使用多個變量和一些不變的時間將數據框重新整形爲面板

Question

這是Stata在一步處理的數據分析中的一個基本問題。

d1 <- data.frame(subject = c("id1", "id2"), 
x0 = c("male", "female"), 
x1_2000 = 1:2, 
x1_2005 = 5:6, 
x2_2000 = 1:2, 
x2_2005 = 5:6  
) 

S.T.：

了2000年和2005年（X1，X2）創建時間不變的數據（X0）和隨時間變化的數據範圍內的數據幀

subject x0 x1_2000 x1_2005 x2_2000 x2_2005 
1  id1 male   1  5  1  5 
2  id2 female  2  6  2  6 

我想塑造它像一個面板，使數據看起來是這樣的：

 subject  x0 time x1 x2 
1  id1 male 2000 1 1 
2  id2 female 2000 2 2 
3  id1 male 2005 5 5 
4  id2 female 2005 6 6 

我可以reshape S.T.做到這一點

d2 <-reshape(d1, 
idvar="subject", 
varying=list(c("x1_2000","x1_2005"), 
    c("x2_2000","x2_2005")), 
    v.names=c("x1","x2"), 
    times = c(2000,2005), 
    direction = "long", 
    sep= "_") 

我主要關心的是當你有幾十個變量時，上面的命令變得很長。在stata人會簡單地鍵入：

reshape long x1 x2, i(subject) j(year) 

是否有R，使得一個簡單的解決方案？

Answer 1

reshape能猜出它的許多論點。在這種情況下，指定以下內容即可。沒有包被使用。

reshape(d1, dir = "long", varying = 3:6, sep = "_") 

，並提供：

 subject  x0 time x1 x2 id 
1.2000  id1 male 2000 1 1 1 
2.2000  id2 female 2000 2 2 2 
1.2005  id1 male 2005 5 5 1 
2.2005  id2 female 2005 6 6 2 

Answer 2

這裏是使用reshape2包一個簡單的例子：

library(reshape2) 
library(stringr) 

# it is always useful to start with melt 
d2 <- melt(d1, id=c("subject", "x0")) 

# redefine the time and x1, x2, ... separately 
d2 <- transform(d2, time = str_replace(variable, "^.*_", ""), 
        variable = str_replace(variable, "_.*$", "")) 

# finally, cast as you want 
d3 <- dcast(d2, subject+x0+time~variable) 

現在你不需要甚至指定X1和X2。
此代碼的工作，如果變量增加：

> d1 <- data.frame(subject = c("id1", "id2"), x0 = c("male", "female"), 
+ x1_2000 = 1:2, 
+ x1_2005 = 5:6, 
+ x2_2000 = 1:2, 
+ x2_2005 = 5:6, 
+ x3_2000 = 1:2, 
+ x3_2005 = 5:6, 
+ x4_2000 = 1:2, 
+ x4_2005 = 5:6 
+) 
> 
> d2 <- melt(d1, id=c("subject", "x0")) 
> d2 <- transform(d2, time = str_replace(variable, "^.*_", ""), 
+      variable = str_replace(variable, "_.*$", "")) 
> 
> d3 <- dcast(d2, subject+x0+time~variable) 
> 
> d3 
    subject  x0 time x1 x2 x3 x4 
1  id1 male 2000 1 1 1 1 
2  id1 male 2005 5 5 5 5 
3  id2 female 2000 2 2 2 2 
4  id2 female 2005 6 6 6 6