- R Programming - Week 3 Assignment
- by Ken Wood
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R Programming Week 3 Programming Assignment 2: Lexical Scoping
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.
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 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
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.
Assignment: 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 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 by makeCacheMatrix above. If the inverse has already been calculated (and the matrix has not changed), then cacheSolve 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.
Suggested Solution —
Tips for submitting assignment:
- Download GitHub Desktop
- Fork the GitHub repository containing the stub R files 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. *Clone or Download, revise it in R or Rstudio*
- Commit( or Copy ) your completed R file into YOUR git repository and push your git branch to the GitHub repository under your account. *Or you can do pull request on Github.
Love to help more and more people as I keep going on the Specialization Track! Hope this helps!
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Programming Assignment 3: Hospital Quality
My code repository for coursera data science specialization by john hopkins university, assignment instructions.
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 and information 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 determining whether hospitals should be fined for not providing high quality care to patients (see http://goo.gl/jAXFX for 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 this assignment. The zip file for this assignment contains three files
- outcome-of-care-measures.csv: Contains information about 30-day mortality and readmission rates for 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 named Hospital_Revised_Flatfiles.pdf. This document contains information about many other files that are not included with this programming assignment. You will want to focus on the variables for Number 19 (“Outcome of Care Measures.csv”) and Number 11 (“Hospital Data.csv”). You may find it useful to print out this document (at least the pages for Tables 19 and 11) to have next to you while you work on this assignment. In particular, the numbers of the variables for each table indicate column indices in each table (i.e. “Hospital Name” is column 2 in the outcome-of-care-measures.csv file)
More information about the assignment here
Data zip file - link
1 Plot the 30-day mortality rates for heart attack - outcome.R
2 Finding the best hospital in a state - best.R
Write a function called best that take two arguments: the 2-character abbreviated name of a state and an outcome name. The function reads the outcome-of-care-measures.csv file and returns a character vector with the name of the hospital that has the best (i.e. lowest) 30-day mortality for the specified outcome in that state. The hospital name is the name provided in the Hospital.Name variable. The outcomes can be one of “heart attack”, “heart failure”, or “pneumonia”. Hospitals that do not have data on a particular outcome should be excluded from the set of hospitals when deciding the rankings.
3 Ranking hospitals by outcome in a state - rankhospital.R
Write a function called rankhospital that takes three arguments: the 2-character abbreviated name of a state (state), an outcome (outcome), and the ranking of a hospital in that state for that outcome (num). The function reads the outcome-of-care-measures.csv file and returns a character vector with the name of the hospital that has the ranking specified by the num argument. For example, the call rankhospital(“MD”, “heart failure”, 5) would return a character vector containing the name of the hospital with the 5th lowest 30-day death rate for heart failure. The num argument can take values “best”, “worst”, or an integer indicating the ranking (smaller numbers are better). If the number given by num is larger than the number of hospitals in that state, then the function should return NA. Hospitals that do not have data on a particular outcome should be excluded from the set of hospitals when deciding the rankings.
4 Ranking hospitals in all states - rankall.R
Write a function called rankall that takes two arguments: an outcome name (outcome) and a hospital ranking (num). The function reads the outcome-of-care-measures.csv file and returns a 2-column data frame containing the hospital in each state that has the ranking specified in num. For example the function call rankall(“heart attack”, “best”) would return a data frame containing the names of the hospitals that are the best in their respective states for 30-day heart attack death rates. The function should return a value for every state (some may be NA). The first column in the data frame is named hospital, which contains the hospital name, and the second column is named state, which contains the 2-character abbreviation for the state name. Hospitals that do not have data on a particular outcome should be excluded from the set of hospitals when deciding the rankings.
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In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
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This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.
