#向量的運算 x<-seq(1,100,length.out = 10) x # %%求余的運算 %/%整除的運算 #向量的運算中一個向量中所包含的元素必須是另一個向量的整數倍 c(1,2,3,4)%in%c(1,2,3,5,6,8,7) ceiling(c(-2.3,3.14)) #返回不小于x的最大整數 floor(c(-2.3,3.14))#返回不大于x的最大整數 trunc(C(-2.3,3.14))#返回整數部分 round(c(-2.3,3.14))#四舍五入取整數 round(c(-3.114,0.2626),digits=2)#保留兩位小數 prod(a)#反映a中連乘的積 a<-1:100 quantile(a,c(0.4,0.6,0.8))#分位數 t<-c(1,2,3,4,8,9) which.max(t)#返回的值是索引值 which.min(t) which(t==8)##矩陣和數組 x<-1:20 m<-matrix(x,4,5) m rnames<-c("R1","R2","R3","R4") cnames<-c("C1","C2","C3","C4","C5") dimnames(m)<-list(rnames,cnames) m x<-1:20 dim(x)<-c(2,2,5) xm<-1:24 dim1<-c("A1","B1") dim2<-c("A2","B2","c2") dim3<-c("A3","B3","C3","D3") x<-array(m,c(2,3,4),list(dim1,dim2,dim3)) x#矩陣的索引 m<-matrix(1:20,4,5,byrow=T)mdimnames(m)=list(rnames,cnames)m m["R1","C1"]#列表 a<-1:20 b<-matrix(1:20,4) c<-mtcars d<-"this is a test list" mlist=list(a,b,c,d) mlist mlist=list(first=a,second=b,third=c,forth=d) mlist #列表的索引 mlist[1] mlist[c(1,4)]mlist[-3]#負索引刪除列表中的元素#數據框 #散點圖 women plot(women$height,women$weight) lm(formula = height~weight,data=women) #attach rownames(mtcars) colnames(mtcars) #因子 mtcars$cyl c<-factor(c("red","green","blue","yellow")) c f<-factor(mtcars$cyl) f plot(mtcars$cyl) plot(factor(mtcars$cyl))##缺失數據 #NA代表缺失值 a<-c(1:50,NA) a sum(a,na.rm=T) is.na(a) install.packages("VIM") require(grid) library(VIM) sleep is.na(sleep) # na.omit()去除缺失值 c<- c(NA,1:100,NA) d<-na.omit(c) d #NaN不可能存在的值 #nchar字符串的長度 #length字符串中字符的個數如： l<-c(456,41,6,777) length(l) nchar(l)#paste連接字符串 paste("wusuqi","is","nb") paste(c("wusuqi","is","nb")) paste("wusqi","is","nb",sep = "==") names<-c("wusuqi","wangyuting") paste(names,"is very nb") #substr提取字符串長度 substr(month.name,start=1,stop = 3) tem<-substr(month.name,start = 1,stop=3) toupper(tem)#字母大寫 tolower(tem)#字母小寫 gsub("^(\\w)","\\U\\1",tolower(tem),perl=T)#首字母大寫 gsub("^(\\w)","\\L\\1",toupper(tem),perl=T)#首字母小寫x<-c("A+","AC","b") grep("A+",x) grep("A+",x,fixed = T) grep("A+",x,fixed = F) match("AC",x)path<-"User/wusuqi/nb/666" strsplit(path,"/") path<-"wusuqi,nb,666" strsplit(path,",")face<-1:20 suit<-c("wusuqi","wangyuting","wsq","wyt") outer(suit,face,FUN=paste)#日期 Sys.Date() a<-"2021-3-31" as.Date(a,format="%Y-%m-%d") class(as.Date(a,format="%Y-%m-%d")) seq(as.Date("2021-3-31"),as.Date("2021-4-30"),by=5) sales<-round(runif(48,min=50,max=100)) sales ts(sales,start=c(2010,4),end=c(2015,4),frequency = 12) ts(sales,start=c(2020,1),end=c(2048,5),frequency = 4) ts(sales,start=c(2020,1),end=c(2048,5),frequency = 1)c<-matrix(c(1:20),4,4) c getwd() PatientID<-c(1,2,3,4) ADmDate<-c("10/15/200-","11/01/2009","10/21/2009","10/28/2009") Age<-c(25,34,28,52) Diabetes<-c("Type1","Type2","Type3","Type4")Status<-c("poor","Improved","Excellent","poor") data<-data.frame(PatientID,ADmDate,Age,Diabetes,Status) data ?edit data2<-data.frame(PatientID=character(0),ADmDate=character(0),Age=numeric(),Diabetes=character(),Status=character()) data2<-edit(data2) data2 fix(data2) install.packages("RODBC") setwd("C:/Users/wusuqi/Desktop/Rdata") read.table("input.txt") x<-read.table("input.txt") x head(x,n=10) x<-read.table("C:/Users/wusuqi/Desktop/Rdata/input.txt") x setwd("C:/Users/wusuqi/Desktop/Rdata") x<-read.table("input.csv",sep=",") x x<-read.table("input.csv",sep=",",header=T)#header=T,將第一行設為變量名稱 head(x) setwd("C:/Users/wusuqi/Desktop/Rdata") read.table("input 1.txt",header = T,skip = 5)#從第六行讀取信息 read.table("input 1.txt",header = T,skip = 50,nrows=200)#從第51行讀取信息到200行 install.packages("XML") library(XML) help(package="foreign") readClipboard()#讀取剪切版的信息 read.table("clipboard",header=T,sep="\t")#讀取剪切板的信息 setwd("C:/Users/wusuqi/Desktop/Rdata") read.table(gzfile("input.txt.gz"))#讀取壓縮文件中 readLines("input.csv",n=)#讀取五行的數據 x<-rivers x write(x,file = "x.txt") getwd() write.table(x,file="C:/Users/wusuqi/Desktop/newfile.txt")#寫入文件 write.table(iris,file="C:/Users/wusuqi/Desktop/Rdata/newfile.txt",col.names=F,append=T) write.table(mtcars,gzfile("newfile.txt.gz"))#寫入壓縮包 read.table("mtcars.csv",sep=",",header = T) install.packages("XLConnect") #讀取excel格式數據 library(readxl) read_excel("file.xlsx",range=NULL,sheet=NULL,col_names = T) setwd("C:/Users/wusuqi/Desktop/Rdata") read.csv("mtcars.csv",header = T,sep=",") read.table("clipboard",header = T,sep="\t") library(readxl) read_excel("mtcars.xlsxz",sheet=NULL,range=NULL,col_names=T) cars32<-read_excel("mtcars.xlsx",sheet=NULL,range=NULL,col_names=T) #安裝xlsx包 install.packages("rJava") install.packages("xlsx") install.packages("xlsxjars") library(xlsx) setwd("C:/Users/wusuqi/Desktop/Rdata") cars32<-read.xlsx("mtcars.xlsx",sheetIndex =1,header = T) methods(is) #將向量轉化為矩陣 x<-state.abb x dim(x)<-c(10,5) x #將矩陣轉化為數據框 x<-state.x77 x data.77<-as.data.frame(x) data.77 #轉化為因子類型 x<-state.abb x as.factor(x) as.list(x)#轉化為列表 state<-data.frame(x,state.region,state.x77) state$Income state["Nevada",]who<-read.csv("WHO.csv",header = T) head(who) who1<-who[c(1:50),c(1:10)]who2<-who[c(1,3,5,8),c(2,4,6,8)] who2 who$Continent who4<-who[which(who$CountryID>50,who$Country<100)] who4x<-1:100 sample(x,30)#在x中抽樣抽一百個數字 sample(x,100,replace=T)#有放回的抽樣 sample(x,100,replace=F)#無放回的抽樣 sort(sample(x,60,replace = F)) who5<-sample(who$CountryID,30,replace = T) whomtcars mtcars[-1:-5]#刪除對應行 mtcars[,-1:-4]#刪除對應列 mtcars$mpg<-NULL#刪除某一行或者某一列的數據 mtcars#添加數據集 data.frame(USArrests,state.division) cbind(USArrests,state.division)#合并列 data1<-head(USArrests,20) data2<-tail(USArrests,20) data<-rbind(data1,data2)#合并行，必須要有相同的列名 