If you've watched any of the demos for SAS Visual Analytics (or even tried it yourself!), you have probably seen this nifty exploration of multiple measures.

It's a way to look at how multiple measures are correlated with one another, using a diagonal heat map chart. The "stronger" the color you see in the matrix, the stronger the correlation.

You might have wondered (as I did): can I build a chart like this in Base SAS? The answer is **Yes** (of course). It won't match the speed and interactivity of SAS Visual Analytics, but you might still find this to be a useful way to explore your data.

#### The approach

There are four steps to achieving a similar visualization in the 9.3 version of Base SAS. (Remember that ODS Graphics procedures are part of **Base SAS** in SAS 9.3!)

- Use the CORR procedure to create a data set with a correlations matrix. Actually, several SAS procedures can create TYPE=CORR data sets, but I used PROC CORR with Pearson's correlation in my example.
- Use DATA step to rearrange the CORR data set to prepare it for rendering in a heat map.
- Define the graph "shell" using the Graph Template Language (GTL) and the HEATMAPPARM statement. You've got a lot of control over the graph appearance when you use GTL.
- Use the SGRENDER procedure to create the graph by applying the CORR data you prepared in the first two steps.

Here's an example of the result:

#### The program

I wrapped up the first two steps in a SAS macro. The macro first runs PROC CORR to create the matrix data, then uses DATA step to transform the result for the heat map.

**Note:** By default, the PROC CORR step will treat all of the numeric variables as measures to correlate. That's not always what you want, especially if your data contains categorical columns that just happen to be numbers. You can use DROP= or KEEP= data set options when using the macro to narrow the set of variables that are analyzed. The examples (near the end of this post) show how that's done.

/* Prepare the correlations coeff matrix: Pearson's r method */ %macro prepCorrData(in=,out=); /* Run corr matrix for input data, all numeric vars */ proc corr data=&in. noprint pearson outp=work._tmpCorr vardef=df ; run; /* prep data for heat map */ data &out.; keep x y r; set work._tmpCorr(where=(_TYPE_="CORR")); array v{*} _numeric_; x = _NAME_; do i = dim(v) to 1 by -1; y = vname(v(i)); r = v(i); /* creates a lower triangular matrix */ if (i<_n_) then r=.; output; end; run; proc datasets lib=work nolist nowarn; delete _tmpcorr; quit; %mend; |

You have to define the graph "shell" (or template) only once in your program. The template definition can then be reused in as many PROC SGRENDER steps as you want.

This heat map definition uses the fact that correlations are always between -1 and 1. Negative numbers show a negative correlation (ex: cars of higher weight will achieve a lower MPG). It's useful to select a range of colors that make it easier to discern the relationships. In my example, I went for "strong" contrasting colors on the ends with a muted color in the middle.

/* Create a heat map implementation of a correlation matrix */ ods path work.mystore(update) sashelp.tmplmst(read); proc template; define statgraph corrHeatmap; dynamic _Title; begingraph; entrytitle _Title; rangeattrmap name='map'; /* select a series of colors that represent a "diverging" */ /* range of values: stronger on the ends, weaker in middle */ /* Get ideas from http://colorbrewer.org */ range -1 - 1 / rangecolormodel=(cxD8B365 cxF5F5F5 cx5AB4AC); endrangeattrmap; rangeattrvar var=r attrvar=r attrmap='map'; layout overlay / xaxisopts=(display=(line ticks tickvalues)) yaxisopts=(display=(line ticks tickvalues)); heatmapparm x = x y = y colorresponse = r / xbinaxis=false ybinaxis=false name = "heatmap" display=all; continuouslegend "heatmap" / orient = vertical location = outside title="Pearson Correlation"; endlayout; endgraph; end; run; |

You can then use the macro and template together to produce each visualization. Here are some examples:

