If a vector in R is created with elements of different data types, R will coerce the elements to the most flexible type, which is ______.
- character
- numeric
- logical
- integer
If a vector in R is created with elements of different data types, R will coerce the elements to the most flexible type, which is the character data type. The character type is considered the most flexible because it can represent other types by converting them to strings.
To fit a linear regression model in R, you would use the ______ function.
- lm()
- regmodel()
- linreg()
- regression()
To fit a linear regression model in R, you would use the lm() function. The lm() function stands for "linear model" and is used for estimating the coefficients of a linear regression model based on the given data.
The ______ function in R can be used to add text annotations to a plot.
- text()
- annotate()
- label()
- add_text()
The text() function in R can be used to add text annotations to a plot. It allows you to specify the coordinates and the text to be displayed at those coordinates, providing additional information or labels within the plot.
Suppose you're asked to write a function in R that takes a vector of numbers and applies a mathematical operation (like squaring or taking the square root) to each number. The mathematical operation itself should also be a function, nested within your main function. How would you do it?
- function_name <- function(numbers, operation) { result <- sapply(numbers, operation); return(result) }
- function_name <- function(numbers, operation) { result <- lapply(numbers, operation); return(result) }
- function_name <- function(numbers, operation) { result <- vapply(numbers, operation, FUN.VALUE = numeric(1)); return(result) }
- All of the above
To write a function in R that takes a vector of numbers and applies a mathematical operation (like squaring or taking the square root) to each number, with the mathematical operation itself nested within the main function, you can use the following code: function_name <- function(numbers, operation) { result <- sapply(numbers, operation); return(result) }. The sapply() function is used to apply the operation function to each element in the numbers vector, and the result is returned.
The & operator in R performs element-wise logical 'AND' operation on ________.
- scalars
- vectors
- strings
- factors
The & operator in R performs element-wise logical 'AND' operation on vectors. When applied to two logical vectors, the & operator compares the corresponding elements and returns a logical vector of the same length, where each element represents the result of the element-wise 'AND' operation.
To handle missing values when finding the max or min value in R, you would use the ______ parameter in the max or min function.
- na.rm = TRUE
- na.exclude = TRUE
- na.action = "ignore"
- na.option = "remove"
To handle missing values when finding the max or min value in R, you would use the na.rm = TRUE parameter in the max() or min() function. Setting na.rm = TRUE instructs R to ignore missing values and calculate the max or min based on the available non-missing values.
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.
How do you represent a double quote within a string in R?
A _____ in Unit Testing is a condition or exception that a unit of code must handle correctly.
- Unit case
- Test suite
- Unit scenario
- Test condition
In Unit Testing, a "unit scenario" is a condition or exception that a unit of code must handle correctly. These scenarios are designed to test specific aspects of the code's behavior, ensuring that it behaves as expected under various conditions.