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s<-as.character(a)	take a Convert to string type and assign to s,a The type of itself remains unchanged
a<-as.character(a)	take a Convert to string type and assign to a,a Its own type becomes a character variable

Two 、 vector

Vectors use <-c To assign a value , among , The separator for each element of the vector is “,”

num<-c(9,2,-3,5,6,7,8,9)	Numerical vectors
ord<-c("a","wdw","23")	Character vector
logv1<-c(T,T,T,TF,F,F)	Logical vector
logv2<-c(True,TRUE,FALSE)	Logical vector

1. Generate regular vectors

Use it directly a:b In the form of , Generated from a To b Sequence of numbers , If a>b One by one +1, Otherwise, one by one -1

From our code below, we can see that our colon has a higher priority than × and － high

2. Use seq(from,to,by) Generating vectors

The first parameter is the starting value , The second parameter is the termination value , The third number is the step size

Of course, we can also directly set the length of the entire vector to generate our vector

3.rep(x,...) Generate repetition vectors that meet the conditions

rep(1,times=10)
x <- c(1,2,3,4)
rep(x,times=3)
rep(x,length.out=10)
rep(x,each=2)
rep(x,times=c(1,2,2,3))
rep("abc",5)
y <- c("a","b","c")
rep(y,2)
rep(y,length.out=10)
rep(y,times=c(1,2,3))

4. Generation of logical vectors

z <- c(8,3,5,7,6,2,8,9)
n <- z > 5
# Judge z Are all elements in greater than 5
all(z>5)
# Judge z Is there any element greater than 5
any(z>5)
# We can see from the following results , our R The following table of language arrays is from 1 At the beginning 
z[2]
z[1:4]
z[c(2,5,7)]
#-3 Indicates that the third element is not taken 
z[-3]
# The second element and the fourth element do not take 
z[c(-2,-4)] 
# The same effect as the previous code , Both the second element and the fourth element do not take 
z[-c(2,4)]

3、 ... and 、 matrix

① A matrix is a two-dimensional array
② The elements in the matrix must be of the same type
③ Generating a matrix requires a function matrix() Realization
mymatrix <- matrix(vector,nrow = number_of_rows,
                                ncol = number_of_columns,
                                byrow = logical_value,
                                dimnames = list(char_vector_rownames, char_vector_colnames))

From the following run results , We'll find out about us R The matrix in the language is placed in the form of columns by default .

A <- matrix(1:20,nrow=5)

Of course, our matrix can also be generated by the following method

values <- c(1,2,3,4)
# Create row index vector 
rnames <- c("r1","r2")
# Create column index vector 
cnames <- c("c1","c2")
# Specify our matrix , Take our values respectively , The number of rows is passed in ,
#byrow The default is FALSE, It means to place by column 
# If TRUE It is placed according to rows 
#dimnames You can specify the names of our columns and rows 
C <- matrix(values,nrow=2,byrow=T,
            dimnames=list(rnames,cnames))
C

# If we were to byrow The parameter of is adjusted to F Will become the following form 
D <- matrix(values,nrow=2,byrow=F,
            dimnames=list(rnames,cnames))
D

The first line of code is to find the data in the first row and the second column

The second line of code is to find all the data in the first line

The third line of code is to find the data in the second column

C[1,2]
C[1,] 
C[,2]

# Find us A Second line in , The values corresponding to the focus of the second and fourth columns 
A[2,c(2,4)] 
# Find us A The values corresponding to the focus of the first and third rows and the third and fourth columns 
A[c(1,3),c(3,4)]
# see A Dimension of 
dim(A) 
# see A The number of rows 
nrow(A)
# see A Columns of 
ncol(A) 
# see C The index of the matrix , Return the row index first , Then return the column index 
dimnames(C)
# see A The index of the matrix , Return the row index first , Then return the column index  
dimnames(A)
# Set up A Row and column index 
dimnames(A) <- list(c("one","two","three","four","five"),
                    c("I","II","III","IV"))
dimnames(A)
A

Straightening of matrix

# Take us c Cast type to vector , And then assign it to our horz, That is to straighten our matrix 
# As can be seen from the following running results , The way we straighten is by column 
horz <- as.vector(C)
horz
C

