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805 lines
23 KiB
805 lines
23 KiB
---
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title: "Task"
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author: "Scary Scarecrow"
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date: "1/12/2022"
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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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"./tasks2/errors/mandatory/", full.names = TRUE
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)))
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do.call(file.remove, list(list.files(
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"./tasks2/errors/codelist/", full.names = TRUE
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)))
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do.call(file.remove, list(list.files(
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"./tasks2/errors/length/", full.names = TRUE
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)))
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do.call(file.remove, list(list.files("./tasks2/summary/", full.names = TRUE)))
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do.call(file.remove, list(list.files("./tasks2/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(2,3))
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contacts<-read.csv("./tasks2/contacts.csv")
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contacts<-
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contacts |>
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mutate(Name=paste(First_Name, Last_Name, sep = " ")) |>
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rename(cont.ext.key=External_Key)
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quotes<-read.csv("./tasks2/quotes.csv") |>
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select(Quote.ID, Account.Number..Customer.) |>
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mutate(Account.Number..Customer.= ifelse(grepl("[A-Za-z*]", Account.Number..Customer.), Account.Number..Customer.,paste0("H",Account.Number..Customer.)))
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```
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### Relationship files
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```{r echo=TRUE, message=FALSE, warning=FALSE}
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# relfilenames <- #Not required in tasks2
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# list.files("./projects/relationship",
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# pattern = "*.xls",
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# full.names = T)
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# print(relfilenames)
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# rel_files <- NULL
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# for (i in seq_along(relfilenames)) {
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# b <- read_excel(path = relfilenames[[i]], sheet = 1)
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# b<-b |> left_join(employeecodes, by=c("Owner"="Name")) |>
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# select(-Owner) |>
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# mutate(Employee.ID=ifelse(is.na(Employee.ID),"99999",Employee.ID)) |>
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# rename(Owner=Employee.ID)
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# rel_files[[i]]<-b
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# }
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# names(rel_files) <- gsub("./projects/relationship/", "", relfilenames)
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# # Names of the files imported
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# names(rel_files)
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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("./tasks2/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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```
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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("./tasks2/raw-data",
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pattern = "*.xls", # Changed from *.xlsx to *.csv
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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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nona<-function(x){
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x<-x[!is.na(x)]
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if(length(x)==0){
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return(NA)
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} else return(unique(x))
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}
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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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select(External_Key, Subject, `Quote ID (Regarding)`,`Start Date`, Due, `External_Key (Regarding)`,
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Recipient, Owner, Category, Priority, `Modified On`, `Project/Opp Number (Regarding)`)
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a<-a |>
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fill(`Quote ID (Regarding)`, .direction = 'up') |>
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group_by(`Quote ID (Regarding)`) |>
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mutate(Recipient=str_c(Recipient, sep="_")) |>
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ungroup() |>
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group_by(`Quote ID (Regarding)`, Recipient) |>
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summarise(External_Key=nona(External_Key),
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Subject=nona(Subject),
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`Start Date`=nona(`Start Date`),
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Due=nona(Due),
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`External_Key (Regarding)`=nona(`External_Key (Regarding)`),
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Owner=nona(Owner),
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Category=nona(Category),
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Priority=nona(Priority),
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`Modified On`=nona(`Modified On`),
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`Project/Opp Number (Regarding)`=nona(`Project/Opp Number (Regarding)`))
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a<-a |>
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ungroup() |>
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left_join(employeecodes, by=c(Owner = "Name")) |>
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rename(owner.emp=Owner) |>
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mutate(Employee.ID=ifelse(is.na(Employee.ID),"90112",Employee.ID)) |> #Changed to Axel's in case of missing employee
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rename(Owner=Employee.ID) |>
