Introduction to Data Cleaning with Julius AI

In our previous tutorial, we discussed data exploration in Julius. We delved into our data to gain a thorough understanding of its content, including its size, structure, variable types, missing data, outliers, and descriptive statistics. If you haven't viewed it yet, I would highly recommend doing so, as this tutorial builds upon the knowledge gained in the previous one.

Introduction to Data Cleaning

Data cleaning is a vital step in the data analysis process to ensure the accuracy of your results. Its purpose is to identify and correct (or remove) errors and inconsistencies in the data, improving its quality. This process is key to ensuring that the conclusions drawn from data analysis are valid and reliable. Data cleaning can include handling missing values, correcting data entry errors, and removing duplicates, among other tasks.

Demonstrating Data Cleaning with an Example Dataset

In this tutorial, we will be using the "Example_Dataset_Tutorial 2.xls" as our dataset. Like the previous tutorial, this dataset contains data collected for a study that examined the relationship between post-partum mortality (death within the first 6 months) of women with peripartum cardiomyopathy (PPCM) and several factors, including maternal age, maternal haemoglobin levels, weight, heart rate, systolic blood pressure (SBP), and C-reactive protein (CRP) levels.

The objective is to prepare this dataset for further analysis by cleaning it using Julius AI. To access the dataset and follow along with this tutorial, please click on the link provided below.

https://docs.google.com/spreadsheets/d/1Ap91NX_YXME0wqdBe0N_RmgApWw5UH1J/edit#gid=866177405

Step-by-Step Walkthrough of Data Cleaning in Julius

After conducting a comprehensive exploration of our data, we have acquired a thorough understanding of its contents. This knowledge, which encompasses the interpretation of each column and the respective data types it contains, will serve as a valuable point of reference for making well-informed decisions about data cleaning.

Step 1: Upload Your Dataset and Initial Data Overview

When you upload your data, Julius will display and show you a preview of the dataset. You can also ask Julius to provide a preview and he will tell you the contents of the dataset (this step was discussed in more detail in tutorial 1).

Now we are ready to clean our dataset using the following steps:

Step 2. Removing duplicates: - Julius is available to help you identify and remove any duplicate records in your dataset, ensuring that each data point is unique. To initiate this process, simply follow the prompt below:

Prompt: I would like us to start by identifying duplicates. Display both originals and duplicates

Note: Julius has identified duplicate entries in the dataset. Specifically, rows 0 and 2 are duplicates, as are rows 1 and 3. These pairs of rows have identical data in all columns. How would you like to handle these duplicates? Julius has suggested two options: removing them or keeping them for further analysis. In this example, we have chosen to remove them, as recommended by Julius (see below).

Prompt: Would you like to remove the duplicates from the dataset?

Note: Julius has reported the successful removal of duplicates from the dataset. He has also provided an update on the current state of the data frame after the removal. Julius mentioned that 2 duplicate rows have been deleted, which has decreased the total number of rows from 106 to 104. However, the number of columns remains unchanged at 31. Julius then asks, "What would you like to do next?"

In our example, we selected the prompt "Identifying and handling missing values in the dataset"

Step 3. Handling missing values includes filling in missing values using data imputation techniques or removing rows/columns with too many missing values.

Note: After receiving our prompt regarding missing data, Julius provided feedback indicating that the dataset contains missing values in multiple columns. The number of missing entries varies across these columns. Notably, columns like IL6av, V6SBP, V6DBP, V6HR, and others have a significant number of missing values. These missing values could potentially affect any analysis relying on these fields.

Julius also suggested various options for handling the missing values, such as:

a. Removing rows that contain missing values.

b. Fill in missing values with a specific value, such as the mean or median of the column.

c. Utilizing a more complex imputation method for filling in missing values.

After considering the options, we decided to proceed with option b, which involved filling in the missing values with the mean of the column.

Prompt: "Fill missing values with a specific value in the mean of the column."