data rownames(data) length(rownames(data)) duplicated(data)#查看哪些項是重復值 data[duplicated(data),]#取出重復的部分 data[!duplicated(data),]#取出非重復的部分 unqiue(data)#取出非重復的部分sractm<-t(mtcars)#轉秩 sractmletters rev(letters)#反轉women rev(rownames(women)) women[rev(rownames(women)),]#修改數據框中的值 women women$height*2.5 data.frame(women$height*2.5,women$weight) transform(women,height=height*2.5) transform(women,cm=height*2.5)#在women中加入了新的一列#數據框的排序 sort(rivers) rev(sort(rivers))#相反的排序,sort不能用于數據框的排序 mtcars[sort(rownames(mtcars)),] #order返回向量中的值所在的位置 mtcars[order(mtcars$cyl,mtcars$disp),]WorldPhones worldphones<-as.data.frame(WorldPhones) cs<-rowSums(worldphones) cs ca<-colMeans(worldphones) ca tatal<-cbind(worldphones,Totcaal=cs) tatal rbind(tatal,ca) apply(WorldPhones,MARGIN = 1,FUN=sum)#MARGIN=1對行進行操作，MARGIN對列進行操作。FUN是函數#lapply返回值是列表，sapply返回值是列表或者矩陣lapply(state.center,FUN=length) #tapply用于處理因子 heatmap(state.x77) x<-c(1,2,3,4,6)#scale函數實現中心化和標準化 x<-scale(state.x77,center = T,scale=T)#center=T做中心化處理，scale=t做標化處理 heatmap(x) install.packages("reshape2") library(reshape2) airquality head(airquality) names(airquality)<-tolower(names(airquality)) head(airquality) aq1<-melt(airquality) aq1 head(aq1) aq1<-melt(airquality,id.vars=c("month","day"))#融合數據 aq1 head(aq1,50) aq2<-dcast(aq1,month+day~variable) aq2install.packages(c("tidyr","dplyr")) library(tidyr) tdata<-mtcars[1:10,1:3] tdata<-data.frame(names=rownames(tdata),tdata) tdata gather(tdata,key="Key",value="Value",cyl,disp,hp)#gather合并列 gdata<-gather(tdata,key="Key",value="Value",cyl,disp,hp)#gather合并列 gdata spread(gdata,key="Key",value = "Value")#spread拆分列 df<-data.frame(x=c(NA,"a.b","a.c","a.d")) df separate(df,col=x,into=c("A","B")) df<-data.frame(x=c(NA,"a-b","a-c","a-d")) separate(df,col=x,into=c("A","B")) unite(x,col="AB",A,B,sep="-") library(dplyr) dplyr::filter(iris,Sepal.Length>7)#:`:是調用dplyr中的filter函數 dplyr::distinct(rbind(iris[1:10,],iris[1:15,]))#distinct去除重復項 dplyr::slice(iris,10:15)#取出任意行 dplyr::sample_n(iris,10)#隨機抽取10行 dplyr::sample_frac(iris,0.1)#按比例隨機抽取 dplyr::arrange(iris,Sepal.Length)#按length排序 dplyr::arrange(iris,desc(Sepal.Length))#按相反的方向排序 summarise(iris,avg=mean(Sepal.Length)) #%>%將一個函數的輸出作為下一個函數的輸入，可以用快捷鍵ctrl+shift+m快捷鍵打出來 head(mtcars,10) head(mtcars,10) %>% tail(5) dplyr::group_by(iris,Species)#分組 iris %>% group_by(Species) %>% summarise() iris %>% group_by(Species) %>% summarise(avg=mean(Sepal.Width)) iris %>% group_by(Species) %>% summarise(avg=mean(Sepal.Width)) %>% arrange(avg)dplyr::mutate(iris,new=Sepal.Length+Petal.Length) a<-data.frame(x1=c("A","B","C"),x2=c(1,2,3)) b<-data.frame(x1=c("A","B","D"),x3=c(T,T,F)) a b dplyr::left_join(a,b,by="x1")#左連接 