More on Spaghetti Plots

In her article Creating Spaghetti Plots Just got Easy, Lelia McConnell has provided us a glimpse into some new useful features in the SAS 9.4M2 release.  The term Spaghetti plots generally refers to cases where time series plots have to be  identified by multiple group classifications.  The support for the GroupLC and GroupLP options, among others make it easy to create such graphs.

The key point to note here is that the GROUP variable is used to decide which observations in the plot should be connected.  So, the group variable should provide the finest grain classification for the series in the plot.  Normally, each individual series is rendered using one of the GraphData elements from the style, providing unique attributes to each series.

Spaghetti_GTLHowever, if multiple series in the graph represent one specific value from another classifier, such as treatment or study, we can provide higher classification roles using the GroupLC  (GroupLineColor) or GroupLP (Group Line Pattern), etc., as shown in the graph on the right.  In this example, the simulated data represents the adoption rate over time for some item classified by location (color) and year (pattern) using the following code for the SERIES plot statement:

series x=x y=y / group=id lineattrs=(thickness=2 pattern=solid)
grouplc=Location grouplp=year smoothconnect;

This graph, along with other graphs using SAS 9.4M2 features are shown in the samples in the Graph Focus page on the SAS Support web site.  Now that SAS 9.4M2 is released (Aug 5), we will be adding more samples demonstrating the features at this location.

Many of you who do not yet have the SAS 9.4M2 release are asking, what does this do for me?  Well, there is good new.  While this feature has now been included in the SGPLOT procedure, it has always been available in GTL. Here is the GTL code you can use at SAS 9.4.  Note the use of the subpixel and itemsize options.

proc template;
  define statgraph MultiClassSeries;
    begingraph / subpixel=on;
      entrytitle 'Adoption Rate over Time by Location and Year';
      layout overlay / yaxisopts=(offsetmin=0.1);
        seriesplot x=x y=y / group=id name='a' lineattrs=(thickness=2) 
                             linecolorgroup=Location linepatterngroup=year;
        discretelegend 'a' / title='Location:' type=linecolor location=inside 
             valign=bottom halign=right;
        discretelegend 'a' / title='Year:' type=linepattern location=inside 
             valign=bottom halign=left itemsize=(linelength=30px);

You can also run this with SAS 9.3, except for the subpixel and itemsize option.  Remove those, and you are good to go.

SAS 9.4 GTL code:  Spaghetti

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Creating Spaghetti Plots Just Got Easy

This article is by guest contributor Lelia McConnell, SAS Tech Support.

Creating Spaghetti Plots Just Got Easy

Sample 38076: “Response by patient and treatment group” illustrates how to generate a spaghetti plot using the SGPLOT procedure.  Sample 40255: “Plot of study results by treatment group” illustrates how to generate a spaghetti plot with PROC SGPLOT prior to SAS® 9.4 TS1M2.  In both samples, a custom style template is necessary in order to get the desired results.

Beginning in SAS 9.4 TS1M2, spaghetti plots are easier than ever to create, thanks to the SGPLOT and SGPANEL procedures and new syntax that was added to the Graph Template Language (GTL).

First, let’s look at how to generate this graph using GTL.  The following options were added to the SERIESPLOT statement in SAS 9.4 TS1M2:

  •  LINECOLORGROUP= column|expression
  • LINEPATTERNGROUP= column|expression
  • MARKERSYMBOLGROUP= column|expression
  • MARKERCOLORGROUP= column|expression

DataIn addition, the TYPE= option was added to the DISCRETELEGEND statement, giving you the ability to include any or all of the grouping information in your legend.  Here are the values for the TYPE= option:


Let’s now generate a spaghetti plot using GTL.  Data sample is shown on the right.


Spaghetti_GTLproc template;
  define statgraph grouping;
      entrytitle 'Study Results by Treatment Group';
     layout overlay;
        seriesplot x=time y=results/ group=subject
               linecolorgroup=trt_group name='grouping';
        discretelegend 'grouping' / type=linecolor;

proc sgrender data=one template=grouping;

 In the code above, notice that a separate line is to be drawn for each value of the variable SUBJECT and that the line color is determined by the values of the variable TRT_GROUP.    This program can be found in Sample 52962: “Create a spaghetti plot with the Graph Template Language (GTL).”

