Creating Composite Variables with Julius AI: A Step-by-Step Tutorial

Introduction to Composite Variables with Julius AI

Julius AI can create composite variables, which are formed by combining two or more variables from a dataset. These composite variables offer new insights and simplify analyses by consolidating information into a single, more meaningful variable.

Composite variables, also known as derived or constructed variables, are newly created variables that result from mathematical operations, transformations, or combinations of existing variables in a dataset. They are designed to capture specific relationships or patterns that may not be immediately apparent in the original dataset. The importance of composite variables in data analysis can be understood in the following ways:

1. Increased Predictive Power: By combining multiple variables into a single composite variable, you can incorporate more information and enhance the predictive power of your models.

2. Dimensionality Reduction: Creating composite variables helps reduce the number of variables in your dataset. This simplifies analysis and modelling while retaining relevant information.

3. Simplified Interpretation: Sometimes, composite variables represent underlying constructs or phenomena more directly than individual variables. This makes it easier to interpret their effects within the context of your analysis.

4. Handling Nonlinearity: In many cases, relationships between variables are nonlinear. Constructing composite variables allows for the effective capture and representation of these nonlinear relationships.

Getting Started with an Example Dataset

In this tutorial, we will use the "Example dataset_merged.csv" that we obtained in the previous tutorial as our main dataset. To follow along and practice the steps on your own, you can download the dataset from the following link:

Step-by-Step Walkthrough

To easily create composite variables using Julius AI, follow these key steps. Each step is accompanied by screenshots and a narrative, ensuring that you can navigate through the process effortlessly.

1. Step 1: Load your dataset into Julius AI using the provided upload feature.

First, you need to upload your dataset to Julius AI. Go to the upload section and select "Example dataset_merged.csv" from your computer. Once uploaded, Julius will automatically load the dataset into its workspace.

2. Step 2: Identify the variables you wish to combine into a composite variable.

Review your dataset to identify the variables you wish to combine into a composite variable. For example, when examining our health dataset, we may choose to create the following composite variables in Julius:

a. Body Mass Index (BMI): A widely used measure that calculates the ratio of weight in kilograms to the square of height in meters, used to classify individuals as underweight, normal weight, overweight, or obese. This composite variable provides insights into the general health status of individuals in the dataset.

b. Cardiovascular Risk Score: This composite variable can be derived from multiple factors such as age, blood pressure (both systolic SBP and diastolic DBP), cholesterol levels, and smoking status (if data on smoking is available or can be inferred). Each factor contributes to the overall risk of cardiovascular disease.

c. Metabolic Syndrome Indicator: Metabolic syndrome is a cluster of conditions that occur together, increasing the risk of heart disease, stroke, and type 2 diabetes. A composite variable can be created to indicate the presence of metabolic syndrome, based on criteria such as waist circumference (which can be inferred from BMI), high blood pressure (using SBP and DBP), high blood sugar (using Gluc), and abnormal cholesterol levels.

d. Health Score: A more general composite variable that can be created by combining various health indicators such as blood pressure, cholesterol, glucose levels, and BMI. Each variable can be scored based on health guidelines, and a cumulative score can provide a general overview of an individual's health status.

e. Step 3: Create the Composite Variable

With Julius AI, you have the ability to create composite variables effortlessly. This can be done through basic arithmetic operations or by utilising the more advanced built-in functions. All you need to do is instruct Julius on the variable you desire and its intended purpose. You can add or customise variables according to your preferences or as allowed by your data. In our example, we have included a brief description of the variable.

a. Body Mass Index (BMI):

Prompt: " Julius can we create the following composite variables? Let's start with Body Mass Index (BMI): A widely used measure to classify underweight, normal weight, overweight, and obesity by calculating the ratio of weight in kilograms to the square of height in meters ()"

Narrative: Julius AI has successfully calculated the Body Mass Index (BMI) for a few individuals in the dataset. He explained that the BMI is obtained by dividing the weight in kilograms by the square of the height in meters. This calculation helps to classify individuals as underweight, normal weight, overweight, or obese.

b. Cardiovascular Risk Score variable.

Next, we asked Julius AI to create the Cardiovascular Risk Score variable.

Prompt: "Create a Cardiovascular Risk Score: This is a composite variable derived from multiple factors such as age, blood pressure (both systolic SBP and diastolic DBP), cholesterol levels, and smoking status (if data on smoking is available or can be inferred). Each factor contributes to the overall risk of cardiovascular diseases. Let us use the available data for demonstration purposes"

Narrative: Julius AI has successfully calculated the Cardiovascular Risk Score and has added it to the dataset. He explains that this score is determined using normalized values of age, systolic blood pressure (SBP), diastolic blood pressure (DBP), and cholesterol levels. Each of these factors contributes to the overall risk of cardiovascular diseases. The score is then calculated by taking the average of these normalized values, offering a simplified yet insightful measure of cardiovascular health risk.

c. Metabolic Syndrome Indicator

Additionally, we asked Julius AI to create the variable Metabolic Syndrome Indicator.

