What is the primary use case for nested functions in R?

  • Encapsulating helper functions within a larger function
  • Reducing code duplication and improving modularity
  • Implementing complex algorithms or workflows
  • All of the above
The primary use case for nested functions in R is to encapsulate helper functions within a larger function. Nested functions can help in reducing code duplication, improving code modularity, and organizing related functionality together. They are especially useful when implementing complex algorithms or workflows that require multiple steps or subroutines.

What is the difference between "==" and "=" in R?

  • "=" is not used in R
  • "==" is used for assignment and "=" is used for comparison
  • "==" is used for comparison and "=" is used for assignment
  • There is no difference
In R, "==" is a comparison operator used to test for equality, while "=" is used for assignment, especially in the context of function arguments. However, "<-" is more commonly used for assignment.

To calculate the mean of each row in a matrix in R, you would use the ______ function.

  • rowMeans()
  • colMeans()
  • mean()
  • apply()
To calculate the mean of each row in a matrix in R, you would use the rowMeans() function. The rowMeans() function computes the mean values across each row of the matrix.

The ______ function in R can be used to generate a histogram of a numeric vector.

  • hist()
  • plot()
  • barplot()
  • boxplot()
The hist() function in R can be used to generate a histogram of a numeric vector. The hist() function divides the range of the data into equal intervals called bins and counts the number of observations falling into each bin, creating a visual representation of the distribution of the data.

Imagine you're working with a large data set in R and need to create a bar chart that clearly communicates the key findings. How would you approach this task?

  • Simplify the chart by focusing on the most important categories
  • Use distinct colors or patterns to enhance differentiation
  • Provide clear labels and a legend for better understanding
  • All of the above
When working with a large data set in R and aiming to create a bar chart that clearly communicates the key findings, it is important to simplify the chart by focusing on the most important categories. Use distinct colors or patterns to enhance differentiation between the bars. Provide clear labels and a legend to ensure better understanding of the chart. The combination of these approaches will help create an effective bar chart that effectively communicates the key findings.

In R, the ______ function can be used to create a scatter plot with a smooth line fitted to the data.

  • scatterplot()
  • smoothplot()
  • lines()
  • loess()
The loess() function in R can be used to fit a smooth line to a scatter plot. It uses the locally weighted scatterplot smoothing technique to estimate a smooth curve that captures the general trend in the data.

In R, to prematurely exit a for loop, you can use the ______ keyword.

  • Next
  • Skip
  • Break
  • Exit
In R, the break keyword is used to prematurely exit a for loop. When encountered, the break statement immediately terminates the loop and execution continues with the next statement after the loop.

In R, the ______ function can be used to check if an object is a matrix.

  • is.matrix()
  • is.vector()
  • is.data.frame()
  • is.array()
In R, the is.matrix() function can be used to check if an object is a matrix. It returns TRUE if the object is a matrix and FALSE otherwise. This function is useful for verifying the type of an object before applying operations specific to matrices.

How would you find the max or min value in each column or row of a matrix or data frame in R?

  • Use the apply() function with the appropriate margin argument
  • Use the max() or min() function with the appropriate argument
  • Use the colMax() or rowMax() function for matrices
  • Use the max.col() or min.col() function for data frames
To find the max or min value in each column or row of a matrix or data frame in R, you can use the apply() function. By specifying the appropriate margin argument (1 for rows, 2 for columns), you can apply the max() or min() function across the specified dimension.

Imagine you're asked to optimize a slow-running for loop in R. What are some strategies you could use to improve its performance?

  • Use vectorized operations
  • Preallocate output objects
  • Minimize unnecessary calculations inside the loop
  • All of the above
To optimize a slow-running for loop in R, you can use strategies such as converting the loop to vectorized operations when possible, preallocating output objects to reduce memory reallocation, and minimizing unnecessary calculations or redundant checks inside the loop. These strategies can significantly improve the performance of the loop.