Now let’s create this same graph using PROC SGPLOT.

The following options were added to the SERIES statement in PROC SGPLOT and PROC SGPANEL:

  • GROUPLC - equivalent to LINECOLORGROUP in GTL

In addition, the following values are now supported in the TYPE option in the KEYLEGEND statement:

  • FILL
  • LINE

Spaghetti_SGThe following sample code illustrates how to produce a spaghetti plot with PROC SGPLOT:

proc sgplot data=one;
  title 'Study Results by Treatment Group';
  series x=time y=results / group=subject
               grouplc=trt_group name='grouping';
  keylegend 'grouping' / type=linecolor;

This program can be found in Sample 52964: “Create a spaghetti plot with the SGPLOT procedure.”

Full SAS 9.4M2 code: Spaghetti  

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Epidemic Curve Graph

A few weeks back I wrote an article on Grouped Timeline for creating a stacked timeline for onset of different virus.  The idea in that article was to display a stacked needle on a time axis using a HighLow plot. Such graphs are also referred to as EPI or Epidemic Curve Graphs.

ByDate_93In that article, I restricted the weeks in the year for onset to 52, and plotted each value on the equivalent location on a time axis.  That all works fine, but really, an year will have 53 weeks for onsets as shown in the graph on the right. Gaps are shown where the data is missing.

The problem is that a start or end week of the year may have smaller number of days.   This causes the bars (with fixed width) for these weeks to be overlapped by neighboring weeks.  Click on the graph to see this in the higher resolution image.  You will see near "Jan 2014", the bar for week 53 of 2013 is overlapped by the bar for week 1 of 2014.  This will happen if the X axis is a real time axis, and week 53 has only 1 or 2 days in it.

ByYearWeek2_93Another way to address this is to draw a BAR graph by the YearWeek variable.  This variable is a combination of the year and week values so as to avoid values form the two different years from being consolidated into one bar, as shown on the right.

Such a graph is easier to make, as the bar chart already does stacked groups using GROUP=Virus.  The X axis is suppressed, and the week values are shown below each bar using another overlaid bar chart.  If you click on the graph for a higher resolution image, you will notice that in this case (as expected) the axis is discrete, and only the weeks that are present in the data are displayed, without gaps for the missing weeks.  A bar or a gap for week 16 is not displayed in the graph.

Let us see if we can get the best of both worlds.  First, let us create a data set that has all weeks in the data with missing response values for the frequency.  Then, we merge this with the actual data.  This ensures all weeks are present in the data and are represented in the graph either with data or a gap.

Virus_BarChartLabelBelow93SAS 9.3 version of this graph is shown on the right.  Click on the graph for a higher resolution image and you will see that all weeks are now represented, with gaps where there is no data.  Week 53 and week 1 are not overlapped, and can be seen distinctly.  However, it is clear that the axis is not a scaled time axis, but is discrete, so the 53 weeks will take up more space than a real year on a time axis.  Also, the 53rd week may have less number of days, but has the same width as all other bars.

Epidemic_GTL_94The final graph is created using SAS 9.4 GTL, and I have added some labeling to indicate the year for the data.  Click on the graph for a higher resolution view.  I believe this should be doable with SG, but I ran into an issue with bar labels that needs investigation.

I used a reference line with scatterplot markercharacter to display the boundary between the 2013 and 2014 data.

Epidemic_Block_GTL_94As usual with SG or GTL, there are other ways to display such demarcation as shown in the graph on the right.

SAS 9.3 program: EPI_93

SAS 9.4 program: EPI_94

Data: Test_dataset

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Legend Order in SGPLOT Procedure

This article is by guest contributor Lelia McConnell, SAS Tech Support.

Several users have called recently to ask the question, “Can I reorder the legend entries on the bar chart that I created with PROC SPLOT?”

