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588 lines
17 KiB
588 lines
17 KiB
---
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title: "Competition"
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author: "Scary Scarecrow"
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date: "1/10/2022"
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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$set(echo = TRUE)
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library(readxl)
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library(dplyr)
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library(lubridate)
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library(DT)
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library(tidyr)
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library(stringr)
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mutlstxlrdr <- function() {
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for (i in seq_along(sheet.na)) {
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colnames <-
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unique(saptemplate[saptemplate$`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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"./competition/errors/mandatory/", full.names = TRUE
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)))
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do.call(file.remove, list(list.files(
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"./competition/errors/codelist/", full.names = TRUE
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)))
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do.call(file.remove, list(list.files(
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"./competition/errors/length/", full.names = TRUE
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)))
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do.call(file.remove, list(list.files("./competition/summary/", full.names = TRUE)))
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do.call(file.remove, list(list.files("./competition/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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### 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("./competition/CodeList",
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pattern = "*.xls",
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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$Customer_type_I
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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<-list.files("./competition/raw-data", pattern="*.xls", full.names = T) # Change the path, check pattern
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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}
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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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old_files[[i]] <- read_excel(path = oldfilepath[[i]], sheet = 1)
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}
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old_files
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names(old_files) <-
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gsub("./competition/raw-data/", "", oldfilepath) # Change path
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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("./competition/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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## Don't have Status column defined
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## There could be issue in line of business
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```{r createmptySAPfiles, echo=TRUE, message=FALSE, warning=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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strt <- Sys.time()
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snames <- unique(saptemplate$`Sheet Name`)
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for (h in seq_along(old_files)) {
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print("Importing new")
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# Copy original data
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old.copy <- old_files[[h]]
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print(paste0(names(old_files[h]), " imported"))
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err.summ <-
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data.frame(
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Country = NULL,
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Name = NULL,
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Expected = NULL,
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Actual = NULL
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) #Error Cal
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# Creates data frame for each sheet in snames
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for (i in seq_along(snames)) {
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print(paste0("Processing ..", snames[i]))
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if (snames[i] %in% c("Competitor")) {
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# Select the column names from the field description sheet
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print("Creating template")
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sel.template.desc <-
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saptemplate[saptemplate$`Sheet Name` == snames[i],]
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print("Creating column names")
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sel.template.desc.colnames <- sel.template.desc$Header
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# Create a list by adding values from corresponding legacy data
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temp <- NULL
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print("adding values to template ")
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if (snames[i] == "Competitor") {
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for (j in seq_along(sel.template.desc.colnames)) {
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temp[j] <-
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ifelse(
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!is.na(sel.template.desc$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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}
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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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# In case there are any default values (of mandatory) they need to be added here
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# if(essential.columns[k]=="International_Version"){
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# print("Found International Version. Adding 0.")
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# df$International_Version<-"0"
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# }
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# if(essential.columns[k]=="Status"){
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# print("Found Status")
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# df$Status<-"2"
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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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"./competition/errors/mandatory/",
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#Change path
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substr(names(old_files[h]), 1, 2),
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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]), 1, 2)
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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 essetial 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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"./competition/errors/codelist/",
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#Change path
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substr(names(old_files[h]), 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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"_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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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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# 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 street")
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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]), 1, 2)
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# fname<-pull(df, 8)
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# lname<-pull(df, 9)
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print(" Values identified")
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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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print("Trimmed")
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}
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lenght.issue.df <-
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rbind(lenght.issue.df,
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data.frame(rowval, ival, rval, colnm, colval, cntr))
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err.type <- paste0("Length error ", colnames(df)[k]) # Error cal
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err.count <- sum(ival > rval, na.rm = T) # 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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}
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lenght.issue.df <- dplyr::filter(lenght.issue.df, ival > rval)
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if (nrow(lenght.issue.df) > 0) {
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write.csv(
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lenght.issue.df,
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paste0(
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"./competition/errors/length/",
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# Change path
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substr(names(old_files[h]), 1, 2),
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"_",
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snames[i],
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"_length_error.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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assign(snames[i], df)
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write.csv(
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df,
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paste0(
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"./competition/output/",
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substr(names(old_files[h]), 1, 2),
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"_",
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snames[i],
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".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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) #Chnage path
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if (nrow(error.df) > 0) {
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write.csv(
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error.df,
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paste0(
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"./competition/summary/",
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substr(names(old_files[h]), 1, 2),
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"_",
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snames[i],
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"_error",
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".csv"
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),
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row.names = F,
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na = ""
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) # Error write
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}
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err.summ <-
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rbind(
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err.summ,
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data.frame(
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Country = Country,
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Name = Name,
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Expected = Expected,
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Actual = nrow(df)
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)
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) #Error Cal
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}
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write.csv(
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err.summ,
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paste0(
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"./competition/summary/" ,
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substr(names(old_files[h]), 1, 2),
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"_",
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snames[i],
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"_sumerror",
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".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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) # Error Write
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}
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}
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end <- Sys.time()
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end - strt
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|
```
|
|
*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"
|
|
)
|
|
|
|
```
|
|
|
|
|
|
|