Add script to combine states over time for a sample dataset

processing_hd.R

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+# Load libraries
+library(tidyverse)
+library(arrow)
+
+# Set seed for reproducibility
+set.seed(2024)
+
+# Read cycleIntervention health output as an example
+hd <- read_csv("C:/Users/ajnab/RMIT University/JIBE working group - General/manchester/simulationResults/ForUrbanTransition/cycleIntervention/health/04_death_and_disease/pp_healthDiseaseTracker_2029.csv")
+
+# Same for quicker run
+shd <- hd |> slice_sample(n = 100)
+
+# Loop through each row
+for (i in 1:nrow(shd)){
+  # Create a long df for each row
+  ur <- pivot_longer(shd[i,], cols = -id)
+  # Get a table for each disease state with the first year when it was appeared
+  fv <- ur |> group_by(value) |> summarise(fy = first(name)) |> arrange(fy)
+  # Loop through the first value dataset with first year for the disease appearance
+  for (j in 1:nrow(fv)){
+    # Ignore healty and null states
+    if (!fv$value[j] %in% c("healthy", "null"))
+    {
+      # Do not carry forward the healthy state to any other state
+      if (fv$value[j - 1] != "healthy")
+        # Join previous state with the current one with a | as a separator
+        fv$value[j] <- paste(fv$value[j], fv$value[j - 1], sep = "|")
+    }
+  }
+  # Update the long format for each ID with the new states
+  for (k in 1:nrow(fv)){
+    if (!fv$value[k] %in% c("healthy", "null"))
+        ur[(ur$name >= as.numeric(fv$fy[k]) & ur$name < (as.numeric(fv$fy[k + 1]))),]$value <- fv$value[k]
+  }
+
+  # Update the states
+  shd <- rows_update(shd, pivot_wider(ur, id_cols=id))
+}