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

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
  • 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 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):

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

The 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 corrr API.


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

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