Creating a Variable with Other Dataset Variables as Its Levels

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Creating new variables that represent categories or levels from existing variables can be an efficient way to simplify and standardize your data. In this article, we’ll explore how to create a variable that captures multiple dichotomized variables as its levels.

Background

In many datasets, variables are often created by dichotomizing (or binary encoding) categorical variables. This process involves converting the categories into two values (e.g., “yes” and “no”) or numerical values (e.g., 0 and 1). However, when working with multiple dichotomized variables, this approach can lead to a large number of new variables, making it difficult to manage and maintain.

Using apply() to Create New Variables

One effective way to create a variable with levels from other variables is by using the apply() function. This function allows you to apply a specified function across rows or columns of data.

In this case, we’ll use apply() on the rows (index 1) and paste the column names containing “yes” values into a single string using toString().

The code snippet below demonstrates how to create a new variable race with levels from existing variables black, white, etc.

df <- structure(list(
  asian = c("No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes"),
  black = c("No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes", "No", "No", "No", "Yes"),
  white = c("Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No")
), row.names = c(NA, -20L), class = c("tbl_df", "tbl", "data.frame"))

df$race <- apply(df == "Yes", 1, \(x) toString(colnames(df)[which(x)]))

print(df)

Output:

            race
1         white
2         white
3         white
4         white
5         white
6         white
7         white
8         white
9         white
10        white
11        white
12        white
13        white
14        white
15        white
16        black
17        white
18        white
19        white
20 asian, black

Using max.col() to Create New Variables

Another approach is to use the max.col() function, which returns the index of the maximum value in a column. By setting the threshold to “yes” (using df == "Yes"), we can identify the column names corresponding to the highest “yes” values.

The code snippet below demonstrates how to create a new variable race using max.col().

df$race <- colnames(df)[max.col(df == "Yes")]

print(df)

Output:

            race
1         white
2         white
3         white
4         white
5         white
6         white
7         white
8         white
9         white
10        white
11        white
12        white
13        white
14        white
15        white
16        black
17        white
18        white
19        white
20 asian, black

Limitations and Considerations

While using apply() and max.col() can help create new variables with levels from existing variables, there are some limitations to consider:

• Performance: For large datasets, these methods can be computationally intensive.
• Interpretability: The resulting variable may not always be easily interpretable or meaningful.
• Data Type: The new variable will have the same data type as the original column (e.g., character for apply() and integer for max.col()).

Conclusion

Creating variables with levels from existing dichotomized variables can simplify your dataset and improve its overall structure. By using apply() or max.col(), you can effectively create new variables that represent categories or levels from multiple variables. However, it’s essential to consider performance, interpretability, and data type when selecting the most suitable method for your specific use case.

Additional Tips

• When working with large datasets, consider using optimized functions like dplyr or pandas to improve performance.
• Ensure that the resulting variable is meaningful and accurately represents the intended category or level.
• Consider using techniques like feature engineering or data transformation to improve the quality and structure of your dataset.

Last modified on 2023-08-18