Myujth

I am trying to run rcorr as part of a function over multiple dataframes, extracting p-values for each test but am receiving an NA values when piping into rcorr.

For example if I create a matrix and run rcorr on this matrix, extracting the pvalue table with $P and the pvalue with [2] it works...

library(Hmisc)
library(magrittr)

mt <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), ncol=2)
 rcorr(mt, type="pearson")$P[2]
 [1] 0

But if I try and pipe this I only recieve NAs.

mt %>% rcorr(., type="pearson")$P[2]
[1] NA NA

mt %>% rcorr(., type="pearson")$P
Error in .$rcorr(., type = "pearson") : 
 3 arguments passed to '$' which requires 2

Can someone explain to me why this doesnt work or give a workaround? Ideally I don't want to have to create variables for each of my matrices before running rcorr

Thanks in advance.

Solution

(mt %>% mcor(type = "pearson"))$P[2]
# [1] 0

Explanation

Notice that both

mt %>% rcorr(., type = "pearson")

and

mt %>% rcorr(type = "pearson")

work as expected. The problem is that you add $ and [ to the second object, which basically are like subsequent function calls. For instance,

s <- function(x) c(1, 1 + x)
1 %>% s
# [1] 1 2

works as expected, but

1 %>% s[1]
# Error in .[s, 1] : incorrect number of dimensions

doesn't return 1 since we are trying to do something like s[1](1) instead.

Now

1 %>% s(x = .)[1]
# Error in .[s(x = .), 1] : incorrect number of dimensions

just as yours

mt %>% rcorr(., type = "pearson")$P[2]
# [1] NA NA

is trickier. Notice that it can be rewritten as

mt %>% `[`(`$`(rcorr(., type = "pearson"), "P"), 2)
# [1] NA NA

So, now it becomes clear that the latter doesn't work because it basically is

`[`(mt, `$`(rcorr(mt, type = "pearson"), "P"), 2)
# [1] NA NA

which, when deciphered, is

mt[rcorr(mt, type = "pearson")$P, 2]
# [1] NA NA

A tidy solution, at least I hope!

library(dplyr) 
library(broom) 
library(Hmisc) 

mtcars[, 5:6] %>%
as.matrix()%>%
rcorr()%>%
tidy() %>%
select(estimate) 

A simple solution using %$% from magrittr:

library(Hmisc)
library(magrittr)
mt <- matrix(1:10, ncol=2)

mt %>% rcorr(type="pearson") %$% P[2]
[1] 0