Statistical Analysis with R

The digital world we live in has given companies and people access to staggering amounts of data, and anyone who can make use of that to drive valuable insights stands to benefit. The ability to work with large amounts of data is becoming a standard requirement for countless jobs across multiple industries.

R, probably the most well-known language for data analysis, has been used for years due to its robust statistical functionality, outstanding graphing ability, and extensibility through packages.

This interactive module will get us up to speed with R and assumes no prior knowledge of it. We'll start with the very basics and work our way up to advanced concepts like exception handling. By the time we're done, we'll be able to write detailed, useful code in R.

Learn R from Scratch

recursiveFibonacci <- function(testVariable) 
{
    if(testVariable <= 1) 
    {
        return(testVariable)
    } else 
    {
        return(recursiveFibonacci(testVariable - 1) + recursiveFibonacci(testVariable - 2))
    }
}

# Driver Code
recursiveFibonacci(0)
recursiveFibonacci(1)
recursiveFibonacci(2)
recursiveFibonacci(5)
recursiveFibonacci(6)

recursiveFibonacci <- function(testVariable) 
{
    if(testVariable <= 1) 
    {
        return(testVariable)
    } else 
    {
        return(recursiveFibonacci(testVariable - 1) + recursiveFibonacci(testVariable - 2))
    }
}

### Explanation
To find the element placed at the input index $n$ in the Fibonacci series we need the elements placed at $(n-1)$ and $(n-2)$ positions.

For example, if current index = $n$
then
```
fibonacciSeries[n] = fibonacciSeries[n-1] + fibonacciSeries[n-2].
```
This is a good situation where recursion can be used. 
Recursion means _function calling itself_, in the code above `recursiveFibonacci()` function calls itself with a lesser value several times. A termination condition is very important in recursion function: in this case, it is
```
if(testVariable <= 1) 
{
   return(testVariable)
}
```
so that the code returns directly for the lowest indexes.


## Explanation
To find the element placed at the input index $n$ in the Fibonacci series we need the elements placed at $(n-1)$ and $(n-2)$ positions.

For example, if current index = $n$
then
```
fibonacciSeries[n] = fibonacciSeries[n-1] + fibonacciSeries[n-2].
```
This is a good situation where recursion can be used. 
Recursion means _function calling itself_, in the code above `recursiveFibonacci()` function calls itself with a lesser value several times. A termination condition is very important in recursion function: in this case, it is
```
if(testVariable <= 1) 
{
   return(testVariable)
}
```
so that the code returns directly for the lowest indexes.


In this review, we give a detailed analysis of the solution to this problem.

Getting Started

Variables

Exploring R Objects

R Operators and Notation

Conditional Statements

Loops

Functions

Input/Output

Exception Handling

Classes

Fun Challenges

Conclusion

Solution Review: Recursion

Solution: Recursion

Explanation