# Generate E matrix , The number of rows is 2
E <- matrix(c("a","b","c","d","e","f","g","h"),nrow=2)
E
# Generate G matrix , The number of columns is 2
G <- matrix(c("i","j","k","l","m","n","o","p"),nrow=2)
G
# Vertical splicing 
rbind(E,G) 
# Transverse splicing 
cbind(E,G) 
# Generate H matrix 
H <- matrix(1:6,nrow=2)
H
# Generate J matrix 
J <- matrix(5:8,ncol=2)
J
# Here we can find , If we have different columns , We can't splice vertically 
rbind(H,J)
# Here we have the same number of lines , So it can be spliced horizontally 
cbind(H,J)
#
cbind(E,H)

Four 、 Array

① Arrays are similar to matrices , But the dimension must be greater than 2
② The elements in the array must be of the same type
③ The way to select the uniform velocity in the array is the same as that in the matrix
④ Arrays can be passed through functions array establish
General form
myarray <- array(vector, dimensions, dimnames)

dim1 <- c("A1","A2")
dim2 <- c("B1","B2","B3")
dim3 <- c("C1","C2","C3","C4")
dim1
dim2
dim3

# Will we from 1-24 The data is divided into four matrices with two rows and three columns , The indexes are dim1,dim2,dim3
exarray <- array(1:24, c(2,3,4),
                 dimnames=list(dim1,dim2,dim3))
exarray
# Here we take out the data of the first row and the second column in the third matrix 
exarray[1,2,3]

5、 ... and 、 Data frame

A data frame is a class matrix composed of different types of data columns , It can be understood as a loose data set
Create instructions mydata<-data.frame(col1,col2,col3,……)

ID <- c(1,2,3,4)
age <- c(25,34,28,52)
status <- c("Poor","Improved","Excellent","Poor")
diabetes <- c("Type1","Type2","Type1","Type1")
patientdata <- data.frame(ID,age,diabetes,status)
patientdata

# Extract our patientdata The first and second columns in 
patientdata[1:2]
# Of course, we can also specify the index name to be searched as a specific object 
patientdata[c("diabetes","status")]
# Direct access to patientdata Medium age The elements of the column 
patientdata$age
# Modify the data of a column 
patientdata$age[2] <- 33
patientdata$diabetes[3] <- "Type2"

Now our data table has been modified to look like the following

6、 ... and 、 factor

factor ： Categorical variables and ordered variables are in R It's called a factor
Categorical variables ： Also known as nominal variable , The value of a variable has no size relationship , There is no sequence relationship , Just like type I and type II of cases in the above table
Ordered variable ： There is a certain order relationship between the values of variables , But there is no quantitative relationship , As in the code above status Evaluation in （poor,Improved,excellent）
function ：factor(), Store category values as an integer vector , The value range of an integer is [1,k],k Is the number of horizontal values .

#diabetes What is stored here is the type of our case , The default is no order 
diabetes <- factor(diabetes)
#status What is stored here is the therapeutic effect , And it's sequential , But the default sort is in character order 
status <- factor(status, ordered=T) 
status
# Of our function level The order of can also be specified by us 
status <- factor(status, ordered=T,
                 levels=c("Poor","Improved","Excellent"))
diabetes
status

7、 ... and 、 list

Lists can be viewed as objects （R Any data structure in ） Ordered set of
Create a list
mylist <- list(object1, object2,......)
Create a list , And name each object in the list
mylist <- list(name1=object1, name2=object2,......)

g <- "my list"
h <- c(25,26,18,39)
j <- matrix(1:10,nrow=5)
k <- c("one", "two", "three")
mylist <- list(title=g, ages=h, j, k)
mylist

Get the second element in the list

mylist[[2]]

# In the following code , We created a list 
# The first element is ID
# The second element is the name 
# The third element is a matrix , And the matrix is twoorthree lines , And the incoming data is arranged in rows , Not by column first 
# And specify the list names of the index of our dimension as semester name and subject name respectively 
stu <- list(ID="12345678",name="Ün",
            grade=matrix(c(99,96,88,92,93,87),nrow=2,byrow=T,
                         dimnames=list(c(" The first semester "," The second semester "),
                                       c(" Mathematical analysis "," Advanced algebra "," probability theory "))))
stu

# View... In the list ID Elements 
stu$ID
# View the name element in the list 
stu$name
# Look at the second element in our list , Here the subscript of the list is from 1 At the beginning , Not from 0 At the beginning , That is our name 
stu[[2]]
# Check out our score elements 
stu$grade
# ditto 
stu[[3]]
# Look at the elements in the second row and the third column of our grade elements 
stu[[3]][2,3]
# View the data of the first row and the first column in the grade element 
stu$grade[1,1]
# Replace our name element with Li Si 
stu$name <- " Li Si "
# Change the data of the position of the first row and the second column of the grade element to 78
stu$grade[1,2] <- 78
stu