Understanding the Problem and Requirements

The problem presented involves cleaning phone numbers in a dataset by creating a new column based on multiple if conditions. The existing code attempts to unify the format of phone numbers using three columns: CountryCode, AreaCode, and MobileNumber.

Code Review and Issues

The provided R function has several issues:

1. Incorrect condition usage: When checking if(nchar(data$MobileNumber >= 12)), only the first element of the length greater than or equal to 12 is considered.
2. Incorrect use of vectors: The use of three separate vectors for CountryCode, AreaCode, and MobileNumber does not guarantee correct pairing with their respective values in data.
3. Inefficient loop approach: Using a for loop to iterate over the data can be inefficient compared to vectorized operations.

A Better Approach: Vectorized Operations

The problem can be solved more efficiently using vectorized operations provided by base R and modern packages like dplyr.

Solution in Base R

data$Number <- vapply(1:nrow(data), function(k) {
  if(nchar(data$MobileNumber[k]) >= 12)
    return(paste("+", data$MobileNumber[k]))
  if(nchar(data$MobileNumber[k]) >= 9)
    return(paste("+", data$CountryCode[k], data$MobileNumber[k]))
  if (data$CountryCode[k] == data$AreaCode[k])
    return(paste("+", data$CountryCode[k], data$MobileNumber[k]))
  paste("+", data$CountryCode[k], data$AreaCode[k], data$MobileNumber[k])
}, character(1))

This solution uses vapply, which is equivalent to sapply but applies a function to each element of the input vector. This approach eliminates the need for explicit looping and makes the code more readable.

Solution using dplyr

The dplyr package provides a grammar-based approach to data manipulation, making it easier to express complex operations like this one.

library(dplyr)

data <- data %>% 
  mutate(Number = ifelse(nchar(MobileNumber) >= 12 & is.na(CountryCode), 
                        paste("+", MobileNumber),
                        ifelse(nchar(MobileNumber) >= 9, paste("+", CountryCode, MobileNumber), 
                               if (CountryCode == AreaCode) {paste("+", CountryCode, MobileNumber)} else {paste("+", CountryCode, AreaCode, MobileNumber)})))

This solution uses the mutate function to create a new column named Number, which is created using conditional expressions. The syntax of these conditions is similar to the original code but uses & for logical AND and ~ for negation.

Additional Considerations

• Phone numbers may not always follow a consistent pattern, so the current solution might produce incorrect results if there are variations in formatting.
• Data validation should be included as part of this process. This includes checking if phone numbers contain any non-alphanumeric characters and ensuring they adhere to common formats (e.g., country codes).
• Cleaning phone numbers may also involve removing or transforming special characters, such as commas, semicolons, etc.

Conclusion

Creating a new column in a dataset based on multiple conditions can be achieved using vectorized operations provided by base R. However, it’s essential to consider the potential variations and validate the output. The dplyr package offers an additional solution that provides more flexibility and readability for complex data manipulation tasks.

Last modified on 2024-03-01