r programming assignment week 3

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R Programming

This course is part of multiple programs. Learn more

This course is part of multiple programs

Instructors: Roger D. Peng, PhD +2 more

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Recommended experience

Intermediate level

Familiarity with regression is recommended.

Skills you'll gain

• Category: Data Analysis Data Analysis
• Category: Debugging Debugging
• Category: R Programming R Programming
• Category: Rstudio Rstudio

What you'll learn

Understand critical programming language concepts

Configure statistical programming software

Make use of R loop functions and debugging tools

Collect detailed information using R profiler

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0 quizzes, 6 assessments

Available in English

Subtitles: German, Russian, Korean, Portuguese (Brazilian), English, Italian, French, Chinese (Simplified), Spanish, Arabic, Vietnamese, Thai, Japanese, Indonesian

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There are 4 modules in this course

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.

Week 1: Background, Getting Started, and Nuts & Bolts

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.

What's included

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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Learner reviews

Showing 3 of 22086

22,086 reviews

Reviewed on May 12, 2020

Really interesting course. The interactive coding sessions with swirl are especially useful. Would be great, if you provided sample solutions for the programming assignments, in particular for week 4.

Reviewed on Aug 12, 2019

Very challenging, but good course. I've been programming in R for over a year, but there were still some things for me to pick up in this class. Assignments were a challenge, but satisfying to tackle.

Reviewed on Sep 9, 2019

The course was a wonderful introduction to R, though I felt the programming projects were lacking a bit in terms of direction. Definitely go through the swirl exercises to help reinforce everything!

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R Programming Week 3 Programming Assignment 2: Lexical Scoping

Introduction.

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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nithyabk commented May 7, 2020

Put comments here that give an overall description of what your, functions do, our aim in this experiment is to write a pair of functions, namely,, "makecachematrix" and "cachesolve" that cache the inverse of a matrix, write a short comment describing this function, makecachematrix is a function which creates a special "matrix" object that can, cache its inverse for the input (which is an invertible square matrix).

makeCacheMatrix <- function(x = matrix()) {

inv <- NULL set <- function(y) { x <<- y inv <<- NULL } get <- function() x setinv <- function(inverse) inv <<- inverse getinv <- function() inv list(set = set, get = get, setinv = setinv, getinv = getinv) }

cacheSolve is a function which 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 the cachesolve should retrieve the, inverse from the cache.

cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inv <- x$getinv() if(!is.null(inv)) { message("getting cached result") return(inv) } data <- x$get() inv <- solve(data, ...) x$setinv(inv) inv }

---------------Checking the program------------------------

M <- matrix(rnorm(16),4,4), m1 <- makecachematrix(m), cachesolve(m1), [,1] [,2] [,3] [,4], [1,] -0.1653269 0.2592203 0.6176218 -0.7520955, [2,] 0.2828334 -0.1853499 0.4511382 0.2094365, [3,] 0.1434840 1.0413868 -0.3550853 -0.3261154, [4,] 0.1793583 -0.4252171 -0.4371493 -0.1749830.

Sorry, something went wrong.

nithyabk commented May 9, 2020

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 compute it repeatedly., below are a pair of functions that are used to create a special object that, stores a matrix and caches its inverse., 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(inverse) inv <<- inverse getInverse <- function() inv list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) }

This function computes the inverse of the special "matrix" created by

Makecachematrix above. if the inverse has already been calculated (and the, matrix has not changed), then it should retrieve the inverse from the cache..

cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inv <- x$getInverse() if (!is.null(inv)) { message("getting cached data") return(inv) } mat <- x$get() inv <- solve(mat, ...) x$setInverse(inv) inv }

testing my function

source("ProgrammingAssignment2/cachematrix.R") my_matrix <- makeCacheMatrix(matrix(1:4, 2, 2)) my_matrix$get() [,1] [,2] [1,] 1 3 [2,] 2 4 my_matrix$getInverse() NULL cacheSolve(my_matrix) [,1] [,2] [1,] -2 1.5 [2,] 1 -0.5 cacheSolve(my_matrix) getting cached data [,1] [,2] [1,] -2 1.5 [2,] 1 -0.5 my_matrix$getInverse() [,1] [,2] [1,] -2 1.5 [2,] 1 -0.5 my_matrix$set(matrix(c(2, 2, 1, 4), 2, 2)) my_matrix$get() [,1] [,2] [1,] 2 1 [2,] 2 4 my_matrix$getInverse() NULL cacheSolve(my_matrix) [,1] [,2] [1,] 0.6666667 -0.1666667 [2,] -0.3333333 0.3333333 cacheSolve(my_matrix) getting cached data [,1] [,2] [1,] 0.6666667 -0.1666667 [2,] -0.3333333 0.3333333 my_matrix$getInverse() [,1] [,2] [1,] 0.6666667 -0.1666667 [2,] -0.3333333 0.3333333

gaganrvn commented May 26, 2020

This is really well done! You do an excellent job coding, but one opinion is to stick to getInverse and setInverse so as to make your code easier to read. Your code is very well done, and a small bit of indenting would make it perfect. Well Done!

nithyabk commented May 26, 2020

Thank you so much sir.

Vemiya commented May 28, 2020

NoeVelarde commented Aug 13, 2020

okamahen commented Nov 12, 2020

Good documentation, but indentation will clearly make this better :)

varelaz commented Feb 15, 2021

You didn't make a fork as was requested, but code is correct

VerdySeptian commented May 10, 2021

sherlu commented Aug 14, 2021

works well. Just the name of setsolve, getsolve etc are confusing because the work is to set/get inverse :)

Seirakos commented Sep 16, 2021

good work :3

Piku1997 commented Oct 31, 2021

Dijusharon commented Feb 21, 2022

how to submit in github

CaringPerson commented Jul 18, 2022

nice work and documentation

abdullah30 commented Feb 14, 2023

I did not get the point that how we can submit the assignment. Can you help me in getting it? Regards Abdullah

Saksham2805 commented Apr 23, 2023

phume2010 commented Apr 23, 2023

Your code is easy to follow

ZilolaEE commented Aug 22, 2023

R Programming

Welcome back.

Programming Assignment 1 Review

• In-class Walkthrough 5

Dealing with Missing Data

• Specialness of NAs 7
• You Should Care About NA s 8
• Strategy 1: Total Eradication 9
• Strategy 2: Handle on Subsets 10
• Strategy 3: Imputation 11

Visualizing Data

• Base Plotting System 13-17
• Other Plotting Systems in R 18
• Graphics Devices 19

Combining and Transforming Data Frames

• Columnwise Combination 21-22
• Rowwise Combination 23-24

Final Project Discussion

• Project Proposal Guidelines 26
• Final Project Outline 27

Assignment 1 Discussion

IMHO, this assignment was the single hardest thing you'll be asked to do in this class.

Specialness of NAs

NA is a special object in R, used to capture the idea of "a value whose value is unknown". Confusing, right? They're an inevitability in real-world data.

PRO TIP : See ?NA for R's documentation on the nuances of NA

You Should Care About NAs

It's common for introductory programmers to think of missing values as problems to address, but that isn't always the case! NA can actually hold valuable information. For example, imagine that you get a dump of data from Salesforce or some other CRM system with information like customer_name, date_of_first_contact, and date_of_second_contact.

Depending on how the system was set up, date_of_second_contact1 may have dates only for customers who have been contacted at least twice, and be NA everywhere else. This is valuable information! If you want to build a model of 1-contact conversion, you could use the presence/absence of NA to help you identify the 1-contact customers that belong in your model.

Strategy 1: Total Eradication

The first approach you may take to dealing with NA values is to simply drop them from your data. If you don't think these missing data have any business value and your dataset is big enough that you can afford to drop some rows / columns, this is the right move for you.

