R package for exploring correlations
Switch branches/tags
Nothing to show
Clone or download
Simon Jackson
Latest commit 0c269a2 Aug 11, 2018

README.md

corrr

CRAN_Status_Badge Build Status Downloads

corrr is a package for exploring correlations in R. It focuses on creating and working with data frames of correlations (instead of matrices) that can be easily explored via corrr functions or by leveraging tools like those in the tidyverse. This, along with the primary corrr functions, is represented below:

You can install:

  • the latest released version from CRAN with
install.packages("corrr")
  • the latest development version from github with
install.packages("devtools")  # run this line if devtools is not installed
devtools::install_github("drsimonj/corrr")

Using corrr

Using corrr typically 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() the correlations with shapes in place of the values.
  • network_plot() the correlations in a network.

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       v6
#>   <chr>        <dbl>     <dbl>     <dbl>      <dbl>    <dbl>    <dbl>
#> 1 v1       NA          0.710     0.709     0.000195  0.0214   -0.0435
#> 2 v2        0.710     NA         0.697    -0.0133    0.00928  -0.0338
#> 3 v3        0.709      0.697    NA        -0.0253    0.00109  -0.0201
#> 4 v4        0.000195  -0.0133   -0.0253   NA         0.421     0.442 
#> 5 v5        0.0214     0.00928   0.00109   0.421    NA         0.425 
#> 6 v6       -0.0435    -0.0338   -0.0201    0.442     0.425    NA

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      v6
#>   <chr>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>   <dbl>
#> 1 v2      0.710 NA      0.697 -0.0133 0.00928 -0.0338
#> 2 v3      0.709  0.697 NA     -0.0253 0.00109 -0.0201

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
#> 
#> Correlation method: 'pearson'
#> Missing treated using: 'pairwise.complete.obs'
       
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)
#> 
#> Correlation method: 'pearson'
#> Missing treated using: 'pairwise.complete.obs'