In R, you can define a custom function called customMode() (or any preferred name) to calculate the mode of a numeric vector. This allows you to implement your own logic for identifying the mode based on the frequency of values.

In R, the <- operator is commonly used to assign a value to a variable. For example, x <- 5 assigns the value 5 to the variable x. The = operator can also be used for assignment, but the <- operator is more commonly used.

Pie charts have the advantage of being easy to understand and visually appealing, making them suitable for displaying proportions. However, they have disadvantages such as being limited to a few categories, making it difficult to compare values accurately. Additionally, pie charts may not be suitable for showing hierarchical data or dealing with whole numbers, and they can be space-consuming. The choice of using a pie chart depends on the specific data and the purpose of visualization.

Some potential risks or downsides of using global variables in R include difficulty in tracking and managing dependencies between functions, increased potential for naming conflicts if multiple global variables have the same name, and reduced code modularity and reusability since functions become dependent on specific global variables. It is important to carefully manage and control the usage of global variables to minimize these risks.

Functions like subset(), filter(), and mutate() are specifically designed to operate on data frames in R. They allow for data manipulation, subsetting, and creating new variables within a data frame.

Starting with the installation of R and RStudio, the basics of R programming should be the first focus. Online resources, such as free tutorials, R documentation, or forums like Stack Overflow can be incredibly helpful. A mix of hands-on practice and theoretical learning usually works best.

All of the mentioned scenarios may require handling missing values when calculating the median in R. For example, when analyzing survey data, it's common to have missing responses that need to be handled appropriately. Similarly, when calculating the median income, missing income values should be accounted for. Handling missing values ensures accurate median calculations and prevents biased results.

To write a recursive function in R that calculates the factorial of a number, you can use the following code: factorial <- function(n) { if (n <= 1) { return(1) } else { return(n * factorial(n - 1)) } }. The function checks if the input n is less than or equal to 1. If it is, it returns 1 (base case). Otherwise, it multiplies n with the factorial of n - 1 (recursive case). This recursive calculation continues until the base case is reached.

R handles missing values in statistical calculations by providing functions that allow for exclusion of missing values from calculations. For example, many statistical functions in R have an na.rm parameter that, when set to TRUE, ignores missing values and performs calculations on the available data. This allows for accurate statistical analyses even when data contain missing values.

You can use conditional statements like if-else in combination with the print() function to display different messages based on the value of a variable. This allows you to make the output more user-friendly and informative.