When nested if statements become too complex, it is advisable to use alternative functions or techniques to handle the conditions. This may include using functions like case_when() or switch(), breaking down the conditions into smaller if statements, or leveraging vectorization and logical operators for efficient conditional operations. The choice depends on the specific scenario and the complexity of the conditions.

To customize the x-axis labels in an R plot, you would use the xlab parameter. It allows you to specify a custom label for the x-axis, providing a descriptive name for the variable or quantity being represented.

In R, CSV data can be imported using the read.csv() function. The read.csv() function reads the data from a CSV file and creates a data frame in R containing the imported data.

The 'paste()' function is used in R to concatenate strings. It converts its arguments to character strings and concatenates them, separating them with a space by default.

The mad() function in R can be used to calculate the median absolute deviation. The median absolute deviation is a robust measure of variability that is less influenced by outliers compared to the standard deviation.

Recursion in the context of R functions refers to the process of a function calling itself within its own definition. This allows the function to solve a problem by breaking it down into smaller sub-problems of the same type. Recursion involves the concept of a base case and a recursive case, where the function keeps calling itself until the base case is reached.

A while loop in R continues to execute as long as the specified condition is true. The condition is checked before each iteration of the loop, and if it evaluates to true, the loop's code block is executed. If the condition is false, the loop is exited, and the program continues with the next statement.

There are many scenarios where you would need to use a vector in R. For example, when storing a set of measurement values, representing categorical variables in a dataset, performing calculations on multiple values simultaneously, or organizing related information. Vectors are a fundamental data structure in R that allow for efficient storage and manipulation of data.

In R, we create a vector of numbers using the c() function, and then calculate the mean and median using the mean() and median() functions. For example, x <- c(1, 2, 3, 4, 5); mean(x); median(x) would create a vector and compute the mean and median of its elements.

In R, the t.test() function can be used to conduct a t-test. The t.test() function is used for hypothesis testing with continuous variables, comparing means between two groups and determining if the difference is statistically significant.