recipe r-tidyheatmap

This is a tidy implementation for heatmap. At the moment it is based on the (great) package 'ComplexHeatmap'. The goal of this package is to interface a tidy data frame with this powerful tool. Some of the advantages are: Row and/or columns colour annotations are easy to integrate just specifying one parameter (column names). Custom grouping of rows is easy to specify providing a grouped tbl. For example: df %>% group_by(…). Labels size adjusted by row and column total number. Default use of Brewer and Viridis palettes.



GPL3 / GPL-3.0-only



package r-tidyheatmap

(downloads) docker_r-tidyheatmap



depends bioconductor-complexheatmap:


depends r-base:


depends r-circlize:


depends r-dendextend:

depends r-dplyr:


depends r-lifecycle:


depends r-magrittr:


depends r-patchwork:

depends r-purrr:


depends r-rcolorbrewer:


depends r-rlang:


depends r-tibble:

depends r-tidyr:


depends r-viridis:




You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install r-tidyheatmap

and update with::

   mamba update r-tidyheatmap

To create a new environment, run:

mamba create --name myenvname r-tidyheatmap

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `r-tidyheatmap/tags`_ for valid values for ``<tag>``)

Download stats