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 have two logical vectors and you need to perform an element-wise 'AND' operation. What would the syntax look like?
- a && b
- a && b
- a & b
- a and b
In R, the syntax for performing an element-wise 'AND' operation between two logical vectors is a & b. For example, if a and b are logical vectors, a & b would return a vector where each element is the result of the 'AND' operation between the corresponding elements of a and b.
How would you handle missing values when calculating the mean in R?
- Use the na.rm = TRUE parameter in the mean() function
- Replace missing values with 0 before using the mean() function
- Exclude missing values from the vector before using the mean() function
- All of the above
When calculating the mean in R, you can handle missing values by using the na.rm = TRUE parameter in the mean() function. Setting na.rm = TRUE instructs R to ignore missing values and compute the mean based on the available non-missing values. This ensures that missing values do not impact the calculation.
What is a data frame in R?
- A graphical representation of data
- A collection of data elements of the same data type
- A two-dimensional table-like data structure
- A statistical model used for forecasting
A data frame in R is a two-dimensional table-like data structure where columns can contain different data types. It is similar to a spreadsheet or a database table, where each column represents a variable and each row represents an observation.
In R, the ________ function can be used to check if a value is numeric.
- is.character()
- is.factor()
- is.logical()
- is.numeric()
The is.numeric() function in R is used to check if a value is numeric. It returns TRUE if the value is numeric and FALSE otherwise.