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
- the latest development version from GitHub with
# install.packages("remotes") remotes::install_github("tidymodels/corrr")
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
tblwith an additional class,
- An extra “term” column
- Standardized variances (the matrix diagonal) set to missing values
NA) so they can be ignored.
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
tbl (or output like a plot). These functions serve one of three
Internal changes (
shave()the upper or lower triangle (set to NA).
rearrange()the columns and rows based on correlation strengths.
Reshape structure (
focus()on select columns and rows.
stretch()into a long format.
Output/visualizations (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.
Databases and Spark
correlate() function also works with database tables. The function
will automatically push the calculations of the correlations to the
database, collect the results in R, and return the
cor_df object. This
allows for those results integrate with the rest of the
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) #>  "cor_df" "tbl_df" "tbl" "data.frame" x #> # A tibble: 6 × 7 #> term v1 v2 v3 v4 v5 v6 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 v1 NA 0.684 0.716 0.00187 -0.00769 -0.0237 #> 2 v2 0.684 NA 0.702 -0.0248 0.00495 -0.0161 #> 3 v3 0.716 0.702 NA -0.00171 0.0205 -0.0566 #> 4 v4 0.00187 -0.0248 -0.00171 NA 0.452 0.442 #> 5 v5 -0.00769 0.00495 0.0205 0.452 NA 0.424 #> 6 v6 -0.0237 -0.0161 -0.0566 0.442 0.424 NA
NOTE: Previous to corrr 0.4.3, the first column of a
dataframe was named “rowname”. As of corrr 0.4.3, the name of this first
column changed to “term”.
tbl, we can use functions from data frame packages like
library(dplyr) # Filter rows by correlation size x %>% filter(v1 > .6) #> # A tibble: 2 × 7 #> term v1 v2 v3 v4 v5 v6 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 v2 0.684 NA 0.702 -0.0248 0.00495 -0.0161 #> 2 v3 0.716 0.702 NA -0.00171 0.0205 -0.0566
corrr functions work in pipelines (
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 computed with #> • Method: 'pearson' #> • Missing treated using: 'pairwise.complete.obs' fashion(x) #> term mpg drat am gear qsec carb hp wt disp #> 1 mpg #> 2 drat .68 #> 3 am .60 .71 #> 4 gear .48 .70 .79 #> 5 qsec .42 .09 -.23 -.21 #> 6 carb -.55 -.09 .06 .27 -.66 #> 7 hp -.78 -.45 -.24 -.13 -.71 .75 #> 8 wt -.87 -.71 -.69 -.58 -.17 .43 .66 #> 9 disp -.85 -.71 -.59 -.56 -.43 .39 .79 .89 rplot(x)
datasets::airquality %>% correlate() %>% network_plot(min_cor = .2) #> Correlation computed with #> • Method: 'pearson' #> • Missing treated using: 'pairwise.complete.obs'
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