---
title: "Competition"
author: "Scary Scarecrow"
date: "1/10/2022"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(readxl)
library(dplyr)
library(lubridate)
library(DT)
library(tidyr)
library(stringr)

mutlstxlrdr <- function() {
  for (i in seq_along(sheet.na)) {
    colnames <-
      unique(saptemplate[saptemplate$`Sheet Name` == snames[i], ]$Header)
    df <- read.table("", col.names = colnames)
    assign(snames[i], df)
    
  }
}
do.call(file.remove, list(list.files(
  "./competition/errors/mandatory/", full.names = TRUE
)))
do.call(file.remove, list(list.files(
  "./competition/errors/codelist/", full.names = TRUE
)))
do.call(file.remove, list(list.files(
  "./competition/errors/length/", full.names = TRUE
)))
do.call(file.remove, list(list.files("./competition/summary/", full.names = TRUE)))
do.call(file.remove, list(list.files("./competition/output/", full.names = TRUE)))
```


## Data transformation workflow

Following is the proposed preliminary workflow for the data transformation project.


>All file of a segment (contacts/accounts etc..) should be inside the relevant folder. Each folder should have one folder for all codelist files. All legacy data (one file for each country) should be inside the raw-data folder, named after each country. Another file having field definitions including name of the matching column from the legacy file should also be there.

>*Make sure that there are no hidden files inside the directory.*


### Code Lists


```{r Create List of Files, echo=TRUE, message=FALSE, warning=FALSE}

filenames <-
  list.files("./competition/CodeList",
             pattern = "*.xls",
             full.names = T) # We can avoid creating a separate directory for code list. But organizing may be difficult. However, this can be explored further if we want transform all the data in one go i.e. not by functions (contacts, accounts etc.).

# File paths
print(filenames)
```


Check manually if the above list includes all the codelist files
If correct, then read the files.

```{r codelistreader, echo=TRUE, message=FALSE, warning=FALSE}
sheet_names <-
  lapply(filenames, excel_sheets) # Creates a list of the sheet names
codelist_files <- NULL
for (i in seq_along(filenames)) {
  a <-
    lapply(excel_sheets(filenames[[i]]),
           read_excel,
           path = filenames[[i]],
           col_types = "text") # Reads the sheets of the excel files
  names(a) <-
    c(sheet_names[[i]]) # Renames them according to the sheet names extracted above
  codelist_files <- c(codelist_files, a)
}
# Names of the files imported
names(codelist_files)
#codelist_files<-unique(codelist_files)
codelist_files$Customer_type_I
```



### Templates


Let us now extract the data. Below we are reading only one file having all data related to `Contacts` from the legacy system.

```{r readlegacyfilepath, echo=TRUE, message=FALSE, warning=FALSE}
oldfilepath<-list.files("./competition/raw-data", pattern="*.xls", full.names = T) # Change the path, check pattern
print(oldfilepath)
```

Check it the list matches the actual files, manually.

```{r readlegacyfiles, echo=TRUE}

old_files <- NULL

#read_excel(path = oldfilepath[[i]], sheet = 1)
for (i in seq_along(oldfilepath)) {
  old_files[[i]] <- read_excel(path = oldfilepath[[i]], sheet = 1)
}
old_files
names(old_files) <-
  gsub("./competition/raw-data/", "", oldfilepath) # Change path
```


*Some errors in the legacy file noticed. Columns with similar or same name exists.*



```{r readSAPtemplate, echo=TRUE, message=FALSE, warning=FALSE}
saptemplate <-
  read_excel("./competition/template.xlsx", sheet = "Field_Definitions")
# First few rows of the imported data
head(saptemplate)
```


*Please note that the format of the tables (sheet) has been slightly changed. Earlier the corresponding sheet name was mentioned in a row before the actual table. Now, all the rows mention the corresponding sheet name. This was done manually for convenience of data extraction*


## Don't have Status column defined
## There could be issue in line of business
```{r createmptySAPfiles, echo=TRUE, message=FALSE, warning=FALSE}
#orilo<-"en_US.UTF-8"
#Sys.setlocale(locale="en_US.UTF-8")
strt <- Sys.time()
snames <- unique(saptemplate$`Sheet Name`)

