How would you customize the appearance of an R scatter plot, including changing colors, markers, and sizes?
- By using the col, pch, and cex parameters in the plot() function
- By using the legend() function
- By using the theme() function from the ggplot2 package
- By using the par() function and graphical parameters
To customize the appearance of an R scatter plot, including changing colors, markers, and sizes, you can use the col parameter to change colors, the pch parameter to change markers, and the cex parameter to change the size of the points. These graphical parameters can be specified within the plot() function.
How can you handle situations where your calculations result in 'Inf' or 'NaN'?
- Both of these methods
- None of the above
- Use ifelse() function to handle such situations
- Use is.finite() function to check the result
One way to handle this is by using the is.finite() function which checks whether the value is finite or not. This function returns FALSE if the value is Inf or NaN and TRUE otherwise. Depending on the use case, you can then decide how to handle these non-finite values.
How does R handle lists that contain elements of different data types?
- R allows lists to contain elements of different data types without coercion
- R coerces the elements to the most flexible type within the list
- R assigns each element a unique data type within the list
- R throws an error if a list contains elements of different data types
R allows lists to contain elements of different data types without coercing them. Unlike vectors, where elements are coerced to a common type, lists retain the individual data types of their elements. This means you can have a list with elements that are numeric, character, logical, etc., all coexisting without being coerced.
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.