Setting up the defaults:
knitr::opts_chunk$set(echo = TRUE, results = "asis")Second programming assignment will require you to write an R function is able to cache potentially time-consuming computations. For example, taking the mean of a numeric vector is typically a fast operation. However, for a very long vector, it may take too long to compute the mean, especially if it has to be computed repeatedly (e.g. in a loop). If the contents of a vector are not changing, it may make sense to cache the value of the mean so that when we need it again, it can be looked up in the cache rather than recomputed. In this Programming Assignment will take advantage of the scoping rules of the R language and how they can be manipulated to preserve state inside of an R object. Review criteria
This assignment will be graded via peer assessment. During the evaluation phase, you must evaluate and grade the submissions of at least 4 of your classmates. If you do not complete at least 4 evaluations, your own assignment grade will be reduced by 20%. Example: Caching the Mean of a Vector In this example we introduce the <<- operator which can be used to assign a value to an object in an environment that is different from the current environment. Below are two functions that are used to create a special object that stores a numeric vector and cache’s its mean.
The first function, makeVector creates a special “vector”, which is really a list containing a function to:
makeVector <- function(x = numeric()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setmean <- function(mean) m <<- mean
getmean <- function() m
list(set = set, get = get,
setmean = setmean,
getmean = getmean)
}The following function calculates the mean of the special “vector” created with the above function. However, it first checks to see if the mean has already been calculated. If so, it gets the mean from the cache and skips the computation. Otherwise, it calculates the mean of the data and sets the value of the mean in the cache via the setmean function.
cachemean <- function(x, ...) {
m <- x$getmean()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- mean(data, ...)
x$setmean(m)
m
}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 (there are also alternatives to matrix inversion that we will not discuss here). Your assignment is to write
Write the following functions:
## A pair of functions that cache the inverse of a matrix.
## This function creates a special "matrix" object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y){
x <<- y
inv <<- NULL
}
get <- function() x
setInverse <- function(solveMatrix) inv <<- solveMatrix
getInverse <- function() inv
list(set = set, get = get, setInverse = setInverse, getInverse = getInverse)
}## This function computes the inverse of the special "matrix" returned by makeCacheMatrix above.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inv <- x$getInverse()
if(!is.null(inv)){
message("getting cached data")
return(inv)
}
data <- x$get()
inv <- solve(data)
x$setInverse(inv)
inv
}For this assignment, assume that the matrix supplied is always invertible.
In order to complete this assignment, you must do the following:
In addition to submitting the URL for your GitHub repository, you will need to submit the 40 character SHA-1 hash (as string of numbers from 0-9 and letters from a-f) that identifies the repository commit that contains the version of the files you want to submit. You can do this in GitHub by doing the following:
A valid submission will look something like (this is just an example!)
This assignment will be graded via peer assessment. During the evaluation phase, you must evaluate and grade the submissions of at least 4 of your classmates. If you do not complete at least 4 evaluations, your own assignment grade will be reduced by 20%.