All of the mentioned techniques can be used in R to visualize high-dimensional data. Scatter plots can encode additional dimensions through color or size. Parallel coordinate plots can show relationships between multiple variables. Dimensionality reduction techniques like PCA or t-SNE can project high-dimensional data into lower dimensions for visualization.

To write a function in R that uses a global variable, you would define the global variable outside of the function and access it within the function. Since global variables are accessible from anywhere in the program, the function can directly reference and modify the global variable as needed.

To create a function in R that checks if a number is prime, you can use the following code: is_prime <- function(n) { if (n <= 1) { return(FALSE) } for (i in 2:sqrt(n)) { if (n %% i == 0) { return(FALSE) } } return(TRUE) }. The function takes a number n as input and iterates from 2 to the square root of n, checking if any of these numbers divides n. If a divisor is found, the function returns FALSE; otherwise, it returns TRUE.

Logical operations in R are commonly used in situations such as checking conditions in control flow statements, filtering data based on specific criteria, and creating boolean variables for flagging or indicating certain conditions.

In R, the operator != is used to check if two values are not equal. For example, 3 != 4 would return TRUE.

The 'print()' function is used in R to print the output to the console. This can be used to display the value of a variable, a message, or the output of a function.

In R, true/false values are stored as logical data type. This data type only has two possible values: TRUE and FALSE.

In R, to prematurely exit a while loop, you can use the 'break' keyword. When the 'break' statement is encountered within the loop's code block, it immediately terminates the loop execution, and the program continues with the next statement after the loop. This allows for early termination of the loop based on certain conditions.

A scenario where you would need to use a nested if statement in R is when you have multiple conditions to evaluate and need to perform different actions based on each condition. Nested if statements allow for more complex branching logic and the ability to handle intricate sets of conditions.

In R, scoping rules follow lexical scoping, also known as static scoping. This means that functions can access objects in their enclosing environment, including objects defined outside the function but within the parent environment. This allows functions to access variables defined in higher-level environments.