correlation matrix with p-value in R

Question

Suppose I want conduct correlation matrix

library(dplyr)

data(iris)
iris %>% 
  select_if(is.numeric) %>%
  cor(y =iris$Petal.Width, method = "spearman") %>%  round(2)

now we see

              [,1]
Sepal.Length  0.83
Sepal.Width  -0.29
Petal.Length  0.94
Petal.Width   1.00

i want that statistical significant correlation were marked by * where

*<0,05
**<0,01
*** <0,001

ho to do it?

Answer 1

A solution using tidyverse. We can convert the data frame to long format, create list column using nest, and then use map to perform cor.test for each subset. After that, map_dbl can extract the P value by specifying the name "p.value". dat1 is the final output.

library(tidyverse)

data(iris)
dat1 <- iris %>% 
  select_if(is.numeric) %>%
  gather(Column, Value, -Petal.Width) %>%
  group_by(Column) %>%
  nest() %>%
  mutate(Cor = map(data, ~cor.test(.x$Value, .x$Petal.Width, method = "spearman"))) %>%
  mutate(Estimate = round(map_dbl(Cor, "estimate"), 2), 
         P_Value = map_dbl(Cor, "p.value"))

dat1
# # A tibble: 3 x 5
#   Column       data               Cor         Estimate  P_Value
#   <chr>        <list>             <list>         <dbl>    <dbl>
# 1 Sepal.Length <tibble [150 x 2]> <S3: htest>    0.83  4.19e-40
# 2 Sepal.Width  <tibble [150 x 2]> <S3: htest>   -0.290 3.34e- 4
# 3 Petal.Length <tibble [150 x 2]> <S3: htest>    0.94  8.16e-70

If you don't need the list columns, you can use select to remove them.

dat1 %>% select(-data, -Cor)
# # A tibble: 3 x 3
#   Column       Estimate  P_Value
#   <chr>           <dbl>    <dbl>
# 1 Sepal.Length    0.83  4.19e-40
# 2 Sepal.Width    -0.290 3.34e- 4
# 3 Petal.Length    0.94  8.16e-70

Now we can use mutate and case_when to add the label to show significance.

dat2 <- dat1 %>%
  select(-data, -Cor) %>%
  mutate(Significance = case_when(
    P_Value < 0.001  ~ "*** <0,001",
    P_Value < 0.01   ~ "** <0,01",
    P_Value < 0.05   ~ "*<0,05",
    TRUE             ~ "Not Significant"
  ))
dat2
# # A tibble: 3 x 4
#   Column       Estimate  P_Value Significance
#   <chr>           <dbl>    <dbl> <chr>       
# 1 Sepal.Length    0.83  4.19e-40 *** <0,001  
# 2 Sepal.Width    -0.290 3.34e- 4 *** <0,001  
# 3 Petal.Length    0.94  8.16e-70 *** <0,001 

Answer 2

You could adapt corstarsl() to your needs.

corFun <- function (x) {
  library(Hmisc)
  x <- as.matrix(x)
  R <- rcorr(x, type="spearman")$r
  p <- rcorr(x, type="spearman")$P
  stars <- ifelse(p < 0.001, "***", ifelse(p < 0.01, "** ", 
                                             ifelse(p < 0.05, "* ", " ")))
  R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[, -1]
  Rnew <- matrix(paste(R, stars, sep = ""), ncol = ncol(x))
  diag(Rnew) <- paste(diag(R), " ", sep = "")
  rownames(Rnew) <- colnames(x)
  colnames(Rnew) <- paste(colnames(x), "", sep = "")
  Rnew <- as.matrix(Rnew)
  Rnew <- as.data.frame(Rnew)
  return(Rnew)
}

Yielding

> data.frame(r=corFun(iris[, -5])[, 4])
                    r
Sepal.Length  0.83***
Sepal.Width  -0.29***
Petal.Length  0.94***
Petal.Width     1.00