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Vectorized Operations

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La programmation en R

Université Johns-Hopkins

4.6 (15,530 évaluations) | 430K étudiants inscrits

Cours 2 de 5 dans Data Science: Foundations using R Spécialisation

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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Compétences que vous apprendrez

Data Analysis, Debugging, R Programming, Rstudio

Avis

4.6 (15,530 évaluations)

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WH

Feb 03, 2016

"R Programming" forces you to dive in deep.\n\nThese skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!

AK

May 27, 2017

This was very engaging, however, the level of expectation and effort needed is much greater than course 1 - ToolBox.\n\nThis is perhaps the best course on R Programming designed for a small duration.

À partir de la leçon

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.

Introduction1:20

Overview and History of R16:07

Getting Help13:53

R Console Input and Evaluation4:46

Data Types - R Objects and Attributes4:43

Data Types - Vectors and Lists6:27

Data Types - Matrices3:24

Data Types - Factors4:31

Data Types - Missing Values2:10

Data Types - Data Frames2:44

Data Types - Names Attribute1:49

Data Types - Summary0:43

Reading Tabular Data5:51

Reading Large Tables7:08

Textual Data Formats4:58

Connections: Interfaces to the Outside World4:35

Subsetting - Basics4:13

Subsetting - Lists4:45

Subsetting - Matrices2:50

Subsetting - Partial Matching1:42

Subsetting - Removing Missing Values3:59

Vectorized Operations3:46

Enseigné par

Roger D. Peng, PhD
Associate Professor, Biostatistics
Jeff Leek, PhD
Associate Professor, Biostatistics
Brian Caffo, PhD
Professor, Biostatistics

Transcription

This is just a simple note on vectorized operations in R. So, vectorized operations, is one of the features of the R language that make it, that makes it easy to use, on the command line. It makes very, kind of, nice to write code, without having to do lots of looping, and things like that. And so, it's kind of a natural thing to have in a computational language. Many other types of languages, like MATLAB have this kind of feature. so, the idea with vectorized operations is, is that things can happen in parallel, when you, for example want to do a computation. For example, suppose I got two vectors here x and y. x is the sequence one through four and y is the sequence six through nine. And I want to add the two vectors together. Now, when I say I want to add them, what I mean is I want to add the first element of x to the first element of y, the second element of x to the second element of y, et cetera, the third element to the third element. So I want to kind of do things in parallel like that. So, in other languages you might have to use a loop to do that, so you'd loop through each element and kind of add them one by one. But in R you can just use the plus to, on the two vectors, and it'll just add them together so x plus y kind of does what you would expect. It adds 1 to 6, 2 to 7, 3 to 8, and 4 to 9, so you get the vector 7, 9, 11, 13. similarly, you can use the greater than, or less than symbols to, give you logical vectors. For example, x greater than 2. So well x is actually a, a vector of 4 numbers. So, which one, so, which number are you comparing to 2? Well, the, the vectorized operation compares all the numbers to 2, and it gives you a vector of falses and trues depending on which numbers happen to be bigger than 2. So you can also use greater than equal to, and that'll tell you which numbers are greater than and equal to 2, and the double equals sign, tests for equality. So it'll take each element of y and test to see whether it's equal to 8. other, and the other kind of, or arithmetic operations like multiplication, by the asterisk, and division, by the solidus, are all vectorized types of operation. So when you want to multiply or divide, add, subtract, vectors, you just you can do the natural thing, just add them together or multiply them together, and they will be, and they will be the operation will be done in parallel. Similarly you can do, you can do You can add make, you can add and subtract, and multiply and divide matrices together. So it's useful to know this because there are different types of mult matrix multiplication. So I've created two matrices here, x and y. X is the matrix 1 through 4, it's a two by two matrix. And y is a, is a matrix that's all tens, it's also a two by two matrix. So if I just do x times y, this is not a mat, matrix multiplication. This is an element-wise multiplication. So the first, the kind of the 1, 1 element of x is multiplied by the 1, 1 element of y. And the 2,2 element is multiplying the 2,2 element of, of the other matrix, et cetera. So, each element is multiplied, together in parallel. Same when you do division. This is not a matrix inverse or something like that, this is just dividing one matrix, literally element by element by another. So if you want to do a true matrix multiplication, you have to use the %*%, that's the symbol for a matrix multiplication. So when you, so, this is just, that's it for vectorized operations for now, You'll see these a lot more often later on, but the idea, but I just wanted to introduce this idea, because it makes code easier to write. And for those of you who are kind of used to other types of programming languages, if you've programmed in languages where you can't do this kind of thing, it's sometimes, it's common to kind of reflexively go to something like a for loop or a while loop or whatever it is. But in the but in a language like R, you can just use the vectorized operations to make the code a lot simpler.

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