setwd("~/Desktop/cdsc/health literacy/")

best = read.csv("Best_Data.csv")

# subsetting desired years

years = c("2007", "2010", "2013", "2016")

best_subsetted = best[best$Year %in% years,]

# creating empty rows for interpolation + interpolating the mean (stigma) scores 

expand = function(df){
  total_years = seq(min(df$Year), max(df$Year))
  expanded_df = data.frame(Year = total_years)
  unique_diseases = unique(df$Reconciled_Name)
  expanded_data = data.frame()
  
  for (disease in unique_diseases){
    disease_data = df[df$Reconciled_Name == disease, ]
    expanded_disease_data = merge(expanded_df, disease_data, by = "Year", all.x = TRUE)
    expanded_disease_data$Reconciled_Name = disease
    expanded_data = rbind(expanded_data, expanded_disease_data)
  }
  
  for (disease in unique_diseases) {
    disease_data = expanded_data[expanded_data$Reconciled_Name == disease, ] 
    known_year = which(!is.na(disease_data$mean))
    # sort known_year it by year
    
    for (i in 1:(length(known_year) - 1)) {
      before_year = known_year[i]
      after_year = known_year[i + 1]
      if (is.na(after_year)){
        print("break")
        break
      }
      start_mean = disease_data$mean[before_year]
      end_mean = disease_data$mean[after_year]
      num_missing = after_year - before_year - 1
      print(after_year)  
      
      if (num_missing > 0) {
        increment = (end_mean - start_mean) / (num_missing + 1)
        
        for (j in 1:num_missing) {
          disease_data$mean[before_year + j] = start_mean + increment * j
        }
      }
    }
    
    expanded_data[expanded_data$Reconciled_Name == disease, ] = disease_data
  }
  
  return(expanded_data)
}

best_interpolated = expand(best_subsetted)