Although there is no option that does this directly in PROC SGPLOT, the answer to this question is “YES, you can define the order of your legend entries.”

Graph_1In this post, I present an example that illustrates the syntax that you would use to define the order of your legend entries.  For this example, we begin by sub setting the data set SASHELP.CARS to include only observations in which TYPE is not equal to HYBRID.  To do this, we use the WHERE option in the SET statement, thus creating the data set CARS.

By bringing the CARS data set into PROC SGPLOT, I can create a vertical bar chart of the values of ORIGIN, where the height of the bars is based on the mean values of the variable MPG_CITY and the group variable is TYPE.

data cars;
  set ne 'Hybrid'));  
proc sgplot data=cars;
  vbar origin / response=mpg_city  group=type 
  groupdisplay=cluster stat=mean ;
  xaxis display=(nolabel);
  title 'Mileage by Origin and Type';

Instead of the default order of SUV, Sedan, Sports, Truck, and Wagon, I want the order of the legend entries to be Wagon, Sports, SUV, Truck, and Sedan.  To make this change, I create a numeric variable that contains the values 1-5, based on the order in which I want my vehicle types to be displayed.

I need to create a format in order to display the values of TYPE in the legend instead of 1-5.  The most efficient way to do this is to create a control data set that I can use with PROC FORMAT.  Since I need only one observation for each value of MYTYPE in this data set, I will sort the data by MYTYPE so that I can use the FIRST logic in the DATA step that follows.  In the DATA step, I need to create the columns FMTNAME, START, and LABEL.  These are used to define the format name, original value, and format values, respectively.  When you create a format, the automatic variable TYPE defines the variable as numeric or character, so we need to include the statement DROP TYPE to remove the variable TYPE from the control data set.

Graph_2The CNTLIN option in the PROC FORMAT statement specifies the SAS data set from which PROC FORMAT builds the format.

Now I can resubmit my original PROC SGPLOT code along with the FORMAT statement to create the legend in the correct order and the KEYLEGEND statement with the TITLE option in order to keep the original title in my legend.

data newcars;
set cars;
if type='Wagon' then mytype=1;
else if type='Sports' then mytype=2;
else if type='SUV' then mytype=3;
else if type='Truck' then mytype=4;
else if type='Sedan' then mytype=5;
proc sort data=newcars out=sortcars;
by mytype;
data myfmt;
set sortcars;
  by mytype;
  if first.mytype then do;
    drop type;
proc format cntlin=myfmt;
proc sgplot data=newcars;
   vbar origin / response=mpg_city group=mytype 
                 groupdisplay=cluster stat=mean;
   xaxis display=(nolabel);
   keylegend /title='Type';
   format mytype typefmt.;
   title 'Mileage by Origin and Type';

Full SAS 9.3 SGPLOT code:  Legend_93

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Overlay Bar Charts

A couple of days back, Rick Wicklin forwarded me a link to an article on the BadHessian Blog on creating a Bar Chart using six different freeware packages in R, Python and Julia.   The target bar chart was one produced by the Jetpack stat module with WordPress.  The graph is shown below.


The unique feature of this graph that had caught the eye of the author was the overlaying of two bar charts, one within the other.  The author's goal was to investigate the capabilities of other graphics packages to create a similar graph, such as R base graphics package, GGPLOT2, Python - Matplotlib, Python - Seaborn, Julie - Gadfly and Julia -

As users of SAS SG Procedures and GTL are aware, such graphs are very easy with the SGPLOT procedure, and examples of such graphs have been shown in this blog and in other places.  Here is the same graph created using the SGPLOT procedure.


SAS 9.3 SGPLOT program:

proc sgplot data=visits nowall noborder;
  styleattrs datacolors=(%rgbhex(140, 185, 202) %rgbhex(19, 85, 137));
  vbar month / response=views nostatlabel nooutline;
  vbar month / response=visitors nostatlabel barwidth=0.5 nooutline;
  keylegend / location=outside position=topright noborder valueattrs=(size=5);
  xaxis fitpolicy=thin display=(nolabel noticks) valueattrs=(size=6 color=gray);
  yaxis grid display=(noline noticks nolabel) valueattrs=(size=6  color=gray);

The %RGBHEX macro is supplied by Perry Watts, and converts a RGB value to CX color value.  It is included in the attached full code.  Many options used here are needed to make the graph visually similar to the original, and are not necessary if one was to accept the default settings for the procedure.  That would reduce the code by a large fraction.

