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ukbiobank/calculate_time_to_diagnosis2.R

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+
+library(dplyr)
+
+diagnosis_dates2 <- read.csv("~/data/ukbiobank/diagnosis_dates2.csv")
+dates <- read.csv("~/data/ukbiobank/dates.csv")
+
+dates <- dates %>% arrange(eid)
+diagnosis_dates2 <- diagnosis_dates2 %>% arrange(eid)
+
+# Save column name for later
+names <- colnames(diagnosis_dates2)
+
+
+# Convert date strings to Date objects
+dates$baseline_date <- as.Date(dates$Date.of.attending.assessment.centre...Instance.0, format = "%m/%d/%Y")
+diagnosis_dates2 <- lapply(diagnosis_dates2[,-1], function(x) as.Date(x, format = "%m/%d/%Y", origin = "1970-01-01"))
+
+# Calculate time difference in years for each column
+time_to_diagnosis <- mapply(function(diagnosis_date) {
+  as.numeric(difftime(diagnosis_date, dates$baseline_date, units = "days")) / 365.25
+}, diagnosis_dates2)
+
+# Combine results into a table
+time_to_diagnosis <- as.data.frame(time_to_diagnosis)
+
+# Add eid column to time_to_diagnosis
+time_to_diagnosis$eid <- dates$eid
+
+# Reorder columns with eid as the first column
+time_to_diagnosis <- time_to_diagnosis[, c("eid", names(diagnosis_dates2))]
+
+# Rename columns
+colnames(time_to_diagnosis) <- names
+
+# View the resulting table
+setwd("~/data/ukbiobank")
+write.csv(time_to_diagnosis, "time_to_diagnosis.csv")
+