for (h in seq_along(old_files)) {
  print("Importing new")
  # Copy original data
  old.copy <- old_files[[h]]
  print(paste0(names(old_files[h]), " imported"))
  err.summ <-
    data.frame(
      Country = NULL,
      Name = NULL,
      Expected = NULL,
      Actual = NULL
    ) #Error Cal
  # Creates data frame for each sheet in snames
  for (i in seq_along(snames)) {
    print(paste0("Processing ..", snames[i]))
    if (snames[i] %in% c("Competitor")) {
      # Select the column names from the field description sheet
      print("Creating template")
      sel.template.desc <-
        saptemplate[saptemplate$`Sheet Name` == snames[i],]
      print("Creating column names")
      sel.template.desc.colnames <- sel.template.desc$Header
      
      # Create a list by adding values from corresponding legacy data
      temp <- NULL
      print("adding values to template ")
      
      
      if (snames[i] == "Competitor") {
        for (j in seq_along(sel.template.desc.colnames)) {
          temp[j] <-
            ifelse(
              !is.na(sel.template.desc$default[j]),
              as.character(as.vector(sel.template.desc$default[j])),
              ifelse(
                sel.template.desc$oldkey[j] == "NA" |
                  is.na(sel.template.desc$oldkey[j]),
                NA,
                as.vector(old.copy[, sel.template.desc$oldkey[j]])
              )
            )
        }
        # Rename the columns according to field description
        print("renaming template ")
        names(temp) <- sel.template.desc.colnames
        
        # Create data frame from the list
        df <- as.data.frame(temp)
        print("Converted to data frame")
        
      }

      
      # Error summary file
      Expected <- nrow(df)
      
      #Select essential rows
      print("Identifying essential rows")
      sel.template.desc |>
        filter(Mandatory == "Yes") |>
        pull(Header) -> essential.columns
      
      error.mandatory <- NULL
      error.df <-
        data.frame(
          Country = NULL,
          Name = NULL,
          Rows = NULL,
          Expected = NULL
        )
      
      # Operate on essential columns including creation of error file
      for (k in seq_along(essential.columns)) {
        # In case there are any default values (of mandatory) they need to be added here
        #   if(essential.columns[k]=="International_Version"){
        #     print("Found International Version. Adding 0.")
        #   df$International_Version<-"0"
        #   }
        #   if(essential.columns[k]=="Status"){
        #     print("Found Status")
        #   df$Status<-"2"
        # }
        
        print("Creating and writing data with missing mandatory values")
        manerrdt <- df[is.na(df[, essential.columns[k]]),]
        if (nrow(manerrdt > 0)) {
          manerrdt <-
            manerrdt |> mutate(error = paste0(essential.columns[k], " missing"))
        }
        assign(
          paste0(
            "error_mandatory_",
            substr(names(old_files[h]), 2, 3),
            "_",
            snames[i],
            "_",
            essential.columns[k]
          ),
          manerrdt
        )
        # TO be saved in error files
        
        if (nrow(manerrdt) > 0) {
          write.csv(
            manerrdt,
            paste0(
              "./competition/errors/mandatory/",
              #Change path
              substr(names(old_files[h]), 1, 2),
              "_",
              snames[i],
              "_",
              essential.columns[k],
              "_error_mandatory.csv"
            ),
            row.names = F,
            na = "",
            fileEncoding = "UTF-8"
          )
        }
        # Error summary file
        Country <- substr(names(old_files[h]), 1, 2)
        Name <- snames[i]
        err.type <- paste0("Missing ", essential.columns[k])
        err.count <- nrow(df[is.na(df[, essential.columns[k]]),])
        
        print("Removing rows with empty essetial columns")
        df <- df[!is.na(df[, essential.columns[k]]),]
        if (err.count > 0) {
          error.df <-
            rbind(
              error.df,
              data.frame(
                Country = Country,
                Name = Name,
                err.type = err.type,
                err.count = err.count
              )
            ) #Error cal
        }
      }
      
      print("Identifying columns associated with codelists")
      # List of columns that have a codelist
      codelistcols <- sel.template.desc |>
        filter(!is.na(`CodeList File Path`)) |> pull(Header)
      
