I have a df that is long where each participant is a row.

set.seed(123)  # for reproducibility

participants <- c("Participant1", "Participant2", "Participant3")
symptoms <- paste0("Symptom", 1:5)

# Create wide format dataframe
main_cl_db <- data.frame(
  participant_id = participants,
  sapply(symptoms, function(sym) sample(c(0, 1), 3, replace = TRUE))  # Generate binary data for symptoms
)

# Display the main_cl_db
print("Original Data (wide format):")
print(main_cl_db)

I then create
main_cl_db_no_id <- main_cl_db %>% select(-participant_id) – removing id so it’s only binary data of symptoms

main_cl_db_t <- t(main_cl_db_no_id) – transposed for clustering where I am clustering the symptoms

I now want to go back to my original df called new_main_cl_db where it’s the same format but it has the cluster number derived from the analysis. Essentially, I want the same frame as main_cl_db but it now has a new column called cluster

All the code for my analysis is below

# data prep ---------------------------------------------------------------
# Select relevant columns and prepare data
main_cl_db <- main_survey %>%
  select(participant_id, starts_with("symptoms_new")) %>%
  mutate(across(starts_with("symptoms_new"), ~ ifelse(is.na(.), 0, .))) %>% 
  mutate_if(is.double, as.factor) #ensures they are treated as factors/binary 


# Remove participant_id column for clustering
main_cl_db_no_id <- main_cl_db %>% select(-participant_id)

# Transpose the data
main_cl_db_t <- t(main_cl_db_no_id)

#calcualte dissim matrix
jaccard_dist_symptoms <- dist(main_cl_db_t, method = "binary")


##function wrapped with set.seed
setseed_fct <- function(data) {
  jaccard_dist <- dist(data, method = "binary")
  
  set.seed(1)
  
  hc <- hclust(jaccard_dist, method = "complete")
  
  return(hc)
}

# use func
hc_symptoms <- setseed_fct(main_cl_db_t)

# cut tree
grp_symp <- cutree(hc_symptoms, k = 6)
table(grp_symp)