funkyheatmap

funkyheatmap: Visualising data frames with mixed data types - Published in JOSS (2025)

https://github.com/funkyheatmap/funkyheatmap

Science Score: 93.0%

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Published in Journal of Open Source Software

Last synced: 6 months ago · JSON representation

Repository

Visualising data frames as funky heatmaps 📊

Basic Info

Host: GitHub
Owner: funkyheatmap
License: other
Language: R
Default Branch: main
Homepage: https://funkyheatmap.github.io/funkyheatmap/
Size: 91.7 MB

Statistics

Stars: 184
Watchers: 6
Forks: 12
Open Issues: 11
Releases: 7

Created over 3 years ago · Last pushed 7 months ago

Metadata Files

Readme Changelog Contributing License

README.Rmd

---
output: github_document
editor_options: 
  chunk_output_type: console
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
# dynbenchmark::reformat_tribbles("README.Rmd")
```

# {funkyheatmap} 


[![cran](https://www.r-pkg.org/badges/version-last-release/funkyheatmap)](https://cran.r-project.org/package=funkyheatmap)
[![R-CMD-check](https://github.com/funkyheatmap/funkyheatmap/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/funkyheatmap/funkyheatmap/actions/workflows/R-CMD-check.yaml)


**{funkyheatmap}** allows generating heatmap-like visualisations for data 
frames. Funky heatmaps can be fine-tuned by providing annotations of the 
columns and rows, which allows assigning multiple palettes or geometries
or grouping rows and columns together in categories.

## Installation

You can install funkyheatmap like so:

``` r
install.packages("funkyheatmap")
```

You can also download **{funkyheatmap}** as a [standalone executable](https://funkyheatmap.github.io/funkyheatmap/articles/executable.html) 
or a [Nextflow pipeline](https://funkyheatmap.github.io/funkyheatmap/articles/nextflow.html).

## Example

Let's use the `mtcars` dataset as an example of what a funky heatmap looks like.

```{r example}
library(funkyheatmap)
library(dplyr, warn.conflicts = FALSE)
library(tibble, warn.conflicts = FALSE)

data("mtcars")

data <- mtcars %>%
  rownames_to_column("id") %>%
  arrange(desc(mpg))
```

You need to provide some information on how each column should be rendered, for example:

```{r example-with-colinfo}
column_info <- tribble(
  ~id,     ~group,         ~name,                      ~geom,        ~palette,    ~options,
  "id",    "",             "",                         "text",       NA,          list(hjust = 0, width = 6),
  "mpg",   "overall",      "Miles / gallon",           "bar",        "palette1",  list(width = 4, legend = FALSE),
  "cyl",   "overall",      "Number of cylinders",      "bar",        "palette2",  list(width = 4, legend = FALSE),
  "disp",  "group1",       "Displacement (cu.in.)",    "funkyrect",  "palette1",  lst(),
  "hp",    "group1",       "Gross horsepower",         "funkyrect",  "palette1",  lst(),
  "drat",  "group1",       "Rear axle ratio",          "funkyrect",  "palette1",  lst(),
  "wt",    "group1",       "Weight (1000 lbs)",        "funkyrect",  "palette1",  lst(),
  "qsec",  "group2",       "1/4 mile time",            "circle",     "palette2",  lst(),
  "vs",    "group2",       "Engine",                   "circle",     "palette2",  lst(),
  "am",    "group2",       "Transmission",             "circle",     "palette2",  lst(),
  "gear",  "group2",       "# Forward gears",          "circle",     "palette2",  lst(),
  "carb",  "group2",       "# Carburetors",            "circle",     "palette2",  lst()
)
```

Now you can generate the funky heatmap:

```{r include=FALSE}
g <- funky_heatmap(data, column_info = column_info, position_args = position_arguments(expand_xmax = 4))
knitr::opts_chunk$set(fig.width = g$width, fig.height = g$height)
```

```{r eval=FALSE}
funky_heatmap(
  data,
  column_info = column_info,
  position_args = position_arguments(expand_xmax = 4)
)
```

