创建共现矩阵 [英] Creating co-occurrence matrix
问题描述
我正在尝试解决具有共现矩阵的问题.我有一个交易和项目的数据文件,我想查看一个项目一起出现的交易数量的矩阵.
我是R编程的新手,我很有趣地发现了R的所有快捷方式,而不是创建特定的循环(我几年前曾经使用C,现在只坚持使用Excel宏和SPSS) .我已经在这里检查了解决方案,但是没有找到一个可行的解决方案(最接近的解决方案是这里给出的解决方案: 如上所述,cbind可能不成功,因此projecting_tm无法给我任何结果. 是否有其他替代方法或对我的方法有更正? 非常感谢您的帮助! 我将结合使用reshape2包和矩阵代数: 也许是图形... I'm trying to solve the problem of having a co-occurence matrix. I have a datafile of transactions and items, and I want to see a matrix of the number of transactions where items appear together. I'm a newbie in R programming and I'm having some fun finding out all the shortcuts that R has, rather than creating specific loops (I used to use C years ago and only sticking to Excel macros and SPSS now). I have checked the solutions here, but haven't found one that works (the closest is the solution given here: Co-occurrence matrix using SAC? - but it produced an error message when I used projecting_tm, I suspected that the cbind wasn't successful in my case. Essentially I have a table containing the following: I want to create something like: What I did was (and you'd probably laugh at my rookie R approach): As mentioned above the cbind was probably unsuccessful, so the projecting_tm couldn't give me any result. Any alternative approach or a correction to my method? Your help would be much appreciated! I'd use a combination of the reshape2 package and matrix algebra: For the graphing maybe...
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dat <- read.table(text="TrxID Items Quant
Trx1 A 3
Trx1 B 1
Trx1 C 1
Trx2 E 3
Trx2 B 1
Trx3 B 1
Trx3 C 4
Trx4 D 1
Trx4 E 1
Trx4 A 1
Trx5 F 5
Trx5 B 3
Trx5 C 2
Trx5 D 1", header=T)
#making the boolean matrix
library(reshape2)
dat2 <- melt(dat)
w <- dcast(dat2, Items~TrxID)
x <- as.matrix(w[,-1])
x[is.na(x)] <- 0
x <- apply(x, 2, function(x) as.numeric(x > 0)) #recode as 0/1
v <- x %*% t(x) #the magic matrix
diag(v) <- 0 #repalce diagonal
dimnames(v) <- list(w[, 1], w[,1]) #name the dimensions
v
g <- graph.adjacency(v, weighted=TRUE, mode ='undirected')
g <- simplify(g)
# set labels and degrees of vertices
V(g)$label <- V(g)$name
V(g)$degree <- degree(g)
plot(g)
TrxID Items Quant
Trx1 A 3
Trx1 B 1
Trx1 C 1
Trx2 E 3
Trx2 B 1
Trx3 B 1
Trx3 C 4
Trx4 D 1
Trx4 E 1
Trx4 A 1
Trx5 F 5
Trx5 B 3
Trx5 C 2
Trx5 D 1, etc.
A B C D E F
A 0 1 1 0 1 1
B 1 0 3 1 1 0
C 1 3 0 1 0 0
D 1 1 1 0 1 1
E 1 1 0 1 0 0
F 0 1 1 1 0 0
library(igraph)
library(tnet)
trx <- read.table("FileName.txt", header=TRUE)
transID <- t(trx[1])
items <- t(trx[2])
id_item <- cbind(items,transID)
item_item <- projecting_tm(id_item, method="sum")
item_item <- tnet_igraph(item_item,type="weighted one-mode tnet")
item_matrix <-get.adjacency(item_item,attr="weight")
item_matrix
#read in your data
dat <- read.table(text="TrxID Items Quant
Trx1 A 3
Trx1 B 1
Trx1 C 1
Trx2 E 3
Trx2 B 1
Trx3 B 1
Trx3 C 4
Trx4 D 1
Trx4 E 1
Trx4 A 1
Trx5 F 5
Trx5 B 3
Trx5 C 2
Trx5 D 1", header=T)
#making the boolean matrix
library(reshape2)
dat2 <- melt(dat)
w <- dcast(dat2, Items~TrxID)
x <- as.matrix(w[,-1])
x[is.na(x)] <- 0
x <- apply(x, 2, function(x) as.numeric(x > 0)) #recode as 0/1
v <- x %*% t(x) #the magic matrix
diag(v) <- 0 #repalce diagonal
dimnames(v) <- list(w[, 1], w[,1]) #name the dimensions
v
g <- graph.adjacency(v, weighted=TRUE, mode ='undirected')
g <- simplify(g)
# set labels and degrees of vertices
V(g)$label <- V(g)$name
V(g)$degree <- degree(g)
plot(g)