knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-" )
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:
install.packages("corrr")
# install.packages("remotes") remotes::install_github("tidymodels/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:
tbl
with an additional class, cor_df
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 r 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/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.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.
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) x
NOTE: Previous to corrr 0.4.3, the first column of a cor_df
dataframe was named "rowname". As of corrr 0.4.3, the name of this first column changed to "term".
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)
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 fashion(x) rplot(x) datasets::airquality %>% correlate() %>% network_plot(min_cor = .2)
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