corrr

corrr is a package for exploring correlations in R. It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualising the matrix in terms of the strength of the correlations.

You can install:

install.packages("corrr")
install.packages("devtools")  # run this line if devtools is not installed
devtools::install_github("drsimonj/corrr")

Using corrr

Using corrr starts with correlate(), which acts like the base correlation function cor(). It differs by defaulting to pairwise deletion, and returning a correlation data frame (cor_df) of the following structure:

API

The corrr API is designed with data pipelines in mind (e.g., to use %>% from the magrittr package). After correlate(), the primary corrr functions take a cor_df as their first argument, and return a cor_df or tbl (or output like a plot). These functions serve one of three purposes:

Internal changes (cor_df out):

Reshape structure (tbl or cor_df out):

Output/visualisations (console/plot out):

Examples

library(MASS)
library(corrr)
set.seed(1)

# Simulate three columns correlating about .7 with each other
mu <- rep(0, 3)
Sigma <- matrix(.7, nrow = 3, ncol = 3) + diag(3)*.3
seven <- mvrnorm(n = 1000, mu = mu, Sigma = Sigma)

# Simulate three columns correlating about .4 with each other
mu <- rep(0, 3)
Sigma <- matrix(.4, nrow = 3, ncol = 3) + diag(3)*.6
four <- mvrnorm(n = 1000, mu = mu, Sigma = Sigma)

# Bind together
d <- cbind(seven, four)
colnames(d) <- paste0("v", 1:ncol(d))

# Insert some missing values
d[sample(1:nrow(d), 100, replace = TRUE), 1] <- NA
d[sample(1:nrow(d), 200, replace = TRUE), 5] <- NA

# Correlate
x <- correlate(d)
class(x)
#> [1] "cor_df"     "tbl_df"     "tbl"        "data.frame"
x
#> # A tibble: 6 × 7
#>   rowname            v1          v2           v3            v4          v5
#>     <chr>         <dbl>       <dbl>        <dbl>         <dbl>       <dbl>
#> 1      v1            NA  0.70986371  0.709330652  0.0001947192 0.021359764
#> 2      v2  0.7098637068          NA  0.697411266 -0.0132575510 0.009280530
#> 3      v3  0.7093306516  0.69741127           NA -0.0252752456 0.001088652
#> 4      v4  0.0001947192 -0.01325755 -0.025275246            NA 0.421380212
#> 5      v5  0.0213597639  0.00928053  0.001088652  0.4213802123          NA
#> 6      v6 -0.0435135083 -0.03383145 -0.020057495  0.4424697437 0.425441795
#> # ... with 1 more variables: v6 <dbl>

As a tbl, we can use functions from data frame packages like dplyr, tidyr, ggplot2:

library(dplyr)

# Filter rows by correlation size
x %>% filter(v1 > .6)
#> # A tibble: 2 × 7
#>   rowname        v1        v2        v3          v4          v5
#>     <chr>     <dbl>     <dbl>     <dbl>       <dbl>       <dbl>
#> 1      v2 0.7098637        NA 0.6974113 -0.01325755 0.009280530
#> 2      v3 0.7093307 0.6974113        NA -0.02527525 0.001088652
#> # ... with 1 more variables: v6 <dbl>

corrr functions work in pipelines (cor_df in; cor_df or tbl out):

x <- datasets::mtcars %>%
       correlate() %>%    # Create correlation data frame (cor_df)
       focus(-cyl, -vs, mirror = TRUE) %>%  # Focus on cor_df without 'cyl' and 'vs'
       rearrange() %>%  # rearrange by correlations
       shave() # Shave off the upper triangle for a clean result
       
fashion(x)
#>   rowname   am drat gear   wt disp  mpg   hp qsec carb
#> 1      am                                             
#> 2    drat  .71                                        
#> 3    gear  .79  .70                                   
#> 4      wt -.69 -.71 -.58                              
#> 5    disp -.59 -.71 -.56  .89                         
#> 6     mpg  .60  .68  .48 -.87 -.85                    
#> 7      hp -.24 -.45 -.13  .66  .79 -.78               
#> 8    qsec -.23  .09 -.21 -.17 -.43  .42 -.71          
#> 9    carb  .06 -.09  .27  .43  .39 -.55  .75 -.66
rplot(x)

datasets::airquality %>% 
  correlate() %>% 
  network_plot(min_cor = .2, legend = TRUE)