Vectorized Operations

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En provenance du cours de Johns Hopkins University

R Programming

12282 notes

Johns Hopkins University

R Programming

12282 notes

Cours 2 sur 10 dans Specialization Data Science

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.

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.

Rencontrer les enseignants

Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health

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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