Subsetting - Basics

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來自 Johns Hopkins University 的課程

R Programming

11735 個評分

Johns Hopkins University

R Programming

11735 個評分

課程 2（共 10 門，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.

從本節課中

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.

與講師見面

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

[NOISE] I'm going to continue to talk about data types, and

basic operations in R.

In particular in this video I'm going to talk about subsetting objects in R.

So there are a couple of different operators that you can use,

to extract subsets of diff, of R objects.

There's the single bracket.

Sorry, the single square bracket.

The double square bracket,

which we saw in the previous video, and there's the dollar sign.

So the sing, the basic kind of principles to remember here is that the single

square bracket always returns an object of the same class as the original.

So the subset a vector, you're going to get back a vector.

If you subset a list, you're going to get back a list.

Any time you used the single bracket operator to subset an object,

you'll get the same, an object of the same class back.

si, furthermore the single bracket operator can be used to

select more than one element of an object.

With one ex, exception that we'll get to later.

But double bracket operator is used to extract elements of a list or

a data frame.

It can only be used to exa, extract a single element and.

Of that object, either the list or the data frame.

And the class of the returned object will not necessarily be a list or a data frame.

So the idea with the double bracket operator is that, remember that lists can,

can, can hold things that are of many different classes.

They don't all have to be the same.

So, the first element might be a vec a numeric vector, the second element might

be a data frame, the third element might be a complex vector, et cetera.

And so when you use the double bracket operator to extract an element of a list,

the oh, the object that comes back maybe,

may not be a list, it may be an object of a totally different class.

So that's what the double brack operator is useful for.

The dollar sign is used to extract elements of a list, again of a list or

data frame that have a name.

Very similar objects can have names and the reason,

one of the reasons you've used names in an object is so

that you can reference elements of the object by the different names.

Otherwise the, the semantics of the dollar sign are similar to the double bracket in

the sense that when you use the dollar sign to extract an element of

an object it may or may not be of the same class as the original object.

So, here is the first,

the first example, a very simple vector, a character vector called x.

And and

I'm going to use the single bracket operator to extract the first element.

So here, what I get back is a,

is another character vector with the single element a in it.

If I, if I use, if I try to extract the second element of x,

what I would get returned back to me is a character vector with the element b in it.

I could also extract a sequence of elements so

if I say, If I, If I want to get the first four elements of x I can cre,

construct the sequence one through four and then I get a, b, c, c.

So in these three examples here what I've done is I,

I, is I subset the vector x using a numeric index so the numeric index is one,

two or the sequence one through four.

The oth, another type of index that you can use is the, is a logical index.

So, in this next example here, I'm going to subset the vector x and I want,

I only want all the elements were, that are greater than or

equ, sorry, that are greater than the letter a, right?

So, you might, it might seem strange to you that

I'm using the greater than sign with letters instead of numbers but

there is a lexicographical ordering to the letters, and

all the letters that are greater than a are letters like b, c, d, e, et cetera.

So what I get returned to me is a character vector that only

contains the letters that are greater than a.

So, here I've got b, c, c, and d.

The other thing I can do, is I can create a logical vector,

which here I call u, which is just the it's a, it's, it tells it's a true or

false vector, which tells me,

which tells me which elements of the vector x are greater than a.

So, if I print out u here I can see that the, the first element is equal to a, so

it's not greater than a.

Then, the next four are greater than a,

but then the last element is equal to a, so again, that's false.

And so, I can subset the vector x with this u vector, and

then I get out all the elements that are greater than a.

So there are two types of indices that I use here,

one, the first type with the numeric index.

And the second type was the logical index.

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