JMM - 18 days ago 4x
R Question

# Correlation between two corresponding columns from seperate datasets

I have two sets of data, which contain columns with the same names, but differing values in those columns. e.g:

``````m1 <- matrix(1:9, nrow = 3, ncol = 3, byrow = TRUE,
dimnames = list(c("s1", "s2", "s3"),c("cow", "dog","cat")))
m2 <- matrix(1:9, nrow = 3, ncol = 3, byrow = FALSE,
dimnames = list(c("s1", "s2", "s3"),c("dog", "cow","cat")))
> m1
cow dog cat
s1   1   2   3
s2   4   5   6
s3   7   8   9
> m2
dog cow cat
s1   1   4   7
s2   2   5   8
s3   3   6   9
``````

I would like to create a function using cor.test() to calculate the correlation between corresponding columns. E.g. cow vs cow, dog vs dog. The reason for using cor.test() is I want to obtain the correlation coefficient and p-value. So, if there are other ways to obtain this information, i'm open to those too. The actual data set has thousands of columns, which are randomly organized, so im looking for a way to match the columns first and then calculate the correlation. Any ideas?

Here is a solution, using `lapply` on common columns:

``````# Common columns
cols <- intersect(colnames(m1), colnames(m2))

# For each column, compute cor test
res <- lapply(cols, function(x) cor.test(
m1[, x],
m2[, x]
))

names(res) <- cols
``````

The result is a list of `htest` objects that you can access this way: `res[["cow"]]`