Note: Julius reported that he has successfully filled all missing values in the dataset with the mean of their respective columns. He also mentioned that there are no remaining columns with missing values, except for an 'index' column which seems to have been created during the operation but doesn't exist in the dataframe. As a result, the dataset is now complete with no missing entries for any health-related measurements or demographic information.

Julius then asked if we would like to continue with any other data cleaning or analysis tasks.

Step 4. Correcting errors: We chose to focus on identifying and correcting errors, which could be typographical errors or inconsistencies in naming conventions.

Prompt: "Let's identify and correct any errors in the dataset"

Note: Julius offered a comprehensive overview of the variables in our dataset that contained errors. Additionally, he specified the specific errors associated with each variable. To illustrate,

- Age_cat and Hypertension columns are categorical, but lack displayed summary statistics, suggesting a non-standard format.

- Some columns have extreme values, like a maximum weight of 125 kg and a maximum CRP level of 90, which may indicate errors or outliers.

- The NYHAFC column has 6 unique values, with class 3 being the most frequent. Verify data consistency with expected values.

- Certain columns, such as IL6av and FASmean, lack summary statistics, possibly due to non-numeric data types or other issues.

Julius further suggested action items to handle these errors and they included:

a. Verify consistency and correct interpretation of categorical data encoding and format.

b. Investigate outliers to determine their validity.

c. Ensure appropriate data types and expected value ranges for all columns.

For our example we selected the first option (a).

Prompt: "Let us start with verifying the encoding and format of categorical data to ensure it's consistent and correctly interpreted."

Note: Julius noted that categorical columns had inconsistencies in the encoding and format such as:

- Age_cat: Contains misspelt words (e.g., 'Yong', 'Midle', 'young', 'OLD', 'Middle') and numeric values (1, 2, 3). It seems there was an attempt to categorize ages into 'Young', 'Middle', and 'Old', but inconsistencies and typos have led to a fragmented categorization.

- Hypertension: Includes both numeric (0, 1) and textual representations ('no', 'yes', 'Yes', 'YES', 'No'). This inconsistency could lead to confusion in analysis, as the same condition is represented in multiple ways.

- NYHAFC: Shows a mix of numeric (1, 2, 3, 4) and Roman numeral (II, IV) representations for the New York Heart Association Functional Classification. This inconsistency might affect analyses that rely on this classification.

Julius also suggested ways to address these inconsistencies by standardizing the values in these columns. This could involve converting all the 'Age_cat' values to 'Young', 'Middle', or 'Old', converting 'Hypertension' values to boolean, and converting 'NYHAFC' values to numeric. For our example, we decided to be more specific in fixing the data errors (see prompt).

Prompt: "Can we fix the all errors and standardize the following variables as follows: Age_cat (Young, Middle, Old), Hypertension (Yes or No) and NYHAFC replace with the numeric values"

Note: Julius confirmed that he successfully corrected and standardized the dataset. The Age_cat column now consistently uses the terms "Young," "Middle," and "Old." The Hypertension column has been standardized to only include "Yes" or "No." Additionally, the Roman numerals in the NYHAFC column have been replaced with their corresponding numeric values.

To verify the changes, we requested Julius to display the modifications he made.

Prompt: "Please display and show me the corrections we just made"

Note: We observed from this display that the variable Age_cat now consistently uses the terms "Young," "Middle," and "Old." However, it still includes numeric values (1, 2, 3) that have not been corrected. Therefore, we requested Julius to standardize the options for this variable as well.

Step 5. Standardising data refers to ensuring that all data follows a consistent format, which is crucial for categorical data.

Prompt: "Please standardize Age_cat and replace all numeric values (1 2 3) with (Young, Middle, Old respectively)"

Note: Julius successfully standardized the variable Age_cat column, replacing all numeric values (1, 2, 3) with their respective textual representations ('Young', 'Middle', 'Old'). This guaranteed consistency in categorizing age groups within the dataset.