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left_join(contacts, by=c(Recipient = "Name")) |>
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mutate(cont.ext.key=ifelse(is.na(cont.ext.key),Recipient,cont.ext.key)) |>
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rename(recip.con=Recipient) |>
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rename(Recipient=cont.ext.key) |>
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left_join(quotes, by=c("Quote ID (Regarding)"="Quote.ID"))
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old_files[[i]]<-a
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}
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names(old_files) <- gsub("./tasks2/raw-data/", "", oldfilepath)
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```
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```{r readSAPtemplate, echo=TRUE, message=FALSE, warning=FALSE}
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saptemplate <-
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read_excel("./tasks2/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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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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# 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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# 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] == "Task"){
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for (j in seq_along(sel.template.desc.colnames)) {
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print(paste("Processing ", sel.template.desc.colnames[j]))
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# if (sel.template.desc.colnames[j] == "Main_Account_ID") {
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# temp[j] <-
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# ifelse(
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# !is.na(old.copy$`Account Number (Regarding)`),
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# old.copy$`Account Number (Regarding)`,
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# old.copy$`Full Name (Regarding)`
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# )
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# next
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# }
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temp[j] <-
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ifelse(
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!is.na(sel.template.desc$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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}
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if(snames[i] %in% c("Task_Follow_Up_and_Related_Item", "Task_Involved_Parties", "Tasks_Notes", "Task_Attachment")){
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for (j in seq_along(sel.template.desc.colnames)) {
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print(paste("Processing ", sel.template.desc.colnames[j]))
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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$oldkey[j]])
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)
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)
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}
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}
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# Rename the columns according to field description
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print("renaming template ")
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names(temp) <- sel.template.desc.colnames
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# Create data frame from the list
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df <- as.data.frame(temp)
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print("Converted to data frame")
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if(c("Subject") %in% sel.template.desc.colnames){
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print("Found Subject")
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df$Subject <- gsub(",","/",df$Subject)
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df$Start_DateTime_Time_Zone_Code <- ifelse(is.na(df$Start_DateTime), NA, df$Start_DateTime_Time_Zone_Code)
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df$Due_Date_Time_Time_Zone_Code <- ifelse(is.na(df$Due_Date_Time), NA, df$Due_Date_Time_Time_Zone_Code)
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df$Last_Changed<-NA
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acc<-read.csv("accounts.csv") |> select(-1)
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fulldf<-
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df |>
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mutate(Main_Account_ID= ifelse(grepl("[A-Za-z*]", Main_Account_ID), Main_Account_ID,paste0("H",Main_Account_ID))) |>
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left_join(acc, by=c("Main_Account_ID"="Former_CRM_reference"))
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df<-fulldf |>
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# filter(!is.na(Account_ID)) |>
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mutate(Main_Account_External_Key=NA) |>
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select(-Main_Account_ID) |>
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rename(Main_Account_ID=Account_ID) |>
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mutate(Main_Employee_Responsible_ID=Processor_ID) |>
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#mutate(External_Key=paste("NEW",External_Key,sep = "_")) |> # TEMPORARY. TO BE DELETED IN PROD
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select(External_Key,
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Document_Type,
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Subject,
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Status,
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Start_DateTime,
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Start_DateTime_Time_Zone_Code,
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Due_Date_Time,
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Due_Date_Time_Time_Zone_Code,
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Planned_Duration,
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Actual_Duration,
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Completion_Date_Time,
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Main_Employee_Responsible_ID,
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MainEmployeeResponsiblePartyExternalKey,
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Main_Account_External_Key,
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Main_Account_ID,
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Main_Contact_ID_External_Key,
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Main_Contact_ID,
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Processor_ID,
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ProcessorPartyExternalKey,