/* Build the graphs */ ods graphics /height=600 width=800 imagemap; %prepCorrData(in=sashelp.cars,out=cars_r); proc sgrender data=cars_r template=corrHeatmap; dynamic _title="Corr matrix for SASHELP.cars"; run; %prepCorrData(in=sashelp.iris,out=iris_r); proc sgrender data=iris_r template=corrHeatmap; dynamic _title= "Corr matrix for SASHELP.iris"; run; /* example of dropping categorical numerics */ %prepCorrData( in=sashelp.pricedata(drop=region date product line), out=pricedata_r); proc sgrender data=pricedata_r template=corrHeatmap; dynamic _title="Corr matrix for SASHELP.pricedata"; run; |

Download complete program: corrmatrix_gtl.sas for SAS 9.3

*Spoiler alert*: These steps will only get easier in a future version of SAS 9.4, where similar built-in visualizations are planned for PROC CORR and elsewhere.

#### Related resources

You can apply a similar "heat-map-style" coloring to ODS tables by creating custom table templates.

If you haven't yet tried SAS Visual Analytics, it's worth a test-drive. Many of the visualizations are inspiring (as this blog post proves).

Finally, while I didn't dissect the GTL heat map definition in detail in this post, you can learn a lot more about GTL from Sanjay Matange and his team at the Graphically Speaking blog.

#### Acknowledgments

Big thanks to Rick Wicklin, who helped me quite a bit with this example. Rick validated my initial approach, and also provided valuable suggestions to improve the heat map and the statistical meaning of the example. He pointed me to http://colorbrewer.org, which provides examples of useful color ranges that you can apply in maps -- colors that are easy to read and don't distract from the meaning.

Rick told me that he is working on some related work coming up on his blog and within SAS 9.4, so you should watch his blog for additional insights.

## 6 Comments

Two points.

1. How can the heat map be labelled by variable labels instead of names?

2. Without specifying a variable list (which saves needing drops or keeps) the order of variables is the same as in the dataset as seen by position option in proc contents. I would prefer a varlist approach so as to match PROC CORR output from its given var list. At the moment I have not amended the macro but use KEEP and RETAIN in a prior data step so get the right order.

I'm not smart enough to solve point 1 though!

Hans,

I think you can get what you want for the labels on one axis by changing this line:

to this:

But for the other axis, you have to reintroduce the labels into the corr matrix data set. Here's one way.

It looks like you have what you need for controlling the order: specify the numeric vars that you want, in the sequence you want, on a KEEP= option. For example:

Love this! Exactly what I needed. Thank you!

An alternative method to custom table templates is much simpler: define a format and use that to set the background of the table cells. This works in PROC TABULATE and PROC PRINT. I use a non-linear 18-point scale from red to blue through white coded as HSV colours.

.

In PROC PRINT use (where the format is

corr.):var Corr / style(data)={background=corr.};

Hello Chris

Thanks. Your example is amazing.

I want to make a panel graph of this heatmap.

For example I am trying to display child mortality rates by year and states by gender in a panel graph. I mean 2 graphs for separate genders with a common Y-axis 'state', male and female graphs show side by side. Basic graph works with the following codes.

`ods graphics /height=800 width=1200 imagemap;`

proc sgpanel data=heat_map_data_q;

format StateCode $Statef. deceasedsex $Sexf.;

panelby deceasedsex;

heatmapparm x=survey_Year y=StateCode

colorresponse=Death_rate_LB_cat;

run;

I was tried to bring your template to Proc SGrender to show a panel graph but failed all my attempts.

Would you be able to advice me what changes to be done in your proc template scripts and what changes to be done in Proc SGrender or Proc SGpanel procedures to produce a panel graph.

Wilson, this is a great question to post into the SAS Support Communities. Try the board about ODS Graphics.

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[...] Visualizing a correlation or covariance matrix reveals relationships between variables. Chris Hemedinger has written an article that describes how to visualize correlation matrices by using a heat map. [...]

[...] use the RANGEATTRMAP and RANGEATTRVAR statements to specify colors for a gradient color ramp, as shown in Chris Hemedinger's blog post. This is my last post until after the Winter Break. I am taking a 10-day hiatus from blogging to [...]

[…] number of unique values, such as certain covariance matrices and sparse matrices. You can also use heat maps with a continuous color ramp to visualize correlation matrices or data […]