Explore correlations in R with corrr
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Earlier this week, my first package, corrr
, was made available on CRAN. Below are the introductory instructions provided on the README for this firstrelease 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 #> <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 x 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(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)
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