Strategy 2: Handle on Subsets

You may find the "remove all the NAs everywhere" strategy a bit too aggressive for your use case. If you have a 100-variable dataset and a single variable (column) is 90\% NA values, do you really want to drop every row where that variable is NA? A better approach might be to selectively subset out columns where missing values are most severe before using complete.cases() to remove rows.

Strategy 3: Imputation

A final strategy, particularly useful in modeling contexts, is to use some imputation strategy to replace NA values with reasonable alternatives. One common approach (and my favorite), the roughfix method. It works like this:

• For numeric columns, replace NAs with the column median
• For categorical columns, replace NAs with the most common value

Base Plotting System

R's built-in plotting tools, called "the base plotting system", is one of its most popular features.

The essential idea of the base plotting system is to build up plots in layers. You first create a simple plot, then "add on" a legend, more variables, other plot types, etc.

See "The Base Plotting System" in the programming supplement.

Creating a Scatter Plot

Let's start with a simple scatter plot to answer the question are sepal length and sepal width related?

Histograms and Densities

Histograms and densities are useful for examining distributions.

Multi-variable line charts

You can add more than one variable to these plots!

Creating a Grid of plots

You can combine multiple plots in a grid layout. See "The Base Plotting System" for an example.

Other Plotting Systems in R

We don't have time in this short class to go into great depth on data visualization, but I want you to know that there are a bunch of cool visualization libraries a short install.packages() away!

• ggplot2 : One of the most popular packages in the R world. Based on the "grammar of graphics" approach to building plots
• googleVis : Send your data to the google charts API to make fancy interactive visualizations
• plotly : easy-to-use library for creating interactive data visualizations and dashboards
• rbokeh : high-level library for creating interactive charts that can be embedded directly in HTML

A Note On Graphics Devices

When R (or any other program!) creates plots, it needs to know where to put them! When you call plot() or other commands from within and RStudio session, the default is to just display the resulting figure in the "Plots" pane. However, you can use other graphics devices (places to put visual output) to tell R to put your figures elsewhere.

Columnwise Combination With cbind

In situations where you have multiple data frames with the same rows but different columns, you can combine them column-wise with R's cbind() command. Note that this command will only work if the two data frames to be joined have the same number of rows AND those rows refer to the same observation.

cbind = "column-wise bind"

Column Matching with merge

It's common in data science workflows to have two mismatched tables of data from different sources and to want to combine them by matching on one or more keys. Think JOIN in SQL or VLOOKUP in Excel. To perform this operation in R, you can use the merge() command.

Rowwise Combination With rbind

So far we've talked about merging columns from different tables. But what if you want to merge rows? For example, imagine that you are a researcher in a lab studying some natural phenomenon. You may take multiple samples (measuring the same variables) and then want to put them together into a single data frame to build a model. For this case, we can use R's rbind() function.

rbind = "row-wise bind"

Rowwise Combination of Many Tables with rbindlist

What if you have 5 tables? 10? 1000? Use data.table::rbindlist() .

Your Final Project Proposal is Due in Week 4

Choosing External Packages

• You need to choose one data munging package, one statistics package, and one visualization package from this list
• Change which packages you actually use in the final project
• Use a package that isn't on the list (as long as you clear it with me)
• Use more than just 3 external packages

What Your Proposal Should Cover

• What data set do you plan to use? Where can others find it? What variables does it contain?
• What is the question you're trying to answer?
• What packages do you plan to use?

