Imagine you need to create a function in R that calculates the mean of a vector, then subtracts the mean from each element of the vector. How would you use a nested function to do this?
- subtract_mean <- function(vector) { mean_value <- mean(vector); subtracted <- function(x) { x - mean_value }; subtracted(vector) }
- subtract_mean <- function(vector) { mean_value <- mean(vector); subtracted <- lapply(vector, function(x) { x - mean_value }); return(subtracted) }
- subtract_mean <- function(vector) { mean_value <- mean(vector); subtracted <- sapply(vector, function(x) { x - mean_value }); return(subtracted) }
- All of the above
To use a nested function in R to calculate the mean of a vector and subtract the mean from each element, you can use the following code: subtract_mean <- function(vector) { mean_value <- mean(vector); subtracted <- function(x) { x - mean_value }; subtracted(vector) }. The nested function subtracted is defined within the main function subtract_mean. It captures the mean_value from the outer function's environment and subtracts it from each element of the vector. Finally, the nested function is called with the vector as the argument.
In R, the mean of a numeric vector is calculated using the ______ function.
- mean()
- median()
- sum()
- mode()
In R, the mean of a numeric vector is calculated using the mean() function. The mean() function calculates the arithmetic average of the values in the vector.
The ______ function in R is a faster alternative to a for loop for repetitive computations.
- apply()
- sapply()
- vapply()
- rep()
The vapply() function in R is a faster alternative to a for loop for repetitive computations. It applies a function to each element of a vector or a list and returns a vector of the desired type and length. It is particularly useful when the result of the function is known in advance.
To add a title to a plot in R, you would use the ______ parameter.
- main
- title
- label
- plot.title
To add a title to a plot in R, you would use the main parameter. It allows you to provide a descriptive title that summarizes the content or purpose of the plot.
How does R handle matrices that contain elements of different data types?
- R coerces the elements to the most flexible type within the matrix
- R assigns each element a unique data type within the matrix
- R throws an error if a matrix contains elements of different data types
- None of the above
When a matrix is created in R with elements of different data types, R coerces the elements to the most flexible type within the matrix. This means that if the matrix contains elements of different data types, R will automatically convert them to a common type that can accommodate all the values in the matrix.
Imagine you're working with a vector in R that contains missing values. How would you handle the missing values when finding the maximum or minimum value?
- Use the na.rm = TRUE parameter in the max() or min() function
- Exclude missing values from the vector before using the max() or min() function
- Replace missing values with 0 before using the max() or min() function
- All of the above
When handling missing values in a vector while finding the maximum or minimum value in R, you can 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 maximum or minimum based on the available non-missing values. This ensures that missing values do not impact the calculation.
Imagine you need to find the index of the maximum value in a vector in R. How would you do this?
- Use the which.max() function to find the index of the maximum value
- Use the which.min() function to find the index of the maximum value
- Use the index_max() function to find the index of the maximum value
- Use the max_index() function to find the index of the maximum value
To find the index of the maximum value in a vector in R, you would use the which.max() function. The which.max() function returns the index of the first occurrence of the maximum value in the vector.
Suppose you're asked to optimize a piece of R code that performs complex calculations on large arrays. What are some strategies you could use to improve its performance?
- Vectorization to perform operations on entire arrays at once
- Using parallel processing techniques to distribute the calculations across multiple cores or machines
- Implementing efficient algorithms specific to the problem domain
- All of the above
When optimizing code that operates on large arrays, you can use strategies such as vectorization to perform operations on entire arrays at once, leveraging the efficiency of R's internal operations. Additionally, you can utilize parallel processing techniques to distribute the calculations across multiple cores or machines, which can significantly speed up computations. Implementing efficient algorithms specific to the problem domain can also help improve performance. By combining these strategies, you can optimize the code and enhance the performance of complex calculations on large arrays.
A ________ in R is a collection of elements of different data types.
- Array
- Data frame
- List
- Matrix
A list in R is a data type that can contain elements of different types - like strings, numbers, vectors and another list inside it.
Does the median function in R handle missing values?
- Yes, the median() function automatically ignores missing values
- No, missing values cause an error in the median() function
- Yes, but missing values are treated as 0 in the median calculation
- Yes, but missing values need to be explicitly removed before using the median() function
Yes, the median() function in R automatically handles missing values by ignoring them in the calculation. It computes the median based on the available non-missing values in the vector or column.