The author of the post has set the X axis spacing of 5 months.  The reason for this is not clear, maybe it is to allow different months to be displayed.   For a discrete axis, SGPlot will try to show all the values on the axis, unless they don't fit cleanly.  Then, as in this case, the values are thinned symmetrically.  If the axis was numeric with a time format, you will get thinned axis tick values.

The author mentions a preference for the outer Y grid line (for Y=10000), and has made an extra effort to include this in the graphs.  For SGPLOT, the preferred default is to include a tick value outside the data range only if the extreme data point goes beyond 30% of the tick interval with inner ticks.  In this case, since the data does not seem to go very much past 8000, the tick value at 10000 is not shown by default.  This prevents wasteful white space outside the data.  Of course this can be changed to produce an outer tick value if a user really wants it using the Threshold option.

SGPLOT has a way to customize the tick values one wants to see on the discrete axis using TickValueList and TickDisplayList.  However it is clear we could use a simpler option to do this.  This can be useful when the discrete data has sequential numeric, time or some other predictable values.

Another noteworthy item in the SGPLOT graph is the outline on the color swatches in the legend.  This is done to allow swatches of very light color to be visible.  However, a case could be made to provide an option to suppress the outline to match the bar.

Users looking for a bit more aesthetic rendering can use skins and gradients without distorting the data as shown below.


For graphs with a smaller amount of data, it may be desirable (based on individual preference) to offset the two bars by a small amount to show overlapped bars.  This too is easily done with SGPLOT procedure by using the DiscreteOffset option as shown in the graph below.


Full SAS 9.3 Program:  Bars

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Group order in GTL

This post could be titled something like "Everything you wanted to know about Group Order in GTL - and more."   The group ordering shows up in three different ways in your graph.

  1. Assignment of attributes (color, marker symbol) to group values.
  2. Position of group values in the graph.
  3. Display of the group values in the legend.

Unique group values are assigned their visual attributes (color, marker symbol) from the GraphData1 - GraphDataN elements defined in the active style.  Most SAS shipped styles like LISTING or HTMLBLUE have 12 group elements.

Starting with SAS 9.3M1, assignment of attributes to group values is based only on the order in which these group values are present in the data  So, if the group value "B" is encountered first in the data, it gets the attributes from GraphData1 style element, and so on.  This happens even if an observation cannot actually be drawn in the graph due to other other reasons.  Also, missing group values get the "GraphMissing" element, and do not impact assignment of the other non-missing values.

I have created all graphs for this article using the GTL code shown below to keep the discussion simple.  The SGPLOT procedure uses the SAS summary object to compute some statistics under the covers.  This can change the order of the data received by the graph renderer.  Here is the GTL program.

proc template;
  define statgraph bar;
    dynamic _resp _footnote _order;
      entrytitle 'Mileage by Origin and Type';
      entryfootnote halign=left "Data Set = " _footnote;
      layout overlay / xaxisopts=(display=(tickvalues));
	barchart category=Origin response=_resp / group=Type groupdisplay=cluster 
                 stat=mean outlineattrs=graphdatadefault name='a' ;
        discretelegend 'a' / sortorder=_order;

Note, _resp, _footnote and _order are dynamics so we can use the same GTL template to produce all the graphs in this article.  These dynamics are set in the SGRENDER procedure.  If a dynamic value for an option is not set, that option is ignored.

The template itself uses the following:

  • A BARCHART statement, with Category, Response and Group options, using cluster groups and stat=mean.
  • A DiscreteLegend, with the sort option.

