Which practice is recommended when transforming messy data to tidy data?

The assertion that all listed practices are recommended when transforming messy data to tidy data is not accurate, as these practices do not align with the principles of tidy data.

In tidy data principles, each variable should have its own column, and each observation should have its own row. Therefore, combining multiple variables in one column or comprising variables in both rows and columns would contravene these guidelines. Additionally, having multiple types of observational units in the same table can lead to confusion and detract from the clarity that tidy data aims to achieve.

In ideal data transformation processes guided by these principles, the focus is on ensuring that the structure of the data facilitates analysis and interpretation. This typically involves restructuring the dataset so that each variable is represented in its own column and that the table reflects a consistent observational unit throughout, ensuring a clear and organized dataset that is conducive for analysis and modeling tasks.