      for (k in seq_along(codelistcols)) {
        print(paste0("Identifying errors ", codelistcols[k]))
        def.rows <-
          which(!df[, codelistcols[k]] %in% c(pull(codelist_files[codelistcols[k]][[1]], Description), NA))
        def.n <- df[def.rows, 1]
        def.rows.val <-
          df[!df[, codelistcols[k]] %in% c(pull(codelist_files[codelistcols[k]][[1]], Description), NA), codelistcols[k]]
        def.colname <-
          rep(codelistcols[k], length.out = length(def.rows))
        def <- data.frame(def.rows, def.n, def.rows.val, def.colname)
        if (nrow(def > 0)) {
          assign(paste0(
            "error_codematch_",
            substr(names(old_files[1]), 1, 2),
            "_",
            snames[i],
            "_",
            codelistcols[k]
          ),
          def) # TO be saved
          write.csv(
            def,
            paste0(
              "./competition/errors/codelist/",
              #Change path
              substr(names(old_files[h]), 1, 2),
              "_",
              snames[i],
              "_",
              codelistcols[k],
              "_error_codematch_.csv"
            ),
            row.names = F,
            na = "",
            fileEncoding = "UTF-8"
          )
        }
        err.type <-
          paste0("Codelist Mismatch ", codelistcols[k]) #Error cal
        err.count <- nrow(def) #Error cal
        if (err.count > 0) {
          error.df <-
            rbind(
              error.df,
              data.frame(
                Country = Country,
                Name = Name,
                err.type = err.type,
                err.count = err.count
              )
            ) #Error cal
        }
        
        print(paste0("Removing errors ", codelistcols[k]))
        # Removes any mismatch
        df[!df[, codelistcols[k]] %in% c(pull(codelist_files[codelistcols[k]][[1]], Description), NA), codelistcols[k]] <-
          NA
        
        # Matches each column with the corresponding code list and returns the value
        df[, codelistcols[k]] <-
          pull(codelist_files[codelistcols[k]][[1]], 2)[match(pull(df, codelistcols[k]),
                                                              pull(codelist_files[codelistcols[k]][[1]], Description))]

        
        
      }
      max.length <- as.numeric(sel.template.desc$`Max Length`)
      dtype <- sel.template.desc$`Data Type`
      rowval <- NULL
      ival <- NULL
      rval <- NULL
      lenght.issue.df <- NULL
      # fname<- NULL
      # lname<- NULL
      # Changing the data class
      for (k in 1:ncol(df)) {
        if (dtype[k] == "String") {
          df[, k] <- as.character(pull(df, k))
        }
        if (dtype[k] == "Boolean") {
          df[, k] <- as.logical(pull(df, k))
        }
        if (dtype[k] == "DateTime") {
          df[, k] <- lubridate::ymd_hms(pull(df, k))
        }
        if (dtype[k] == "Time") {
          df[, k] <- lubridate::hms(pull(df, k))
          
        } # This list will increase and also change based on input date and time formats
        
        
        
      }
      
      print("Rectifying streetname")
      # Street and House Number
      if (any(colnames(df) == "Street")) {
        print("found street")
        # stop()
        
        #df$Streetname<-NA
        #df$HouseNumber<-NA
        #df |> extract("Street", "(\\D+)(\\d.*)")
        df <- tidyr::extract(df,
                             "Street",
                             c("Streetname", "HouseNumber"),
                             "(\\D+)(\\d.*)")
        df <- df |>
          select(-c("House_Number")) |>
          rename(Street = Streetname, House_Number = HouseNumber) |>
          select(all_of(sel.template.desc.colnames))
      }
      
      # Rectifying Phone, Mobile and Fax numbers
      if (any(colnames(df) == "Phone")) {
        print("Found Phone")
        df$Phone <- gsub("[+]", "00", df$Phone)
      }
      
      if (any(colnames(df) == "Mobile")) {
        print("Found Mobile")
        df$Mobile <- gsub("[+]", "00", df$Mobile)
      }
      
      if (any(colnames(df) == "Mobile")) {
        print("Found Mobile")
        df$Mobile <- gsub("[+]", "00", df$Mobile)
      }
      
