data.filename <- '../../data/cohort-sanitised.csv'

require(data.table)

COHORT <- fread(data.filename)

percentMissing <- function(x, sf = 3) {

  round(sum(is.na(x))/length(x), digits = sf)*100

}

# Remove the patients we shouldn't include

COHORT <-

  COHORT[

    # remove negative times to death

    COHORT$time_death > 0 &

      # remove patients who should be excluded

      !COHORT$exclude

    ,

    ]

# Age, 5, 50, 95, %missing

print(quantile(COHORT$age, c(0.5, 0.025, 0.975)))

# Gender

print(table(COHORT$gender))

print(table(COHORT$gender)/nrow(COHORT)*100)

# Deprivation, 5, 50, 95, %missing

print(quantile(COHORT$imd_score, c(0.5, 0.025, 0.975), na.rm = TRUE))

print(percentMissing(COHORT$imd_score))

# SCAD subtype

print(table(COHORT$diagnosis)/nrow(COHORT)*100)

# PCI

print(sum(COHORT$pci_6mo)/nrow(COHORT)*100)

# CABG

print(sum(COHORT$cabg_6mo)/nrow(COHORT)*100)

# previous/recurrent MI

print(sum(COHORT$hx_mi)/nrow(COHORT)*100)

# nitrates (listed as 1 and NA not T and F)

print(sum(COHORT$long_nitrate, na.rm = TRUE)/nrow(COHORT)*100)

# Smoking, by category, %missing

print(table(COHORT$smokstatus)/nrow(COHORT)*100)

print(percentMissing(COHORT$smokstatus))

# Hypertension

print(sum(COHORT$hypertension)/nrow(COHORT)*100)

# Diabetes, yes/no

print(

  (sum(COHORT$diabetes == 'Diabetes unspecified type') +

    sum(COHORT$diabetes == 'Type 1 diabetes') +

    sum(COHORT$diabetes == 'Type 2 diabetes')) /nrow(COHORT)*100

)

# Total cholesterol

print(quantile(COHORT$total_chol_6mo, c(0.5, 0.025, 0.975), na.rm = TRUE))

print(percentMissing(COHORT$total_chol_6mo))

# HDL

print(quantile(COHORT$hdl_6mo, c(0.5, 0.025, 0.975), na.rm = TRUE))

print(percentMissing(COHORT$hdl_6mo))

# Heart failure

print(sum(COHORT$heart_failure)/nrow(COHORT)*100)

# Peripheral arterial disease

print(sum(COHORT$pad)/nrow(COHORT)*100)

# Atrial fibrillation

print(sum(COHORT$hx_af)/nrow(COHORT)*100)

# Stroke

print(sum(COHORT$hx_stroke)/nrow(COHORT)*100)

# Chronic kidney disease

print(sum(COHORT$hx_renal)/nrow(COHORT)*100)

# COPD

print(sum(COHORT$hx_copd)/nrow(COHORT)*100)

# Cancer

print(sum(COHORT$hx_cancer)/nrow(COHORT)*100)

# Chronic liver disease

print(sum(COHORT$hx_liver)/nrow(COHORT)*100)

# Depression

print(sum(COHORT$hx_depression)/nrow(COHORT)*100)

# Anxiety

print(sum(COHORT$hx_anxiety)/nrow(COHORT)*100)

# Heart rate

print(quantile(COHORT$pulse_6mo, c(0.5, 0.025, 0.975), na.rm = TRUE))

print(percentMissing(COHORT$pulse_6mo))

# Creatinine

print(quantile(COHORT$crea_6mo, c(0.5, 0.025, 0.975), na.rm = TRUE))

print(percentMissing(COHORT$crea_6mo))

# WCC

print(quantile(COHORT$total_wbc_6mo, c(0.5, 0.025, 0.975), na.rm = TRUE))

print(percentMissing(COHORT$total_wbc_6mo))

# Haemoglobin

print(quantile(COHORT$haemoglobin_6mo, c(0.5, 0.025, 0.975), na.rm = TRUE))

print(percentMissing(COHORT$haemoglobin_6mo))

# Follow-up, 5, 50, 95

print(quantile(COHORT$endpoint_death_date, c(0.5, 0.025, 0.975)))/365.25

# Death vs censored, %

print(table(COHORT$endpoint_death)) /nrow(COHORT)*100