Note:vectors are one-dimensional arrays,scalars are one-element vectors.
创建use c() function
a<-c(1,2,3) #a numeric vector b<-c("one",'two","three") # a charactor vector c<-(TRUE,TRUE,FALSE) # a logical vector) f<-3 # scalars are used to hold constants(常量)mymatrix<- matrix(vector,nrow=number_of_rows,ncol=number_of_columns,byrow=logical_value,dimname=list(char_vector_rownames,char_vector_colnames)) 默认：byrow=FALSE
> y<-matrix(1:20,nrow=5,ncol=4) > y [,1] [,2] [,3] [,4] [1,] 1 6 11 16 [2,] 2 7 12 17 [3,] 3 8 13 18 [4,] 4 9 14 19 [5,] 5 10 15 20 > cells<-c(1,2,3,4) > rnames<-c("R1","R2") > cnames<-c("C1,","C2") > mymatrix<-matrix(cells,nrow=2,ncol=2,byrow = TRUE,dimnames=list(rnames,cnames)) > mymatrix C1, C2 R1 1 2 R2 3 4Arrays are similar to matrices but can have more than two dimensions. Like matrices,they must be asingle mode. - 创建
myarray<-array(vertor,dimensions,dimnames)
dimensions is a numeric vector giving the maximal index for each dimension dimnames is an optional list of dimension lables
> dim1<-c("a1","a2") > dim2<-c("b1","b2","b3") > dim3<-c("c1","c2","c3","c4") > z<-array(1:24,c(2,3,4),dimnames = list(dim1,dim2,dim3)) > z , , c1 b1 b2 b3 a1 1 3 5 a2 2 4 6 , , c2 b1 b2 b3 a1 7 9 11 a2 8 10 12 , , c3 b1 b2 b3 a1 13 15 17 a2 14 16 18 , , c4 b1 b2 b3 a1 19 21 23 a2 20 22 24a data from is more general than a matrix in that different columns can contain different modes of data.
创建mydata<- data.frame(col1,clo2,clo3,…)
> ID<-c(1,2,3,4) > age<-c(25,34,28,52) > diabetes<-c("type1","type2","type1","type1") > status<-c("poor","improved","excellent","poor") > patientdata<-data.frame(ID,age,diabetes,status) > patientdata ID age diabetes status 1 1 25 type1 poor 2 2 34 type2 improved 3 3 28 type1 excellent 4 4 52 type1 pooreach column must have only one mode,but you can put colmns of different modes together to form the data frame.
variables canbe described as nominal(名义型),ordinal(有序型),or continuous. Categorical(nominal) and ordered categorical(ordinal) variables in R are called factors. norminal:上个例子中的diabetes(type1,type2),是无序的 ordinal:上个例子中的status(poor,improved,excellent)，是有序的，但不表示数量
factor() myfactor<-factor(factor_vector,order=TRUE,levels)
> ID<-c(1,2,3,4) > age<-c(25,34,28,52) > diabetes<-c("type1","type2","type1","type1") > status<-c("poor","improved","excellent","poor") > diabetes<-factor(diabetes) > status<-factor(status,order=TRUE) > patientdata<-data.frame(patientdata,age,diabetes,status) > str(patientdata) 'data.frame': 4 obs. of 7 variables: $ ID : num 1 2 3 4 $ age : num 25 34 28 52 $ diabetes : Factor w/ 2 levels "type1","type2": 1 2 1 1 $ status : Factor w/ 3 levels "excellent","improved",..: 3 2 1 3 $ age.1 : num 25 34 28 52 $ diabetes.1: Factor w/ 2 levels "type1","type2": 1 2 1 1 $ status.1 : Ord.factor w/ 3 levels "excellent"<"improved"<..: 3 2 1 3 > summary(patientdata) ID age diabetes status Min. :1.00 Min. :25.00 type1:3 excellent:1 1st Qu.:1.75 1st Qu.:27.25 type2:1 improved :1 Median :2.50 Median :31.00 poor :2 Mean :2.50 Mean :34.75 3rd Qu.:3.25 3rd Qu.:38.50 Max. :4.00 Max. :52.00 age.1 diabetes.1 status.1 Min. :25.00 type1:3 excellent:1 1st Qu.:27.25 type2:1 improved :1 Median :31.00 poor :2 Mean :34.75 3rd Qu.:38.50 Max. :52.00注意，对factor指定order=TRUE，并规定level，是为了让factor的排序方式与逻辑顺序一致，默认情况是依照字母顺序创建的
A list allows you to gather a variety of objects under one name.For example,a list may contain a conbination of vectors,matrices,data frames,and even other lists.
创建 mylist<-list(object1,object2,…) # Optionally,you can name the objects in a list mylist<-list(name1=object1,name2=object2,…) > g<-"my first list" > h<-c(1,2,3,4) > j<-matrix(1:10,nrow=5) > k<-c("one","two","three") > mylist<-list(title=g,ages=h,j,k) > mylist $title [1] "my first list" $ages [1] 1 2 3 4 [[3]] [,1] [,2] [1,] 1 6 [2,] 2 7 [3,] 3 8 [4,] 4 9 [5,] 5 10 [[4]] [1] "one" "two" "three" > mylist[[2]] [1] 1 2 3 4 > mylist[["ages"]] [1] 1 2 3 4R refers to case identifiers as rownames and categorical variables(nominal[名义型] ,ordinal[有序型]) as factors A dataset is usually a rectangular array of data with rows representing observations and columns representing variables. R has a wide variaty of objects for hoding data,including scalars(标量),vectors,matrices,arrays,data frame,and lists.——《R in Action》
