r-cran-heatmaply 1.1.0+dfsg-2build1 source package in Ubuntu

Changelog

r-cran-heatmaply (1.1.0+dfsg-2build1) groovy; urgency=medium

  * No-change rebuild against r-api-4.0

 -- Steve Langasek <email address hidden>  Sun, 31 May 2020 06:16:53 +0000

Upload details

Uploaded by:
Steve Langasek
Uploaded to:
Groovy
Original maintainer:
Ubuntu Developers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Groovy release universe misc

Builds

Groovy: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-heatmaply_1.1.0+dfsg.orig.tar.xz 69.4 KiB cb00b1028abb583aebb938f2837a3296d227aeb81f20d7a2ceab4440d057b7b1
r-cran-heatmaply_1.1.0+dfsg-2build1.debian.tar.xz 3.0 KiB debc7722450ff0b3ce846f6c67a67eb266873a0de33b9d8013615e444403008c
r-cran-heatmaply_1.1.0+dfsg-2build1.dsc 2.5 KiB 98df8dc18e29f5cf05dc935cd1a0def166878a04a081ee02efeec13beeff282a

View changes file

Binary packages built by this source

r-cran-heatmaply: GNU R interactive cluster heat maps using 'plotly'

 Create interactive cluster 'heatmaps' that can be saved as a stand alone
 HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and
 available in the 'RStudio' viewer pane. Hover the mouse pointer over a
 cell to show details or drag a rectangle to zoom. A 'heatmap' is a
 popular graphical method for visualizing high-dimensional data, in which
 a table of numbers are encoded as a grid of colored cells. The rows and
 columns of the matrix are ordered to highlight patterns and are often
 accompanied by 'dendrograms'. 'Heatmaps' are used in many fields for
 visualizing observations, correlations, missing values patterns, and
 more. Interactive 'heatmaps' allow the inspection of specific value by
 hovering the mouse over a cell, as well as zooming into a region of the
 'heatmap' by dragging a rectangle around the relevant area. This work is
 based on the 'ggplot2' and 'plotly.js' engine. It produces similar
 'heatmaps' as 'heatmap.2' or 'd3heatmap', with the advantage of speed
 ('plotly.js' is able to handle larger size matrix), the ability to zoom
 from the 'dendrogram' panes, and the placing of factor variables in the
 sides of the 'heatmap'.