uploaded r code for interpolation

dataset/interpolation_function.R

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+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)

dataset/map_and_merge.R

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+setwd("~/Desktop/cdsc/health literacy/")
+
+mapping = read.delim("Mapping - Sheet1.tsv")
+mapping <- mapping[,colnames(mapping) != "Notes"]
+
+IHME = read.csv("IHME_Data.csv")
+best <- read.csv("Best_Data.csv")
+
+# first, create the list of all the one-to-ones
+d <- merge(best, mapping[!is.na(mapping$Mapping_Type) & mapping$Mapping_Type == "one-to-one",], by.x="Reconciled_Name", by.y="Best_Disease_Name", all=TRUE)
+
+
+# now merge the imhe data on
+d <-  merge(d, IHME[IHME$measure_name == "DALYs (Disability-Adjusted Life Years)", 
+                  c("cause_id", "measure_name", "year", "val")],
+          by.x=c("IHME_Cause_ID", "Year"),
+          by.y=c("cause_id", "year"))
+# next, handle the one-to-manys but summing up 
+