An analyst writes the following code to study average star ratings over time:

pet_supplies |
group_by(review_date) |
summarise(avg_rating = mean(star_rating),
n_reviews = n()) |
ggplot(aes(x = review_date, y = n_reviews)) +
geom_point()

What is the main problem with this code?

Question

An analyst writes the following code to study average star ratings over time:

pet_supplies |>
    group_by(review_date) |>
    summarise(avg_rating = mean(star_rating),
              n_reviews = n()) |>
    ggplot(aes(x = review_date, y = n_reviews)) +
    geom_point()

What is the main problem with this code?

BlackTom AI · Accepted Answer

The code computes avg_rating but maps y to n_reviews, so the plot shows counts of reviews by date instead of the average star rating over time.

1) Relevant knowledge points and concepts
- group_by + summarise creates one row per group with summary statistics (here avg_rating and n_reviews).
- ggplot aesthetics (aes) determine which variables are plotted; y = specifies the vertical axis.
- The computed summaries must be the variables referenced in aes to appear on the plot.
- Choice of geom (point, line, bar) affects presentation but not which summary is plotted.

2) Step-by-step reasoning
- pet_supplies |> group_by(review_date) |> summarise(avg_rating = mean(star_rating), n_reviews = n()) produces a data frame with columns: review_date, avg_rating, and n_reviews.
- The subsequent ggplot call is ggplot(aes(x = review_date, y = n_reviews)) + geom_point(). This maps the y-aesthetic to n_reviews.
- Therefore each point represents the number of reviews on that date, not the average star rating on that date (avg_rating).

3) Intermediate steps / calculation details
- For a given review_date d: avg_rating[d] = sum(star_rating for date d) / n_reviews[d].
- The code calculates avg_rating correctly but never uses it in aes. Instead ggplot plots n_reviews[d], i.e., the integer count.

4) Why the correct answer is correct
- The main problem is the mismatch between the intended analysis (average star ratings over time) and the plotted variable (review counts). To fix it, change the aesthetic to y = avg_rating (and optionally format review_date and choose geom_line()).

5) Why other options are incorrect
- "Group by product_id": grouping by date is correct for time series of averages; grouping by product_id would give per-product summaries, not per-date.
- "Use filter() before summarise()": filtering is unnecessary unless you want a subset; it doesn't address plotting the wrong variable.
- "Use geom_bar() instead of geom_point()": geom choice affects display but not the fact that the code plots n_reviews rather than avg_rating.

6) Conclusion
The main problem is that the code plots the number of reviews by date, not the average star rating by date, which matches the provided answer.

An analyst writes the following code to study average star ratings over time: pet_supplies |> group_by(review_date) |> summarise(avg_rating = mean(star_rating), n_reviews = n()) |> ggplot(aes(x = review_date, y = n_reviews)) + geom_point() What is the main problem with this code?单项选择题

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