coursera r programming week 3 peer graded assignment

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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

markarecio commented Jan 18, 2024

Data Visualization Week 3 Programming Assignment 2

An information flow model for conflict and fission in small group.

This file implements Programming Assignment 2: Visualize Network Data for the Coursera Data Visualization Class.

This assignment uses R Programming Language to visualize data.

Q1. What is the data that you chose? Why?

Data from a voluntary association are used to construct a new formal model for a traditional anthropological problem, fission in small groups. The process leading to fission is viewed as an unequal flow of sentiments and information across the ties in a social network. This flow is unequal because it is uniquely constrained by the contextual range and sensitivity of each relationship in the network. The subsequent differential sharing of sentiments leads to the formation of subgroups with more internal stability than the group as a whole and results in fission.

Q2. Did you use a subset of the data? If so, what was it?

Ans: No I used the full data set as available from this link.

Q3. Are there any particular aspects of your visualization to which you would like to bring attention?

To improve the visualization and make it more clear:

• Colors (Hue,): I’ve used colors to indicate which conferences the nodes belong to. The colors corespondent to the colors as seen in the legend on the left.
• Clustering: All nodes are clustered.

The chart is made interactive:

• Hovering a node will highlight and color the links to it’s linked nodes.
• Hovering a node will temporary enlarge a node.

Q4. What do you think the data and your visualization show?

An edge is drawn if two individuals consistently were observed to interact outside the normal activities of the club (karate classes and club meetings). That is, an edge is drawn if the individuals could be said to be friends outside the club activities. All the edges in Figure 1 are non-directional (they represent interaction in both directions), and the graph is said to be symmetrical.

Loading the Data

The Data is obtained from University of Michigan Network Data: Zachary’s karate club social network of friendships between 34 members of a karate club at a US university in the 1970s. Please cite W. W. Zachary, An information flow model for conflict and fission in small groups, Journal of Anthropological Research 33, 452-473 (1977).

Loading the packages which will be used in analyzing the data

Convert the igraph data into something more suitable for networkD3

networkD3 requires edge references to nodes start from 0.

Create the interactive D3 plot.

Figure 1: This is the graphic representation of the social relationships among the 34 individuals in the karate club. A line is drawn between two points when the two individuals being represented consistently interacted in contexts outside those of karate classes, workouts, and club meetings. Each such line drawn is referred to as an edge.

Note: You can Zoom with the scroll wheel.

• Coursera 'Data Visualization in R with ggplot2' course by Johns Hopkins University: Week 3 Peer Review
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Peer assignments not getting graded

Hi. I'm currently doing a course on responsive website development. While in the first 2 parts everything was going smoothly, now in the 3rd part my peer reviewed assignments aren't getting graded.

I've submitted 4 assignments way before the "deadline", it's been at least a week and not even the first assignment has been graded, even though I asked for reviews in the discussion section. I've already reviewed others work. The date in the "your assignment should be graded by" part also changes and gets pushed back each day.

How long does it take to get graded? What happens if I've finished everything else but can't get thw certificate cause the assignments aren't getting graded? Do I contact coursera? I really don't wanna waste money here.

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