Masi - 11 months ago 68

R Question

I have 2x N amount of 1D Signals in

`files`

Code 1 is simplified example about 1x N amount of 1D signals, while Code 2 is the actual target with two pieces of pseudocode about:

- to create two dimensional vector () - just two integer data units in each row separated by comma, and
`files[[i]] = i,i+1`

- and then accessing the data there later () where I cannot refer to all columns 2 of all signals
`tcrossprod( files[[]][, 2], files[[]][, 2] )`

Simplified Code 1 works as expected

`## Example with 1D vector in Single column`

N <- 7

files <- vector("list", N)

# Make a list of two column data

for (i in 1:N) {

files[[i]] = i

}

str(files)

# http://stackoverflow.com/a/40323768/54964

tcrossprod( files, files )

`## Example with 2x1D vectors in two columns`

N <- 7

files <- vector("list", N)

# Make a list of two column data

for (i in 1:N) {

files[[i]] = i,i+1 # PSEUDOCODE

}

str(files)

# access one signal single columns by files[[1]][,1] and files[[1]][,2]

tcrossprod( files[[]][, 2], files[[]][, 2] ) # PSEUDOCODE

Assume Vector 1 dimensions are Nx1 and Vector 1 1xM.

Each cell, accessed for instance for Signal 2 Column 2 by

`files[[1]][,2]`

Mutiply all such signals of Column 2 by

`trossprod`

`NxM`

Data: a list of two columns, where first column is 1D signal; 2nd column is improved 1D signal. I want to compare those improved 1D signals all together in the matrix.

Expected output

`cor Improved 1 Improved 2 ...`

Improved 1 1 0.55

Improved 2 0.111 1

...

I am not tied to any particular R data structures .

Column and cell are just my descriptions of the items in the data units. So not precise because I am newbie in R.

Output of tchakravarty's graphic code in my system where you see x-axis is correct but not y-axis

OS: Debian 8.5

R: 3.1.1

Answer Source

I am still not sure of your question, so I will first try to make sure of the data structure that you have in mind.

I have created a list of length M (= 100) each element of which with an N x 2 matrix (where N = 1000) which represents the 2D signals.

```
N = 1000
li_matrices = setNames(
lapply(paste("Improved", 1:100), function(x) matrix(rnorm(N*2), nrow = N, ncol = 2, byrow = TRUE)),
paste("Improved", 1:100))
> str(li_matrices, list.len = 5, max.level = 1)
List of 100
$ Improved 1 : num [1:1000, 1:2] 0.228 -0.44 0.713 -0.118 -0.918 ...
$ Improved 2 : num [1:1000, 1:2] 0.928 0.362 -0.105 -0.1 0.165 ...
$ Improved 3 : num [1:1000, 1:2] 0.0881 -0.1466 1.8549 -0.3376 -1.1626 ...
$ Improved 4 : num [1:1000, 1:2] 0.0575 -0.7809 0.4221 0.5378 -0.7882 ...
$ Improved 5 : num [1:1000, 1:2] 0.6739 1.4515 -0.0704 -0.1596 0.2157 ...
[list output truncated]
```

Then, I have extracted the second dimension of the signals from each of the M list elements, and computed their correlations across the M replicates.

```
> cor(sapply(li_matrices, function(x) x[, 2]))
Improved 1 Improved 2 Improved 3 Improved 4 Improved 5 Improved 6 Improved 7
Improved 1 1.0000000000 -0.0181724914 0.0307864778 -0.0235266506 0.0681155904 -0.0654758679 -0.0416660418
Improved 2 -0.0181724914 1.0000000000 0.0837086793 -0.0310760562 0.0035757641 -0.0303866471 -0.0345608009
Improved 3 0.0307864778 0.0837086793 1.0000000000 -0.0093528744 0.0282039040 -0.0525328267 0.0410787784
Improved 4 -0.0235266506 -0.0310760562 -0.0093528744 1.0000000000 -0.0139707732 -0.0145970712 -0.0022037703
Improved 5 0.0681155904 0.0035757641 0.0282039040 -0.0139707732 1.0000000000 -0.0406468255 0.0381800143
Improved 6 -0.0654758679 -0.0303866471 -0.0525328267 -0.0145970712 -0.0406468255 1.0000000000 -0.0534592829
Improved 7 -0.0416660418 -0.0345608009 0.0410787784 -0.0022037703 0.0381800143 -0.0534592829 1.0000000000
Improved 8 -0.0320972342 -0.0344929079 -0.0204718584 -0.0007383034 0.0223386392 -0.0361548831 0.0090484961
Improved 9 0.0068743021 -0.0109232340 0.0071627901 0.0102613137 0.0265829001 -0.0443782611 0.0266421500
Improved 10 -0.0228804070 -0.0163596866 0.0066448268 0.0137962914 0.0357421845 0.0403325013 -0.0391002841
```

Here is the plotting code requested by OP:

```
m_corr = cor(sapply(li_matrices, function(x) x[, 2]))
m_corr %>%
as.data.frame() %>%
rownames_to_column(var = "Var1") %>%
as_data_frame() %>%
gather(key = Var2, value = Value, -Var1) %>%
ggplot(
aes(
x = reorder(Var1, as.numeric(gsub("Improved ", "", Var1))),
y = reorder(Var2, as.numeric(gsub("Improved ", "", Var2))),
fill = Value
)
) +
geom_tile() +
theme_bw() +
theme(
axis.text.x = element_text(angle = 90, size = 5, hjust = 1),
axis.text.y = element_text(size = 5)
) +
xlab("Variable 1") +
ylab("Variable 2")
```

This gives: