180 search results for "heatmap"

Heatmap tables with ggplot2, sort-of

August 27, 2012
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Heatmap tables with ggplot2, sort-of

I wrote before about heatmap tables as a better way of producing frequency or other tables, with a solution which works nicely in latex. It is possible to do them much more easily in ggplot2, like this library(Hmisc) library(ggplot2) library(reshape) data(HairEyeColor) P=t(HairEyeColor) Pm=melt(P) ggfluctuation(Pm,type="heatmap")+geom_text(aes(label=Pm$value),colour="white")+ opts(axis.text.x=theme_text(size = 15),axis.text.y=theme_text(size = 15)) Note that ggfluctuation will also take

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Heatmap tables with ggplot2

August 20, 2012
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Heatmap tables with ggplot2

I wrote before about heatmap tables as a better way of producing frequency or other tables, with a solution which works nicely in latex. It is possible to do them much more easily in ggplot2, like this library(Hmisc) library(ggplot2) library(reshape) data(HairEyeColor) P=t(HairEyeColor) Pm=melt(P) ggfluctuation(Pm,type="heatmap")+geom_text(aes(label=Pm$value),colour="white")+ opts(axis.text.x=theme_text(size = 15),axis.text.y=theme_text(size = 15)) Note that ggfluctuation will also take … Continue reading...

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ggplot2 Time Series Heatmaps

April 15, 2012
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ggplot2 Time Series Heatmaps

How do you easily get beautiful calendar heatmaps of time series in ggplot2? E.g:From MarginTaleI was impressed by the lattice-based  implementation from Paul Bleicher of Humedica, which you can find referenced in http://blog.revolutionanalytics.c...

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heatmaps: controlling the color representation with set data range

January 27, 2012
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heatmaps: controlling the color representation with set data range

Often you want to set the fixed colors for particular range of your dataset to be sure that the visual output is correctly represented. This is particularly useful for time series data, where the range or your dataset might drastically … Continue reading →

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Simple Heatmap in R with Formula One Dataset

October 24, 2011
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Simple Heatmap in R with Formula One Dataset

Now, that the 2011 F1 season is over I decided to quickly scrub the Formula 1 data of the F1.com website, such as the list of drivers, ordered by the approximate amount of salary driver is getting (top list driver is making the most, approx. 30MM) and position at the end of each race. There

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S&P 500 components heatmap in R

October 12, 2011
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S&P 500 components heatmap in R

In this article, Hans Gilde exposes the clever use of a heatmap hidden in the Bioconductor library. In his example, he describes a way to show different ‘observations’ on subjects, with the concept of time. Financial indices, like the S&P 500 or the Dow Jones indices, are mathematically some kind of measure of overall market

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k-mean clustering + heatmap

October 10, 2011
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If you want more info about clustering, I have another post about "Clustering analysis and its implementation in R". Here is the link:  http://onetipperday.blogspot.com/2012/04/clustering-analysis-2.html------------Several R functions in this...

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Heatmap tables done better, in Sweave and latex

July 10, 2011
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Heatmap tables done better, in Sweave and latex

  I wrote before about using heatmap tables to combine the strengths of tables and graphics for nominal data. Here is a neat approach using Sweave and latex to produce an effect like in the picture. This latex code is self-contain...

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Heatmap tables done better, in Sweave and latex

July 10, 2011
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Heatmap tables done better, in Sweave and latex

  I wrote before about using heatmap tables to combine the strengths of tables and graphics for nominal data. Here is a neat approach using Sweave and latex to produce an effect like in the picture. This latex code is self-contained. Just save it as myfile.Rnw, run Sweave(myfile.Rnw) from inside R and then pdflatex myfile.tex

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Drawing heatmaps in R

June 24, 2011
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Drawing heatmaps in R

A while back, while reading chapter 4 of Using R for Introductory Statistics, I fooled around with the mtcars dataset giving mechanical and performance properties of cars from the early 70's. Let's plot this data as a hierarchically clustered heatmap. # scale data to mean=0, sd=1 and convert to matrix mtscaled <- as.matrix(scale(mtcars)) # create...

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