用R表示复杂相关矩阵

Question

我有一个代码，它创建了几个项目之间的相关矩阵，我想以最好的方式可视化它，我试图使用corrplot函数，但我有一个问题，并且无法理解任何事情来自它， 这是我的代码，包含我的数据样本：

library(corrplot)
Orders<- structure(list(WHWorkOrderHeaderId = c(137413L, 137413L, 137413L, 
137413L, 137413L, 137413L, 137413L, 137413L, 137413L, 137413L, 
137413L, 137413L, 137429L, 137429L, 137429L, 137429L, 137429L, 
137429L, 137429L, 137429L, 137429L, 137260L, 137260L, 137260L, 
137260L, 137260L, 137260L, 137260L, 137260L, 137260L, 137260L, 
136729L, 136729L, 136729L, 136729L, 136729L, 136729L, 136729L, 
136729L, 136729L, 136729L, 136729L, 136729L, 136729L, 137902L, 
137902L, 137902L, 137902L, 137902L, 137902L, 137902L, 137974L, 
137974L, 138837L, 138837L, 138837L, 138837L, 138837L, 138837L, 
139424L, 139424L, 139424L, 139424L, 139424L, 139424L, 139424L, 
139424L, 139424L, 139642L, 139642L, 139642L, 139642L, 139642L, 
139642L, 139642L, 140676L, 140676L, 140676L, 140676L, 140676L, 
140676L, 140938L, 140938L, 140938L, 140938L, 140938L, 140938L, 
140938L, 140938L, 140938L, 140938L, 141302L, 141302L, 141302L, 
141302L, 141302L, 141302L, 138297L, 138297L, 138297L), OtherLangDescription = structure(c(17L, 
16L, 34L, 19L, 25L, 32L, 18L, 35L, 15L, 27L, 13L, 22L, 16L, 26L, 
5L, 20L, 19L, 14L, 29L, 35L, 27L, 17L, 16L, 30L, 26L, 5L, 19L, 
14L, 31L, 29L, 27L, 23L, 24L, 16L, 30L, 8L, 19L, 14L, 32L, 9L, 
31L, 35L, 27L, 21L, 24L, 30L, 26L, 5L, 14L, 32L, 31L, 26L, 5L, 
11L, 24L, 31L, 15L, 27L, 13L, 11L, 17L, 24L, 10L, 19L, 32L, 6L, 
31L, 33L, 23L, 11L, 17L, 4L, 10L, 19L, 31L, 23L, 11L, 4L, 10L, 
19L, 31L, 11L, 17L, 16L, 14L, 25L, 12L, 31L, 7L, 1L, 2L, 23L, 
3L, 35L, 15L, 27L, 28L, 17L, 24L, 16L), .Label = c(" Green Beans", 
"Baladi Cabbage", "Baladi Garlic", "Banati Grape", "Barshomi Figs", 
"Black Eggplant", "Cantaloupe", "Capsicum", "Carrot", "Chili Pepper", 
"Classic Eggplant", "Cooking Potato", "Coriander", "Cucumber", 
"Dill", "Flame Grape", "frying Potato", "Golden Onion", "Green pepper", 
"Hot Pepper", "Local Celery ", "Local Eggplant", "Local Lemon", 
"Local Pear", "Molokhia", "Momtaza Owais Mango", "Parsley", "Red Globe Grape", 
"Red Onion", "Superior Grape", "Tomato", "White Eggplant ", "Zaghlol Dates", 
"Zebdaya Mango", "Zucchini"), class = "factor")), .Names = c("WHWorkOrderHeaderId", 
"OtherLangDescription"), row.names = c(NA, -100L), class = "data.frame")

Orders$OtherLangDescription <- as.factor(Orders$OtherLangDescription)
orderList <- unique(Orders$OtherLangDescription) 
ListId <- lapply(orderList, function(x) subset(Orders, OtherLangDescription == x)$WHWorkOrderHeaderId) 
Initial_Tab <- lapply(ListId, function(x) subset(Orders, WHWorkOrderHeaderId %in% x)$OtherLangDescription) 
Correlation_Tab <- mapply(function(Product, ID) table(Product)/length(ID),
                          Initial_Tab, ListId) 
colnames(Correlation_Tab) <- orderList
cor_per<- round(Correlation_Tab*100,2)
#View(cor_per)
#plot cor matrix
corrplot(Correlation_Tab, tl.pos="lt", type="upper",        
         tl.col="black", tl.cex=0.6, tl.srt=45, is.corr = FALSE,
         addCoef.col="black", addCoefasPercent = TRUE,
         sig.level=0.50, insig = "blank")

Answer 1

您可以尝试使用网络分析。这是来自＆＃34; http://www.r-graph-gallery.com＆＃34;。

的示例

这是示例代码：

# library
library(igraph)

# data
head(mtcars)

# Make a correlation matrix:
mat=cor(t(mtcars[,c(1,3:6)]))
# Keep only high correlations
mat[mat<0.995]=0

# Make an Igraph object from this matrix:
network=graph_from_adjacency_matrix( mat, weighted=T, mode="undirected", diag=F)

plot(network)

这将是结果：

您可以将此示例改编为您的示例，以便更好地查看相关性。