To create a scatter plot in R that shows the relationship between two numeric variables in a data set, you would use the plot() function. Specify the two numeric variables as the x and y arguments in the function call, and R will generate the scatter plot with the corresponding data points.

R provides a wide range of statistical functions for mathematical computations. This includes functions to calculate the mean(), median(), mode(), minimum (min()), maximum (max()), sum(), standard deviation (sd()), variance (var()), correlation (cor()), and many others.

For loops are generally slower than vectorized operations in R. R is optimized for vectorized operations, which can perform operations on entire vectors or matrices at once, leading to more efficient and faster execution.

Vectorization in R allows you to apply functions or operations directly to vectors or data frames, which eliminates the need for explicit if-else statements. The ifelse() function is specifically designed for vectorized conditional operations, providing a concise and efficient alternative to if-else statements when working with vectors or data frames.

Including double quotes within a string in R is commonly required when writing a string that represents a quote or dialogue. For example, "She said, "Hello!"" represents the string She said, "Hello!".

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.

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 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.