You are a newly employed data scientist working at Google!

Your team is responsible for analysing user engagement. One of the key metrics your team tracks is the number of daily active users (DAUs) across different regions.

You have the dataset, google_dau, which contains the following columns:

Date: Date of the observation (e.g. 2024-07-01, 2024-07-02...)
Region: Region of the users (North America, Europe, Asia)
DAUs: Number of daily active users (e.g. 10000, 11000...)

What code best matches the following graph (assuming you have loaded the necessary packages)?

Question

You are a newly employed data scientist working at Google!

Your team is responsible for analysing user engagement. One of the key metrics your team tracks is the number of daily active users (DAUs) across different regions.

You have the dataset, google_dau, which contains the following columns:

Date: Date of the observation (e.g. 2024-07-01, 2024-07-02...)
Region: Region of the users (North America, Europe, Asia)
DAUs: Number of daily active users (e.g. 10000, 11000...)

What code best matches the following graph (assuming you have loaded the necessary packages)?

BlackTom AI · Accepted Answer

Reframing the problem helps clarify the task: we want to match code that visually represents the distribution of daily active users (DAUs) across different regions (North America, Europe, Asia) using the google_dau dataset.

Option analysis:
- Given option: ggplot(google_dau, aes(x = Region, y = DAUs)) + 
   geom_boxplot()
  - This choice is appropriate because it uses Region as the x-axis, DAUs as the y-axis, and overlays a boxplot to compare the distribution of DAUs across regions. A boxplot reveals medians, quartiles, and potential outliers for each region, which is a common way to compare distributions across groups.
  - The mapping aes(x = Region, y = DAUs) correctly defines the grouping variable (Region) and the numeric measurement (DAUs). geom_boxplot() then summarizes those DAU values within each region, producing separate boxes side by side for North America, Europe, and Asia.
- Potential alternative approaches (not shown in the options but useful to contrast):
  - A bar plot with stat = 'summary' (e.g., geom_bar with stat = 'summary' and fun = mean) would show average DAUs per region but hides distributional details like spread and outliers.
  - A violin plot (geom_violin) would also convey distribution shape and density, but if the goal is a straightforward comparison of central tendency and spread across regions, a boxplot is typically preferred for its simplicity and interpretability.
  - A facet or facet_wrap with a separate plot per region would be unnecessary here because the regions are already categories for direct comparison on the same axes.
- Why this option is suitable for the graph described: the graph in mind appears to be a distributional comparison with distinct regions on the x-axis and a numerical DAUs measure on the y-axis, which aligns with a boxplot that groups data by Region.
- Why other structural choices would be less appropriate in this context: choosing a scatter plot across Region (categorical) vs DAUs (numeric) would be inappropriate because scatter plots assume numeric x-variables or a meaningful continuous axis; using a line plot would imply a temporal or ordered relationship rather than categorical grouping; fixing the region order manually is unnecessary when the default factor levels provide a sensible order.
- In summary, the provided code snippet cleanly produces a boxplot that compares the distribution of DAUs across regions, matching the described graph and the dataset columns.

DATA1001/1901 (ND) Evaluate Quiz 2

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