A data analyst is considering using tibbles instead of basic data frames. What are some of the limitations of tibbles? Select all that apply.
1 point
Tibbles can overload a console
Tibbles can never create row names
Tibbles won’t automatically change the names of variables
Tibbles can never change the input type of the data
The correct answer and explanation is :
Correct Answer:
- Tibbles won’t automatically change the names of variables
- Tibbles can never change the input type of the data
Explanation:
Tibbles are an improved version of data frames in R, provided by the tibble package. They come with several advantages, such as better printing behavior, automatic prevention of accidental column name modifications, and resistance to data type coercion. However, they do have some limitations.
- Tibbles won’t automatically change the names of variables – Unlike base R data frames, tibbles do not modify variable names to make them syntactically valid. In a base R data frame, names like
"1stColumn"might automatically get adjusted to"X1stColumn"to conform to valid variable name rules. However, tibbles retain variable names exactly as provided, which can lead to errors if column names are not valid. - Tibbles can never change the input type of the data – In base R data frames, character vectors are automatically converted to factors unless explicitly prevented (
stringsAsFactors = FALSE). However, tibbles always keep character data as characters and do not perform automatic coercion. This behavior ensures that the input type remains unchanged, providing greater consistency but requiring explicit conversions when necessary.
Incorrect Options:
- Tibbles can overload a console – This is incorrect. Tibbles are designed to improve console readability by displaying only the first 10 rows and only as many columns as fit on the screen. This prevents overloading the console with excessive output, unlike base data frames, which might print large datasets in an unreadable manner.
- Tibbles can never create row names – This is misleading. While tibbles do not support row names in the traditional sense (as data frames do), row names can still be stored as a separate column if needed.
By understanding these limitations, a data analyst can decide whether to use tibbles or stick with base R data frames, depending on the specific use case.