Welcome to today's lesson on grouping data frames and performing analyses. Most real-world data is chaotic. Grouping data enables us to analyze large datasets. By grouping data, slicing information at the macro or micro level becomes a breeze. Let's delve further into this.

Grouping data means analyzing it through the lens of certain categories. In R, group_by() from dplyr aids us in doing this. Consider a dataset sales_df that comprises sales information for different products. If we group it by product_name, we can compare products without turning the analysis into an apples-to-oranges comparison.

The grouped_df contains an object that knows how to work with different groups in data. We can print it, but it won't differ from the original sales_df. The difference is in the inner structure, which allows us to use a magical summarize function.