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| Whenever you're working with a new dataset, the first thing you should do is look at it! What is the format of the data? What are the dimensions? What are the
| variable names? How are the variables stored? Are there missing data? Are there any flaws in the data?

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| One of the great advantages of using a statistical programming language like R is its vast collection of tools for simulating random numbers.

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| R has a special way of representing dates and times, which can be helpful if you're working with data that show how something changes over time (i.e. time-series
| data) or if your data contain some other temporal information, like dates of birth.

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| One of the greatest strengths of R, relative to other programming languages, is the ease with which we can create publication-quality graphics. In this lesson,
| you'll learn about base graphics in R.

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## Caching the Inverse of a Matrix:
## Matrix inversion is usually a costly computation and there may be some 
## benefit to caching the inverse of a matrix rather than compute it repeatedly.
## Below are a pair of functions that are used to create a special object that 
## stores a matrix and caches its inverse.

## This function creates a special "matrix" object that can cache its inverse.

makeCacheMatrix <- function(x = matrix()) {
        inv <- NULL