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705 lines
20 KiB
705 lines
20 KiB
---
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title: "Contacts"
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author: "Scary Scarecrow"
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date: "12/27/2021"
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output: html_document
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE)
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strt <- Sys.time()
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library(readxl)
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library(dplyr)
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library(lubridate)
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library(DT)
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library(tidyr)
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mutlstxlrdr <- function() {
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for (i in seq_along(sheet.na)) {
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colnames <-
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unique(saptemplate[saptemplate$`Sheet Name` == snames[i], ]$Header)
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df <- read.table("", col.names = colnames)
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assign(snames[i], df)
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}
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}
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do.call(file.remove, list(list.files(
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"./contacts/errors/mandatory/", full.names = TRUE
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)))
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do.call(file.remove, list(list.files(
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"./contacts/errors/codelist/", full.names = TRUE
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)))
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do.call(file.remove, list(list.files(
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"./contacts/errors/length/", full.names = TRUE
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)))
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do.call(file.remove, list(list.files("./contacts/summary/", full.names = TRUE)))
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do.call(file.remove, list(list.files("./contacts/output/", full.names = TRUE)))
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```
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## Data transformation workflow
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Following is the proposed preliminary workflow for the data transformation project.
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>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.
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>*Make sure that there are no hidden files inside the directory.*
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### Employees
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```{r}
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# employeecodes<-read.csv("emp.csv")
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# employeecodes<-employeecodes |> select(c(1,2))
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# employeecodesnew<-read.csv("./employees/empoct.csv") |>
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# select(c(Employee_ID,First_Name,Last_Name)) |>
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# mutate(Name=paste(First_Name, Last_Name)) |>
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# select(Employee_ID,Name) |>
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# rename(Employee.ID=Employee_ID)
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# employeecodes<-rbind(employeecodes,employeecodesnew) |>
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# unique()
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employeecodes<-read.csv("./employees/empoct.csv") |>
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mutate(Name=paste(First_Name, Last_Name)) |>
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select(Employee_ID,Name)
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```
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### Code Lists
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```{r Create List of Files, echo=TRUE, message=FALSE, warning=FALSE}
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filenames <-
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list.files("./contacts/CodeList",
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pattern = "*.xlsx",
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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.).
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# File paths
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print(filenames)
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```
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Check manually if the above list includes all the codelist files
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If correct, then read the files.
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```{r codelistreader, echo=TRUE, message=FALSE, warning=FALSE}
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sheet_names <-
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lapply(filenames, excel_sheets) # Creates a list of the sheet names
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codelist_files <- NULL
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for (i in seq_along(filenames)) {
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a <-
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lapply(excel_sheets(filenames[[i]]),
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read_excel,
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path = filenames[[i]],
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col_types = "text") # Reads the sheets of the excel files
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names(a) <-
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c(sheet_names[[i]]) # Renames them according to the sheet names extracted above
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codelist_files <- c(codelist_files, a)
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}
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# Names of the files imported
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names(codelist_files)
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#codelist_files<-unique(codelist_files)
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codelist_files$Title
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```
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### Templates
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Let us now extract the data. Below we are reading only one file having all data related to `Contacts` from the legacy system.
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```{r readlegacyfilepath, echo=TRUE, message=FALSE, warning=FALSE}
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oldfilepath <-
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list.files("./contacts/raw-data/",
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pattern = "*.xls",
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full.names = T)
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print(oldfilepath)
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```
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Check it the list matches the actual files, manually.
