Understanding the Problem and Desired Output

The problem at hand involves two data frames, df1 and df2. The goal is to create a new column in df1 that contains a value from df2 based on a matching substring in df1$.messy.

Data Frame Creation

To begin with, we need to create sample data frames. Let’s assume the desired output:

df1:
-----------------
|   messy    |    new_str |
|-------------|------------|
|    abc.'123_c |      aa     |
|    def.'456_c |     NULL    |
|    hij.'789_c |      cc     |

df2:
-----------------
|   old_str    |    new_str    |
|--------------|---------------|
|    123         |      aa       |
|    789         |      cc       |

We can create these data frames using the following code:

# Create df1
df1 <- data.frame(
  messy = c('abc.'123_c', 'def.'456_c', 'hij.'789_c'),
  stringsAsFactors = FALSE
)

# Create df2
df2 <- data.frame(
  old = c(123, 789),
  new = c("aa", "cc"),
  stringsAsFactors = FALSE
)

Understanding the Approach

To solve this problem, we need to find a way to match each substring in df1$messy with the corresponding value in df2$old. Once a match is found, we can use the corresponding value from df2$new to fill in the new column.

Using Regular Expressions for Matching

One approach to this problem involves using regular expressions (regex) to find matching substrings. However, regex alone may not be enough, as we need to ensure that the matched substring is unique within each row of df1.

Creating a Lookup Table

To overcome this limitation, we can create a lookup table for df2$old and then use this table to match substrings in df1$messy. We will also need to handle cases where no match is found.

Collapsing Search Terms and Matching

We can collapse the search terms into a single string using the pipe character (|). Then, we can use the grep function to find matching substrings. This approach allows us to efficiently search for matches within each row of df1.

# Create a lookup table for df2$old
lookup_table <- grepl(paste(df2$old, collapse = "|"), df1$messy, value = T)

# Use the lookup table to join df2 with df1
left_join(df2, df1)

However, this approach does not directly address our problem. Instead, it shows how regex can be used for matching.

Creating a Function for Matching and Filling

To solve our problem, we need to create a function that takes df1$messy as input and finds the corresponding value in df2$old. If a match is found, it fills in the new column with the corresponding value from df2$new.

We can use a for loop or vectorized operations to achieve this. Here’s an example of how we can create a function using vectorized operations:

# Create a function to fill the new column
fill_new_column <- function(df1, df2) {
  # Collapse search terms into a single string
  lookup_table <- grepl(paste(df2$old, collapse = "|"), df1$messy, value = T)

  # Fill in the new column using vectorized operations
  df1$new_str[lookup_table] <- df2$new[lookup_table]
}

# Apply the function to our data frames
fill_new_column(df1, df2)

Conclusion

In this article, we explored how to replace part of a string using a lookup table. We discussed the problem, created sample data frames, and walked through different approaches to solving the problem.

The key insight here is to use regex for collapsing search terms into a single string and then creating a lookup table. This allows us to efficiently match substrings within each row of df1.

By using vectorized operations and creating a function for matching and filling, we can efficiently solve this problem even with large datasets.

Last modified on 2024-01-31