Reshaping an array to data.frame
Solution 1
Yes, 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
Solution 2
Use as.data.frame.table().
d0 <- as.data.frame.table(x)
head(d0)
# 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)
d1 <- pivot_wider(data = d0, names_from = "Var4", values_from = "Freq")
head(d1)
# 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
Solution 3
df = melt(x) gives you something very similar to what you want. Then you could compute the various measure variables from the different levels of measure.
Using the "reshape2" package, try:
dcast(melt(x), Subject + Cond + Item ~ Var4)
Solution 4
ftable pretty much gets you where you need to be:
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
But, most people would probably prefer their data in a data.frame. Using as.data.frame.matrix extracts the values, but not the row and column names. ftable stores that information in row.vars and col.vars attributes.
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"
We can use this information to write a function that converts an ftable to a 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)
}
Here it is in use directly on your original "x":
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
Amyunimus
I'm an assistant professor at University of Wisconsin-Madison studying visual cognition. I'm interested in what visual information we can acquire without conscious awareness, and how awareness contributes to representing information in the brain. I'm also interested in disambiguating how neural populations encode different types of information using methods like fMRI adaptation and multi-voxel pattern analysis.
Updated on February 04, 2021Comments
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Amyunimus about 3 years
I have the following data structure (an "atomic vector?") output from
daplyinplyr, in which I had the function return three different measures for each subject, condition, and item.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", "")))I want to change it to look like:
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 0.27etc.
Is there an easy way to do this?