      # Length Rectification
      colclasses <- lapply(df, class)
      print("Rectifying Length")
      for (k in 1:ncol(df)) {
        if (colclasses[[k]] == "character") {
          print("found character column ")
          rowval <- pull(df, 1)
          ival <-
            ifelse(nchar(pull(df, k)) == 0 |
                     is.na(nchar(pull(df, k))), 1, nchar(pull(df, k)))
          rval <- max.length[k]
          colval <- pull(df, k)
          colnm <- colnames(df)[k]
          cntr <- substr(names(old_files[h]), 1, 2)
          # fname<-pull(df, 8)
          # lname<-pull(df, 9)
          print(" Values identified")
          # rectifying data length
          df[, k] <-
            ifelse(nchar(pull(df, k)) > max.length[k],
                   substring(pull(df, k), 1, max.length[k]),
                   pull(df, k))
          print("Trimmed")
        }
        
        lenght.issue.df <-
          rbind(lenght.issue.df,
                data.frame(rowval, ival, rval, colnm, colval, cntr))
        err.type <- paste0("Length error ", colnames(df)[k]) # Error cal
        err.count <- sum(ival > rval, na.rm = T) # Error cal
        if (err.count > 0) {
          error.df <-
            rbind(
              error.df,
              data.frame(
                Country = Country,
                Name = Name,
                err.type = err.type,
                err.count = err.count
              )
            ) #Error cal
        }
        
      }
      
      lenght.issue.df <- dplyr::filter(lenght.issue.df, ival > rval)
      if (nrow(lenght.issue.df) > 0) {
        write.csv(
          lenght.issue.df,
          paste0(
            "./competition/errors/length/",
            # Change path
            substr(names(old_files[h]), 1, 2),
            "_",
            snames[i],
            "_length_error.csv"
          ),
          row.names = F,
          na = "",
          fileEncoding = "UTF-8"
        )
      }
      
      assign(snames[i], df)
      
      write.csv(
        df,
        paste0(
          "./competition/output/",
          substr(names(old_files[h]), 1, 2),
          "_",
          snames[i],
          ".csv"
        ),
        row.names = F,
        na = "",
        fileEncoding = "UTF-8"
      ) #Chnage path
      if (nrow(error.df) > 0) {
        write.csv(
          error.df,
          paste0(
            "./competition/summary/",
            substr(names(old_files[h]), 1, 2),
            "_",
            snames[i],
            "_error",
            ".csv"
          ),
          row.names = F,
          na = ""
        ) # Error write
      }
      err.summ <-
        rbind(
          err.summ,
          data.frame(
            Country = Country,
            Name = Name,
            Expected = Expected,
            Actual = nrow(df)
          )
        ) #Error Cal
    }
    write.csv(
      err.summ,
      paste0(
        "./competition/summary/" ,
        substr(names(old_files[h]), 1, 2),
        "_",
        snames[i],
        "_sumerror",
        ".csv"
      ),
      row.names = F,
      na = "",
      fileEncoding = "UTF-8"
    ) # Error Write
    
  }
}

end <- Sys.time()

end - strt



```
*The code failed because Department Column appears several times in the data and while importing R renamed them to Department..xx).*
*Manually verify if these are the required templates*


```{r}
stop("Change code")
opfilepath <-
  list.files("./competition/output",
             pattern = "*.csv",
             full.names = T)
opfiles <- lapply(opfilepath, read.csv)
opdf <-
  do.call(rbind.data.frame, opfiles[c(1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31)])
readr::write_excel_csv(
  opdf,
  "./competition/output/combined/combinedsalesdata.csv",
  row.names = F,
  na = "",
  fileEncoding = "UTF-8"
)

opdf <-
  do.call(rbind.data.frame, opfiles[c(2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32)])
write.csv(
  opdf,
  "./competition/output/combined/combinedaccountteam.csv",
  row.names = F,
  na = "",
  fileEncoding = "UTF-8"
)


opdf <-
  do.call(rbind.data.frame, opfiles[c(3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33)])
write.csv(
  opdf,
  "./competition/output/combined/combinedaccount.csv",
  row.names = F,
  na = "",
  fileEncoding = "UTF-8"
)

```