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```{r readlegacyfiles, echo=TRUE, message=FALSE, warning=FALSE}
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old_files <- NULL
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#read_excel(path = oldfilepath[[i]], sheet = 1)
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for (i in seq_along(oldfilepath)) {
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a<- read_excel(path = oldfilepath[[i]], sheet = 1)
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a<-a |>
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left_join(employeecodes, by=c(`Full Name (Owning User)` = "Name")) |>
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select(-`Full Name (Owning User)`) |>
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mutate(Employee_ID=ifelse(is.na(Employee_ID),"99999",Employee_ID)) |>
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rename(`Full Name (Owning User)`=Employee_ID)
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old_files[[i]]<-a
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}
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names(old_files) <- gsub("./contacts/raw-data/", "", oldfilepath)
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```
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*Some errors in the legacy file noticed. Columns with similar or same name exists.*
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```{r readSAPtemplate, echo=TRUE, message=FALSE, warning=FALSE}
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saptemplate <-
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read_excel("./contacts/template.xlsx", sheet = "Field_Definitions")
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# First few rows of the imported data
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head(saptemplate)
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```
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*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*
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```{r createmptySAPfiles, message=FALSE, warning=FALSE, include=FALSE}
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#orilo<-"en_US.UTF-8"
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#Sys.setlocale(locale="en_US.UTF-8")
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isValidEmail <- function(x) {
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grepl("\\<[A-Z0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,}\\>", as.character(x), ignore.case=TRUE)
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}
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snames <- unique(saptemplate$`Sheet Name`)
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for (h in seq_along(old_files)) {
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# Copy original data
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old.copy <- old_files[[h]]
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print(paste0(names(old_files[h]), " imported"))
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err.summ <-
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data.frame(
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Country = NULL,
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Name = NULL,
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Expected = NULL,
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Actual = NULL
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) #Error Cal
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# Creates data frame for each sheet in snames
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for (i in seq_along(snames)) {
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print(paste0("Processing ..", snames[i]))
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if (snames[i] == "Contact") {
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# Select the column names from the field description sheet
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print("Creating template")
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sel.template.desc <-
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saptemplate[saptemplate$`Sheet Name` == snames[i],]
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print("Creating column names")
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sel.template.desc.colnames <- sel.template.desc$Header
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# Create a list by adding values from corresponding legacy data
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temp <- NULL
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print("adding values to template ")
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for (j in seq_along(sel.template.desc.colnames)) {
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temp[j] <-
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ifelse(
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!is.na(sel.template.desc$default[j]),
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as.character(as.vector(sel.template.desc$default[j])),
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ifelse(
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sel.template.desc$oldkey[j] == "NA" |
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is.na(sel.template.desc$oldkey[j]),
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NA,
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as.vector(old.copy[, sel.template.desc$oldkey[j]])
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)
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)
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}
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# Rename the columns according to field description
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print("renaming template ")
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names(temp) <- sel.template.desc.colnames
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# Create data frame from the list
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df <- as.data.frame(temp)
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print("Converted to data frame")
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df$Title <- ifelse(df$Title == "Mrs.", "Ms.", df$Title)
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# df$CountryRegion <- ifelse(!is.na(df$CountryRegion),
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# toupper(substr(names(old_files[h]), 2, 3)),
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# df$CountryRegion)
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df$CountryRegion <- toupper(substr(names(old_files[h]), 2, 3))
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# if(names(old_files)=="/CN.xlsx"){
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# df$Contact_Owner_ID<-"226"
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# }
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# if(names(old_files)=="/CZ.xlsx"){
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# df$Contact_Owner_ID<-"390"
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# }
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# if(names(old_files)=="/FI.xlsx"){
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# df$Contact_Owner_ID<-"325"
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# }
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# if(names(old_files)=="/DE.xlsx"){
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# df$Contact_Owner_ID<-"289"
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# }
