---
title: "Support"
author: "Scary Scarecrow"
date: "1/12/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(readxl)
library(dplyr)
library(lubridate)
library(DT)
library(tidyr)
mutlstxlrdr<-function(){
for( i in seq_along(sheet.na)){
colnames<-unique(saptemplate[saptemplate$`Sheet Name`==snames[i],]$Header)
df<-read.table("", col.names = colnames)
assign(snames[i], df)
}
}
do.call(file.remove, list(list.files("./support/errors/mandatory/", full.names = TRUE)))
do.call(file.remove, list(list.files("./support/errors/codelist/", full.names = TRUE)))
do.call(file.remove, list(list.files("./support/errors/length/", full.names = TRUE)))
do.call(file.remove, list(list.files("./support/summary/", full.names = TRUE)))
do.call(file.remove, list(list.files("./support/output/", full.names = TRUE)))
```
## Data transformation workflow
Following is the proposed preliminary workflow for the data transformation project.
>All file of a segment (support/accounts etc..) should be inside the relevant folder. Each folder should have one folder for all codelist files. All legacy data (one file for each country) should be inside the raw-data folder, named after each country. Another file having field definitions including name of the matching column from the legacy file should also be there.
>*Make sure that there are no hidden files inside the directory.*
### Code Lists
```{r Create List of Files, echo=TRUE, message=FALSE, warning=FALSE}
filenames <- list.files("./support/CodeList", pattern="*.xlsx", 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 (support, accounts etc.).
# File paths
print(filenames)
```
Check manually if the above list includes all the codelist files
If correct, then read the files.
```{r codelistreader, echo=TRUE, message=FALSE, warning=FALSE}
sheet_names<-lapply(filenames, excel_sheets) # Creates a list of the sheet names
codelist_files<-NULL
for(i in seq_along(filenames)){
a<-lapply(excel_sheets(filenames[[i]]), read_excel, path = filenames[[i]], col_types = "text") # Reads the sheets of the excel files
names(a)<-c(sheet_names[[i]]) # Renames them according to the sheet names extracted above
codelist_files<-c(codelist_files,a)
}
# Names of the files imported
names(codelist_files)
#codelist_files<-unique(codelist_files)
codelist_files$Academic_Title
```
### Templates
Let us now extract the data. Below we are reading only one file having all data related to `support` from the legacy system.
```{r readlegacyfilepath, echo=TRUE, message=FALSE, warning=FALSE}
oldfilepath<-list.files("./support/raw-data", pattern="*.xls", full.names = T) # Change the path, check pattern
print(oldfilepath)
```
Check it the list matches the actual files, manually.
```{r readlegacyfiles, echo=TRUE}
old_files<-NULL
#read_excel(path = oldfilepath[[i]], sheet = 1)
for(i in seq_along(oldfilepath)){
old_files[[i]]<-read_excel(path = oldfilepath[[i]], sheet = 1)
}
names(old_files)<-gsub("./support/raw-data/","",oldfilepath)
```
*Some errors in the legacy file noticed. Columns with similar or same name exists.*
```{r readSAPtemplate, echo=TRUE, message=FALSE, warning=FALSE}
saptemplate<-read_excel("./support/template.xlsx", sheet = "Field_Definitions")
# First few rows of the imported data
head(saptemplate)
```
*Please note that the format of the tables (sheet) has been slightly changed. Earlier the corresponding sheet name was mentioned in a row before the actual table. Now, all the rows mention the corresponding sheet name. This was done manually for convenience of data extraction*
```{r createmptySAPfiles, echo=TRUE, message=FALSE, warning=FALSE}
#orilo<-"en_US.UTF-8"
#Sys.setlocale(locale="en_US.UTF-8")
strt<-Sys.time()
snames <- unique(saptemplate$`Sheet Name`)
for (h in seq_along(old_files)) {
# Copy original data
old.copy <- old_files[[h]]
print(paste0(names(old_files[h])," imported"))
err.summ<-data.frame(Country=NULL, Name=NULL, Expected=NULL, Actual=NULL) #Error Cal
# Creates data frame for each sheet in snames
for (i in seq_along(snames)) {
print(paste0("Processing ..",snames[i]))
# Select the column names from the field description sheet
print("Creating template")
sel.template.desc <-
saptemplate[saptemplate$`Sheet Name` == snames[i], ]
print("Creating column names")
sel.template.desc.colnames <- sel.template.desc$Header
# Create a list by adding values from corresponding legacy data
temp <- NULL
print("adding values to template ")
for (j in seq_along(sel.template.desc.colnames)) {
temp[j] <- ifelse(!is.na(sel.template.desc$default[j]), as.character(as.vector(sel.template.desc$default[j])),
ifelse(
sel.template.desc$oldkey[j]=="NA" | is.na(sel.template.desc$oldkey[j]), NA,
as.vector(old.copy[, sel.template.desc$oldkey[j]])
)
)
}
# Rename the columns according to field description
print("renaming template ")
names(temp) <- sel.template.desc.colnames
# Create data frame from the list
df <- as.data.frame(temp)
print("Converted to data frame")
# Error summary file
Expected<-nrow(df)
#Select essential rows
print("Identifying essential rows")
sel.template.desc |>
filter(Mandatory == "Yes") |>
pull(Header) -> essential.columns
error.mandatory <- NULL
error.df<-data.frame(Country=NULL, Name=NULL, Rows=NULL, Expected=NULL)
# Operate on essential columns including creation of error file
for (k in seq_along(essential.columns)) {
if(essential.columns[k]=="International_Version"){
print("Found International Version. Adding 0.")
