├── README.md
└── cachematrix.R


/README.md:
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  1 | ### Introduction
  2 | 
  3 | This second programming assignment will require you to write an R
  4 | function that is able to cache potentially time-consuming computations.
  5 | For example, taking the mean of a numeric vector is typically a fast
  6 | operation. However, for a very long vector, it may take too long to
  7 | compute the mean, especially if it has to be computed repeatedly (e.g.
  8 | in a loop). If the contents of a vector are not changing, it may make
  9 | sense to cache the value of the mean so that when we need it again, it
 10 | can be looked up in the cache rather than recomputed. In this
 11 | Programming Assignment you will take advantage of the scoping rules of
 12 | the R language and how they can be manipulated to preserve state inside
 13 | of an R object.
 14 | 
 15 | ### Example: Caching the Mean of a Vector
 16 | 
 17 | In this example we introduce the `<<-` operator which can be used to
 18 | assign a value to an object in an environment that is different from the
 19 | current environment. Below are two functions that are used to create a
 20 | special object that stores a numeric vector and caches its mean.
 21 | 
 22 | The first function, `makeVector` creates a special "vector", which is
 23 | really a list containing a function to
 24 | 
 25 | 1.  set the value of the vector
 26 | 2.  get the value of the vector
 27 | 3.  set the value of the mean
 28 | 4.  get the value of the mean
 29 | 
 30 | <!-- -->
 31 | 
 32 |     makeVector <- function(x = numeric()) {
 33 |             m <- NULL
 34 |             set <- function(y) {
 35 |                     x <<- y
 36 |                     m <<- NULL
 37 |             }
 38 |             get <- function() x
 39 |             setmean <- function(mean) m <<- mean
 40 |             getmean <- function() m
 41 |             list(set = set, get = get,
 42 |                  setmean = setmean,
 43 |                  getmean = getmean)
 44 |     }
 45 | 
 46 | The following function calculates the mean of the special "vector"
 47 | created with the above function. However, it first checks to see if the
 48 | mean has already been calculated. If so, it `get`s the mean from the
 49 | cache and skips the computation. Otherwise, it calculates the mean of
 50 | the data and sets the value of the mean in the cache via the `setmean`
 51 | function.
 52 | 
 53 |     cachemean <- function(x, ...) {
 54 |             m <- x$getmean()
 55 |             if(!is.null(m)) {
 56 |                     message("getting cached data")
 57 |                     return(m)
 58 |             }
 59 |             data <- x$get()
 60 |             m <- mean(data, ...)
 61 |             x$setmean(m)
 62 |             m
 63 |     }
 64 | 
 65 | ### Assignment: Caching the Inverse of a Matrix
 66 | 
 67 | Matrix inversion is usually a costly computation and there may be some
 68 | benefit to caching the inverse of a matrix rather than computing it
 69 | repeatedly (there are also alternatives to matrix inversion that we will
 70 | not discuss here). Your assignment is to write a pair of functions that
 71 | cache the inverse of a matrix.
 72 | 
 73 | Write the following functions:
 74 | 
 75 | 1.  `makeCacheMatrix`: This function creates a special "matrix" object
 76 |     that can cache its inverse.
 77 | 2.  `cacheSolve`: This function computes the inverse of the special
 78 |     "matrix" returned by `makeCacheMatrix` above. If the inverse has
 79 |     already been calculated (and the matrix has not changed), then
 80 |     `cacheSolve` should retrieve the inverse from the cache.
 81 | 
 82 | Computing the inverse of a square matrix can be done with the `solve`
 83 | function in R. For example, if `X` is a square invertible matrix, then
 84 | `solve(X)` returns its inverse.
 85 | 
 86 | For this assignment, assume that the matrix supplied is always
 87 | invertible.
 88 | 
 89 | In order to complete this assignment, you must do the following:
 90 | 
 91 | 1.  Fork the GitHub repository containing the stub R files at
 92 |     [https://github.com/rdpeng/ProgrammingAssignment2](https://github.com/rdpeng/ProgrammingAssignment2)
 93 |     to create a copy under your own account.
 94 | 2.  Clone your forked GitHub repository to your computer so that you can
 95 |     edit the files locally on your own machine.
 96 | 3.  Edit the R file contained in the git repository and place your
 97 |     solution in that file (please do not rename the file).
 98 | 4.  Commit your completed R file into YOUR git repository and push your
 99 |     git branch to the GitHub repository under your account.
100 | 5.  Submit to Coursera the URL to your GitHub repository that contains
101 |     the completed R code for the assignment.
102 | 
103 | ### Grading
104 | 
105 | This assignment will be graded via peer assessment.
106 | 


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/cachematrix.R:
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 1 | ## Put comments here that give an overall description of what your
 2 | ## functions do
 3 | 
 4 | ## Write a short comment describing this function
 5 | 
 6 | makeCacheMatrix <- function(x = matrix()) {
 7 | 
 8 | }
 9 | 
10 | 
11 | ## Write a short comment describing this function
12 | 
13 | cacheSolve <- function(x, ...) {
14 |         ## Return a matrix that is the inverse of 'x'
15 | }
16 | 


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