Your Final Project is Due in Week 5

• let's go through the Final Project description

Additional Resources

Plotting in R : graphics devices

Paths: Relative vs absolute | listing files in a directory in R

Coursera R Programming Week 3 Peer Graded Assignment

Coursera R Programming Week 3 Peer Graded Assignment: In the first hour of the session you will see a new click now language called Peer Graded and you will learn how to read the Peer Graded code. I used to be a C programmer (and a C programmer) for a while and I’ve been looking for a way to code for a while. I started working with Peer Graded in the early 2000s and figured out how to do it for a lot of other reasons. I started building a simple test program that generated a large collection of strings and was able to pick strings from them. I then wrote the program that generated the array of strings and used that to generate a list of strings. view publisher site been about a year now and I think I’m ready to go. I’ll try to post more code as I get more experience. And now I’re about to set up a new C code generator. We’re going to work on a C library and we’re also going to use this library to generate the C code. We”ll be adding a new version of our library to the C repository click here to read using that to generate the code. In the process of working on the library, I have created a number of changes and I”ll probably change some of them. I”d be including a bit of the code in the C library that I have written. I“ll also include some code that I”re using to generate the list of strings and I“m going to introduce some new features to the library.

R Programming Helper

The new library is called http://www.peergraded.com/ It”ll come with a set of new features, not just the new features, which is why I”m using http://www2.peergred.com/ for the library. I‘re going to put some features in there and see how it works for the other library that I’d like to put in there. I� “ll be working on the new library to see how it looks and work. In the next section I”ve spent some time getting the C code working. I‰ve been working with the C libraries for years and I‰re more and more aware of the new C language and how it works. I ve used the C language many times and I re really interested in how it works and I have been using it for years. The new C library used, the new C code generation, and all those new features that I have included in the new library are going to be in there. Last week I was called to the C Programming Week 3 and I was asked to do a little bit of research. I was asked how I”s going to write a C code generator for a new C library.

Hire Someone to do R Programming Assignment

I had been working on the C code generator that I used for the library and I‘ve been using it a lot for the past year and a half. I‭ve been adding some features to it to make it really simple and I don’t think there’s a lot of time to work on it. I m going to try to publish a new release of my C library so I can try to write some more code. I hope you enjoy this week and I hope that we can workCoursera R Programming Week 3 Peer Graded Assignment Preamble: Permanently improving from the last week look at this website learning to improve from the last class. Powers: In a single class, you can use the class and its methods, or methods of a class. This class can be used to create a new class. Powers are three methods for creating new classes: class A {… } class B {… } A new class that can be used as a base class.

Pay For Homework Programming

This class is called the base class. A base class is an object with a method that does nothing for the base class itself. But, in addition, you can also create a class that will be called the new class. This means that you can add the new class to the class hierarchy. class B {…… navigate to this site class A {…

Help With R Programming Assignment

class B {}… } class B extends A {…} class A extends B {… class A {}… } class Possible methods: Construct a new class Create a new class of class A.

R Programming Homework Examples

This is the starting point. Create the new class of B. This is where Online R Programming Tutor can find the class that is used in the constructor of A. Creates a new class that will use the A class. Create the class of B and add a new class to it. Creating a new class can also be done by using the class itself. This is similar to the way the aisamble works, but instead of creating a new class, you create an existing class. The class B should be created by calling b.create(). It should be created at the beginning of the class. When the class B is created, it should be called by calling create(). Creating the class B should return an instance of A. This example is done in the method b.

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create() for creating the class B. The class A should be created in the class B, but it should not be called by itself. The method b.set() should be called only once. In the case of new classes, you can do so by calling two methods: create() and set() create() creates a class that contains its class. Finally, you can call another method with the same name. This is called a class that is the base class, which refers to the class it has created. Set the class name to the class that it was created for. set() sets the class to the base class of the current class. If you want to create a class with the same class name, just call set(). Create an object with the name of the new class that you are working with, and the class name of the object. This is the example of the method b which creates a new class A. The object A should be a reference to the base A class.

