R基礎要素

この章の主な内容は、ベクトル、欠落値、マトリクス、インデックスです.主な内容は英語で書かれていて、筆者が勉強しながら整理したもので、理解に難易度はないはずです.Rの基本的な賦値と4則の演算文法を身につけた後に学習することを提案して、Rの中の基本的な演算単位を理解します.
Sequences of number paste(my_name,collapse = " ") paste("Hello", "world!", sep = " ") the first line of code will join the strings in my_name into one strings.and the second line will join different strings from different vectors into one.
Vector
Vectors come in two different flavors: atomic vectors and lists. An atomic vector contains exactly one data type, whereas a list may contain multiple data types. We'll explore atomic vectors further before we get to lists. In previous lessons, we dealt entirely with numeric vectors, which are one type of atomic vector. Other types of atomic vectors include logic, character, integer and complex. In this lesson, we'll take a closer look at logical and character vector.
Logical vectors can contain the values TRUE, FALSE, and NA (for 'not available'). These values are generated as the result of logical 'conditions'.
The < and >= symbols in these examples are called 'logical operators'. Other logical operators include > , <= , == for exact equality, and != for inequality.
If we have two logical expressions, A and B, we can ask whether at least one is TRUE with A | B (logical 'or' a.k.a. 'union') or whether they are both TRUE with A & B (logical 'and' a.k.a. 'intersection'). Lastly, !A is the negation of A and is TRUE when A is FALSE and vice versa(逆も同様).
Missing Value
Missing values play an important role in statistics and data analysis. Often, missing values must not be ignored, but rather they should be carefully studied to see if there's an underlying pattern or cause for their missingness. In R, NA is used to represent any value that is 'not available' or 'missing' (in the statistical sense). In this lesson, we'll explore missing values further. NA
which stands for 'not avaliable' to find NA in variable(let's take it as x), we have is.na(x) Everywhere you see a TRUE, you know the corresponding element of x is NA. Likewise, everywhere you see a FALSE, you know the corresponding element of x is a non-NA. NA is not really a value, if we code x == NA all we have can only be--R has no choice but to return a vector of the same length as x that contains all NAs. NAN
which stands for 'not a number', such as 0/0 or Inf-Inf and so on (Inf means infinity)
Subsetting Vectors
In this lesson,we'll see how to extract(取り出し)elements from a vector based on some conditions that we specify.
For example, we may only be interested in the first 20 elements of a vector, or only the elements that are not NA, or only those that are positive or correspond to a specific variable of interest. By the end of this lesson, you'll know how to handle each of these scenarios.
The way you tell R that you want to select some particular elements(i.e.a'subset')from a vector is by placing an'index vector'in square brackets(かっこ)immediately following the name of the vector.For example, try x[1:10] to view the first ten elements of x
As already shown, to subset just the first ten values of x using x[1:10]. In this case, we're providing a vector of positive integers inside of the square brackets, which tells R to return only the elements of x numbered 1 through 10.
Many programming languages use what's called 'zero-based indexing', which means that the first element of a vector is considered element 0. R uses 'one-based indexing', which (you guessed it!) means the first element of a vector is considered element 1.
Index vectors come in four different flavors -- logical vectors, vectors of positive integers, vectors of negative integers, and vectors of character strings -- each of which we'll cover in this lesson.
indexing with logical vectors
Let's start by indexing with logical vectors. (情景)when working with real-world data is that we want to extract all elements of a vector that are not NA(i.e.missing data).Recall that is.na(x) yields a vector of logical values the same length as x, with TRUEs corresponding to NA values in x and FALSEs corresponding to non-NA values in x.
To view all the "NAs"in x x[is.na(x)] To view all the "non-NAs"in x, and put them in y. Then to find the positive values in y.

y 0]

But if we try x[x>0 directly ,we get a bunch of NAs mixed in with our positive numbers since "NA"is not really a number.
So, what about x[!is.na(x)&x>0] , it's the same with y[y>0] .
positive integer, and negative integer
To subset the 3rd, 5th, and 7th elements of x x[c(3,5,7)]
It's important that when using integer vectors to subset our vector x, we stick with the set of indexes {1, 2, ..., 40} since x only has 40 elements or we get only "NA"which might be useless in most cases.
by x[c(-2,-10)] or x[-c(2,10)] we can get all elements of x EXCEPT for the 2nd and 10 elements.
Named Elements
To create "named"vectors

vect 

now we have both vect and vext2, use identical(vect, vect2) to see if they were the same.
To Find Elements by Name
such as by using vect[c("bar","foo") ] we get 2 11 .
Matrices and Data Frame
Matrices:only contain a single class of data Data Frame:consist of many different classes of data
Matrix dim(x) :to view the dimensions of x To give a vector dim, we have dim(x) this will turn a vector to a matrix.
use class(x) to say the type of x.
Instead of what we mention above, we can create matrix directly by matrix(data, nrow, ncol)
use the cbind() function to 'combine columns'.
data frame
To create a data frame, use my_data
where patients is a vector and my_matrix is matrix in this case.

cnames 

we can simply assigning names to the columns of our data frame.
Logic
Logic Operators
Creating logical expressions requires logical operators. You're probably familiar with arithmetic operators like + , - , * , and / . The first logical operator we are going to discuss is the equality operator, represented by two equals signs == . <= : less than or equal to < :less than >= :greater than or equal to > :greater than != :not equal to & and &&

TRUE & c(TRUE, FALSE, FALSE)
TRUE && c(TRUE, FALSE, FALSE)

use the & operator to evaluate AND across a vector. The && version of AND only evaluates the first member of a vector.
the **Or operator ** | and || work just like the 'and' operator but with a different meaning.
arithmetic has an order of operations and so do logical expressions. All AND operators are evaluated before OR operators. Let's see 5 > 8|| 6!=8 && 4>3.9 this gonna return to a "TRUE"
function isTRUE(x) will tell us if x is true identical() will return TRUE if the two R objects passed to it as arguments are identical. xor() function stands for exclusive OR. If one argument evaluates to TRUE and one argument evaluates to FALSE, then this function will return TRUE, otherwise it will return FALSE.
now we use ints
to create a vector which contains a random sampling of integers from 1 to 10 without replacement.
then which(ints > 7) will give us the indices( )to the elements which are greater 7 in ints.any() function will return TRUE if one or more of the elements in the logical vector is TRUE. all() function will return TRUE if every element in the logical vector is TRUE. Let's try

any(ints < 0)
all(ints > 0)