```{r heatmap2, echo=FALSE}
g
```

## More information 
  
* The [reference documentation](https://funkyheatmap.github.io/funkyheatmap/reference/index.html)
  on `funky_heatmap()` details the exact formats of each annotation object that you can pass to it.

* Check out the [Getting started](https://funkyheatmap.github.io/funkyheatmap/articles/funkyheatmap.html)
  article in the package documentation for more information on how to customize this visualisation.

* In [`vignette("dynbenchmark", "funkyheatmap")`](https://funkyheatmap.github.io/funkyheatmap/articles/dynbenchmark.html) we 
  use funkyheatmap to regenerate the figures from Saelens et al. (2019)
  [doi:10.1038/s41587-019-0071-9](https://doi.org/10.1038/s41587-019-0071-9).

* We used [Viash](https://viash.io) to wrap the `funkyheatmap::funky_heatmap()` function
  as a [standalone executable](https://funkyheatmap.github.io/funkyheatmap/articles/executable.html) and
  [Nextflow module](https://funkyheatmap.github.io/funkyheatmap/articles/nextflow.html).

Owner

Name: funkyheatmap
Login: funkyheatmap
Kind: organization

Repositories: 5
Profile: https://github.com/funkyheatmap

JOSS Publication

funkyheatmap: Visualising data frames with mixed data types

Published

April 11, 2025

DOI

10.21105/joss.07698

Volume 10, Issue 108, Page 7698

Authors

Robrecht Cannoodt*

Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium, Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Belgium, Data Intuitive, Lebbeke, Belgium

Louise Deconinck*

Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium, Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Belgium

Artuur Couckuyt*

Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium, Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Belgium

Nikolay S. Markov*

Division of Pulmonary and Critical Care Medicine, Northwestern University, Chicago, USA

Luke Zappia

Data Intuitive, Lebbeke, Belgium, Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Munich, Germany, Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany

Malte D. Luecken

Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Munich, Germany, Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Comprehensive Pneumology Center (CPC-M), Germany; Member of the German Center for Lung Research (DZL)

Marta Interlandi

Institute of Medical Informatics, University of Muenster, Muenster, Germany

Yvan Saeys†

Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium, Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Belgium

Wouter Saelens†

Laboratory of Myeloid Cell Biology in tissue homeostasis and regeneration, VIB Center for Inflammation Research, Ghent, Belgium, Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium

Editor

Julia Romanowska

GitHub Events

Total

Create event: 23
Release event: 2
Issues event: 11
Watch event: 21
Delete event: 14
Issue comment event: 12
Push event: 112
Pull request review comment event: 39
Pull request review event: 46
Pull request event: 40
Fork event: 2

Last Year

Create event: 23
Release event: 2
Issues event: 11
Watch event: 21
Delete event: 14
Issue comment event: 12
Push event: 112
Pull request review comment event: 39
Pull request review event: 46
Pull request event: 40
Fork event: 2

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 28
Total pull requests: 55
Average time to close issues: 4 months
Average time to close pull requests: 11 days
Total issue authors: 17
Total pull request authors: 6
Average comments per issue: 2.39
Average comments per pull request: 0.25
Merged pull requests: 50
Bot issues: 0
Bot pull requests: 5

Past Year

Issues: 6
Pull requests: 36
Average time to close issues: 21 days
Average time to close pull requests: 7 days
Issue authors: 5
Pull request authors: 4
Average comments per issue: 1.33
Average comments per pull request: 0.06
Merged pull requests: 35
Bot issues: 0
Bot pull requests: 0

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Total packages: 1
Total downloads:
- cran 365 last-month
Total docker downloads: 55

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 7
Total maintainers: 1

cran.r-project.org: funkyheatmap

Generating Funky Heatmaps for Data Frames

Homepage: https://funkyheatmap.github.io/funkyheatmap/
Documentation: http://cran.r-project.org/web/packages/funkyheatmap/funkyheatmap.pdf
License: MIT + file LICENSE
Latest release: 0.5.2
published 11 months ago

Versions: 7
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 365 Last month
Docker Downloads: 55

Rankings

Stargazers count: 11.3%

Forks count: 21.9%

Average: 25.4%

Downloads: 28.4%

Dependent packages count: 29.8%

Dependent repos count: 35.5%

Maintainers (1)

rcannood@gmail.com

Last synced: 6 months ago

Dependencies

.github/workflows/R-CMD-check.yaml actions

actions/checkout v3 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/pkgdown.yaml actions

JamesIves/github-pages-deploy-action v4.4.1 composite
actions/checkout v3 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

DESCRIPTION cran

R >= 2.10 depends
RColorBrewer * imports
assertthat * imports
cli * imports
cowplot * imports
dplyr * imports
dynutils * imports
ggforce * imports
ggplot2 * imports
grDevices * imports
purrr * imports
stringr * imports
tibble * imports
tidyr * imports
knitr * suggests
rmarkdown * suggests
testthat >= 3.0.0 suggests

ecosyste.ms

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funkyheatmap

Science Score: 93.0%

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README.Rmd

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JOSS Publication

funkyheatmap: Visualising data frames with mixed data types

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