Cleaning Wide Data by Rearranging Columns Based on Shared Variables and Time Points

In this blog post, we will explore a technique for cleaning wide data by rearranging columns based on shared variables and time points. We’ll dive into the details of how to approach this task using R and provide examples along the way.

Understanding the Problem

Wide data refers to a dataset where each variable is represented as a separate column. This can be particularly challenging when dealing with multiple variables that share similar characteristics, such as being related to time points or other grouping variables. The goal of rearranging columns in this context is to group together variables based on shared characteristics and then order them by their respective time points.

A Real-World Example

Let’s take a look at the provided example data:

id <- c(100, 101, 102)
variablea_1 <- c(1, 1, 1)
variableb_1 <- c(1, 1, 1)
variablec_1 <- c(1, 1, 1)
varibaled_1 <- c(1, 1, 1)
variablea_2 <- c(1, 1, 1)
variableb_2 <- c(1, 1, 1)
variablec_2 <- c(1, 1, 1)
varibaled_2 <- c(1, 1, 1)
variablea_3 <- c(1, 1, 1)
variableb_3 <- c(1, 1, 1)
variablec_3 <- c(1, 1, 1)

Data <- data.frame(patientid = patientid, variablea_1 = variablea_1, 
                   variableb_1 = variableb_1, variablec_1 = variablec_1,
                   varibaled_1 = varibaled_1, variablea_2 = variablea_2,
                   variableb_2 = variableb_2, variablec_2 = variablec_2,
                   varibaled_2 = varibaled_2, variablea_3 = variablea_3,
                   variableb_3 = variableb_3, variablec_3 = variablec_3)

In this example, we have a dataset with 80 variables (variables variablea_1 to variablec_3) and three time points. We want to rearrange the columns so that they are grouped together based on shared variables and then ordered by their respective time points.

Approaching the Problem

One way to approach this problem is to remove the substring from column names, get a sequence of the remaining characters, order it using ave and order, and use that to reorder the columns. Here’s how you can do it:

Step 1: Removing Substrings from Column Names

v1 <- as.numeric(sub(".*_", "", names(Data)[-1]))

This step removes the substring “_1” from each column name, leaving us with a sequence of characters that represents the group or category label.

Step 2: Ordering the Sequence

Data1 <- Data[c(1, order(ave(v1, v1, FUN = seq_along)) + 1)]

In this step, we use ave to calculate the length of each sequence (i.e., the number of shared variables), and then use order to sort them in ascending order. We add 1 to the first element of the sorted vector because R uses 1-based indexing.

Putting it All Together

Now that we have a function to reorder our columns, let’s take a closer look at how it works:

# Step 1: Define the original data frame
Data <- data.frame(patientid = patientid, variablea_1 = variablea_1,
                   variableb_1 = variableb_1, variablec_1 = variablec_1,
                   varibaled_1 = varibaled_1, variablea_2 = variablea_2,
                   variableb_2 = variableb_2, variablec_2 = variablec_2,
                   varibaled_2 = varibaled_2, variablea_3 = variablea_3,
                   variableb_3 = variableb_3, variablec_3 = variablec_3)

# Step 2: Remove substrings from column names
v1 <- as.numeric(sub(".*_", "", names(Data)[-1]))

# Step 3: Order the sequence and reorder columns
Data1 <- Data[c(1, order(ave(v1, v1, FUN = seq_along)) + 1)]

By following these steps, we can rearrange our wide data into a more organized and consistent format.

Conclusion

Rearranging columns in wide datasets based on shared variables and time points is an important skill to have. By using techniques like removing substrings from column names and ordering sequences, we can make our data more manageable and easier to work with. Whether you’re working with large datasets or just a small subset of data, this technique can help you get your data organized and in order.

Next Steps

Now that you know how to approach the problem, what’s next? You can try applying this technique to your own dataset and see the results for yourself. Remember to always check your work and make sure that the changes you’re making are correct.

Additional Tips and Variations

When working with large datasets, it may be helpful to use a database or data storage solution like SQLite or RDBMS.
If you have multiple variables that share similar characteristics, you can create a hierarchy of categories to better organize your data.
You can also explore other techniques for rearranging columns in wide datasets, such as using pivot tables or data transformation functions.

By following these tips and variations, you can further improve your skills in working with wide datasets and make even more efficient use of this technique.

Last modified on 2023-11-20