Here pink color indicates a negative correlation and blue color indicates a positive correlation.

When we are doing correlation analysis significance also important.

How to measure significant correlation analysis in R?

Load below mentioned package for p-value calculation

library(tidyverse)
library(broom)
dt1 = t(combn(names(dt), 2)) %>%
as_data_frame() %>%
setNames(c("x", "y"))
dt1
cor_result = dt1 %>%
mutate(results = map2(x, y, ~ cor.test(dt[[.x]], dt[[.y]], method = "pearson")),
results = map(results, tidy)) %>%
unnest(results)
cor_result

use the following function to extract estimate and p-value

`cor_result %>% select(x, y, estimate, p.value) %>% filter(p.value < 0.5)`

x y estimate p.value

<chr> <chr> <dbl> <dbl>

1 a b 0.631 0.0504

2 a d -0.332 0.349

3 b c -0.271 0.449

t-test in R