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# if(names(old_files)=="/IT.xlsx"){
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# df$Contact_Owner_ID<-"182"
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# }
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# if(names(old_files)=="/PL.xlsx"){
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# df$Contact_Owner_ID<-"368"
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# }
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# if(names(old_files)=="/ES.xlsx"){
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# df$Contact_Owner_ID<-"447"
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# }
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# if(names(old_files)=="/SE.xlsx"){
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# df$Contact_Owner_ID<-"351"
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# }
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# if(names(old_files)=="/NL.xlsx"){
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# df$Contact_Owner_ID<-"90052"
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# }
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# if(names(old_files)=="/NO.xlsx"){
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# df$Contact_Owner_ID<-"000"
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# }
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# Error summary file
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Expected <- nrow(df)
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#Select essential rows
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print("Identifying essential rows")
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sel.template.desc |>
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filter(Mandatory == "Yes") |>
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pull(Header) -> essential.columns
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error.mandatory <- NULL
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error.df <-
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data.frame(
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Country = NULL,
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Name = NULL,
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Rows = NULL,
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Expected = NULL
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)
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# Operate on essential columns including creation of error file
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for (k in seq_along(essential.columns)) {
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if (essential.columns[k] == "Department") {
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print("Department found")
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#stop()
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df$Department <- paste0("Z", substr(names(old_files[h]), 2, 3))
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}
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print("Creating and writing data with missing mandatory values")
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manerrdt <- df[is.na(df[, essential.columns[k]]),]
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if (nrow(manerrdt > 0)) {
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manerrdt <-
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manerrdt |> mutate(error = paste0(essential.columns[k], " missing"))
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}
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assign(
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paste0(
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"error_mandatory_",
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substr(names(old_files[h]), 2, 3),
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"_",
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snames[i],
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"_",
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essential.columns[k]
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),
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manerrdt
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)
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# TO be saved in error files
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if (nrow(manerrdt) > 0) {
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write.csv(
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manerrdt,
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paste0(
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"./contacts/errors/mandatory/",
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substr(names(old_files[h]), 2, 3),
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"_",
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snames[i],
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"_",
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essential.columns[k],
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"_error_mandatory.csv"
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),
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row.names = F,
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na = "",
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fileEncoding = "UTF-8"
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)
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}
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# Error summary file
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Country <- substr(names(old_files[h]), 2, 3)
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Name <- snames[i]
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err.type <- paste0("Missing ", essential.columns[k])
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err.count <- nrow(df[is.na(df[, essential.columns[k]]),])
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print("Removing rows with empty essential columns")
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df <- df[!is.na(df[, essential.columns[k]]),]
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if (err.count > 0) {
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error.df <-
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rbind(
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error.df,
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data.frame(
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Country = Country,
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Name = Name,
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err.type = err.type,
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err.count = err.count
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)
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) #Error cal
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}
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}
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print("Identifying columns associated with codelists")
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# List of columns that have a codelist
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codelistcols <- sel.template.desc |>
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filter(!is.na(`CodeList File Path`)) |> pull(Header)
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for (k in seq_along(codelistcols)) {
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print(paste0("Identifying errors ", codelistcols[k]))
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def.rows <-
