long - spread r
Remodelación de una matriz a data.frame (4)
Sí, use 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
Tengo la siguiente estructura de datos (¿un "vector atómico"?) De la salida de daply en plyr , en la que hice que la función devuelva tres medidas diferentes para cada sujeto, condición y artículo.
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", "")))
Quiero cambiarlo para que se vea así:
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
etc.
¿Hay una forma fácil de hacer esto?
df = melt(x) te da algo muy similar a lo que quieres. Entonces podrías calcular las diversas variables de medida de los diferentes niveles de medida.
Usando el paquete "reshape2", intente:
dcast(melt(x), Subject + Cond + Item ~ Var4)
ftable te lleva a donde necesitas estar:
y <- ftable(x)
y
#
# Measure1 Measure2 Measure3
# Subject Cond Item
# s1 A 1 -0.930 -0.360 -0.005
# 2 -0.010 -0.190 -0.050
# B 1 -0.690 0.070 0.170
# 2 -0.070 -0.150 0.060
# s2 A 1 0.390 1.043 1.090
# 2 0.790 -1.390 0.110
# B 1 1.020 0.670 0.680
# 2 -0.630 -0.080 0.270
# s3 A 1 0.880 0.330 0.360
# 2 0.320 0.980 0.990
# B 1 0.290 0.480 0.510
# 2 0.260 0.740 0.740
# s4 A 1 0.630 -0.120 0.040
# 2 0.140 0.430 0.620
# B 1 0.940 0.002 0.090
# 2 0.070 0.960 0.960
# s5 A 1 0.860 -0.055 0.090
# 2 0.130 -0.020 0.130
# B 1 0.930 0.008 0.120
# 2 0.870 0.440 0.450
Pero, la mayoría de la gente probablemente prefiera sus datos en un data.frame . El uso de as.data.frame.matrix extrae los valores, pero no los nombres de fila y columna. ftable almacena esa información en los atributos row.vars y col.vars .
attributes(y)$row.vars
# $Subject
# [1] "s1" "s2" "s3" "s4" "s5"
#
# $Cond
# [1] "A" "B"
#
# $Item
# [1] "1" "2"
attributes(y)$col.vars
# [[1]]
# [1] "Measure1" "Measure2" "Measure3"
Podemos usar esta información para escribir una función que convierta un ftable a un data.frame :
ftable2df <- function(mydata) {
ifelse(class(mydata) == "ftable",
mydata <- mydata, mydata <- ftable(mydata))
dfrows <- rev(expand.grid(rev(attr(mydata, "row.vars"))))
dfcols <- as.data.frame.matrix(mydata)
names(dfcols) <- do.call(
paste, c(rev(expand.grid(rev(attr(mydata, "col.vars")))), sep = "_"))
cbind(dfrows, dfcols)
}
Aquí está en uso directamente en su "x" original:
ftable2df(x)
# 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
# 7 s2 B 1 1.02 0.670 0.680
# 8 s2 B 2 -0.63 -0.080 0.270
# 9 s3 A 1 0.88 0.330 0.360
# 10 s3 A 2 0.32 0.980 0.990
# 11 s3 B 1 0.29 0.480 0.510
# 12 s3 B 2 0.26 0.740 0.740
# 13 s4 A 1 0.63 -0.120 0.040
# 14 s4 A 2 0.14 0.430 0.620
# 15 s4 B 1 0.94 0.002 0.090
# 16 s4 B 2 0.07 0.960 0.960
# 17 s5 A 1 0.86 -0.055 0.090
# 18 s5 A 2 0.13 -0.020 0.130
# 19 s5 B 1 0.93 0.008 0.120
# 20 s5 B 2 0.87 0.440 0.450
Use as.data.frame.table . A continuación, puede evitar la carga plyr - desanexar plyr cuando desee utilizar dplyr - cycle.
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 <- df0 %>% spread(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