Correlation matrix with dplyr, tidyverse and broom - P-value matrix

Question

all. I want to obtain the p-value from a correlation matrix using dplyr and/or broom packages and testing multiple variables at the same time. I'm aware of other methods, but dplyr seems easier and more intuitive for me. In addition, dplyr will need to correlate each variable to obtain the specific p-value, what makes the process easier and faster.

I checked other links, but they did not work for this question (example 1, example 2, example 3) When I use this code, the correlation coefficients are reported. However, the P-values are not.

agreg_base_tipo_a %>% 
  dplyr::select(S2.RT, BIS_total, IDATE, BAI, ASRS_total) %>% 
  do(as.data.frame(cor(., method="spearman", use="pairwise.complete.obs")))

Please, check out this reproducible code to word:

set.seed(1164)

library(tidyverse)
ds <- data.frame(id=(1) ,a=rnorm(10,2,1), b=rnorm(10,3,2), c=rnorm(5,1,05))

ds %>% 
  select(a,b,c) %>% 
  do(as.data.frame(cor(., method="spearman", use="pairwise.complete.obs")))

Answer 1

This answer is based on akrun's comment from this post. By using the rcorr function, we can calculate the correlations and P values. To access these components, use ds_cor$r and ds_cor$P.

set.seed(1164)

library(tidyverse)
library(Hmisc)

ds <- data.frame(id=(1) ,a=rnorm(10,2,1), b=rnorm(10,3,2), c=rnorm(5,1,05))

ds_cor <- ds %>%
  select(-id) %>% 
  as.matrix() %>%
  rcorr(type = "spearman")

ds_cor
#    a     b     c
# a  1.00  0.28 -0.42
# b  0.28  1.00 -0.25
# c -0.42 -0.25  1.00
# 
# n= 10 
# 
# 
# P
#   a      b      c     
# a        0.4250 0.2287
# b 0.4250        0.4929
# c 0.2287 0.4929