CarsIn the full data set (minus Hybrids), the first group value is SUV, but the first group value for the category Europe is Sedan.  We use this data set to create the graph using the SGRENDER step shown below.

proc sgrender data=cars template=bar;
dynamic _resp="mpg_city"

The  graph is shown below.   The group colors are assigned in the order the unique group values are encountered in the data.  "SUV" is the first value for TYPE in the data set, and hence gets GraphData1 as the style element, with the blue fill color.  "Sedan" is the second group value, so gets GraphData2, with the red fill color.  All of the group colors are displayed in the legend in the order they are encountered in the data.  Click on the graph for a higher resolution image.

BarCarsThe order of displaying each group value within each category is unique to each category.  So, for the category "Europe", Sedan is the first group value, and hence is displayed first.  The color used for Sedan is consistent in the whole graph.

Now, instead of letting the GTL BarChart summarize the data, let us summarize the data ourselves.  We will use the MEANS procedure to get the MeanMpg by Origin and Type as follows:

CarMeansproc means data=cars noprint;
class origin type;
var mpg_city;
output out=carmeans

Note, in the data shown above, the order of the type values has changed.  Now the first type value for Europe is also SUV.  This is different from the order of the original data.

Now, we use this data to plot the MeanMpg by Origin and Type using the same template and the SGRENDER code below.

BarCarMeansproc sgrender data=carmeans template=bar;
  dynamic _resp="meanMpg" _footnote="CarMeans"; 

Note in the graph above, the order for assigning the group colors has now changed.  Wagon and Truck have now swapped positions and colors.  Also, the position of each type value within each category has changed.  The colors are correct within the graph, but are no longer consistent with the first graph.

BarCarMeansByMpgThe purpose of this exercise was to order the group values within each category by descending value of the response.  Since the order within each category is retained as was in the data, we can now sort the data however we want, and display the values in our custom order.

The graph above shows the car types by origin sorted by the mean mileage within each category.  Now, since Sedan is first in the data, it gets the first color, and so on.  But, every time you do this kind of a custom sort (say by car counts), the incoming data order changes, and so does the color assignment.  How can we retain consistent group colors across all graphs?

AttrMapTo ensure the colors are consistent we can use a Discrete Attr Map.  Extract the original order of the group values using the MEANS procedure, and construct an attr map data set so that the colors are specified using the order.   The attr map data set is shown on the right.

BarCarMeansByMpgMapThe graph created using this attr map is shown on the right, with the legend entries sorted alphabetically.  The colors of each type are exactly as the original graph, though the positions in the legend are now in alphabetical order.

BarCarMeansByMpgMergedIf we are really picky, and want the colors assigned as per the original order AND get positions in the legend in the same data order, we have to play a little trick.   Instead of building an attr map, we use the extracted the original order, and prepend those values into the sorted data, with missing values for the Origin column.   See code in the attached program.

Remember, we said at the top of the article that colors are assigned based on the order of the group values, even if the observations cannot be drawn due to other reasons.  So, prepending the unique group values in the order you want, with other missing values will do this trick.  Now, the color values are assigned in the original data order (or, whatever order we want), and the observations are drawn in the order they are in rest of the data.

SAS 9.4 Code:  Group_Order_94


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Spirals are cool.  And useful.  We use them every day without thinking about it.  Every time the road turns from a straight line to a curve, we go through a transition spiral.  Spirals allow us to change curvature in a steady increasing or decreasing fashion.   Without a spiral, this transition would be abrupt.

Weather_SeriesFor our purpose, spirals can also be useful for visualizing data that is cyclical in nature.  If you are visualizing daily high temperature over a 2 year period, you could plot it on a straight X axis as shown on the right.

The cyclical nature of the data is evident in the graph.  But, this same cyclical behavior may be  easier to understand on a spiral with one cycle per year.   So, that is the plan for today.

A visit to Wikipedia page on spirals reveals there are many kinds of spirals including logarithmic, hyperbolic and many more.  Let us start with the simple Archimedean Spiral.  This has the simple formula:  R = A + B * theta.

Experimenting with this equation yields these different spirals for various values of A and number of cycles.

