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corrr

Explore correlations in R

https://github.com/tidymodels/corrr

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    Low similarity (19.1%) to scientific vocabulary

Keywords from Contributors

tidy-data setup tidyverse
Last synced: 11 months ago · JSON representation

Repository

Explore correlations in R

Basic Info
Statistics
  • Stars: 594
  • Watchers: 18
  • Forks: 58
  • Open Issues: 21
  • Releases: 10
Created about 10 years ago · Last pushed 12 months ago
Metadata Files
Readme Contributing License Code of conduct

README.Rmd 

---
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-"
)
```

# corrr 


[![R-CMD-check](https://github.com/tidymodels/corrr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidymodels/corrr/actions/workflows/R-CMD-check.yaml)
[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/corrr)](https://cran.r-project.org/package=corrr)
[![Codecov test coverage](https://codecov.io/gh/tidymodels/corrr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidymodels/corrr?branch=main)


corrr is a package for exploring **corr**elations 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](https://www.tidyverse.org/). This, along with the primary corrr functions, is represented below:



You can install:

- the latest released version from CRAN with

```{r install_cran, eval = FALSE}
install.packages("corrr")
```

- the latest development version from GitHub with

```{r install_git, eval = FALSE}
# install.packages("remotes") 
remotes::install_github("tidymodels/corrr")
```

## 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 "term" column
- Standardized 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 `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.

## 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.

## Examples

```{r example, message = FALSE, warning = FALSE}
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`:

```{r, message = FALSE, warning = FALSE}
library(dplyr)

# Filter rows by correlation size
x %>% filter(v1 > .6)
```

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

```{r combination, warning = FALSE, fig.height = 4, fig.width = 5}
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)
```

## Contributing

This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on RStudio Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).

- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/corrr/issues).

- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html) (a minimal, reproducible example), to clearly communicate about your code.

- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).

Owner

  • Name: tidymodels
  • Login: tidymodels
  • Kind: organization

GitHub Events

Total
  • Issues event: 1
  • Watch event: 8
  • Issue comment event: 2
  • Push event: 2
  • Pull request event: 2
  • Fork event: 2
Last Year
  • Issues event: 1
  • Watch event: 8
  • Issue comment event: 2
  • Push event: 2
  • Pull request event: 2
  • Fork event: 2

Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 315
  • Total Committers: 18
  • Avg Commits per committer: 17.5
  • Development Distribution Score (DDS): 0.533
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
drsimonj d****n@g****m 147
Edgar Ruiz e****r@r****m 40
Julia Silge j****e@g****m 34
Edgar Ruiz e****z@g****m 21
topepo m****n@g****m 20
Daryn Ramsden t****n@g****m 14
cimentadaj c****j@g****m 13
James Laird-Smith j****h@g****m 6
Kirill Müller k****r@m****g 5
Simon Jackson s****n@b****m 4
Emil Hvitfeldt e****t@g****m 3
jsta s****2@m****u 2
Antoine Sachet a****c@g****m 1
Hannah Frick h****k@g****e 1
Michael Grund 2****d 1
Michael Grund m****d@i****m 1
Matthew T. Warkentin m****n@m****a 1
Samuel Scherrer s****r@p****e 1
Committer Domains (Top 20 + Academic)
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Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 74
  • Total pull requests: 39
  • Average time to close issues: 3 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 48
  • Total pull request authors: 13
  • Average comments per issue: 2.84
  • Average comments per pull request: 1.95
  • Merged pull requests: 34
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
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Pull Request Authors
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Top Labels
Issue Labels
feature (7) bug (5) upkeep (1) reprex (1)
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • cran 7,815 last-month
  • Total docker downloads: 9,985
  • Total dependent packages: 8
    (may contain duplicates)
  • Total dependent repositories: 47
    (may contain duplicates)
  • Total versions: 19
  • Total maintainers: 1
proxy.golang.org: github.com/tidymodels/corrr
  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.4%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 12 months ago
cran.r-project.org: corrr

Correlations in R

  • Versions: 12
  • Dependent Packages: 8
  • Dependent Repositories: 47
  • Downloads: 7,815 Last month
  • Docker Downloads: 9,985
Rankings
Stargazers count: 0.6%
Forks count: 1.3%
Dependent repos count: 3.7%
Downloads: 3.9%
Dependent packages count: 6.1%
Average: 6.5%
Docker downloads count: 23.7%
Maintainers (1)
max@rstudio.com
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.4 depends
  • dplyr >= 1.0.0 imports
  • ggplot2 >= 2.2.0 imports
  • ggrepel >= 0.6.5 imports
  • glue >= 1.4.2 imports
  • purrr >= 0.2.2 imports
  • rlang >= 0.4.0 imports
  • seriation >= 1.2 imports
  • tibble >= 2.0 imports
  • DBI * suggests
  • RSQLite * suggests
  • covr * suggests
  • dbplyr >= 1.2.1 suggests
  • knitr >= 1.13 suggests
  • rmarkdown >= 0.9.6 suggests
  • sparklyr >= 0.9 suggests
  • testthat >= 3.0.0 suggests
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