wlt9037的博客

output = data.frame(nodeID = 0,Weight = 0,seq = 0)        #建立空的输出序列output = output[-1,]                                      #清空数据

网络分析的时候，可能需要提取出网络中的节点或者边，igraph包中其实提供了很多可用的函数。#创建网络方法之一：data.framedata<-data.frame(id1=c(1,1,2,3,4,4,5,5,6,6,7,8,8,9,10,5,15,6,7,16),id2=c(2,11,11,12,13,14,15,16,7,15,16,17,18,18,9,19,19,19,19,19))

两组向量求交集和并集a <- c(1,2,3,4,1)a[1] 1 2 3 4 1b <- c(1,5,7)b[1] 1 5 7intersect(a,b) #交集[1] 1union(a,b) #并集[1] 1 2 3 4 5 7

test <- c(1,2,3,2,1,1,5,6,4,5,1)test [1] 1 2 3 2 1 1 5 6 4 5 1test1 <- table(test)test1test1 2 3 4 5 6 #test中的元素4 2 1 1 2 1 #各个元素出现的频率#1.提取table()中的元素names(test1)[1] "1" "2" "3" "4" "5"

A <- matrix(nrow=0,ncol=2) #创建空的矩阵A [,1] [,2]B <- matrix(c(1:10),ncol=2) #初始化矩阵B [,1] [,2][1,] 1 6[2,] 2 7[3,] 3 8[4,] 4 9[5,] 5 10C <- data.fram

问题：在使用"$"符查看变量列的时候，报错Error in output$nodeID : $ operator is invalid for atomic vectorsoutput <- data$scoreoutput <- cbind(nodeID=dat$nodeID,score=output)head(output$nodeID) Error in output$nod

#Rstudio默认屏幕打印变量数据行*列不超过1000，超出部分会被省略#构造数据data <- cbind(c(1:600),c(600:1),c(0:9))data [,1] [,2] [,3] [1,] 1 600 0 [2,] 2 599 1 [3,] 3 598 2 [4,] 4 597 3

#构造数据data <- cbind(c(1:10),c(10:1))data [,1] [,2] [1,] 1 10 [2,] 2 9 [3,] 3 8 [4,] 4 7 [5,] 5 6 [6,] 6 5 [7,] 7 4 [8,] 8 3 [9,] 9

中心度概念说明：指标名称概念比较实际应用点度中心度在某个点上，有多少条线强调某点单独的价值★作为基本点的描述接近中心度该点与网络中其他点距离之和的倒数，越大说明越在中心，越能够很快到达其他点强调点在网络的价值，越大，越在中心★★基本描述，用户价值中间中心度代表最短距离是否都经过该点，如果都经过说明这个点

library(dplyr)data <- data.frame(year = rep(2016:2017,6),month = seq(1:12),sales=rep(c(10,20,30,40),3)) year month sales1 2016 1 102 2017 2 203 2016 3 304 2017 4

library(data.table)library(dplyr)A<-data.table(X1=c("a","b","c","d","e"),Y1=c(1,2,3,4,5),Z1=c("num1","num2","num3","num4","num5"))AB<-data.table(X2=c("c","d","e","f"),Y2=c(4.4,5.5,6.6,7.7))B#LEF

Error in `[<-.ts`(`*tmp*`, ri, value = c(2.7110254334953, 2.95418132827278, : only replacement of elements is allowed

方法一：x <- rep(0,15)dim(x) <- c(3,5)x [,1] [,2] [,3] [,4] [,5][1,] 0 0 0 0 0[2,] 0 0 0 0 0[3,] 0 0 0 0 0方法二：x <- matrix(0,3,5)x [,1] [,2]

#将变量中的数据导入数据库conn <- dbConnect(MySQL(),dbname = "NETWORKS",username = "root") # 建立数据库连接dbSendQuery(conn, "SET @@sql_mode=ANSI;") # 启动非严格模式dbSendQuery(conn, "SET NAMES GBK") # win7环境下如果汉字乱码，就

> A<-seq(1:20)> A [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20> class(A)[1] "integer"> B<-matrix(A,nr=5)> B [,1] [,2] [,3] [,4][1,] 1 6 11 16[2,] 2 7

library(dplyr)   node_rel    node_rel       #循环node_del   for (m in 1:length(node_del))     {       node_del_tmp        node_rel      }   node_rel    View(node_rel)          

library(RMySQL)                                                                                                       # 载入RMySQL包conn dbSendQuery(conn, "SET @@sql_mode=ANSI;")                    

cbind() 帮助文档中有这么一段话：The cbind data frame method is just a wrapper for data.frame(..., check.names = FALSE). This means that it will split matrix columns in data frame arguments, and convert characte