使用mdply和mutate的data.frame排列（从列表创建）选项

我正在使用

mdply

和

mutate

将列表放入data.frame中，使用以下信息：

虽然我希望结果是这样安排的：

# DFx
DAY MONTH YEAR pLength V1 V2 V3
1     1    1    0.00  1   NA NA
1     1    1    0.25 NA 2.00 NA
1     1    1    1.00 NA   NA  1
2     2    1    0.00  2   NA NA
2     2    1    0.50 NA 2.50 NA
2     2    1    1.00 NA   NA  3
2     3    2    0.00  2   NA NA
2     3    2    0.65 NA 2.35 NA
2     3    2    1.00 NA   NA  3

在下面的代码中，我是否可以采用不同的格式来获得

DFx

？我尝试过预测，但没有成功。或者，除了

mutate

之外，还有哪些选项可以与

mdply

一起使用，从而实现我想要的最终结果

编辑：我当前的解决方案是将预测保存为csv，在Excel中打开，对列进行文本分割，只留下一列名为variable的变量名（即V1、V2、V3），将其带回r，最后dcast

dcast（pred、DATE+pLength~变量）

---

df1如果你只想把
预测
转换成
DFx
，你不能这样做吗

DFx <- predictions
DFx <- cbind(DFx,
            V1=ifelse(substr(DFx$X1,7,8)=="V1",DFx$pred,NA),
            V2=ifelse(substr(DFx$X1,7,8)=="V2",DFx$pred,NA),
            V3=ifelse(substr(DFx$X1,7,8)=="V3",DFx$pred,NA))
DFx <- DFx[,-6]   # delete "pred" column

DFx此解决方案有效。然而，我的真实数据集有名称长度不同的变量，大约有20个。我认为在那一点上，走我愚蠢的Excel路线会更快。
df1 <- structure(list(DAY = c(1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 
2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L), MONTH = c(1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), YEAR = c(1L, 
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L), pLength = c(0L, 
0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 
1L), variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("V1", "V2", "V3"
), class = "factor"), value = c(1L, 2L, 2L, 3L, 1L, 1L, 2L, 3L, 
3L, 2L, 2L, 2L, 3L, 1L, 1L, 1L, 3L, 3L)), .Names = c("DAY", "MONTH", 
"YEAR", "pLength", "variable", "value"), row.names = c(NA, -18L
), class = "data.frame")

df2 <- structure(list(DAY = c(1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L), MONTH = c(1L, 
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), YEAR = c(1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L), pLength = c(0, 0.25, 1, 0, 0.5, 1, 0, 0.65, 
1), X1 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), X2 = c(0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L), X3 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), X4 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("DAY", 
"MONTH", "YEAR", "pLength", "X1", "X2", "X3", "X4"), class = "data.frame", row.names = c(NA, 
-9L))

# choose colums from df2 that will be used to receive the predicted values
recvars <- c("DAY", "MONTH", "YEAR", "pLength")
rec <- df2[recvars]
recList <- dlply(rec, c("DAY", "MONTH", "YEAR", "pLength"))

# create list of models that predict the value by pLength
models <- dlply(df1, c("DAY", "MONTH", "YEAR", "variable"), function(df) 
  lm(value ~ pLength, data = df))

# get predicted values
predictions <- mdply(cbind(mod = models, df = recList), function(mod, df) {
  mutate(df, pred = predict(mod, newdata = df))
})

DFx <- predictions
DFx <- cbind(DFx,
            V1=ifelse(substr(DFx$X1,7,8)=="V1",DFx$pred,NA),
            V2=ifelse(substr(DFx$X1,7,8)=="V2",DFx$pred,NA),
            V3=ifelse(substr(DFx$X1,7,8)=="V3",DFx$pred,NA))
DFx <- DFx[,-6]   # delete "pred" column