Spiral_N1A=0, Cycles=1

The x and y points are computed using the spiral equation, for theta values up to N * 360, where N is the number  of cycles.  The curve is plotted using the series statement of the SGPLOT procedure.

proc sgplot data=spiral nowall noborder pad=0;
  series x=x y=y / smoothconnect;
  xaxis min=-&max max=&max grid display=none;
  yaxis min=-&max max=&max grid display=none;

A=0.5, Cycles=2

The first spiral starts from the center (A=0) and turns through 360 degree cycle.  The second spiral starts at an offset of 0.5 the radius and turns through 720 degrees.

Once the spiral is drawn, now we need to map the data on to the spiral so that the time axis is along the spiral and the response values are drawn normal to the spiral, towards the center.

Spiral_N3_VThe graph on the right shows the basic idea.  At each point along the time axis, we compute the theta and then find the (x, y) point on the spiral.  The direction vector (cx, cy) for the response (the arrows) is towards the center of the spiral (0, 0).   In the example on the right, all arrow heights are half the spiral spacing.  So, we can compute the (x2, y2) location of the arrow heads from (x, y) and (cx, cy) as shown in the program.

I have used some SAS 9.4 features to draw smooth curves and remove background wall and border.   A SAS 9.3 version program is also included.

Spiral_N3_VS5With real time series data, we would normalize the response over the entire range, and plot the data one side of the spiral by using the abs() value and a color to signify sign.  Then, scale the vector by response and plot.  A simulated example is shown on the right.

We will cover the mapping real world time series data (as shown in the first graph on top) on to the spirals in next article on Spirals.

SAS 9.4 Program:  Spiral_Macro_94

SAS 9.3 Program:  Spiral_Macro_93

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Proportional Euler Diagram

The topic of VENN diagrams had come up a while ago.  At that time, I thought it may be interesting to build a proportional VENN diagram.  But, reading up on VENN Diagrams, I learned that VENN diagrams represent all intersections of N sets, regardless of whether there are actually any observations in one of the regions.  So, there did not seem any purpose to make a proportional VENN diagram, and maybe the term itself is an oxymoron.

Euler_30_20_0_SheenI was interested in a graphical representation of the number of different types of subjects in a study, say subjects with Diabetes, or Hypertension or both.   It turns out, Euler Diagrams do represent the real world data, and not all theoretical combination.  So, it would make sense to draw a Proportional Euler Diagram.

I started with the simple 2-Set case, as it seems achievable.  The results are shown on the right.  The values for N1, N2 and NI are also Euler_30_0_20_sheenshown in the footnote, along with the value of the convergence error.  The two special cases are shown on the right, and are straightforward.  Click on the graphs for a higher resolution image.

The two cases with intersecting circles are shown below.  For the first one, the numbers are such that the intersection point of the two circles lies in-between the centers of the two circles.   For the second case, the intersection lies to the right of the smaller circle.

Euler_30_20_10_NoneIn all cases, the radius of the larger circle is set to 10 (arbitrary), and I compute the area of the smaller circle proportional to the number of observations in the circles.

Here are the details of my program:

  • N1, N2 and NI are the number of observations in Set 1, Set 2 and intersection ONLY.
  • So, N1+NI is first circle, and N2+NI is the 2nd circle, and NI is the intersection.
  • Euler_30_10_30_PressN1 >= N2.
  • Special case #1 -> NI=0.  This means the two circles are non-overlapping.
  • Special case #2 -> N2=0.  This means the circle 2 is fully inside circle 1.
  • Case #3 -> the intersecting vertical line is between center 1 and center 2.
  • Case #4 -> the intersection vertical line is to the right of centers #2.

Here is the algorithm:

  • First, I assign v - height of the intersection above centerline = 1.
  • Compute the three different areas.
  • Compute the area per observation in each section.
  • Then, based on the ratio of ANI / AN1, I adjust v by the error ratio.  V is kept < r2.
  • I repeat this while the error is > 0.001 and number of iterations < N.
  • Now, if the error is still > 0.001, convergence is not reached and the intersection is to the right of the center 2.
  • Now, set v=0.99999*r2 and repeat the same computations above, with reducing v.