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which(!df[, codelistcols[k]] %in% c(pull(codelist_files[codelistcols[k]][[1]], Description), NA))
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def.n <- df[def.rows, 1]
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def.rows.val <-
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df[!df[, codelistcols[k]] %in% c(pull(codelist_files[codelistcols[k]][[1]], Description), NA), codelistcols[k]]
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def.colname <-
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rep(codelistcols[k], length.out = length(def.rows))
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def <- data.frame(def.rows, def.n, def.rows.val, def.colname)
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if (nrow(def > 0)) {
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assign(paste0(
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"error_codematch_",
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substr(names(old_files[1]), 1, 2),
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"_",
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snames[i],
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"_",
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codelistcols[k]
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),
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def) # TO be saved
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write.csv(
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def,
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paste0(
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"./contacts/errors/codelist/",
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substr(names(old_files[h]), 2, 3),
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"_",
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snames[i],
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"_",
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codelistcols[k],
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"_error_codematch_.csv"
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),
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row.names = F,
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na = "",
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fileEncoding = "UTF-8"
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)
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}
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err.type <-
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paste0("Codelist Mismatch ", codelistcols[k]) #Error cal
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err.count <- nrow(def) #Error cal
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if (err.count > 0) {
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error.df <-
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rbind(
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error.df,
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data.frame(
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Country = Country,
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Name = Name,
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err.type = err.type,
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err.count = err.count
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)
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) #Error cal
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}
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print(paste0("Removing errors ", codelistcols[k]))
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# Removes any mismatch
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df[!df[, codelistcols[k]] %in% c(pull(codelist_files[codelistcols[k]][[1]], Description), NA), codelistcols[k]] <-
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NA
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# Matches each column with the corresponding code list and returns the value
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df[, codelistcols[k]] <-
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as.character(pull(codelist_files[codelistcols[k]][[1]], 2)[match(pull(df, codelistcols[k]),
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pull(codelist_files[codelistcols[k]][[1]], Description))])
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}
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max.length <- as.numeric(sel.template.desc$`Max Length`)
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dtype <- sel.template.desc$`Data Type`
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rowval <- NULL
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ival <- NULL
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rval <- NULL
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lenght.issue.df <- NULL
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fname <- NULL
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lname <- NULL
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owner <- NULL
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# Changing the data class
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for (k in 1:ncol(df)) {
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if (dtype[k] == "String") {
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df[, k] <- as.character(pull(df, k))
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}
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if (dtype[k] == "Boolean") {
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df[, k] <- as.logical(pull(df, k))
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}
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if (dtype[k] == "DateTime") {
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df[, k] <- lubridate::ymd_hms(pull(df, k))
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}
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if (dtype[k] == "Time") {
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df[, k] <- lubridate::hms(pull(df, k))
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} # This list will increase and also change based on input date and time formats
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}
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print("Rectifying streetname")
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# Street and House Number
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if (any(colnames(df) == "Street")) {
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print("found steet")
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# stop()
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df$Streetname <- NA
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df$HouseNumber <- NA
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#df |> extract("Street", "(\\D+)(\\d.*)")
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df <- tidyr::extract(df,
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"Street",
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c("Streetname", "HouseNumber"),
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"(\\D+)(\\d.*)")
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df <- df |>
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select(-c("House_Number")) |>
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rename(Street = Streetname, House_Number = HouseNumber) |>
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select(all_of(sel.template.desc.colnames))
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}
