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README.Rmd
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README.Rmd
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---
output: github_document
---
# correlation <img src='man/figures/logo.png' align="right" height="139" />
```{r README-1, warning=FALSE, message=FALSE, echo=FALSE}
library(ggplot2)
library(poorman)
library(correlation)
options(digits = 2)
knitr::opts_chunk$set(
collapse = TRUE,
dpi = 300,
message = FALSE,
warning = FALSE,
fig.path = "man/figures/"
)
```
[](https://doi.org/10.21105/joss.02306) [](https://cranlogs.r-pkg.org/) [](https://CRAN.R-project.org/package=correlation)
<!-- [](https://cran.r-project.org/package=correlation) -->
`correlation` is an [**easystats**](https://github.com/easystats/easystats) package focused on correlation analysis. It's lightweight, easy to use, and allows for the computation of many different kinds of correlations, such as **partial** correlations, **Bayesian** correlations, **multilevel** correlations, **polychoric** correlations, **biweight**, **percentage bend** or **Sheperd's Pi** correlations (types of robust correlation), **distance** correlation (a type of non-linear correlation) and more, also allowing for combinations between them (for instance, *Bayesian partial multilevel correlation*).
# Citation
You can cite the package as follows:
Makowski, D., Ben-Shachar, M. S., Patil, I., \& Lüdecke, D. (2020). Methods and algorithms for correlation analysis in R. _Journal of Open Source Software_,
*5*(51), 2306. https://doi.org/10.21105/joss.02306
Makowski, D., Wiernik, B. M., Patil, I., Lüdecke, D., \& Ben-Shachar, M. S. (2022). *correlation*: Methods for correlation analysis [R package]. https://CRAN.R-project.org/package=correlation (Original work published 2020)
# Installation
[](https://cran.r-project.org/package=correlation) [](https://easystats.r-universe.dev) [](https://github.com/easystats/correlation/actions)
[](https://app.codecov.io/gh/easystats/correlation)
The *correlation* package is available on CRAN, while its latest development version is available on R-universe (from _rOpenSci_).
Type | Source | Command
---|---|---
Release | CRAN | `install.packages("correlation")`
Development | R-universe | `install.packages("correlation", repos = "https://easystats.r-universe.dev")`
Once you have downloaded the package, you can then load it using:
```{r, eval=FALSE}
library("correlation")
```
> **Tip**
>
> **Instead of `library(correlation)`, use `library(easystats)`.**
> **This will make all features of the easystats-ecosystem available.**
>
> **To stay updated, use `easystats::install_latest()`.**
# Documentation
Check out package [website](https://easystats.github.io/correlation/) for documentation.
# Features
The *correlation* package can compute many different types of correlation,
including:
✅ **Pearson's correlation**<br>
✅ **Spearman's rank correlation**<br>
✅ **Kendall's rank correlation**<br>
✅ **Biweight midcorrelation**<br>
✅ **Distance correlation**<br>
✅ **Percentage bend correlation**<br>
✅ **Shepherd's Pi correlation**<br>
✅ **Blomqvist’s coefficient**<br>
✅ **Hoeffding’s D**<br>
✅ **Gamma correlation**<br>
✅ **Gaussian rank correlation**<br>
✅ **Point-Biserial and biserial correlation**<br>
✅ **Winsorized correlation**<br>
✅ **Polychoric correlation**<br>
✅ **Tetrachoric correlation**<br>
✅ **Multilevel correlation**<br>
An overview and description of these correlations types is [**available here**](https://easystats.github.io/correlation/articles/types.html). Moreover,
many of these correlation types are available as **partial** or within a
**Bayesian** framework.
# Examples
The main function is [`correlation()`](https://easystats.github.io/correlation/reference/correlation.html), which builds on top of [`cor_test()`](https://easystats.github.io/correlation/reference/cor_test.html) and comes with a number of possible options.
## Correlation details and matrix
```{r README-4}
results <- correlation(iris)
results
```
The output is not a square matrix, but a **(tidy) dataframe with all correlations tests per row**. One can also obtain a **matrix** using:
```{r README-5}
summary(results)
```
Note that one can also obtain the full, **square** and redundant matrix using:
```{r README-6}
summary(results, redundant = TRUE)
```
```{r README-7}
library(see)
results %>%
summary(redundant = TRUE) %>%
plot()
```
## Correlation tests
The `cor_test()` function, for pairwise correlations, is also very convenient for making quick scatter plots.
```{r README-corr}
plot(cor_test(iris, "Sepal.Width", "Sepal.Length"))
```
## Grouped dataframes
The `correlation()` function also supports **stratified correlations**, all within the
*tidyverse* workflow!
```{r README-8}
iris %>%
select(Species, Sepal.Length, Sepal.Width, Petal.Width) %>%
group_by(Species) %>%
correlation()
```
## Bayesian Correlations
It is very easy to switch to a **Bayesian framework**.
```{r README-9}
correlation(iris, bayesian = TRUE)
```
## Tetrachoric, Polychoric, Biserial, Biweight...
The `correlation` package also supports different types of methods, which can
deal with correlations **between factors**!
```{r README-10}
correlation(iris, include_factors = TRUE, method = "auto")
```
## Partial Correlations
It also supports **partial correlations** (as well as Bayesian partial correlations).
```{r README-11}
iris %>%
correlation(partial = TRUE) %>%
summary()
```
## Gaussian Graphical Models (GGMs)
Such partial correlations can also be represented as **Gaussian Graphical
Models** (GGM), an increasingly popular tool in psychology. A GGM traditionally
include a set of variables depicted as circles ("nodes"), and a set of lines
that visualize relationships between them, which thickness represents the
strength of association (see [Bhushan et al., 2019](https://www.frontiersin.org/articles/10.3389/fpsyg.2019.01050/full)).
```{r README-12}
library(see) # for plotting
library(ggraph) # needs to be loaded
plot(correlation(mtcars, partial = TRUE)) +
scale_edge_color_continuous(low = "#000004FF", high = "#FCFDBFFF")
```
## Multilevel Correlations
It also provide some cutting-edge methods, such as Multilevel (partial)
correlations. These are are partial correlations based on linear mixed-effects
models that include the factors as **random effects**. They can be see as
correlations *adjusted* for some group (*hierarchical*) variability.
```{r README-13}
iris %>%
correlation(partial = TRUE, multilevel = TRUE) %>%
summary()
```
However, if the `partial` argument is set to `FALSE`, it will try to convert the
partial coefficient into regular ones.These can be **converted back** to full
correlations:
```{r README-14}
iris %>%
correlation(partial = FALSE, multilevel = TRUE) %>%
summary()
```
# Contributing and Support
In case you want to file an issue or contribute in another way to the package, please follow [this guide](https://easystats.github.io/correlation/CONTRIBUTING.html). For questions about the functionality, you may either contact us via email or also file an issue.
# Code of Conduct
Please note that this project is released with a
[Contributor Code of Conduct](https://easystats.github.io/correlation/CODE_OF_CONDUCT.html). By participating in this project you agree to abide by its terms.