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optimised code
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B. Zapata-Diomedi committed Nov 6, 2024
1 parent e7c9c71 commit 36806fe
Showing 1 changed file with 66 additions and 60 deletions.
126 changes: 66 additions & 60 deletions processing_hd.R
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,11 @@ library(arrow)
set.seed(2024)

# Read cycleIntervention health output as an example

### Ali to bring in the fix dataset
# 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")
### Ali to add cycling intervention health data.


## Belen folder
directory <- "C:/Users/mbzd2/OneDrive - RMIT University/JIBE/JIBE-WP6/healthmicrosim/"
Expand All @@ -19,65 +23,65 @@ health_base <- read_csv(paste0(directory, "manchester/simulationResults/ForUrban
# Same for quicker run
# shd <- hd |> slice_sample(n = 100)

####### Referecen data #######

# Loop through each row
for (i in 1:nrow(health_base)){
# Create a long df for each row
ur <- pivot_longer(health_base[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
health_base <- rows_update(health_base, pivot_wider(ur, id_cols=id))
}



####### Scenario data #######

# Loop through each row
for (i in 1:nrow(health_intervention)){
# Create a long df for each row
ur <- pivot_longer(health_intervention[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
health_intervention <- rows_update(health_intervention, pivot_wider(ur, id_cols=id))
}
# ####### Referecen data #######
#
# # Loop through each row
# for (i in 1:nrow(health_base)){
# # Create a long df for each row
# ur <- pivot_longer(health_base[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
# health_base <- rows_update(health_base, pivot_wider(ur, id_cols=id))
# }
#
#
#
# ####### Scenario data #######
#
# # Loop through each row
# for (i in 1:nrow(health_intervention)){
# # Create a long df for each row
# ur <- pivot_longer(health_intervention[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
# health_intervention <- rows_update(health_intervention, pivot_wider(ur, id_cols=id))
# }


### Optimised version
Expand All @@ -86,6 +90,8 @@ library(data.table)
library(dplyr)

# Convert health_base to data.table for fast processing


health_base_dt <- as.data.table(health_base)

# Melt (pivot_longer) the entire dataset once to create the long format
Expand Down Expand Up @@ -127,5 +133,5 @@ health_base_updated <- dcast(setDT(ur), id ~ year, value.var = "final_state")
health_base_updated[is.na(health_base_updated)] <- health_base[is.na(health_base_updated)]

# Assign the result back to the original health_base variable if desired
health_base_plot <- health_base_updated
health_base_plot <- as.data.frame(health_base_updated)

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