# Functions

rxl <- function(path, ...) {
  tryCatch(
    read_excel(path, ...),
    error = function(c) {
      c$message <- "No Data"
      print("No Data")
      stop(c)
    }
  )
}

ltodf <- function(path, ...) {
  tryCatch(
    rbind.data.frame(path, ...),
    error = function(c) {
      c$message <- "No Data"
      print("No Data")
      stop(c)
    }
  )
}





# Data import

contactinputpath <-
  list.files("./contacts/raw-data",
             pattern = "*.xlsx",
             full.names = T)
accountinputpath <-
  list.files("./accounts/raw-data",
             pattern = "*.xlsx",
             full.names = T)
projectinputpath <-
  list.files("./projects/raw-data",
             pattern = "*.xlsx",
             full.names = T)


# Operations on Data and creation of other data

# S4HANA Sales with integration to C4 Quote configuration and integration process. Support controlling and project team towards impletation and go live.
# Little bit knowledge with Finance and SAP implementation. Data migration, testing, Go live.... ()
# BAM  of S4 HANA, support key users with data migration, testing, training (On Site)

rawformat<- function(inputpath, inputdata="Contact", pathname="./contacts/raw-data/"){
  conta <- lapply(inputpath, read_excel)
  names(conta) <- gsub(pathname, "", inputpath)
  c <- lapply(conta, nrow)
  Input_data <- inputdata
  #Country<-gsub(".xlsx","",names(conta))
  Observations <- c
  
  temp <- data.frame(Input_data, Observations) |>
    pivot_longer(cols = (-1),
                 names_to = "Country",
                 values_to = "Observations") |>
    mutate(Country = gsub(".xlsx", "", Country))
  
  return(temp)
}

input.summary<-
  rbind(
  rawformat(inputpath=contactinputpath, inputdata="Contacts",pathname="./contacts/raw-data/"),
  rawformat(inputpath=accountinputpath, inputdata="Accounts", pathname="./accounts/raw-data/"),
  rawformat(inputpath=projectinputpath, inputdata="Projects", pathname="./projects/raw-data/")
) |> arrange(desc(Observations))

input.summary.summ <- input.summary |>
  group_by(Input_data) |>
  summarise(Observations = sum(Observations)) |>
  arrange(desc(Observations))


# Contacts


error.function<- function(filepath="./contacts/summary", filename="Error_Contact", country){
  errfilepath <-
    list.files(filepath,
               pattern = "*_error.csv",
               full.names = T)
  errfiles <- lapply(errfilepath, read.csv)
  tdf <- unique(do.call(ltodf, errfiles))
  
  tdf <- tdf |>
    mutate(err.deep = word(err.type, -1)) |>
    mutate(err = word(err.type, 1, -2)) |> select(-err.type) |>
    select(c(Name, Country, err, err.deep, err.count)) |>
    arrange(desc(err.count)) |> 
    filter(Country %in% country)
  
  tdf.country <- tdf |>
    group_by(Country) |>
    summarize(err.count = sum(err.count)) |>
    arrange(err.count)
  Lvl1dfStatus <-
    tibble(
      name = tdf.country$Country,
      y = tdf.country$err.count,
      drilldown = tolower(name)
    )
  Level_2_Drilldowns <-
    lapply(unique(tdf$Country), function(x_level) {
      tdf.err <- tdf[tdf$Country == x_level, ]
      tdf.err <- tdf.err |>
        group_by(err) |>
        summarize(err.count = sum(err.count)) |> arrange(err.count)
      
      Lvl2dfStatus <-
        tibble(
          name = tdf.err$err,
          y = tdf.err$err.count,
          drilldown = tolower(paste(x_level, name, sep = "_"))
        )
      list(
        id = tolower(x_level),
        type = "column",
        data = list_parse(Lvl2dfStatus),
        name = "High Level Error"
      )
    })
  Level_3_Drilldowns <-
    lapply(unique(tdf.country$Country), function(x_level) {
      tdf.err <- tdf[tdf$Country == x_level, ]
      lapply(unique(tdf.err$err), function(y_level) {
        tdf.err.deep <- tdf.err[tdf.err$err == y_level, ]
        tdf.err.deep <- tdf.err.deep |>
          group_by(err.deep) |>
          summarize(err.count = sum(err.count)) |> arrange(err.count)
        
        Lvl3dfStatus <-
          tibble(name = tdf.err.deep$err.deep, y = tdf.err.deep$err.count)
        list(
          id = tolower(paste(x_level, y_level, sep = "_")),
          type = "column",
          data = list_parse2(Lvl3dfStatus),
          name = "Deep Dive"
        )
      })
    }) |> unlist(recursive = FALSE)
  
  highchart() |>
    hc_xAxis(type = "category") |>
    hc_add_series(
      Lvl1dfStatus,
      "column",
      hcaes(x = name, y = y),
      name = "Country View",
      showInLegend = F
    ) |>
    hc_plotOptions(column = list(stacking = "normal"),
                   lang = list(drillUpText = "Back")) |>
    hc_drilldown(
      allowPointDrilldown = TRUE,
      series = c(Level_2_Drilldowns, Level_3_Drilldowns)
    ) |>
    hc_yAxis(title = list(text = "Number of errors")) |>
    hc_title(text = "Error Count") |>
    hc_subtitle(text = "Click on bar for deep dive") |>
    hc_credits(enabled = TRUE,
               text = "LaNubia Data Science",
               href = "https://www.lanubia.com/") |>
    hc_tooltip() |>
    hc_exporting(enabled = TRUE, # always enabled
                 filename = filename)
  
}






contdup<-read.csv("duplicatecontacts.csv")
accdup<-read.csv("duplicateaccounts.csv")
empmissing<-read.csv("empmissinginsap.csv")