仅转置data.frame中的某些列

这是我的数据：

           am   group  v1  v2  v3    v4
1  2015-10-31       A 693 803 700   17%
2  2015-10-31       B 524 859 302   77%
3  2015-10-31       C 266 675 86    7%
4  2015-10-31       D 376 455 650   65%
5  2015-11-30       A 618 715 200   38%
6  2015-11-30       B 249 965 215   54%
7  2015-11-30       C 881 106 184   24%
8  2015-11-30       D 033 047 492   46%
9  2015-12-31       A 229 994 720   19%
10 2015-12-31       B 539 543 332   57%
11 2015-12-31       C 100 078 590   24%
12 2015-12-31       D 517 413 716   57%

问题： 我如何转换数据以便

转置

v1-v4

和

将

am

中的值作为列变量

group

变量由

v1-v4

我想得出的结果是：

group metric 2015-10-31 2015-11-30 2015-12-31
    A     v1        693        618        229
    A     v2        803        715        994 
    A     v3        700        200        720
    A     v4        17%        38%        19%
    B     v1        524        249        539
    B     v2        859        965        543 
    B     v3        302        215        332
    B     v4        77%        54%        57%
    ...

到目前为止我所尝试的：

name <- mydata$am
data <- as.data.frame(t(mydata[, -1]))
colnames(mydata) <- name

---

name基本思想是先使用“长”格式，然后使用“宽”格式

这里有一些方法可以做到这一点

melt+
dcast

library(data.table) ## or library(reshape2)
dcast(melt(as.data.table(mydf), id.vars = c("am", "group")), 
      group + variable ~ am, value.var = "value")

library(dplyr)
library(tidyr)

mydf %>%
  gather(key, value, v1:v4) %>%
  spread(am, value)

reshape(cbind(mydf[c(1, 2)], stack(mydf[-c(1, 2)])), 
        direction = "wide", idvar = c("group", "ind"), timevar = "am")

重铸

library(data.table) ## or library(reshape2)
dcast(melt(as.data.table(mydf), id.vars = c("am", "group")), 
      group + variable ~ am, value.var = "value")

library(dplyr)
library(tidyr)

mydf %>%
  gather(key, value, v1:v4) %>%
  spread(am, value)

reshape(cbind(mydf[c(1, 2)], stack(mydf[-c(1, 2)])), 
        direction = "wide", idvar = c("group", "ind"), timevar = "am")

（基本上与上述相同，但只需一步。）

聚集+
分散

library(data.table) ## or library(reshape2)
dcast(melt(as.data.table(mydf), id.vars = c("am", "group")), 
      group + variable ~ am, value.var = "value")

library(dplyr)
library(tidyr)

mydf %>%
  gather(key, value, v1:v4) %>%
  spread(am, value)

reshape(cbind(mydf[c(1, 2)], stack(mydf[-c(1, 2)])), 
        direction = "wide", idvar = c("group", "ind"), timevar = "am")

重塑

library(data.table) ## or library(reshape2)
dcast(melt(as.data.table(mydf), id.vars = c("am", "group")), 
      group + variable ~ am, value.var = "value")

library(dplyr)
library(tidyr)

mydf %>%
  gather(key, value, v1:v4) %>%
  spread(am, value)

reshape(cbind(mydf[c(1, 2)], stack(mydf[-c(1, 2)])), 
        direction = "wide", idvar = c("group", "ind"), timevar = "am")

是否确实要在生成的数据集中混合数据类型？这似乎是个坏主意。我正在为
htmlTable
准备一个表输出。