programming assignment 3 r programming

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Cosmin11 commented Mar 21, 2015

best <- function(state, outcome ) {

Read outcome data----------------------------------------

Error msg for outcomes-----------------------------------, error msg for state--------------------------------------, create data frame with hosp., state, h. attack, h. failure & pneumonia-----, replace 'not available with na---------------------------------------------, find minimum value of the outcome--------------------------------------------, display all hospitals with the minimum dead rate-----------------------------, display result in alphabetical ordered------------------------------------------, display first value from the result vector-------------------------------------.

Sorry, something went wrong.

bbrewington commented Jul 10, 2015

You should take this down, since you're enabling other people to copy it and cheat. Also, this goes against the Coursera Honor Code (link: https://www.coursera.org/about/terms/honorcode )

It says "I will not make solutions to homework, quizzes, exams, projects, and other assignments available to anyone else (except to the extent an assignment explicitly permits sharing solutions). This includes both solutions written by me, as well as any solutions provided by the course staff or others."

rahul351987 commented Feb 15, 2016

Good one but calling functions in other codes makes it difficult plus time consuming!

R Programming Assignment 3: Hospital Quality

Introduction.

Download the file ProgAssignment3-data.zip file containing the data for Programming Assignment 3 from the Coursera web site. Unzip the file in a directory that will serve as your working directory. When you start up R make sure to change your working directory to the directory where you unzipped the data.

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 essentially 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).

Part 1: Plot the 30-day mortality rates for heart attack

Read the outcome data into R via the read.csv function and look at the first few rows.

There are many columns in this dataset. You can see how many by typing ncol(outcome) (you can see the number of rows with the nrow function). In addition, you can see the names of each column by typing names(outcome) (the names are also in the PDF document).

To make a simple histogram of the 30-day death rates from heart attack (column 11 in the outcome dataset), run

Because we originally read the data in as character (by specifying colClasses = “character” we need to coerce the column to be numeric. You may get a warning about NAs being introduced but that is okay.

There is nothing to submit for this part.

Part 2: Finding the best hospital in a state

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.

Handling ties. If there is a tie for the best hospital for a given outcome, then the hospital names should be sorted in alphabetical order and the first hospital in that set should be chosen (i.e. if hospitals “b”, “c”, and “f” are tied for best, then hospital “b” should be returned).

The function should use the following template.

The function should check the validity of its arguments. If an invalid state value is passed to best , the function should throw an error via the stop function with the exact message “invalid state”. If an invalid outcome value is passed to best , the function should throw an error via the stop function with the exact message “invalid outcome”.

Here is some sample output from the function.

Save your code for this function to a file named best.R .

Part 3: Ranking hospitals by outcome in a state

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

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.

Handling ties . It may occur that multiple hospitals have the same 30-day mortality rate for a given cause of death. In those cases ties should be broken by using the hospital name. For example, in Texas (“TX”), the hospitals with lowest 30-day mortality rate for heart failure are shown here.

Note that Cypress Fairbanks Medical Center and Detar Hospital Navarro both have the same 30-day rate (8.7). However, because Cypress comes before Detar alphabetically, Cypress is ranked number 3 in this scheme and Detar is ranked number 4. One can use the order function to sort multiple vectors in this manner (i.e. where one vector is used to break ties in another vector).

The function should check the validity of its arguments. If an invalid state value is passed to rankhospital , the function should throw an error via the stop function with the exact message “invalid state”. If an invalid outcome value is passed to rankhospital , the function should throw an error via the stop function with the exact message “invalid outcome”.

Save your code for this function to a file named rankhospital.R .

Part 4: Ranking hospitals in all states

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.

Handling ties . The rankall function should handle ties in the 30-day mortality rates in the same way that the rankhospital function handles ties.

NOTE : For the purpose of this part of the assignment (and for efficiency), your function should NOT call the rankhospital function from the previous section.

The function should check the validity of its arguments. If an invalid outcome value is passed to rankall , the function should throw an error via the stop function with the exact message “invalid outcome”. The num variable 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 .

Save your code for this function to a file named rankall.R .

My Solution

A Good Tutorial

Link: https://github.com/DanieleP/PA3-tutorial

• 1. Introduction
• 2. Part 1: Plot the 30-day mortality rates for heart attack
• 3. Part 2: Finding the best hospital in a state
• 4. Part 3: Ranking hospitals by outcome in a state
• 5. Part 4: Ranking hospitals in all states
• 6. My Solution
• 8. A Good Tutorial

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.

Secure Your Spot in Our Data Manipulation in R Online Course Starting on July 15 (Click for More Info)

Assignment Operators in R (3 Examples) | Comparing = vs. ＜- vs. ＜＜-

On this page you’ll learn how to apply the different assignment operators in the R programming language .

The content of the article is structured as follows:

Let’s dive right into the exemplifying R syntax!

Example 1: Why You Should Use ＜- Instead of = in R

Generally speaking, there is a preference in the R programming community to use an arrow (i.e. ＜-) instead of an equal sign (i.e. =) for assignment.

In my opinion, it makes a lot of sense to stick to this convention to produce scripts that are easy to read for other R programmers.

However, you should also take care about the spacing when assigning in R. False spacing can even lead to error messages .

For instance, the following R code checks whether x is smaller than minus five due to the false blank between ＜ and -:

A properly working assignment could look as follows:

However, this code is hard to read, since the missing space makes it difficult to differentiate between the different symbols and numbers.

In my opinion, the best way to assign in R is to put a blank before and after the assignment arrow:

As mentioned before, the difference between ＜- and = is mainly due to programming style . However, the following R code using an equal sign would also work:

In the following example, I’ll show a situation where ＜- and = do not lead to the same result. So keep on reading!

Example 2: When ＜- is Really Different Compared to =

In this Example, I’ll illustrate some substantial differences between assignment arrows and equal signs.

Let’s assume that we want to compute the mean of a vector ranging from 1 to 5. Then, we could use the following R code:

However, if we want to have a look at the vector x that we have used within the mean function, we get an error message:

Let’s compare this to exactly the same R code but with assignment arrow instead of an equal sign:

The output of the mean function is the same. However, the assignment arrow also stored the values in a new data object x:

This example shows a meaningful difference between = and ＜-. While the equal sign doesn’t store the used values outside of a function, the assignment arrow saves them in a new data object that can be used outside the function.

Example 3: The Difference Between ＜- and ＜＜-

So far, we have only compared ＜- and =. However, there is another assignment method we have to discuss: The double assignment arrow ＜＜- (also called scoping assignment).

The following code illustrates the difference between ＜- and ＜＜- in R. This difference mainly gets visible when applying user-defined functions .

Let’s manually create a function that contains a single assignment arrow:

Now, let’s apply this function in R:

The data object x_fun1, to which we have assigned the value 5 within the function, does not exist:

Let’s do the same with a double assignment arrow:

Let’s apply the function:

And now let’s return the data object x_fun2:

As you can see based on the previous output of the RStudio console, the assignment via ＜＜- saved the data object in the global environment outside of the user-defined function.

Video & Further Resources

I have recently released a video on my YouTube channel , which explains the R syntax of this tutorial. You can find the video below:

The YouTube video will be added soon.

In addition to the video, I can recommend to have a look at the other articles on this website.

• R Programming Examples

In summary: You learned on this page how to use assignment operators in the R programming language. If you have further questions, please let me know in the comments.

assignment-operators-in-r How to use different assignment operators in R – 3 R programming examples – R programming language tutorial – Actionable R programming syntax in RStudio

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From Reddit threads to roundtable events, debating the merits of programming languages is not a new phenomenon.

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