Prompt: "Can we create a Metabolic Syndrome Indicator, a composite variable to indicate the presence of metabolic syndrome, based on criteria such as waist circumference (which could be inferred from BMI), high blood pressure (using SBP and DBP), high blood sugar (using Gluc), and abnormal cholesterol levels."

Narrative: Julius A has successfully created the Metabolic Syndrome Indicator and added it as a variable to the dataset. According to Julius AI, this indicator is based on specific criteria such as waist circumference (estimated from BMI), high blood pressure (using SBP and DBP), high blood sugar (using Gluc), and abnormal cholesterol levels. The indicator is assigned a value of 1 if the criteria for metabolic syndrome are met, and 0 otherwise. In the initial rows shown, all individuals have a Metabolic Syndrome Indicator value of 0, indicating that they do not meet the criteria for metabolic syndrome based on the provided thresholds.

Next, we decided to ask Julius AI about the thresholds he utilized in developing the Metabolic Syndrome Indicator. If necessary for your analysis or research question, you have the option to provide Julius AI with specific thresholds instead of relying on his suggestions.

Prompt:" Please share all thresholds that you used to create the composite variable "Metabolic Syndrome Indicator"

Narrative: Julius explained that the composite variable "Metabolic Syndrome Indicator" was created using specific thresholds based on the criteria for metabolic syndrome:

1. Waist Circumference (Inferred from BMI): A BMI of 25 or higher was used as an indicator of increased waist circumference.

2. High Blood Pressure: Systolic Blood Pressure (SBP) greater than 130 mmHg or Diastolic Blood Pressure (DBP) greater than 85 mmHg.

3. High Blood Sugar: Blood glucose (Gluc) level greater than 5.6 mmol/L.

4. Abnormal Cholesterol Levels: Total cholesterol (Cholest) greater than 5.2 mmol/L.

Julius also explained the reason why he chose these thresholds. He said these criteria are commonly used to diagnose metabolic syndrome, a cluster of conditions that increase the risk of heart disease, stroke, and diabetes.

d. Health Score

Lastly, we asked Julius AI to create the Health score variable.

Prompt: "Can we create a Health Score to provide a general overview of an individual's health status: A more general composite variable that combines various health indicators such as blood pressure, cholesterol, glucose levels, and BMI."

Narrative: Julius has successfully created The Health Score and incorporated it into the dataset. He clarified that this score is a composite variable that considers multiple health indicators, including both systolic and diastolic blood pressure, cholesterol levels, glucose levels, and BMI. To make sure these factors can be compared accurately, they have all been standardized. The Health Score is then calculated as the average of these standardized values. This score provides a general overview of an individual's health status, with higher scores indicating potentially better health conditions based on the selected indicators.

Our next step is to review the composite variables we have just created.

Step 4: Review the newly created composite variable for accuracy and relevance.

Prompt: "Display the dataset with all 4 new variables"

Narrative: Julius provided a preview of the dataset with the four new variables added: BMI, Health Score, Metabolic Syndrome Indicator, and Cardiovascular Risk Score. He explained that these variables offer a holistic perspective on an individual's health status, encompassing key indicators like blood pressure, cholesterol, glucose levels, and BMI, as well as a calculated risk score for cardiovascular diseases.

Step 5: Save and Export Your Updated Dataset. Export your updated dataset with the new composite variable included.

Prompt: "Can we save the dataset with all 4 new variables as Example_Dataset_new and export it as an Excel file"

As requested, the dataset, which included all four new variables, was saved under the name 'Example_Dataset_new.xlsx'. A link was provided by Julius AI for downloading the dataset in Excel format.

Note: Julius AI can create all the necessary variables using just one prompt, as long as you provide clear and sufficient instructions. With its extensive knowledge in any subject, Julius AI can also suggest additional composite variables that can be derived from your data, helping to guide you.

Tips for Creating Composite Variables

Ensure that your research question and objectives are clearly defined. Generate composite variables that address your research questions or are relevant to your objectives.

Understand Your Data: Before creating composite variables, make sure you understand the variables in your dataset and how they relate to each other.

Use Meaningful Combinations: Create composite variables that provide additional insights or simplify your analysis. Avoid combining variables that don't logically fit together.

Check for Errors: After creating a composite variable, check for any errors or unexpected values. This can help ensure the accuracy of your analyses.

Julius AI is capable of creating all the required variables with just one prompt. So long as you provide it with clear and adequate instructions. With his knowledge of any subject, He can also suggests other composite variables that your data can create to help guide you

Conclusion

Creating composite variables is a powerful method to improve your data analysis. Julius AI simplifies this process, enabling you to concentrate on deriving valuable insights from your data. Whether you're an experienced data scientist or new to data analysis, Julius AI offers the necessary tools to effectively work with your datasets.