I assume convergence is reached, and based on this value of v, I compute the horizontal distance from center of each circle to the intersection, d1 and d2 and other numbers needed to plot the details.

I can use the ELLIPSEPARM or BUBBLE (RelativeScale=False)  statement to draw the plot.  However, SGPLOT procedure does not support these statements (not in the 80-20 range for simple plots).  So, I used GTL, with the BubblePlot because I wanted to use skins.

I made it into a macro, with three parameters N1, N2 and NI.  Skin is optional.  If you have a need for Proportional Euler Diagrams in your work, please chime in and let me know if this is useful to you.  Maybe you have made one of your own and I would love to hear how you went about solving for the intersection areas.

VENN diagram shapes for 2, 3, 4 and more sets are available on the web, would be possible to make these using EllipseParm statement for both circles and ellipses.

I plan to tackle the case of the 3 set Proportional Euler Diagram.  This same algorithm may not extend to this case.  I would love to hear your ideas.

Full GTL Macro program:  Euler_Bubble_Macro

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Graphs are easy in SAS University Edition

By now you have heard all about the SAS(R) STUDIO software that provides access to the power of SAS analytics in a Web browser.  The SAS(R) University Edition is also available free for higher education teaching, learning and  research.

This software includes ODS Graphics software for creating graphs.  You can use the familiar program window to write your own SAS data step and procedures.  An example of running your own SGPLOT program is shown in Robert's recent article on How to create a Histogram using the SAS University Edition.

Studio_UI_2Making graphs gets even easier in SAS Studio by using the graph tasks that are included with the software.  When you first launch the software, you will see the user interface shown on the right.  Click on the "Tasks" button on the left, and you will see a list of tasks by category.

Here I have highlighted the Tasks folder and the Graph subfolder under it.

Multiple graph tasks are available including Bar Chart, Bar-Line Chart, Box Plot, Histogram, Line Chart, Pie Chart, Scatter Plot, Series Plot and more.

Histogram_TaskEach of these tasks presents you with a form to set the data and various options as shown on the right.  Here, I have launched the "Histogram" task as shown highlighted in blue.  This starts the Histogram task.

Each task presents you with an easy to use visual interface to set the parameters and options necessary to make the graph.  These are all collected under two tabs - The Data tab and the Options Tab.  In the image on the right, the Data tab for the Histogram task is highlighted in yellow.

Each graph task allows you to provide the name of the data set and the required variables to create the graph.  In case of Histogram, you need to provide only one numeric variable for the Analysis Role.  In the example above, we have selected the SASHELP.CARS data set and the MPG_CITY column for graphing.

Histogram_ResultsOnce the required parameters are provided, you can submit the task by pressing the run button or 'F3'.  The task will render the histogram using the default settings for styles and present the graph to you in the Results window as shown on the right.

Each task also supports optional settings which are included under the "Options" tab.  These options can be used to customize your graph, including setting of titles, footnotes and  graph size.  In this case, I have set the graph size to 4" x 3" to fit the small region.

Each graph task generates the required SGPLOT code needed to render the graph.  This code is available in the Code window under the "Code / Results" tab.  This code is built and updated as you apply the settings in the Data and Options panels.  So, this is a good way to get started with learning the SGPLOT procedure.

The tasks cover many of the features available in the SGPLOT procedure, but not all.  So, you can cut and paste the code into the program window and customize it to your own needs.

You can learn more about creating graphs using SG procedures right here in this blog.  Learn all about the procedures themselves in the book on Statistical Graphics Procedures by Example.




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Swimmer plot

At PharmaSUG 2014 in San Diego, I had the pleasure of attending "Swimmer Plot: Tell a Graphical Story of Your Time to Response Data Using PROC SGPLOT", by Stacey Phillips.  In this paper, Stacey presented an interesting graph showing the effects of a study drug on patients' tumor size.

