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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

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Selection: 15


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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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Selection: 14


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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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Selection: 13


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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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| 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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Selection: 11


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| In the last lesson, you learned about the two most fundamental members of R's
| *apply family of functions: lapply() and sapply(). Both take a list as input,
| apply a function to each element of the list, then combine and return the
| result. lapply() always returns a list, whereas sapply() attempts to simplify

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| In this lesson, we'll cover matrices and data frames. Both represent
| 'rectangular' data types, meaning that they are used to store tabular data,
| with rows and columns.

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| In this lesson, we'll see how to extract elements from a vector based on some
| conditions that we specify.

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Selection: 10


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| In this lesson, you'll learn how to use lapply() and sapply(), the two most
| important members of R's *apply family of functions, also known as loop
| functions.

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| Missing values play an important role in statistics and data analysis. Often,
| missing values must not be ignored, but rather they should be carefully
| studied to see if there's an underlying pattern or cause for their
| missingness.