Melt 正在用我的数据创造 Cronenburg 怪兽

Melt is creating Cronenburg monsters out of my data

我有一个数据框:

structure(list(diag = structure(list(svm.ca = 0.894598155467721,     rpart.ca = 1), .Names = c("svm.ca", "rpart.ca")), kappa = structure(list(    svm.ca = 0.838953088986906, rpart.ca = 1), .Names = c("svm.ca", "rpart.ca")), rand = structure(list(svm.ca = 0.871202561348254,     rpart.ca = 1), .Names = c("svm.ca", "rpart.ca")), crand = structure(list(    svm.ca = 0.715005579974998, rpart.ca = 1), .Names = c("svm.ca", "rpart.ca"))), .Names = c("diag", "kappa", "rand", "crand"), row.names = c("svm", "rpart"), class = "data.frame")

看起来像这样:

              diag     kappa      rand     crand
svm.ca   0.8945982 0.8389531 0.8712026 0.7150056
rpart.ca         1         1         1         1

我正在尝试遵循 svm 方向 here(第 3 页),并且我想显示这些值的分组直方图。所以,我融化了我的 df:

df <- melt(as.matrix(histdf))

这让我觉得很奇怪:

     X1    X2 value.diag value.kappa value.rand value.crand
1   svm  diag  0.8945982   0.8389531  0.8712026   0.7150056
2 rpart  diag          1           1          1           1
3   svm kappa  0.8945982   0.8389531  0.8712026   0.7150056
4 rpart kappa          1           1          1           1
5   svm  rand  0.8945982   0.8389531  0.8712026   0.7150056
6 rpart  rand          1           1          1           1
7   svm crand  0.8945982   0.8389531  0.8712026   0.7150056
8 rpart crand          1           1          1           1

我也试过:

> df <- melt(histdf)
Using  as id variables
> df
  variable     value NA
1     diag 0.8945982  1
2     diag 0.8945982  1
3    kappa 0.8389531  1
4    kappa 0.8389531  1
5     rand 0.8712026  1
6     rand 0.8712026  1
7    crand 0.7150056  1
8    crand 0.7150056  1

来自

rownames(histdf)<-c("svm","rpart")
histdf$rn <- row.names(histdf)
df <- melt(histdf, id.vars = "rn")

这给了我与上次尝试相同的输出。我的意思是我应该得到一个名称,值 table,对吧?不变量,值,NA - 怎么绘制它?

我知道这不适用于像这样的分组 ggplot 直方图(我尝试了很多次):

我从classAgreement重构了原始变量如下:

svm.ca <- structure(list(diag = 0.704225352112676, kappa = 0.589482378854626, 
    rand = 0.746881287726358, crand = 0.367673479030732), .Names = c("diag", 
"kappa", "rand", "crand"))
rpart.ca <- structure(list(diag = 0.732394366197183, kappa = 0.607049228080396, 
    rand = 0.720724346076459, crand = 0.361367036482943), .Names = c("diag", 
"kappa", "rand", "crand"))

这些都是列表。假设我们 rbind 列表中的每个元素,然后将它们放入数据框中:

dd <- as.data.frame(Map(rbind,svm.ca,rpart.ca))

现在这是一个正常的、不包含列表的数据框。 reshape2::melt(dd)tidyr::gather(dd,"variable","value"),工作正常。


这是最终代码:

df<-as.data.frame(Map(rbind,svm.ca,rpart.ca))
df$Names<-c("svm","rpart")
df.m<-melt(df)
ggplot(df.m, aes(Names, value),group) +   
  geom_bar(aes(fill = variable), position = "dodge", stat="identity")