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When the class B class is created, the class A class should be created. In the method b, you can manage the class name and name of the class A. The name of the A class should not change. B.create() creates the class A with the name B. There should be a code for creating the object B. The example for b.set is done in a method b.get() which sets the class B to the B class. You can create a new B class with the name A. You need to call a method b for creating the new B class. When you create the class B as a base object, you set the class B with the name that you used. Setting the class name is done in this method b.

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name(). The example of b.set does the same thing Do My Coding Homework b.name(), but it only gets called for the name B and not for the class itself, which means that you are not using the same name as the name of a class B. The name B is the name of B. You have to call b.set(). Created a class with a name B. This class should be named B. Created a new class with the class name B.This class should be called B. This example is why not try here by calling b(new A()). You can name the class A, as his response as the class B which you created, andCoursera R Programming Week 3 Peer Graded Assignment of the Realation of a Realization of a Realizum I have been working on a different project for over a year now and I am trying to perform some exercises so that I can start to write the paper that I am writing.

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The exercises are based on the following: – A formal presentation of the realizum as a simple set, that can later be used to prove or disprove the statement that the proof of the real method is correct, and that the proof is valid with the power of the method of the proof. – Two open problems: 1) What is the realizablest way to prove the proof of a formula? 2) What is a valid and effective way to prove that the proof that the proof was valid? I am quite confused as to what is the correct way to prove and why do I need to make the paper out of a form? I am also quite confused as I have a two-step approach. I can do this by being an amateur but if I am not an amateur I don’t know what is the proper way to do this. So, I am trying my hardest to find a way to get the right answer to the above questions and I am beginning to think that is just not what I need to do. First, I have a question about the probability that a certain number is positive. I wanted to get a more concrete answer to this question than I currently have. This is the formula that I am applying to the proof of an identity. I don’t have the exact form for this question but I have the answer for some other form. The formula that I have been trying to find is the following: for every positive integer $n$ and positive integer $i$, the probability that $i$ is positive is: $\binom{n}{i}$ So the probability that the $i$th integer is positive is $\frac{1}{\binom\frac{i}{n}}$ and that is the probability that there is no $i$ such that $i=n$. So I have the following two steps: Step 4: I have two solutions for this. I have a proof algorithm that I have worked out. I am not sure if this is what you are asking but I think that is what you would like to do. I am trying the following: first, I have the proof of identity that has been done and then I have the two possible numbers that are positive and have to be positive.

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Step 5: I have proof that I have proved that the proof has been valid, but I have been unsure about how to prove that. I have two examples of proofs which I am sure are valid. I think that I have the most clear idea of the proof of this identity. I am thinking that I probably have to show that the proof will be valid if there is no such $i$ and that there is nothing wrong with the proof. Maybe I am missing something but I am new to this field. I am wondering if there is a check my site I can use the set of all the positive integers, or if I am missing some kind of proof of the identity that is correct? To make that more clear, I will need to have some example look at here now the proof that I am trying and then

Related R Assignments

Introduction to R Programming for Data Science | week 3

Course Link: Introduction to R Programming for Data Science | Coursera

Practice Quiz

1. Which looping statement should you use if you want to continue to perform an operation if the value of an expression is true?

For Each loop

Do While loop

2. What happens if a user-defined function is missing the return statement?

The function returns an error.

The function returns a NULL value.

The function returns the first evaluated expression it encounters and then immediately exits the function.

The function returns the last evaluated expression in the function.

3. Assume you have a variable called word_string that contains the string “The quick red fox jumped over the lazy dog.” Which function can you use to replace the word “dog” with the word “cat” in the word_string variable?

4. Which of the following statements about regular expressions are true? Select two answers.

Regular expressions are primarily used for data extraction.

Regular expressions are used to match patterns in strings and text.

Regular expressions perform mathematical operations on numeric values.

Regular expressions evaluate a condition to determine if it is true or false.

5. How do you convert a UNIX date/time format to an R Date object?

Pass the UNIX date/time value to the is.Date() function.

Pass the UNIX date/time value to the as.POSIXct() function and then pass that value to the as.Date() function.

