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

calculateLog <- function(test)
{
    tryCatch(
        {
            cat(log(test))
        }, 
        error = function(e) 
        {
            cat("This operation is not allowed!")
        },
        warning = function(w) 
        {
            cat("This operation is not allowed!")
        })
}

# Driver Code
calculateLog(10)
cat("\n")
calculateLog(-10)
cat("\n")
calculateLog("a")
cat("\n")

calculateLog <- function(test)
{
    tryCatch(
        {
            cat(log(test))
        }, 
        error = function(e) 
        {
            cat("This operation is not allowed!")
        },
        warning = function(w) 
        {
            cat("This operation is not allowed!")
        })
}

### Explanation

> We use `log()` to calculate the log of a variable or constant. 

Here, `log(test)` is the function call which may throw an exception, therefore we have used `tryCatch()` to handle errors and warnings.

We have to write both the **error** and **warning** functions because for negative numbers the `log()` function throws a warning and for non-integers, it throws an error.

---
In the next lesson, we will be learning other methods for exception handling.

## Explanation

> We use `log()` to calculate the log of a variable or constant. 

Here, `log(test)` is the function call which may throw an exception, therefore we have used `tryCatch()` to handle errors and warnings.

We have to write both the **error** and **warning** functions because for negative numbers the `log()` function throws a warning and for non-integers, it throws an error.

---
In the next lesson, we will be learning other methods for exception handling.

In this review, we provide 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: Try Catch

Solution: Try Catch Block

Explanation