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Completion_Percent,
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Category,
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Priority,
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Sales_Territory_ID,
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Sales_Organization_External_Key,
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Sales_Organization_ID,
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Distribution_Channel,
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Division,
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Data_Origin,
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Processor_Email,
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Processor_Name,
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Last_Changed)
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missaccdf<- df |>
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filter(is.na(Main_Account_ID))
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if (nrow(missaccdf) > 0) {
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write.csv(
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missaccdf,
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paste0(
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"./tasks2/errors/missingacc/",
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substr(names(old_files[h]), 1, 2),
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"_",
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"_missing_account.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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}
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if(c("Text") %in% sel.template.desc.colnames){
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print("Found Text")
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df$Text<-paste("des",old.copy$Description,"recip",old.copy$recip.con, "own",old.copy$owner.emp, sep = "_")
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df$Text<-gsub(",",".",df$Text)
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#df$Task_External_Key<-paste("NEW", df$Task_External_Key, sep = "_")# TEMPORARY. TO BE DELETED IN PROD
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df$External_Key<-paste(df$Task_External_Key,"TA01", sep = "_")
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}
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if(c("BTD_ID") %in% sel.template.desc.colnames | c("Party_ID") %in% sel.template.desc.colnames){
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print("Found BTD_ID")
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df$External_Key<-paste(df$Task_External_Key,"TA01", sep = "_")
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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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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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"./tasks2/errors/mandatory/",
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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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|
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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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"./tasks2/errors/codelist/",
|
|
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
|
|
# 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] <-
|
|
strftime(pull(df, k), format = "%Y-%m-%d")
|
|
#strftime(pull(df, k), format = "%Y-%m-%d %H:%M:%S", tz = "CET")
|
|
#pull(df, k)
|
|
#paste0(sub(" ","T",as.character(pull(df, k))),"Z")
|
|
print(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
|
|
|
|
}
|
|
|
|
|
|
# Length Rectification
|
|
colclasses <- lapply(df, class)
|
|
print("Rectifying Length")
|
|
for (k in 1:ncol(df)) {
|
|
if(colnames(df)[k] %in% c("Start_DateTime","Due_Date_Time","Completion_Date_Time","Last_Changed","Updated_On")){
|
|
next
|
|
}
|
|
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)
|
|
# rectifying data length
|
|
df[, k] <-
|
|
ifelse(nchar(pull(df, k)) > max.length[k],
|
|
substring(pull(df, k), 1, max.length[k]),
|
|
pull(df, k))
|
|
}
|
|
|
|
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 | !is.null(lenght.issue.df)) {
|
|
write.csv(
|
|
lenght.issue.df,
|
|
paste0(
|
|
"./tasks2/errors/length/",
|
|
substr(names(old_files[h]), 1, 2),
|
|
"_",
|
|
snames[i],
|
|
"_length_error.csv"
|
|
),
|
|
row.names = F,
|
|
na = ""
|
|
)
|
|
}
|
|
|
|
assign(snames[i], df)
|
|
|
|
write.csv(
|
|
df,
|
|
paste0(
|
|
"./tasks2/output/",
|
|
substr(names(old_files[h]), 1, 2),
|
|
"_",
|
|
snames[i],
|
|
".csv"
|
|
),
|
|
sep=";",
|
|
row.names = F,
|
|
na = "",
|
|
fileEncoding = "UTF-8"
|
|
)
|
|
if (nrow(error.df) > 0) {
|
|
write.csv(
|
|
error.df,
|
|
paste0(
|
|
"./tasks2/summary/",
|
|
substr(names(old_files[h]), 1, 2),
|
|
"_",
|
|
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(
|
|
"./tasks2/summary/" ,
|
|
substr(names(old_files[h]), 1, 2),
|
|
"_",
|
|
snames[i],
|
|
"_sumerror",
|
|
".csv"
|
|
),
|
|
row.names = F,
|
|
na = ""
|
|
) # 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("./tasks2/output",
|
|
pattern = "*_Task.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,
|
|
"./tasks2/output/combined/combinedtask.csv",
|
|
row.names = F,
|
|
na = "",
|
|
fileEncoding = "UTF-8",
|
|
sep = ",",
|
|
quote = FALSE
|
|
)
|
|
|
|
openxlsx::write.xlsx(opdf,"./tasks2/output/combined/combinedtask.xlsx")
|
|
|
|
opfilepath <-
|
|
list.files("./tasks2/output",
|
|
pattern = "*_Task_Involved_Parties.csv",
|
|
full.names = T)
|
|
opfiles <- lapply(opfilepath, read.csv, colClasses = "character")
|
|
|
|
|
|
opdf <- do.call(rbind.data.frame, opfiles)
|
|
|
|
write.csv(
|
|
opdf,
|
|
"./tasks2/output/combined/combinedDTaskInvolvedParties.csv",
|
|
row.names = F,
|
|
na = "",
|
|
fileEncoding = "UTF-8",
|
|
sep = ",",
|
|
quote = FALSE
|
|
)
|
|
|
|
openxlsx::write.xlsx(opdf,"./tasks2/output/combined/combinedDTaskInvolvedParties.xlsx")
|
|
|
|
opfilepath <-
|
|
list.files("./tasks2/output",
|
|
pattern = "*_Task_Follow_Up_and_Related_Item.csv",
|
|
full.names = T)
|
|
opfiles <- lapply(opfilepath, read.csv, colClasses = "character")
|
|
|
|
|
|
opdf <- do.call(rbind.data.frame, opfiles)
|
|
|
|
write.csv(
|
|
opdf,
|
|
"./tasks2/output/combined/combinedTaskFollowUpandRelatedItem.csv",
|
|
row.names = F,
|
|
na = "",
|
|
fileEncoding = "UTF-8",
|
|
sep = ",",
|
|
quote = FALSE
|
|
)
|
|
|
|
openxlsx::write.xlsx(opdf,"./tasks2/output/combined/combinedTaskFollowUpandRelatedItem.xlsx")
|
|
|
|
opfilepath <-
|
|
list.files("./tasks2/output",
|
|
pattern = "*_tasks2_Notes.csv",
|
|
full.names = T)
|
|
opfiles <- lapply(opfilepath, read.csv, colClasses = "character")
|
|
|
|
|
|
opdf <- do.call(rbind.data.frame, opfiles)
|
|
write.csv(
|
|
opdf,
|
|
"./tasks2/output/combined/combinedTaskNotes.csv",
|
|
row.names = F,
|
|
na = "",
|
|
fileEncoding = "UTF-8",
|
|
sep = ",",
|
|
quote = FALSE
|
|
)
|
|
|
|
openxlsx::write.xlsx(opdf,"./tasks2/output/combined/combinedTaskNotes.xlsx")
|
|
|
|
```
|
|
|
|
|
|
|
|
|