Step 6. Outlier detection: Another crucial step in our data cleaning process involved identifying and evaluating outliers to determine whether they indicate errors or genuine extreme values. We enlisted Julius's assistance in identifying the variables in our dataset that contained outliers.

Prompt: "Can we identify and list only variables with outliers in our dataset"

Note: Julius identified the variables with outliers in the dataset using the Z-score method and the number of outliers detected for each. These were the variables that had values that deviated significantly from the mean, indicating potential data entry errors or exceptional cases in the dataset.

- WeightInkg: 2 outliers

- nrofchilnrofepisodes: 1 outlier

- BLVEDD: 1 outlier

- BLVESD: 1 outlier

Julius also suggested some potential next steps. We could either investigate the specific details of the outliers found in the variables or explore the possible reasons for these outliers in the dataset. Furthermore, we could evaluate the influence of outliers on the analysis and modelling process or discuss strategies for effectively managing them. After careful consideration, we decided to pursue the latter option as an example:

Prompt: "Discuss strategies for handling outliers in the dataset"

Note: Julius shared a list of strategies for managing outliers in our dataset. He also emphasized that outliers can sometimes be legitimate and reflect significant variations in the data. In these situations, it is advisable to retain them and acknowledge their potential influence on the analysis. He noted that the selection of an appropriate strategy depends on the nature of the data and the specific analysis being conducted. Additionally, he mentioned that consulting domain experts can be immensely helpful in discerning whether outliers are valid data points or errors.

For our example, we decided to find out how we can determine if an outlier is a genuine data point or an error

Prompt: "How can I determine if an outlier is a genuine data point or an error?"

Note: Julius provided a list of steps and considerations to help determine if a data point is an error or a genuine data point. He emphasised that differentiating between genuine data and errors ultimately requires a blend of statistical analysis, domain knowledge, and sometimes detective work.

Step 7. Validating accuracy: After addressing all the points mentioned above, we are finally able to proceed with the last step of our data cleaning process: validating the accuracy of the data. This involves checking whether the data accurately represents real-world scenarios or constraints.

Note: Data cleaning and preparation is a crucial step in data analysis as it directly impacts the quality of the analysis. However, it can be a complex and time-consuming process. The specific steps required may vary depending on the issues present in your data and the type of analysis you intend to conduct. Additionally, it is important to remember that these steps may vary further based on your research questions and the desired outcomes of your analysis.

Tips for Effective Data Cleaning

Here are some tips to make the most out of your data cleaning with Julius AI:

- Understand Your Data: Before starting the cleaning process, take the time to understand your dataset. Knowing what each column represents and the type of data it contains can guide your cleaning decisions.

- Document Your Process: Keep a record of the cleaning steps you take. This is crucial for reproducibility and understanding the changes made to the dataset.

- Be Cautious with Deletions: When removing data, whether it's duplicates or outliers, proceed with caution. Ensure that the removal is justified and won't negatively impact your analysis.

- Ask, Ask, Ask: Ask Julius to display each step you take during your data cleaning process to ensure that you obtain precisely what you requested. Additionally, inquire with Julius about any steps that you do not comprehend or are unsure about. Furthermore, consult with experts as needed.

- Regularly Save Your Progress: Data cleaning can be a lengthy process. Regularly save your progress to avoid losing any work.

Conclusion

Data cleaning is an indispensable step in the data analysis process, ensuring the integrity and reliability of your findings. With Julius AI, data analysts can streamline the data cleaning process, making it more efficient and less prone to errors. By following the steps outlined in this tutorial, you can prepare your dataset for analysis, paving the way for insightful and accurate results. Remember, clean data is the foundation of meaningful analysis. Happy cleaning!

(Note: This tutorial is a conceptual guide. The specific commands and steps might vary based on the dataset and the analysis objectives. Always refer to Julius AI's documentation and support for the most accurate and up-to-date information.)