Swimmer_StaceyStacey says in her paper that often "investigators prefer to dig deeper and look at an individual subject’s pattern of response. A swimmer plot is a graphical way of showing multiple pieces of a subject’s response 'story' in one glance."    The final graph includes a bar showing the length of treatment duration for each patient, classified by the disease stage at baseline, one for each patient in the study.  Graph also includes indicators for the start and end of each response episode, classified by complete or partial response, and an indicator showing whether the patient is a "Durable responder".

Stacey uses a combination of HBarParm, Scatter and annotations to create this graph.  The annotation is used for adding the "Continued response" arrow, and for the display of the inner legend for decoding of the various symbols in the graph.

Along with many of the attendees of the presentation, I was impressed and intrigued by this visual.  I was curious if its creation could be simplified using some of the new features released with SAS 9.3.  In particular, I wanted to see if I could make this graph without any annotation.

DataSAS 9.3 includes some versatile plot statements and features to create graphs.  Two of these are the HIGHLOW plot and the Discrete Attributes Map for controlling the color of the group values.

The data used to create the graph is is eyeballed from Stacey's graph and shown above.  Updated graph is shown below. Click on the graph for a higher resolution image.

Swimmer_93Here are the features of this graph:

  1. This graph uses the High Low plot to draw the bar representing the duration of the response for each subject.
  2. The bar has a arrow on the right side to indicate a continued response.  This is explained in the 1st footnote.
  3. Each response episode is represented by start and end events joined by a line classified by the type of response - Complete or Partial.  Connecting the start and end event and using a common classification color groups these together as one event, and is easier for the eye to consume.  Continuing response does not have an end event on the right.
  4. All event lines and markers are included in the inner legend.

Swimmer2_94It is also possible to place the indicator for continued event into the key legend using a "TriangleRightFilled" marker in the graph.  This marker is drawn outside the plot region, but is included in the legend.  Some items in the legend are shown in grey, to indicate the meaning of the shape since the actual marker will have different colors in the graph based on other criteria.

The graph on the right uses SAS 9.4 with a few aesthetic features for bar skins and filled, outlined markers.  Note the shorter line segments in the legend.

Note, the marker for the right arrow in intentionally made bigger to match the right arrows of the HighCap of the HighLow plot.

SAS 9.3 Code:

footnote  J=l h=0.8 'Each bar represents one subject in the study.';
footnote2 J=l h=0.8 'A durable responder is a subject who has confirmed response for at least 183 days (6 months).';
proc sgplot data= swimmer dattrmap=attrmap nocycleattrs;
  highlow y=item low=low high=high / highcap=highcap type=bar group=stage fill nooutline
          lineattrs=(color=black) name='stage' nomissinggroup transparency=0.3;
  highlow y=item low=startline high=endline / group=status lineattrs=(thickness=2 pattern=solid) 
          name='status' nomissinggroup attrid=status;
  scatter y=item x=start / markerattrs=(symbol=trianglefilled size=8 color=darkgray) name='s' legendlabel='Response start';
  scatter y=item x=end / markerattrs=(symbol=circlefilled size=8 color=darkgray) name='e' legendlabel='Response end';
  scatter y=ymin x=low / markerattrs=(symbol=trianglerightfilled size=14 color=darkgray) name='x' legendlabel='Continued response ';
  scatter y=item x=durable / markerattrs=(symbol=squarefilled size=6 color=black) name='d' legendlabel='Durable responder';
  scatter y=item x=start / markerattrs=(symbol=trianglefilled size=8) group=status attrid=status;
  scatter y=item x=end / markerattrs=(symbol=circlefilled size=8) group=status attrid=status;
  xaxis label='Months' values=(0 to 20 by 1) valueshint;
  yaxis reverse display=(noticks novalues noline) label='Subjects Received Study Drug' min=1;
  keylegend 'stage' / title='Disease Stage';
  keylegend 'status' 'd' 's' 'e'  'x' / noborder location=inside position=bottomright across=1;

The part that I believe makes this version easier to consume is the continuity of the response events.  Joining the start and end events with a line segment, all having the same color as per the event classification allows the eye to see each event and its duration clearly.

The part I like best is the graph uses no annotation.

Full SAS 9.3 Code:Swimmer_93




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