To calculate the median of each column in a data frame in R, you would use the ______ function.
- apply()
- colMedian()
- median()
- colMeans()
To calculate the median of each column in a data frame in R, you would use the apply() function. By specifying the appropriate margin argument (2 for columns), you can apply the median() function across each column of the data frame.
How does the ifelse() function in R differ from the if-else statement?
- The ifelse() function allows vectorized conditional operations, while the if-else statement only works with scalar conditions
- The ifelse() function can only handle logical conditions, while the if-else statement can handle any type of condition
- The if-else statement is more efficient than the ifelse() function for large datasets
- The ifelse() function and the if-else statement are functionally equivalent
The ifelse() function in R allows vectorized conditional operations, which means it can process entire vectors of conditions and return corresponding values based on those conditions. In contrast, the if-else statement in R works with scalar conditions and can only evaluate one condition at a time.
What function is commonly used to create a basic plot in R?
- plot()
- barplot()
- hist()
- scatterplot()
The plot() function is commonly used to create a basic plot in R. It can be used to create a wide range of plots such as scatter plots, line plots, bar plots, and more.
Imagine you need to create a scatter plot in R that shows the relationship between two numeric variables. How would you do this?
- Use the scatterplot() function
- Use the plot() function with type = "scatter"
- Use the points() function
- Use the ggplot2 package
To create a scatter plot in R that shows the relationship between two numeric variables, you would use the plot() function and pass the two numeric variables as the x and y arguments. The points() function can be used to add individual data points to the scatter plot. Alternatively, the ggplot2 package provides a more advanced and customizable approach to creating scatter plots.
The ________ package in R provides functions that can help avoid explicit use of nested loops.
- dplyr
- tidyr
- purrr
- plyr
The purrr package in R provides functions that can help avoid explicit use of nested loops. It offers a variety of functions for functional programming and iteration, such as map(), walk(), and reduce(), which can simplify and streamline operations without the need for nested loops.
The ______ function in R can be used to apply a function to the margins of an array.
- apply()
- lapply()
- sapply()
- tapply()
The apply() function in R can be used to apply a function to the margins of an array. The margins refer to the dimensions of the array, such as rows or columns. By specifying the margin argument in the apply() function, you can apply a function to the rows or columns of an array and obtain the results in a desired format.
Can you describe a scenario where you used logical vectors in R for subsetting data?
- Subsetting a dataset based on a certain condition or criteria
- Creating logical conditions for applying specific transformations
- Filtering out missing values in a dataset
- All of the above
A scenario where logical vectors are used in R for subsetting data is when you want to extract specific rows from a dataset based on a certain condition or criteria. For example, you can use a logical vector to subset a dataset to include only rows where a certain variable meets a specific condition.
To check multiple conditions in an if statement in R, you can use the ________ or ________ operators.
- & and
- | and
- ! and
- %in% and
To check multiple conditions in an if statement in R, you can use the & operator for logical 'AND' and the | operator for logical 'OR'. For example, if (condition1 & condition2) { code to execute } will check if both condition1 and condition2 are true.
Can you describe a scenario where you would need to use a global variable in R?
- Storing program configuration settings
- Sharing data between multiple functions
- Implementing a global counter or identifier
- All of the above
There are various scenarios where you might need to use a global variable in R. For example, when storing program configuration settings that need to be accessed by multiple functions, sharing data between multiple functions or code blocks, or implementing a global counter or identifier to keep track of certain program states. Global variables can be useful in these cases to facilitate communication and data sharing across different parts of the program.
In R, the ______ function can be used to list all the variables in the global environment.
- ls()
- vars()
- objects()
- globals()
In R, the ls() function can be used to list all the variables in the global environment. It returns the names of all the objects or variables defined in the global environment, allowing you to inspect and access the global variables present in your program.