Explore correlations in R with corrr

July 13, 2016
By

(This article was first published on blogR, and kindly contributed to R-bloggers)

Earlier this week, my first package, corrr, was made available on CRAN. Below are the introductory instructions provided on the README for this first-release version 0.1.0. Please contribute to corrr on Github or email me your suggestions!

 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:

  • the latest released version from CRAN with
install.packages("corrr")
  • the latest development version from github with
if (packageVersion("devtools") < 1.6) {
  install.packages("devtools")
}
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:

  • A tbl with an additional class, cor_df
  • An extra “rowname” column
  • Standardised variances (the matrix diagonal) set to missing values (NA) so they can be ignored.

 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):

  • shave() the upper or lower triangle (set to NA).
  • rearrange() the columns and rows based on correlation strengths.

Reshape structure (tbl or cor_df out):

  • focus() on select columns and rows.
  • stretch() into a long format.

Output/visualisations (console/plot out):

  • fashion() the correlations for pretty printing.
  • rplot() plots the correlations.

 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 x 7
#>   rowname            v1          v2           v3            v4          v5
#>                                             
#> 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 

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 x 7
#>   rowname        v1        v2        v3          v4          v5
#>                                  
#> 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 

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(method = "HC", absolute = FALSE) %>%  # arrange by correlations
       shave() # Shave off the upper triangle for a clean result

fashion(x)
#>      disp wt   hp   carb qsec mpg  drat am   gear
#> disp                                             
#> wt    .89                                        
#> hp    .79  .66                                   
#> carb  .39  .43  .75                              
#> qsec -.43 -.17 -.71 -.66                         
#> mpg  -.85 -.87 -.78 -.55  .42                    
#> drat -.71 -.71 -.45 -.09  .09  .68               
#> am   -.59 -.69 -.24  .06 -.23  .60  .71          
#> gear -.56 -.58 -.13  .27 -.21  .48  .70  .79
rplot(x)

posts-combination-1.png

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