Suppose you're asked to optimize a piece of R code that operates on large vectors. What are some strategies you could use to improve its performance?
- Use vectorized functions instead of explicit loops
- Preallocate memory for the resulting vector
- Minimize unnecessary copies of vectors
- All of the above
Some strategies to improve the performance of R code operating on large vectors include using vectorized functions instead of explicit loops, preallocating memory for the resulting vector to avoid dynamic resizing, minimizing unnecessary copies of vectors to reduce memory usage, and optimizing the code logic to avoid redundant calculations. These strategies can significantly enhance the efficiency and speed of code execution.
Describe a situation where you had to use string manipulation functions in R for data cleaning.
- Removing leading and trailing whitespaces from strings
- Converting strings to a consistent case
- Replacing certain patterns in strings
- All of the above
All the options are valid situations where string manipulation functions in R might be used for data cleaning. For example, trimws() can be used to remove leading and trailing whitespaces, tolower() or toupper() can be used to convert strings to a consistent case, and gsub() can be used to replace certain patterns in strings.