This second programming assignment will require you to write an R function that 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 you 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.
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 caches its mean.
The first function, makeVector
creates a special "vector", which is
really a list containing a function to
- set the value of the vector
- get the value of the vector
- set the value of the mean
- get the value of the mean
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 get
s 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 computing it repeatedly (there are also alternatives to matrix inversion that we will not discuss here). Your assignment is to write a pair of functions that cache the inverse of a matrix.
Write the following functions:
makeCacheMatrix
: This function creates a special "matrix" object that can cache its inverse.cacheSolve
: This function computes the inverse of the special "matrix" returned bymakeCacheMatrix
above. If the inverse has already been calculated (and the matrix has not changed), thencacheSolve
should retrieve the inverse from the cache.
Computing the inverse of a square matrix can be done with the solve
function in R. For example, if X
is a square invertible matrix, then
solve(X)
returns its inverse.
For this assignment, assume that the matrix supplied is always invertible.
In order to complete this assignment, you must do the following:
- Fork the GitHub repository containing the stub R files at https://github.com/rdpeng/ProgrammingAssignment2 to create a copy under your own account.
- Clone your forked GitHub repository to your computer so that you can edit the files locally on your own machine.
- Edit the R file contained in the git repository and place your solution in that file (please do not rename the file).
- Commit your completed R file into YOUR git repository and push your git branch to the GitHub repository under your account.
- Submit to Coursera the URL to your GitHub repository that contains the completed R code for the assignment.
This assignment will be graded via peer assessment.
The data for this assignment come from the Hospital Compare web site (http://hospitalcompare.hhs.gov)run by the U.S. Department of Health and Human Services. The purpose of the web site is to provide data andinformation about the quality of care at over 4,000 Medicare-certified hospitals in the U.S. This dataset es-sentially covers all major U.S. hospitals. This dataset is used for a variety of purposes, including determiningwhether hospitals should be fined for not providing high quality care to patients (seehttp://goo.gl/jAXFXfor some background on this particular topic). The Hospital Compare web site contains a lot of data and we will only look at a small subset for thisassignment.
The zip file for this assignment contains three files•outcome-of-care-measures.csv: Contains information about 30-day mortality and readmission ratesfor heart attacks, heart failure, and pneumonia for over 4,000 hospitals.•hospital-data.csv: Contains information about each hospital.•Hospital_Revised_Flatfiles.pdf: Descriptions of the variables in each file (i.e the code book).A description of the variables in each of the files is in the included PDF file namedHospital_Revised_Flatfiles.pdf.This document contains information about many other files that are not included with this programmingassignment. You will want to focus on the variables for Number 19 (“Outcome of Care Measures.csv”) andNumber 11 (“HospitalData.csv”). You may find it useful to print out this document (at least the pages forTables 19 and 11) to have next to you while you work on this assignment. In particular, the numbers ofthe variables for each table indicate column indices in each table (i.e. “Hospital Name” is column 2 in theoutcome-of-care-measures.csv file).
The three r documents contain three functions to find out
- the best hospital in a US state;
- ranking hospitals by outcome in a US state;
- ranking hospitals in all US states.
Have fun.:)