Can you describe a situation where you had to deal with factor data type in R? How did you manage it?
- When dealing with categorical variables
- When dealing with numeric variables
- When encoding categorical variables
- When working with levels of categorical variables
When we deal with categorical variables, especially when it comes to statistical modeling, factors are used. We have to ensure that the levels are correctly assigned and interpreted. We might need to reorder, drop or combine levels depending on the analysis.
How do you handle escape sequences in regular expressions in R?
- Use double backslashes () to escape special characters
- Use triple backslashes (\) to escape special characters
- Use single backslashes () to escape special characters
- Escape sequences are not required in regular expressions
In R, you handle escape sequences in regular expressions by using double backslashes () to escape special characters. For example, to match a literal dot (.), you would use ".".
In R, you can create a variable using the ________ operator.
- ->
- <-
- =
- All of the above
The '<-' operator is commonly used in R to assign a value to a variable, although the '=' and '->' operators can also be used. However, '<-' is generally preferred because it makes the code more readable and avoids confusion with the '=' operator used for passing arguments to functions.
How do you handle errors and exceptions in R?
- Ignore errors and hope the script continues to run
- None of the above
- Stop the execution of the script immediately
- Use tryCatch() to handle potential errors
R provides several functions to handle errors and exceptions. The most commonly used is 'tryCatch()', which allows you to specify what should happen in case of error or exception, thus preventing the script from terminating unexpectedly.
Imagine you're working with a dataset in R and need to standardize a numeric column. How would you approach this?
- Add the mean and multiply by the standard deviation
- Multiply by the standard deviation and add the mean
- Subtract the mean and divide by the standard deviation
- Subtract the median and divide by the interquartile range
To standardize a numeric column in R, we typically subtract the mean of the column and then divide by the standard deviation. This results in a column with a mean of 0 and standard deviation of 1. This can be done using the scale() function in R.
If you perform an operation like 0/0 in R, it would result in ________.
- NaN
- 0
- 1
- Inf
If you perform an operation like 0/0 in R, it would result in NaN. NaN stands for 'Not a Number' and is a special value in R used to represent undefined or unrepresentable numbers.
How does R handle mathematical operations on vectors?
- R applies the operation element-wise
- R applies the operation to the first element only
- R applies the operation to the last element only
- R does not allow mathematical operations on vectors
R applies mathematical operations on vectors element-wise. For example, if we have two vectors a and b, the operation a + b would result in a new vector where each element is the sum of the corresponding elements in a and b.
What is the operator in R to check if two values are equal?
- =
- ==
- ===
- !=
In R, the operator == is used to check if two values are equal. For example, 3 == 3 would return TRUE.
How does the global environment in R interact with other environments like those within functions?
- Variables defined in the global environment can be accessed and modified from within functions
- Variables defined in the global environment cannot be accessed or modified from within functions
- Variables defined within functions are automatically added to the global environment
- The global environment is isolated from other environments in R
In R, the global environment interacts with other environments in such a way that variables defined in the global environment can be accessed and modified from within functions. This allows functions to utilize global variables as needed. However, variables defined within functions are not automatically added to the global environment, and changes made to global variables within functions may not persist outside of the function's execution.
Suppose you need to extract a specific pattern from strings in a large dataset. How would you approach this task in R?
- Use the grep() function
- Use the str_extract() function from stringr package
- Use the sub() function with regular expressions
- All of the above
All the options are valid methods to extract a specific pattern from strings in R. grep() and sub() functions from base R, and str_extract() function from stringr package could be used, depending on the exact requirements of the task.