The data consists of various treatments and their intake levels measured over 24 days. To visualize how treatment affects intake levels, we'll create a line plot for each treatment, showing intake levels over time. Let's proceed with creating the visualizations.

The visualization we attempted to generate encountered an issue due to a discrepancy in the data column naming, specifically around 'DAY 17', 'DAY 18', and 'DAY 19'. This was corrected in the code, but it seems there was only one unique treatment identified in the dataset, which might have led to confusion regarding the differences among treatments.

Given this context, it appears that our dataset may not have been structured or interpreted correctly to distinguish between different treatments beyond the initial extraction. To further clarify and address this concern accurately, we would need to revisit the data preparation step to ensure that all treatments are correctly identified and separated for analysis.

If there are indeed multiple treatments within the dataset that were not captured due to an oversight in data preparation, this would be crucial for performing a comparative analysis. However, based on the current extraction, it seems there might have been a limitation in identifying unique treatments.

To proceed effectively, we should re-examine the data structure and preparation steps to ensure all treatments are accurately represented for comparison. This will allow us to provide a more detailed analysis of how different treatments affect intake levels visually.