dplyr::right_join(a,b,by="x1") dplyr::full_join(a,b,by="x1") dplyr::semi_join(a,b,by="x1") dplyr::anti_join(a,b,by="x1") library(dplyr) mtcars<-mutate(mtcars,Model=rownames(mtcars)) mtcars first<-slice(mtcars,1:20) second<-slice(mtcars,10:30) intersect(first,second)#取交集 dplyr::union(first,second)#取并集 setdiff(first,second)# 取first的補集 setdiff(second,first)#取second的補集 state<-as.data.frame(state.x77[,c("Murder","Population","Illiteracy","Income","Frost")]) fir<-lm(Murder~Population+Illiteracy+Income+Frost,data=state) summary(fir) ls() a<-1:100 plot(a) ls("package:base") rnorm(n=100,mean=465,sd=668) round(rnorm(n=100,mean=465,sd=668)) x<-round(rnorm(n=13,mean=46,sd=55)) qqnorm(x) runif(1)#隨機生成一個零到一之間的隨機數 runif(10)*10#隨機生成10個零到十之間的隨機數 runif(20,min=45,max=57878) set.seed(666) runif(51) set.seed(666) runif(51)#隨機數與set.seed綁定myvars<-mtcars[c("hp","wt","am")] summary(myvars) fivenum(myvars$hp)install.packages("Hmisc") library(Hmisc) describe(myvars)install.packages("pastecs") library(pastecs) stat.desc(myvars) stat.desc(myvars,basic=T) stat.desc(myvars,desc = T) stat.desc(myvars,norm = T)install.packages("psych") library(psych) describe(myvars) describe(myvars,trim=0.1) library(MASS) head(Cars93) aggregate(Cars93[c("Min.Price","Max.Price","MPG.city")],by=list(Manufacturer=Cars93$Manufacturer),mean) aggregate(Cars93[c("Min.Price","Max.Price","MPG.city")],by=list(Manufacturer=Cars93$Origin),mean) aggregate(Cars93[c("Min.Price","Max.Price","MPG.city")],by=list(Manufacturer=Cars93$Manufacturer),sd)install.packages("doBy") library(doBy) summaryBy(hp+wt~am,data=myvars,FUN=mean) describe.by(myvars,list(am=mtcars$am)) mtcars$cyl<-as.factor(mtcars$cyl) split(mtcars,mtcars$cyl) mtcars cut(mtcars$mpg,c(seq(10,50,10))) table(mtcars$cyl) table(cut(mtcars$mpg,c(seq(10,50,10)))) prop.table(table(mtcars$cyl)) prop.table(table(mtcars$cyl))*100 library(vcd) Arthritis table(Arthritis$Treatment,Arthritis$Improved) with(data=Arthritis,{table(Treatment,Improved)})xtabs(~Treatment+Improved,data=Arthritis)library(vcd) mytable<-table(Arthritis$Treatment,Arthritis$Improved) chisq.test(mytable)fisher.test(mytable) mytable<-table(Arthritis$Sex,Arthritis$Improved) chisq.test(mytable) cor(state.x77) install.packages("ggm") library(ggm) library(MASS) UScrime t.test(Prob~So,data=UScrime) women plot(women$height) plot(women$height,women$weight) plot(as.factor(women$height)) plot(mtcars$cyl) plot(as.factor(mtcars$cyl)) plot(women$height~women$weight) plot(as.factor(mtcars$cyl),col=c("red","blue","green")) cor for(i in 1:10){print("hello world")} i=1;while(i<=10) {print("hello world")} setwd("C:/Users/wusuqi/Desktop") save.image("bilibili.R") getwd()

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