#stop()
df$International_Version<-"0"
}
print("Creating and writing data with missing mandatory values")
assign(
paste0(
"error_mandatory_",
substr(names(old_files[h]), 1, 2),
"_",
snames[i],
"_",
essential.columns[k]
),
df[is.na(df[, essential.columns[k]]), ]
)
# TO be saved in error files
if(nrow(df[is.na(df[, essential.columns[k]]), ])>0){
write.csv(
df[is.na(df[, essential.columns[k]]), ],
paste0(
"./support/errors/mandatory/",
substr(names(old_files[h]), 1, 2),
"_",
snames[i],
"_",
essential.columns[k],
"_error_mandatory.csv"
), row.names = F, na=""
)
}
# Error summary file
Country<-substr(names(old_files[h]), 1, 2)
Name<-snames[i]
err.type<-paste0("Missing ",essential.columns[k])
err.count<-nrow(df[is.na(df[, essential.columns[k]]), ])
print("Removing rows with empty essetial columns")
df <- df[!is.na(df[, essential.columns[k]]), ]
if(err.count>0){
error.df<-rbind(error.df,data.frame(Country=Country, Name=Name, err.type=err.type, err.count=err.count)) #Error cal
}
}
print("Identifying columns associated with codelists")
# List of columns that have a codelist
codelistcols <- sel.template.desc |>
filter(!is.na(`CodeList File Path`)) |> pull(Header)
for (k in seq_along(codelistcols)) {
if(codelistcols[k]=="International_Version"){
print("Found International Version. Adding 0.")
df$International_Version<-"0"
}
print(paste0("Identifying errors ",codelistcols[k]))
def.rows <-
which(!df[, codelistcols[k]] %in% c(pull(codelist_files[codelistcols[k]][[1]], Description), NA))
def.n<- df[def.rows, 1]
def.rows.val <-
df[!df[, codelistcols[k]] %in% c(pull(codelist_files[codelistcols[k]][[1]], Description), NA), codelistcols[k]]
def <- data.frame(def.rows, def.n,def.rows.val)
if(nrow(def>0)){
assign(paste0(
"error_codematch_",
substr(names(old_files[1]), 1, 2),
"_",
snames[i],
"_",
codelistcols[k]
),
def) # TO be saved
write.csv(
def,
paste0(
"./support/errors/codelist/",
substr(names(old_files[h]), 1, 2),
"_",
snames[i],
"_",
codelistcols[k],
"_error_codematch_.csv"
), row.names = F, na=""
)
}
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] <- lubridate::ymd_hms(pull(df, k))
}
if (dtype[k] == "Time") {
df[, k] <- lubridate::hms(pull(df, k))
} # This list will increase and also change based on input date and time formats
}
print("Rectifying streetname")
# Street and House Number
if (any(colnames(df) == "Street")) {
print("found steet")
# stop()
df$Streetname<-NA
df$HouseNumber<-NA
#df |> extract("Street", "(\\D+)(\\d.*)")
df<-tidyr::extract(df,
"Street",
c("Streetname", "HouseNumber"),
"(\\D+)(\\d.*)")
df <- df |>
select(-c("House_Number")) |>
rename(Street = Streetname, House_Number = HouseNumber) |>
select(all_of(sel.template.desc.colnames))
}
# Length Rectification
colclasses <- lapply(df, class)
print("Rectifying Length")
for (k in 1:ncol(df)) {
if (colclasses[[k]] == "character") {
print("found character column ")
rowval <- pull(df, 1)
ival <- ifelse(nchar(pull(df, k))== 0 | is.na(nchar(pull(df, k))),1,nchar(pull(df, k)))
rval <- max.length[k]
# 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))
err.type<- paste0("Length error ", colnames(df)[k]) # Error cal
err.count<- sum(ival>rval, na.rm = T) # Error cal
if(err.count>0){
error.df<-rbind(error.df,data.frame(Country=Country, Name=Name, err.type=err.type, err.count=err.count)) #Error cal
}
}
lenght.issue.df <- dplyr::filter(lenght.issue.df,ival>rval)
if(nrow(lenght.issue.df)>0){
write.csv(lenght.issue.df,
paste0(
"./support/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("./support/output/", substr(names(old_files[h]), 1, 2), "_", snames[i],".csv"), row.names = F, na="")
if(nrow(error.df)>0){
write.csv(error.df, paste0("./support/summary/",substr(names(old_files[h]), 1, 2), "_", snames[i],"_error",".csv"), row.names = F, na="") # Error write
}
err.summ<-rbind(err.summ,data.frame(Country=Country, Name=Name, Expected=Expected, Actual=nrow(df))) #Error Cal
}
write.csv(err.summ,
paste0("./support/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*
