To accomplish this task, you can use the select() function from the dplyr package in R. You can specify the column selection criteria to include only numeric columns and obtain a new data frame with the desired columns.

To avoid deep nesting of if statements when writing a function in R to handle complex conditions, you can employ various approaches. This includes breaking down the conditions into smaller functions or helper variables, utilizing functions like switch() or case_when() to handle multiple conditions concisely, and leveraging logical operators and vectorization for efficient conditional operations. These approaches help enhance code readability, maintainability, and performance.

The Recall() function in R is used to call the function itself recursively from within the function. It is commonly used in recursive functions to simplify the syntax and improve readability when making recursive calls. By using Recall(), you can avoid explicitly writing the function name again, making the recursive calls more concise.

R internally represents dates and times as special date/time objects using classes such as 'Date', 'POSIXct', and 'POSIXlt'. These classes store date and time information in a format that can be easily manipulated in R.

First, you'd have to define a function that calculates the square of a number (e.g., square <- function(x) {return(x^2)}). Then you could use print(square(x)) to print the result of squaring a number. Note: 'x' should be replaced with the number you want to square.

To write an if-else statement in R that checks if a number is positive or negative, you can use the condition if (number > 0) { code for positive number } else { code for negative number }. If the number is greater than 0, the code inside the if block will be executed; otherwise, the code inside the else block will be executed.

Instead of using nested if statements, there are alternative approaches in R. These include using the switch() function for handling multiple conditions, utilizing the ifelse() function for vectorized conditional operations, and employing the case_when() function from the dplyr package for conditional operations in data frames. These alternatives can simplify code structure and enhance code readability.

To create a data frame with the first 100 positive integers and their corresponding square values, you can use the data.frame() function. You can create two separate vectors, one for the integers and one for the squares, and then pass them as arguments to the data.frame() function to create the desired data frame.

The apply() function in R can be used to apply a function to each element of a vector or columns of a data frame. The apply() function simplifies repetitive operations by iterating over the elements or columns and applying the specified function.

To calculate the mode of a factor in R, you can apply the table() function to the factor. The table() function counts the frequencies of each level in the factor, allowing you to identify the most frequent level as the mode.