用 R 可视化复相关矩阵

debugcn 发表于 Dev

信徒

我有一个代码，它在几个项目之间创建了一个相关矩阵，我想以最好的方式将它可视化，我尝试使用 corrplot 函数，但我遇到了问题并且无法从中理解任何东西，这是我的带有我的数据示例的代码：

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")

用户 6683331

您可以尝试使用网络分析。这是来自“ http://www.r-graph-gallery.com ”的一个例子。

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)