdomadaire
    as.data.frame(incidence(HAR_br()$consultation_date, 
                            interval = 7, 
                            iso_week = TRUE)) %>%
      select(dates, counts)
  } else if(agg == 30){
    # Agrégation mensuelle
    HAR_br() %>% 
      group_by(dates = floor_date(consultation_date, "month")) %>%
      summarize(counts=n()) %>%
      complete(dates = seq.Date(date1, date2, by = "month"), 
               fill = list(counts = 0))
  } else if(agg == 90){
    # Agrégation trimestrielle
    HAR_br() %>% 
      group_by(dates = floor_date(consultation_date, "quarter")) %>%
      summarize(counts=n()) %>%
      complete(dates = seq.Date(date1, date2, by = "quarter"), 
               fill = list(counts = 0))
  } else if(agg == 366){
    # Agrégation annuelle
    HAR_br() %>% 
      group_by(dates = floor_date(consultation_date, "year")) %>%
      summarize(counts=n()) %>%
      complete(dates = seq.Date(date1, date2, by = "year"), 
               fill = list(counts = 0))
  }
})

# Création des tableaux temporels pour la Colombie
HAR_co_tab <- reactive({
  agg <- as.numeric(input$agg)
  date1 <- as.Date(paste0(input$dates[1],"-01-01"))
  date2 <- as.Date(paste0(input$dates[2],"-12-31"))
  
  # Création d'une série temporelle complète avec des zéros pour les périodes sans cas
  data_co <- HAR_co() %>% 
    group_by(dates = floor_date(consultation_date, "month")) %>%
    summarize(counts = n()) %>%
    # Cette ligne est cruciale : elle crée tous les mois, même ceux sans cas
    complete(dates = seq.Date(date1, date2, by = "month"), 
             fill = list(counts = 0))
  
  return(data_co)
})

##################
### Visualisations et compteurs
##################

# Compteur pour la Colombie
output$HAR_count_co <- renderValueBox({
  valueBox(
    paste(nrow(HAR_co()), tr("cases2")), tr("CO"), color = "purple"
  )
})

# Compteur pour le Brésil
output$HAR_count_br <- renderValueBox({
  valueBox(
    paste(nrow(HAR_br()), tr("cases2")), tr("BR"), color = "purple"
  )
})

# Graphique d'incidence
output$HAR_plot_incidence <- renderPlotly({
  plot_ly() %>%
    add_trace(
      data = HAR_br_tab(), 
      x = ~dates, 
      y = ~counts, 
      type = 'scatter', 
      mode = 'lines',
      line = list(color = 'blue', width = 2),
      name = paste0("Brazil (", text_recup_donnees_BR, ")")
    ) %>%
    add_trace(
      data = HAR_co_tab(), 
      x = ~dates, 
      y = ~counts, 
      type = 'scatter', 
      mode = 'lines',
      line = list(color = 'red', width = 2),
      name = paste0("Colombia (", text_recup_donnees_CO, ")")
    ) %>%
    layout(
      xaxis = list(title = "Date"),
      yaxis = list(
        title = "Cases",
        type = 'linear',  # Échelle linéaire au lieu de logarithmique
        tickformat = 'd'  # Format entier pour les nombres
      ),
      showlegend = TRUE,
      legend = list(x = 0, y = 1.2)
    )
})
# Graphique des âges
output$HAR_plot_age <- renderPlotly({
  # Préparation des données
  HAR_br <- HAR_br() %>% mutate(patient_age = round(patient_age, 0))
  HAR_co <- HAR_co() %>% mutate(patient_age = round(patient_age, 0))
  
  # Création du graphique
  plot_ly() %>%
    add_trace(data = HAR_br, 
              x = ~patient_age, 
              type = "histogram", 
              name = tr("BR")) %>%
    add_trace(data = HAR_co, 
              x = ~patient_age, 
              type = "histogram", 
              name = tr("CO")) %>%
    layout(xaxis = list(title = tr("age"), domain = c(0,100)), 
           yaxis = list(title = tr("cases"))) %>%
    layout(legend = list(x = 0.8, y = 0.9))
})

# Graphique de la répartition par sexe
output$HAR_plot_sex <- renderPlotly({
  # Liste des pays
  HAR_loc <- c("CO","BR")
  
