Adding Insets to Classification Panels

This section requires familiarity with Using Classification Panels. You should skip this section if you are not familiar with the general coding for classification panels.
The DATALATTICE and DATAPANEL layouts provide INSET= and INSETOPTS= options for displaying insets in classification panels. The INSETOPTS= option supports the same placement and appearance features as those documented for the SCATTERPLOTMATRIX statement in Adding Insets to a SCATTERPLOTMATRIX Graph However, unlike the SCATTERPLOTMATRIX statement, the DATALATTICE and DATAPANEL layouts do not have predefined information available. Thus, for the INSET= option, you must create the columns for the information that you want to display in the inset and integrate it with the input data before the graph is rendered. Then, on the INSET= option, you specify the name(s) of the column(s) that contain the desired information.
For example, the following template code uses INSET=(NOBS MEAN) to reference input data columns that are named NOBS and MEAN. When the graph is rendered, the values that are stored in these columns will be displayed in the inset.
In the inset display in this example, one row is displayed for each column that is listed on INSET=, and each row has two columns. The left column shows the column name (column label, if it is defined in the data), and the right column contains the column value for that particular cell of the panel. The number of rows of data for these columns should match the number of cells in the classification panel and the sequence in which the cells are populated.
The following template code defines a template named PANEL. The template "makes room" for the insets in each panel by adding a maximum row axis offset. In this case, OFFSETMAX=0.4 is sufficient, but the setting will vary case-by-case. This is what the first row of the classification panel with insets will look like:
Add an inset to a classification panel
proc template;
 define statgraph panel;
  begingraph;
    entrytitle "Average City MPG for Vehicles";
    entrytitle "by Origin, Cylinders and VehicleType";
    layout datalattice columnvar=origin rowvar=cylinders /
        columndatarange=unionall rowdatarange=unionall
        headerlabeldisplay=value
        headerbackgroundcolor=GraphAltBlock:color
        inset=(cellN cellMean)
        insetopts=(border=true
                   opaque=true backgroundcolor=GraphAltBlock:color)
        rowaxisopts=( offsetmax=.4 offsetmin=.1 display=(tickvalues) )
        columnaxisopts=(display=(label tickvalues)
                        linearopts=( tickvaluepriority=true
                          tickvaluesequence=(start=5 end=30 increment=5))
                        griddisplay=on offsetmin=0 offsetmax=.1);
      layout prototype;
        barchart x=type y=mean / orient=horizontal
                                 barwidth=.5 barlabel=true;
      endlayout;
    endlayout;
  endgraph;
 end;
run;
When this template is used, the input data must contain separate columns for the following:
classification variables
columnvar=origin rowvar=cylinders
inset information
inset=(cellN cellMean)
bar chart
x=type Y=mean
The data for this example is from the SASHELP.CARS data set. To calculate the number of observations and mean for the observations, we can use PROC SUMMARY.
The following PROC SUMMARY step calculates the number of observations and the mean of MPG_CITY for each of the classification interactions listed in the TYPES statement. CYLINDERS*ORIGIN is the crossing needed for the cell summaries, and CYLINDER*ORIGIN*TYPE is the crossing needed by each cell's bar chart.
The COMPLETETYPES option creates summary observations even when the frequency of the classification interactions is zero. Additionally, the code creates subsets in the input data to restrict the number of bars in each bar chart to at most three, and to reduce the number cells in the classification panel. There are three values of ORIGIN (Asia, Europe, and USA) and three values of CYLINDERS (4, 6, and 8).
For the insets to display accurate data, we must ensure that the order of the observations in the data corresponds to the column order for the CLASS statement of PROC SUMMARY. Because the panel cells are populated across one row before proceeding to the next row, the values of the panel's row variable (CYLINDERS) determines the panel order and must be specified first in the SUMMARY procedure's CLASS statement so that the values of CYLINDERS also determine the order for the statistics calculations.
/* compute the barchart data and inset information */
 
proc summary data=sashelp.cars completetypes;
  where type in ("Sedan" "Truck" "SUV") and 
        cylinders in (4 6 8);
  class cylinders origin type;
  var mpg_city;
  output out=mileage mean=Mean n=Nobs / noinherit;
  types cylinders*origin cylinders*origin*type;
run; 
The SAS log displays the following note when the procedure code is submitted:
NOTE: There were 337 observations read from the data set SASHELP.CARS.
      WHERE type in ('SUV', 'Sedan', 'Truck') and cylinders in (4, 6, 8);
NOTE: The data set WORK.MILEAGE has 36 observations and 6 variables.
Confirm the Order of Data Observations
Confirm the Order of Data Observations
Confirm the Order of Data Observations shows the order of observations in the interim data set named MILEAGE. Notice that the first nine observations (where _TYPE_ equals 6) are the cell summaries. The remaining 27 observations (where _TYPE_ equals 7) are for each cell's bar chart.
To create separate columns for the inset, we need to store the _TYPE_= 6 observations in new columns. The following DATA step writes the inset information to another data set named OVERALL.
data mileage
     overall(keep=origin cylinders mean nobs
         rename=(origin=cellOrigin cylinders=cellCyl
                 mean=cellMean nobs=cellNobs ));
  set mileage; by _type_;
  if _type_ eq 6 then output overall;
  else output mileage;
run;
The SAS log displays the following note when the code is submitted:
NOTE: There were 36 observations read from the data set WORK.MILEAGE.
NOTE: The data set WORK.MILEAGE has 27 observations and 5 variables.
NOTE: The data set WORK.OVERALL has 9 observations and 4 variables.
Finally, we create a new data set named SUMMARY, which merges the MILEAGE and OVERALL data sets. Note that this is a non-match merge (no BY statement), and that all columns in the two tables have unique names to prevent overwriting any data values.
data summary;
   merge mileage overall;
   label Mean="MPG (City)"; 
   format mean cellMean 4.1;
run;
NOTE: There were 27 observations read from the data set WORK.MILEAGE.
NOTE: There were 9 observations read from the data set WORK.OVERALL.
NOTE: The data set WORK.SUMMARY has 27 observations and 9 variables.
Modified Input Data Set with Additional Columns
Modified Input Data Set with Additional Columns
The SUMMARY data set can now be used to render a graph from template PANEL:
ods listing style=statistical;
proc sgrender data=summary template=panel;
run;
Insets Generated for Each Panel in the Graph
The following figure shows another example of adding insets to a classification panel. The complete code for this output is presented inUsing Classification Panels.
Insets Displayed at the Bottom of Each Panel