28 videos 9 readings 1 quiz
28 videos • Total 129 minutes
- Installing R on a Mac • 1 minute • Preview module
- Installing R on Windows • 3 minutes
- Installing R Studio (Mac) • 1 minute
- Writing Code / Setting Your Working Directory (Windows) • 7 minutes
- Writing Code / Setting Your Working Directory (Mac) • 7 minutes
- Introduction • 1 minute
- Overview and History of R • 16 minutes
- Getting Help • 13 minutes
- R Console Input and Evaluation • 4 minutes
- Data Types - R Objects and Attributes • 4 minutes
- Data Types - Vectors and Lists • 6 minutes
- Data Types - Matrices • 3 minutes
- Data Types - Factors • 4 minutes
- Data Types - Missing Values • 2 minutes
- Data Types - Data Frames • 2 minutes
- Data Types - Names Attribute • 1 minute
- Data Types - Summary • 0 minutes
- Reading Tabular Data • 5 minutes
- Reading Large Tables • 7 minutes
- Textual Data Formats • 4 minutes
- Connections: Interfaces to the Outside World • 4 minutes
- Subsetting - Basics • 4 minutes
- Subsetting - Lists • 4 minutes
- Subsetting - Matrices • 2 minutes
- Subsetting - Partial Matching • 1 minute
- Subsetting - Removing Missing Values • 3 minutes
- Vectorized Operations • 3 minutes
- Introduction to swirl • 1 minute
9 readings • Total 90 minutes
- Welcome to R Programming • 10 minutes
- About the Instructor • 10 minutes
- Pre-Course Survey • 10 minutes
- Syllabus • 10 minutes
- Course Textbook • 10 minutes
- Course Supplement: The Art of Data Science • 10 minutes
- Data Science Podcast: Not So Standard Deviations • 10 minutes
- Getting Started and R Nuts and Bolts • 10 minutes
- Practical R Exercises in swirl Part 1 • 10 minutes
1 quiz • Total 30 minutes
- Week 1 Quiz • 30 minutes
Week 2: Programming with R
Welcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.
13 videos 3 readings 2 quizzes
13 videos • Total 90 minutes
- Control Structures - Introduction • 0 minutes • Preview module
- Control Structures - If-else • 1 minute
- Control Structures - For loops • 4 minutes
- Control Structures - While loops • 3 minutes
- Control Structures - Repeat, Next, Break • 4 minutes
- Your First R Function • 10 minutes
- Functions (part 1) • 9 minutes
- Functions (part 2) • 7 minutes
- Scoping Rules - Symbol Binding • 10 minutes
- Scoping Rules - R Scoping Rules • 8 minutes
- Scoping Rules - Optimization Example (OPTIONAL) • 9 minutes
- Coding Standards • 8 minutes
- Dates and Times • 10 minutes
3 readings • Total 30 minutes
- Week 2: Programming with R • 10 minutes
- Practical R Exercises in swirl Part 2 • 10 minutes
- Programming Assignment 1 INSTRUCTIONS: Air Pollution • 10 minutes
2 quizzes • Total 60 minutes
- Week 2 Quiz • 30 minutes
- Programming Assignment 1: Quiz • 30 minutes
Week 3: Loop Functions and Debugging
We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice.
8 videos 2 readings 1 quiz
8 videos • Total 61 minutes
- Loop Functions - lapply • 9 minutes • Preview module
- Loop Functions - apply • 7 minutes
- Loop Functions - mapply • 4 minutes
- Loop Functions - tapply • 3 minutes
- Loop Functions - split • 9 minutes
- Debugging Tools - Diagnosing the Problem • 12 minutes
- Debugging Tools - Basic Tools • 6 minutes
- Debugging Tools - Using the Tools • 8 minutes
2 readings • Total 20 minutes
- Week 3: Loop Functions and Debugging • 10 minutes
- Practical R Exercises in swirl Part 3 • 10 minutes
- Week 3 Quiz • 30 minutes
Week 4: Simulation & Profiling
This week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R.
6 videos 4 readings 2 quizzes
6 videos • Total 42 minutes
- The str Function • 6 minutes • Preview module
- Simulation - Generating Random Numbers • 7 minutes
- Simulation - Simulating a Linear Model • 4 minutes
- Simulation - Random Sampling • 2 minutes
- R Profiler (part 1) • 10 minutes
- R Profiler (part 2) • 10 minutes
4 readings • Total 40 minutes
- Week 4: Simulation & Profiling • 10 minutes
- Practical R Exercises in swirl Part 4 • 10 minutes
- Programming Assignment 3 INSTRUCTIONS: Hospital Quality • 10 minutes
- Post-Course Survey • 10 minutes
- Week 4 Quiz • 30 minutes
- Programming Assignment 3: Quiz • 30 minutes
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