Lesson 3: Filtering  Data

• Purpose of filter(): The filter() function is introduced as a powerful tool to reduce large datasets to only the most relevant rows, making the data easier to analyze.

• Selective Column Review: Before filtering, the instructor uses the select() function to isolate columns related to three-point shooting, helping focus the analysis.

• Creating a Subset: A new data frame is created containing only players, positions, three-point stats, and total points—highlighting how to subset data for a specific analysis.

• Filtering with Conditions: The filter() function is used with logical conditions (e.g., 3PA >= 3) to keep only players who meet specific criteria, like attempting a minimum number of three-pointers per game.

• Combining Multiple Filters: Multiple conditions can be added (e.g., PTS >= 15) to further narrow the dataset, resulting in a cleaner and more focused sample.

• Fine-Tuning Filters: The lecturer adjusts thresholds (e.g., increasing 3PA to 4) to bring the dataset down to a more manageable size (~100 players), demonstrating how to refine filters based on analysis needs.

• Encouragement to Experiment: Viewers are encouraged to experiment with their own filters—such as using two-point stats or adjusting values—to get comfortable working with different subsets.