## Not run: 
# import and format a mixed-type data set
library(kamila)
data(Byar, package='clustMD')

ds <- readr::read_csv("2 Longterm/assigndata.csv",na = c("","NA")) |> na.omit()

ds <- ds |> dplyr::mutate(mrs_1 = mrs_1>2)

cat_i <- lapply(ds,is.character) |> purrr::list_c()
con_i <- lapply(ds,is.double) |> purrr::list_c()

xor(cat_i,con_i)

clin_clust=2
# Byar$logSpap <- log(Byar$Serum.prostatic.acid.phosphatase)

# conInd <- c(5,6,8:10,16)
# conVars <- Byar[,conInd]
# conVars <- data.frame(scale(conVars))

conVars <- ds[,con_i]
conVars <- data.frame(scale(conVars))

# catVarsFac <- Byar[,-c(1:2,conInd,11,14,15)]
# catVarsFac[] <- lapply(catVarsFac, factor)
# catVarsDum <- dummyCodeFactorDf(catVarsFac)

catVarsFac <- ds[,cat_i]
catVarsFac <- lapply(catVarsFac, factor) |> dplyr::bind_cols() |> as.data.frame()
catVarsDum <- dummyCodeFactorDf(catVarsFac)

# Modha-Spangler clustering with kmeans default Hartigan-Wong algorithm
gmsResHw <- gmsClust(conVars, catVarsDum, nclust = clin_clust)

# Modha-Spangler clustering with kmeans Forgy-Lloyd algorithm
# NOTE searchDensity should be >= 10 for optimal performance:
# this is just a syntax demo
gmsResLloyd <- gmsClust(conVars, catVarsDum, nclust = clin_clust,
                        algorithm = "Lloyd", searchDensity = 15)

# KAMILA clustering
kamRes <- kamila(conVars, catVarsFac, numClust=2:7, numInit=10, calcNumClust="ps")

# Plot results
# ternarySurvival <- factor(Byar$SurvStat)
# levels(ternarySurvival) <- c('Alive','DeadProst','DeadOther')[c(1,2,rep(3,8))]
plottingData <- cbind(
  conVars,
  catVarsFac,
  KamilaCluster = factor(kamRes$finalMemb))
# plottingData$Bone.metastases <- ifelse(
#   plottingData$Bone.metastases == '1', yes='Yes',no='No')
# 
# # Plot Modha-Spangler/Hartigan-Wong results
# msPlot <- ggplot(
#   plottingData,
#   aes(
#     x=logSpap,
#     y=Index.of.tumour.stage.and.histolic.grade,
#     color=ternarySurvival,
#     shape=MSCluster))
# plotOpts <- function(pl) (pl + geom_point() + 
#                             scale_shape_manual(values=c(2,3,7)) + geom_jitter())
# plotOpts(msPlot)

# Plot KAMILA results
kamPlot <- ggplot(
  plottingData,
  aes(
    x=pase_0,
    y=pase_6,
    color=KamilaCluster,
    shape=KamilaCluster))
plotOpts(kamPlot)


plotting_ls <- tibble(KamilaCluster = factor(kamRes$finalMemb),
                      MSCluster = factor(gmsResHw$results$cluster)) |> 
  purrr::map(\(x) cbind(x, ds))

plotting_ls |> purrr::map(\(y) {
  y |> gtsummary::tbl_summary(by=x) |> gtsummary::add_p() |> gtsummary::add_overall()}) |> 
  gtsummary::tbl_merge()