background preloader

Heatmaps

Facebook Twitter

Superheat: supercharged heatmaps for R. The heatmap is a useful graphical tool in any data scientist's arsenal.

Superheat: supercharged heatmaps for R

It's a useful way of representing data that naturally aligns to numeric data in a 2-dimensional grid, where the value of each cell in the grid is represented by a color. The hourly heatmap with ggplot2. I’ve had a few folk get in touch following my last post, all commenting on the last plot : Most of these enquiries went along the following lines: “I hadn’t thought of doing that.

The hourly heatmap with ggplot2

It looks really easy. But how do you do it?” Unfortunately I was away the last couple of days without access to the code, and I didn’t want to pass on the wrong info. HeatmapHrbyDay.R What is the use case? Some comments : The Interpol dataset is pretty large, so we filter out for the first weather station, which conveniently had 2 full years data.

Static and Interactive Heatmap in R - Unsupervised Machine Learning. ComplexHeatmap is an R/bioconductor package, developed by Zuguang Gu, which provides a flexible solution to arrange and annotate multiple heatmaps.

Static and Interactive Heatmap in R - Unsupervised Machine Learning

It allows also to visualize the association between different data from different sources. Making Faceted Heatmaps with ggplot2. We were doing some exploratory data analysis on some attacker data at work and one of the things I was interested is what were “working hours” by country.

Making Faceted Heatmaps with ggplot2

Now, I don’t put a great deal of faith in the precision of geolocated IP addresses since every geolocation database that exists thinks I live in Vermont (I don’t) and I know that these databases rely on a pretty “meh” distributed process for getting this local data. However, at a country level, the errors are tolerable provided you use a decent geolocation provider. Since a rant about the precision of IP address geolocation was not the point of this post, let’s move on. One of the best ways to visualize these “working hours” is a temporal heatmap. How to easily make beautiful heatmaps with ezplot - Part 8 – Become Great at R. Q: How would you display information in three variables (2 categorical vars and 1 numerical var) in one chart?

How to easily make beautiful heatmaps with ezplot - Part 8 – Become Great at R

A: Heat map. In this post, we’ll look at how to easily make effective heatmaps using ezplot. We’ll use a dataset of NBA basketball statistics hosted at flowingdata.com. Let’s get started. Prerequisites Install a set of development tools On Windows, download and install Rtools. Install and Load ezplot. Getting Genetics Done: R User Group Recap: Heatmaps and Using the caret Package. At our most recent R user group meeting we were delighted to have presentations from Mark Lawson and Steve Hoang, both bioinformaticians at Hemoshear.

Getting Genetics Done: R User Group Recap: Heatmaps and Using the caret Package

All of the code used in both demos is in our Meetup’s GitHub repo. Making heatmaps in R Steve started with an overview of making heatmaps in R. Using the iris dataset, Steve demonstrated making heatmaps of the continuous iris data using the heatmap.2 function from the gplots package, the aheatmap function from NMF, and the hard way using ggplot2. The “best in class” method used aheatmap to draw an annotated heatmap plotting z-scores of columns and annotated rows instead of raw values, using the Pearson correlation instead of Euclidean distance as the distance metric. Classification and regression using caret Mark wrapped up with a gentle introduction to the caret package for classification and regression training. Display Large Matrix as an Image. Plotting a matrix as an image in R. Squash - multivariate visualization package for R. Squash is an add-on package for the R statistical environment.

squash - multivariate visualization package for R

This package provides functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps. Lower-level functions are provided to map numeric values to colors, display a matrix as an array of colors, and draw color keys. Higher-level plotting functions are provided to generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more. The current version is 1.0.6 (2014-08-04). As with many R packages, squash can be obtained from CRAN, or can can be downloaded and installed automatically by entering the following at the R prompt: install.packages('squash') Previous versions are here.

Please send questions or comments about squash to Aron. News Aug 5, 2014: Version 1.0.6 is now available from CRAN. Aug 15, 2011: squash is now available from CRAN. Examples library(squash) The bivariate histogram. Video: Using heat maps to visualize matrices. (This article was originally published at The DO Loop, and syndicated at StatsBlogs.)

Video: Using heat maps to visualize matrices

One of my presentations at SAS Global Forum 2014 was about the new heat map functions in SAS/IML 13.1. Over the summer I created a short video of my presentation, which gives an overview of visualizing matrices with heat maps, and describes how to choose colors for heat maps: If your browser does not support embedded video, you can go directly to the video on YouTube. If you prefer to read articles about heat maps so that you can study the concepts and cut and paste examples, here are a few recent blog posts that are based on my SAS Global Forum presentation:

Interactive Heatmaps (and Dendrograms) – A Shiny App. Heatmaps are a great way to visualize data matrices.

Interactive Heatmaps (and Dendrograms) – A Shiny App

Heatmap color and organization can be used to encode information about the data and metadata to help learn about the data at hand. An example of this could be looking at the raw data or hierarchically clustering samples and variables based on their similarity or differences. There are a variety packages and functions in R for creating heatmaps, including heatmap.2. I find pheatmap particularly useful for the relative ease in annotating the top of the heat map using an arbitrary number of items (the legend needs to be controlled for best effect, not implemented). Controlling heatmap colors with ggplot2. MarginTale: ggplot2 Time Series Heatmaps.

Require(quantmod) require(ggplot2) require(reshape2) require(plyr) require(scales) # Download some Data, e.g. the CBOE VIX getSymbols("^VIX",src="yahoo")

MarginTale: ggplot2 Time Series Heatmaps

Charting time series as calendar heat maps in R. Last month we showcased the JSM Data Expo, where the winning entry was a visualization of airline delays represented as a color-coded calendar. That graphic was created in SAS, but now thanks to reader Paul Bleicher, we can show you how to create the same graphic in R. Paul Bleicher, MD PhD is Chief Medical Officer at Humedica, a next-generation clinical informatics company that provides novel business intelligence solutions to the health care and life science industries.

Paul is leading a team that is using R extensively for a wide variety of predictive analytics and data visualization applications with medical record data. Paul has been kind enough to share his R code that takes a sequence of numeric values indexed by date, and represents them as a calendar with the days filled with colors representing the values. It's easier to explain by example: let's download Microsoft's stock price from 2006 to date from Yahoo, and plot it using Paul's calendarHeat function: stock <- "MSFT" stock, Calender Heatmap with Google Analytics Data. As data analytics consulting firm, we think we are fortunate that we keep finding problems to find.

Recently my team mate found a glaring problem of not having any connector for R with Google. With the inspiration from Michael, Ajay O, it soon become a worth problem to solve. With RGoogleAnalytics package now, we have solved the problem of data extraction into R from Google Analytics a new breed of ideas started emerging primarily around visualization. I have been playing with GGplot2 has been great package to convert data into visualization. Thanks Dr. Using R: Correlation heatmap with ggplot2. Calendar charts with googleVis.