CLiREN-LMS
Data Analysis in R

Data Analysis in R: Summary and Assessment

Knowledge Check

30-60 minutes Applied Step 4 of 7
Quiz

Knowledge Check

4 / 7
Quiz

Knowledge Check

Answer these questions to check understanding.

What is the main purpose of descriptive analysis in clinical data management?
  • A. To summarize collected data for review, monitoring, and reporting.
  • B. To replace the protocol.
  • C. To remove all missing values automatically.
  • D. To guarantee statistical significance.
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Answer: A

Why is the unit of observation important?
  • A. Because rows may represent participants, visits, events, or test results.
  • B. Because R cannot count rows.
  • C. Because all datasets have one row per participant.
  • D. Because it only affects graphics.
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Answer: A

Which function is commonly used to count records by category?
  • A. `count()`
  • B. `install.packages()`
  • C. `read_csv()`
  • D. `getwd()`
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Answer: A

Why should missing values often be shown in data management summaries?
  • A. Because missingness is itself a data quality issue.
  • B. Because missing values should always be treated as zero.
  • C. Because missing values cannot affect interpretation.
  • D. Because R requires missing values to be printed.
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Answer: A

In a visit-level dataset, what does `n()` count?
  • A. Rows or visits.
  • B. Always unique participants.
  • C. Only completed participants.
  • D. Only missing values.
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Answer: A

What does `n_distinct(participant_id)` count?
  • A. The number of unique participant identifiers.
  • B. The number of variables in the dataset.
  • C. The number of missing values.
  • D. The number of R packages loaded.
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Answer: A

What is a common risk when joining enrollment data to laboratory data?
  • A. Participant rows may multiply if there are multiple lab records per participant.
  • B. R will delete all laboratory results automatically.
  • C. Joins cannot affect row counts.
  • D. Joins are only used for graphics.
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Answer: A

Why should counts usually be shown with percentages?
  • A. Percentages can be misleading when denominators are small or unclear.
  • B. Counts are never useful.
  • C. Percentages always add to exactly 100 after rounding.
  • D. R cannot calculate percentages.
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Answer: A