  # Filtrage des données par sexe
  HAR_br_sex_sub <- subset(HAR_br(), patient_sex == "Male" | patient_sex == "Female")
  HAR_co_sex_sub <- subset(HAR_co(), patient_sex == "Male" | patient_sex == "Female")
  
  # Calcul des proportions
  HAR_br_sex_tab <- prop.table(table(HAR_br_sex_sub$patient_sex))*100
  HAR_co_sex_tab <- prop.table(table(HAR_co_sex_sub$patient_sex))*100
  HAR_male <- c(HAR_co_sex_tab["Male"],HAR_br_sex_tab["Male"])
  HAR_female <- c(HAR_co_sex_tab["Female"],HAR_br_sex_tab["Female"])
  HAR_data <- data.frame(HAR_loc, HAR_male, HAR_female)
  
  # Création du graphique
  plot_ly(HAR_data, 
          x = ~HAR_loc, 
          y = ~HAR_male, 
          type = "bar", 
          name = tr("male")) %>%
    add_trace(y = ~HAR_female, name = tr("female")) %>%
    layout(xaxis = list(title = ""), 
           yaxis = list(title = tr("percentage")), 
           barmode = 'stack')
})

# Graphique de la répartition par type de Plasmodium
output$HAR_plot_plasm <- renderPlotly({
  # Liste des pays
  HAR_loc <- c("CO","BR")
  
  # Calcul des totaux
  HAR_br_any <- nrow(consultation_har_filter(HAR_br(), 
                                             diagn = "Any", 
                                             new_attack = input$new_attack))
  HAR_co_any <- nrow(consultation_har_filter(HAR_co(), 
                                             diagn = "Any", 
                                             new_attack = input$new_attack))
  
  # Calcul des pourcentages par type
  HAR_br_falci <- nrow(consultation_har_filter(HAR_br(), 
                                               diagn = "falciparum", 
                                               new_attack = input$new_attack))/HAR_br_any*100
  HAR_br_mixed <- nrow(consultation_har_filter(HAR_br(), 
                                               diagn = "mixed infection with P. falciparum", 
                                               new_attack = input$new_attack))/HAR_br_any*100
  HAR_br_other <- nrow(consultation_har_filter(HAR_br(), 
                                               diagn = "non-falciparum", 
                                               new_attack = input$new_attack))/HAR_br_any*100
  HAR_br_unspe <- nrow(consultation_har_filter(HAR_br(), 
                                               diagn = "unspecified", 
                                               new_attack = input$new_attack))/HAR_br_any*100
  
  HAR_co_falci <- nrow(consultation_har_filter(HAR_co(), 
                                               diagn = "falciparum", 
                                               new_attack = input$new_attack))/HAR_co_any*100
  HAR_co_mixed <- nrow(consultation_har_filter(HAR_co(), 
                                               diagn = "mixed infection with P. falciparum", 
                                               new_attack = input$new_attack))/HAR_co_any*100
  HAR_co_other <- nrow(consultation_har_filter(HAR_co(), 
                                               diagn = "non-falciparum", 
                                               new_attack = input$new_attack))/HAR_co_any*100
  HAR_co_unspe <- nrow(consultation_har_filter(HAR_co(), 
                                               diagn = "unspecified", 
                                               new_attack = input$new_attack))/HAR_co_any*100
  
  # Préparation des données pour le graphique
  HAR_falci <- c(HAR_co_falci, HAR_br_falci)
  HAR_mixed <- c(HAR_co_mixed, HAR_br_mixed)
  HAR_other <- c(HAR_co_other, HAR_br_other)
  HAR_unspe <- c(HAR_co_unspe, HAR_br_unspe)
  HAR_data <- data.frame(HAR_loc, HAR_falci, HAR_other)
  
  # Création du graphique
  plot_ly(HAR_data, 
          x = ~HAR_loc, 
          y = ~HAR_falci, 
          type = "bar", 
          name = "Falci assoc") %>%
    add_trace(y = ~HAR_mixed, name = "Mixed") %>%
    add_trace(y = ~HAR_other, name = "Non falci") %>%
    add_trace(y = ~HAR_unspe, name = "Unspecified") %>%
    layout(xaxis = list(title = ""), 
           yaxis = list(title = tr("percentage")), 
           barmode = 'stack')
})