slice_sample() with prop=0.5 is the clean answer and I got there, but I initially started talking about sample_n() out of habit before catching myself.
Start by clarifying that you would use dplyr's sample_frac() function with a fraction of 0.5 to randomly sample exactly 50% of the rows. Then, demonstrate awareness of the need to set a random seed for reproducibility and discuss potential trade-offs such as sampling with or without replacement.
Pro tip: Mention that sample_frac() is a wrapper around sample_n() and that for large datasets, you might consider using slice_sample() (dplyr 1.0.0+) for better performance and clarity. Also, highlight the importance of setting a seed to ensure reproducible results, which is crucial in collaborative data science projects.
Confirm that the goal is to randomly select exactly 50% of the rows from a data frame, and discuss whether sampling should be with or without replacement.
Explain that sample_frac() is the direct function for sampling a fraction of rows, and note that slice_sample() (with prop argument) is the newer, recommended alternative.
Write the code: df %>% sample_frac(0.5) or df %>% slice_sample(prop = 0.5). Emphasize the use of the pipe operator for readability.
Set a random seed before sampling using set.seed() to make the result reproducible, and explain why this is important.
Mention considerations such as sampling with replacement (replace = TRUE), handling grouped data (use group_by() before sampling), and performance on large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Explain that a left join returns all rows from the left table (products) and matching rows from the right table (discounts). For products with no matching discount, the discount columns will contain NULL values. Emphasize that the product row is still included, ensuring no products are lost.
Pro tip: Mention that left joins are often used to find missing matches, and you can filter for NULLs in the right table to identify products without discounts. This shows practical insight beyond just the mechanics.
State that a left join returns all rows from the left table and matched rows from the right table. Unmatched right table columns are filled with NULLs.
Clarify that products is the left table and discounts is the right table, joined on product_id. This sets the context for the result.
Explain that for products with no discount, the discount-related columns (e.g., discount_id, discount_amount) will be NULL, but the product columns remain intact.
Give a concrete example: if product 101 has no discount, the result row shows product 101's details and NULLs for all discount columns.
Mention that this ensures all products are included, which is useful for reports or analyses where you need to see all products regardless of discounts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the simulation parameters (number of products, number of runs) and then implement a vectorized R simulation where each run randomly assigns half the products to a 10% price increase. Compute the average price per run and then average across runs, ensuring reproducibility with set.seed.
Pro tip: Always set a random seed for reproducibility and use vectorized operations instead of loops for efficiency, especially when the number of runs is large. Also, consider discussing the expected value analytically to validate your simulation results.
Specify the number of products, number of simulation runs, and the price increase percentage. Assume all products start with the same price or define a price vector if needed.
For each run, randomly select half of the products to receive a 10% price increase. Use vectorized operations in R to efficiently generate the price vectors for all runs.
Calculate the average price for each run by summing the prices and dividing by the number of products.
Compute the overall average of the per-run averages to obtain the final simulated average price.
Compare the simulated average with the theoretical expected value (e.g., if all start at $1, expected average is $1.05) and present the result clearly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.