# R – Defining Your Own Color schemes for HeatMaps

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This post is intended at those who are beginners at R, and is inspired by a small post in Martin’s bioblog.**Doodling with Data**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

First, we plot a “correlation heatmap” using the same logic that Martin uses. In our example, let’s use the Movies dataset that comes with ggplot2.

We take the 6 genre columns, and we can compute the correlation matrix for those 6 columns.

Here’s what the matrix looks like:

> cor(movieGenres) # 6×6 cor matrix

Action Animation Comedy Drama

Action 1.000000000 -0.05443315 -0.08288728 0.007760094

Animation -0.054433153 1.00000000 0.17967294 -0.179155441

Comedy -0.082887284 0.17967294 1.00000000 -0.255784957

Drama 0.007760094 -0.17915544 -0.25578496 1.000000000

Documentary -0.069487718 -0.05204238 -0.14083580 -0.173443622

Romance -0.023355368 -0.06637362 0.10986485 0.103545195

Documentary Romance

Action -0.06948772 -0.02335537

Animation -0.05204238 -0.06637362

Comedy -0.14083580 0.10986485

Drama -0.17344362 0.10354520

Documentary 1.00000000 -0.07157792

Romance -0.07157792 1.00000000

When we plot with the default colors we get:

It is difficult to see the details in the tiles. Now, if you want to better control the colors, you can use the handy

**colorRampPalette()**function and combine that with

**scale_fill_gradient2**.

Let’s say that we want “red” colors for negative correlations and “green” for positives.

(We can gray out the 1 along the diagonal.)

Doing this produces:

If there are values close to 1 or to -1, those will pop out visually. Values close to 0 are a lot more muted.

Hope that helps someone.

References: Using R: Correlation Heatmap with ggplot2

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