The organization of variables is a crucial step in the data-cleaning process.
This phase ensures that all variables, typically represented as columns in a dataset, are meticulously structured, maintain consistency, and are optimally prepared for subsequent stages of analysis or modeling. Proper organization of variables enhances the data analysis workflow and minimizes the risk of errors and misinterpretations in the final results.
Organizing variables involves several key steps.
Renaming variables
Variable names should be understandable and intuitive. Often, especially when data comes from automated processes or imports, names might be unclear (e.g., columns labeled V1, V2, etc.).
When providing intuitive names, avoid accented characters, spaces, and special characters that analysis programs might misinterpret.
It’s best to use a consistent naming style (such as snake_case or camelCase) across all variables.
Defining Data Types
Each column’s data must be of a consistent data type (such as numbers, categories, booleans, or date/time). It’s essential to identify and rectify any discrepancies in data types across the columns.
Creating New Variables
Creating new variables is part of “feature engineering,” a process that transforms or combines existing variables to enhance analysis or modeling. This involves:
- Mathematical operations: Combining variables to create new ones (e.g., subtracting costs from revenues to get “profit,” or using height and weight to calculate BMI).
- Date operations: Manipulating date variables to create new time-based metrics (e.g., calculating days between events).
- Binning: Converting numerical variables into categories (e.g., transforming “age” into groups like “young,” “adult,” and “elderly”).
Removing Unnecessary Variables
The inclusion of variables that do not contribute to data analysis should be carefully avoided. Such extraneous variables can introduce complications and inefficiencies into the analytical process. They may lead to confusion, increase computational overhead, and potentially skew results. Moreover, these unnecessary variables can significantly inflate the dataset, making it more cumbersome to manage and process. This added complexity not only slows down analysis but also increases the risk of errors and misinterpretations.
Unnecessary variables include those containing unique identifiers or codes (such as social security numbers or customer codes), those with constant values, and variables that contain duplicate information under different names.
It’s crucial to remove strongly correlated variables to prevent multicollinearity issues. For example, having one variable expressing height in meters and another expressing the same height in centimeters is redundant and potentially problematic.