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# Rectifying Phone, Mobile and Fax numbers
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if (any(colnames(df) == "Phone")) {
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print("Found Phone")
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df$Phone <- gsub("[+]", "00", df$Phone)
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}
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if (any(colnames(df) == "Mobile")) {
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print("Found Mobile")
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df$Mobile <- gsub("[+]", "00", df$Mobile)
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}
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if (any(colnames(df) == "Mobile")) {
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print("Found Mobile")
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df$Mobile <- gsub("[+]", "00", df$Mobile)
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}
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# Length Rectification
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colclasses <- lapply(df, class)
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print("Rectifying Length")
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for (k in 1:ncol(df)) {
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if (colclasses[[k]] == "character") {
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print("found character column ")
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rowval <- pull(df, 1)
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ival <-
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ifelse(nchar(pull(df, k)) == 0 |
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is.na(nchar(pull(df, k))), 1, nchar(pull(df, k)))
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rval <- max.length[k]
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colval <- pull(df, k)
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colnm <- colnames(df)[k]
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cntr <- substr(names(old_files[h]), 2, 3)
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fname <- pull(df, 8)
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lname <- pull(df, 9)
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owner <- pull(df, 47)
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# rectifying data length
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df[, k] <-
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ifelse(nchar(pull(df, k)) > max.length[k],
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substring(pull(df, k), 1, max.length[k]),
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pull(df, k))
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}
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# Add name and email
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lenght.issue.df <-
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rbind(
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lenght.issue.df,
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data.frame(
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rowval,
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ival,
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rval,
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colnm,
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colval,
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cntr,
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fname,
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lname,
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owner
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)
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)
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err.type <-
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paste0("Length error ", colnames(df)[k]) # Error cal
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|
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(
|
|
"./contacts/errors/length/",
|
|
substr(names(old_files[h]), 2, 3),
|
|
"_",
|
|
snames[i],
|
|
"_length_error.csv"
|
|
),
|
|
row.names = F,
|
|
na = "",
|
|
fileEncoding = "UTF-8"
|
|
)
|
|
}
|
|
|
|
assign(snames[i], df)
|
|
df<- df |> mutate(EMail=ifelse(isValidEmail(EMail),EMail,"missing@leviat.com"))
|
|
write.csv(
|
|
df,
|
|
paste0(
|
|
"./contacts/output/",
|
|
substr(names(old_files[h]), 2, 3),
|
|
"_",
|
|
snames[i],
|
|
".csv"
|
|
),
|
|
row.names = F,
|
|
sep=",",
|
|
na = "",
|
|
fileEncoding = "UTF-8"
|
|
)
|
|
if (nrow(error.df) > 0) {
|
|
write.csv(
|
|
error.df,
|
|
paste0(
|
|
"./contacts/summary/",
|
|
substr(names(old_files[h]), 2, 3),
|
|
"_",
|
|
snames[i],
|
|
"_error",
|
|
".csv"
|
|
),
|
|
row.names = F,
|
|
na = "",
|
|
fileEncoding = "UTF-8"
|
|
) # 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(
|
|
"./contacts/summary/" ,
|
|
substr(names(old_files[h]), 2, 3),
|
|
"_",
|
|
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}
|
|
opfilepath <-
|
|
list.files("./contacts/output",
|
|
pattern = "*.csv",
|
|
full.names = T)
|
|
opfiles <- lapply(opfilepath, read.csv, colClasses = "character", header=TRUE, row.names=NULL)
|
|
opdf <- do.call(rbind.data.frame, opfiles)
|
|
write.csv(
|
|
opdf,
|
|
"./contacts/output/combined/combined.csv",
|
|
row.names = F,
|
|
na = "",
|
|
fileEncoding = "UTF-8"
|
|
)
|
|
|
|
openxlsx::write.xlsx(opdf,"./contacts/output/combined/combined.xlsx")
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
# Duplicate check
|
|
|
|
```{r}
|
|
contwav2<-read.csv("./contacts/output/combined/combined.csv") |>
|
|
mutate(FullName=paste(First_Name, Last_Name))
|
|
sapcont<-read.csv("contoct.csv") |>
|
|
mutate(FullName=paste(First_Name, Last_Name))
|
|
contwav2[duplicated(contwav2$FullName) | duplicated(contwav2$FullName, fromLast = TRUE),]
|
|
# write.csv("./contacts/errors/duplicatecontactssinsource.csv")
|
|
contwav2<-
|
|
contwav2 |>
|
|
select(External_Key, Account_External_Key, FullName, CountryRegion, Function, House_Number, Street, City, Postal_Code, EMail,Contact_Owner_ID ) |>
|
|
mutate(source="legacy CRM") #|>
|
|
#unique() # Using unique till Dariusz changes the legacy files
|
|
sapcont<-
|
|
sapcont |>
|
|
select(External_Key, Account_External_Key, FullName, CountryRegion, Function, House_Number, Street, City, Postal_Code, EMail,Contact_Owner_ID) |>
|
|
mutate(source="S4-CAA200") |>
|
|
filter(!CountryRegion %in% c("AT","CH")) |>
|
|
mutate(External_Key=ifelse(External_Key=="","EMPTY IN SAP",External_Key)) |>
|
|
mutate(Account_External_Key=ifelse(Account_External_Key=="","EMPTY IN SAP",Account_External_Key))
|
|
|
|
fullcont<-rbind(contwav2,sapcont)
|
|
|
|
fullcont[duplicated(fullcont$FullName) | duplicated(fullcont$FullName, fromLast = T), ] |>
|
|
select(External_Key, FullName, source, matches(".")) |>
|
|
rename(Source=source) |>
|
|
arrange(FullName) |>
|
|
group_by(FullName) |>
|
|
mutate(same = +(n_distinct(Source) == 1)) |>
|
|
ungroup() |>
|
|
mutate(errorsource=ifelse(same==1, Source, "Both")) |>
|
|
select(-same) |>
|
|
select(External_Key, FullName, Source,errorsource, matches(".")) |> # check if we need to send all, because several are same names in legacy
|
|
#filter(errorsource=="legacy CRM") |>
|
|
#filter(errorsource=="S4-CAA200") |>
|
|
#filter(errorsource=="Both")
|
|
left_join(employeecodes, by=c("Contact_Owner_ID"="Employee_ID")) |>
|
|
mutate(Contact_Owner_ID=ifelse(is.na(Name),Contact_Owner_ID,Name)) |>
|
|
select(-Name) |>
|
|
write.csv("./contacts/errors/duplicatecontacts.csv", row.names = F)
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|