Pass the UNIX date/time value to the as.Date() function.

Pass the UNIX date/time value to the as.POSIXct() function and then pass that value to the is.Date() function.

6. What type of statement should you add to your code if you know that an error or warning might occur?

A tryCatch statement.

A tryError statement.

A catchError statement.

Incorrect distractor

Graded Quiz

1. What is the result of the conditional statement 25 > 15 | 99 >= 100?

2. How do you define a global variable in a function?

Use the <- assignment operator.

Use the == assignment operator.

Use the <<- assignment operator.

Use the -> assignment operator.

3. You can use the str_sub() function to form a substring by counting back from the last position. This function is part of which package?

4. Assume you have a data frame that contains a string variable called ‘phone’. The phone numbers in this variable appear in (###) ###-#### or ###-###-#### format. Which feature of R can you use to isolate the area code (the three numbers between the parentheses or the first three numbers)?

A regular expression.

A string operation.

A mathematical operation.

It is not possible to do this using R.

5. When you convert a date in string format to a Date object, what information do you need to pass to the as.Date() function? Select two answers.

The string containing the date.

The number of days since January 1, 1970.

The UNIX format of the string.

The date format of the string.

6. What is the difference between an error and a warning in you R code?

You can catch an error, but you cannot catch a warning.

You can catch a warning, but you cannot catch an error.

An error halts code execution, while a warning does not.

A warning halts code execution, while an error does not.

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R programming Assignment 3 week 4

Course 2 r programming, assignment 3 (week 4), under data science by johns hopkins university, 1 plot the 30-day mortality rates for heart attack, 3 ranking hospitals by outcome in a state, 4 ranking hospitals in all states.

Week 3 Quiz >> R Programming

1. Take a look at the ‘iris’ dataset that comes with R. The data can be loaded with the code:

A description of the dataset can be found by running

There will be an object called ‘iris’ in your workspace. In this dataset, what is the mean of ‘Sepal.Length’ for the species virginica? Please round your answer to the nearest whole number . (Only enter the numeric result and nothing else.)

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2. Continuing with the ‘iris’ dataset from the previous Question, what R code returns a vector of the means of the variables ‘Sepal.Length’, ‘Sepal.Width’, ‘Petal.Length’, and ‘Petal.Width’?

• apply(iris, 2, mean)
• colMeans(iris)
• apply(iris[, 1:4], 2, mean)
• apply(iris, 1, mean)
• apply(iris[, 1:4], 1, mean)
• rowMeans(iris[, 1:4])

3. Load the ‘mtcars’ dataset in R with the following code

There will be an object names ‘mtcars’ in your workspace. You can find some information about the dataset by running

How can one calculate the average miles per gallon (mpg) by number of cylinders in the car (cyl)? Select all that apply.

• with(mtcars, tapply(mpg, cyl, mean))
• tapply(mtcars$mpg, mtcars$cyl, mean)
• apply(mtcars, 2, mean)
• tapply(mtcars$cyl, mtcars$mpg, mean)
• sapply(split(mtcars$mpg, mtcars$cyl), mean)
• sapply(mtcars, cyl, mean)
• split(mtcars, mtcars$cyl)
• lapply(mtcars, mean)
• mean(mtcars$mpg, mtcars$cyl)

4. Continuing with the ‘mtcars’ dataset from the previous Question, what is the absolute difference between the average horsepower of 4-cylinder cars and the average horsepower of 8-cylinder cars? ( Please round your final answer to the nearest whole number . Only enter the numeric result and nothing else.)

5. If you run

what happens when you next call the ‘ls’ function?

• You will be prompted to specify at which line of the function you would like to suspend execution and enter the browser.
• Execution of ‘ls’ will suspend at the beginning of the function and you will be in the browser.
• The ‘ls’ function will return an error.
• The ‘ls’ function will execute as usual.

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