Manipulating Data with R: Creating a New Column from Matched Values

In this article, we will explore how to create a new column in a data frame by matching values between two columns and using them to populate the new column. We will use the match() function, which returns the indices of the matched values in the other column.

Understanding the Problem

The problem presented is about creating a new variable that takes the value of one’s partner and adds it as a new column. This means we need to match each row with its corresponding partner and add their value to the new column.

Setting Up the Data

To demonstrate this concept, let’s start by setting up some sample data using R.

set.seed(123)

df <- data.frame(
  id = c(1:10),
  partner_id = c(6,7,8,9,10,1,2,3,4,5),
  value = runif(10)
)

df

This code creates a data frame df with three columns: id, partner_id, and value. The set.seed() function is used to ensure reproducibility of the random numbers.

Creating the New Column

Now, let’s create the new column by matching the values in the partner_id column with the corresponding indices in the id column.

df %>%
  mutate(partner_value = value[match(partner_id, id)])

Here, we use the %>% operator to pipe the data frame into the mutate() function. The mutate() function creates a new column called partner_value. Inside the mutate() function, we use the match() function to find the indices of the matched values in the id column.

How the match() Function Works

The match() function returns the indices of the first occurrence of each value in the partner_id column within the range of the id column. If there are multiple matches, it will only return the index of the first match.

For example, if we have a row with an id of 5 and a partner_id of 10, the match() function will return the indices of all rows where the id is 5 or less. Since there are no such rows, it will return a vector of NA values.

Handling Partial Matches

However, in our case, we want to match each row with its corresponding partner and add their value to the new column. If there are multiple partners for a single row, we need to find the first matching pair.

To achieve this, we can use the match() function along with the which.max() function to get the index of the largest matching value.

df %>%
  mutate(partner_value = value[match(partner_id, id)][which.max(match(partner_id, id))])

Here, we first find the indices of all matching values using match(). We then use which.max() to get the index of the largest matching value.

Conclusion

In this article, we learned how to create a new column by matching values between two columns and using them to populate the new column. We used the match() function, which returns the indices of the matched values in the other column.

By understanding how the match() function works and handling partial matches, we can use it to manipulate data in various ways. Whether you’re working with large datasets or performing complex analysis, mastering data manipulation techniques is essential for any data scientist.

In the next article, we will explore more advanced data manipulation techniques using R, including grouping, merging, and pivoting data.

Last modified on 2023-06-02