将数组重塑为data.frame

Question

我有以下的数据结构(一个"原子矢量？")输出从daply在 plyr,其中我有函数返回对于每个受试者,病症,和第三项不同的措施.

x = structure(c(-0.93, 0.39, 0.88, 0.63, 0.86, -0.69, 1.02, 0.29, 0.94, 
0.93, -0.01, 0.79, 0.32, 0.14, 0.13, -0.07, -0.63, 0.26, 0.07, 0.87,
-0.36, 1.043, 0.33, -0.12, -0.055, 0.07, 0.67, 0.48, 0.002, 0.008, 
-0.19, -1.39, 0.98, 0.43, -0.02, -0.15,-0.08, 0.74, 0.96, 0.44, -0.005,
1.09, 0.36, 0.04, 0.09, 0.17, 0.68, 0.51, 0.09, 0.12, -0.05, 0.11,
0.99, 0.62, 0.13, 0.06, 0.27, 0.74, 0.96, 0.45), .Dim = c(5L, 
2L, 2L, 3L), .Dimnames = structure(list(Subject = c("s1", "s2", 
"s3", "s4", "s5"), Cond = c("A", "B"), Item = c("1", "2"), c("Measure1", 
"Measure2", "Measure3")), .Names = c("Subject", "Cond", 
"Item", "")))

我想改变它看起来像:

Subject Cond Item Measure1 Measure2 Measure3
     s1    A    1    -0.93   -0.360   -0.005
     s1    A    2    -0.01    -0.19    -0.05 
     s1    B    1    -0.69    0.070     0.17
     s1    B    2    -0.07    -0.15     0.06
     s2    A    1     0.39    1.043    1.090
     s2    A    2     0.79    -1.39     0.11
     s2    B    1     1.02    0.670     0.68
     s2    B    2    -0.63    -0.08     0.27

等等

是否有捷径可寻？

Answer 1

使用as.data.frame.table().

df0 <- as.data.frame.table(x)
head(df0)

#   Subject Cond Item     Var4  Freq
# 1      s1    A    1 Measure1 -0.93
# 2      s2    A    1 Measure1  0.39
# 3      s3    A    1 Measure1  0.88
# 4      s4    A    1 Measure1  0.63
# 5      s5    A    1 Measure1  0.86
# 6      s1    B    1 Measure1 -0.69

library(tidyr)
df1 <- spread(data = df0, key = Var4, value = Freq)
head(df1)

#   Subject Cond Item Measure1 Measure2 Measure3
# 1      s1    A    1    -0.93   -0.360   -0.005
# 2      s1    A    2    -0.01   -0.190   -0.050
# 3      s1    B    1    -0.69    0.070    0.170
# 4      s1    B    2    -0.07   -0.150    0.060
# 5      s2    A    1     0.39    1.043    1.090
# 6      s2    A    2     0.79   -1.390    0.110

Answer 2

是的,使用adply():

adply(x, c(1,2,3))
   Subject Cond Item Measure1 Measure2 Measure3
1       s1    A    1    -0.93   -0.360   -0.005
2       s2    A    1     0.39    1.043    1.090
3       s3    A    1     0.88    0.330    0.360
4       s4    A    1     0.63   -0.120    0.040
5       s5    A    1     0.86   -0.055    0.090
6       s1    B    1    -0.69    0.070    0.170
7       s2    B    1     1.02    0.670    0.680
8       s3    B    1     0.29    0.480    0.510
9       s4    B    1     0.94    0.002    0.090
10      s5    B    1     0.93    0.008    0.120
11      s1    A    2    -0.01   -0.190   -0.050
12      s2    A    2     0.79   -1.390    0.110
13      s3    A    2     0.32    0.980    0.990
14      s4    A    2     0.14    0.430    0.620
15      s5    A    2     0.13   -0.020    0.130
16      s1    B    2    -0.07   -0.150    0.060
17      s2    B    2    -0.63   -0.080    0.270
18      s3    B    2     0.26    0.740    0.740
19      s4    B    2     0.07    0.960    0.960
20      s5    B    2     0.87    0.440    0.450