02-Palettes.sas

/* Program for 
   Wicklin, Rick (2023) "Ten Tips for Creating Effective Statistical Graphics"
   presented at multiple SAS conferences, including SAS Innovate 2024 in Las Vegas.

   Cite as 
   Wicklin, Rick (2023) "10 tips for creating effective statistical graphics." 
   _The DO Loop_ blog. Published December 6, 2023. 
   Accessed 01MAR2024
   https://blogs.sas.com/content/iml/2023/12/06/10-tips-statistical-graphics.html
*/

title; footnote; ods graphics / push;

/* 2. Use Well-Designed Palettes */

/* The PALETTE function in SAS IML defines the ColorBrewer palettes.
   Create a correlation matrix for variables in cluster order:
   https://blogs.sas.com/content/iml/2018/05/02/reorder-variables-correlation-heat-map.html 
*/
proc iml;
varNames = {'Length' 'Wheelbase' 'Weight' 'EngineSize' 'Cylinders' 'Horsepower' 
            'MSRP' 'Invoice' 'MPG_Highway' 'MPG_City'};
use Sashelp.Cars;  read all var varNames into Y;  close;
corr = corr(Y);
colors = palette("BrBg", 7);
 
call HeatmapCont(corr) xvalues=varNames yvalues=varNames 
            colorramp=colors range={-1.01 1.01} 
            title="Correlation Matrix and a Diverging Color Model";
QUIT;

proc iml;
colors = palette("YlOrRd", 7);
print colors;
/*
colors = { CXFFFFB2 CXFED976 CXFEB24C CXFD8D3C CXFC4E2A CXE31A1C CXB10026 };
*/
QUIT;

data thick;
   input East North Thick @@;
   label Thick='Coal Seam Thickness';
   datalines;
    0.7  59.6  34.1   2.1  82.7  42.2   4.7  75.1  39.5 
    4.8  52.8  34.3   5.9  67.1  37.0   6.0  35.7  35.9
    6.4  33.7  36.4   7.0  46.7  34.6   8.2  40.1  35.4   
   13.3   0.6  44.7  13.3  68.2  37.8  13.4  31.3  37.8
   17.8   6.9  43.9  20.1  66.3  37.7  22.7  87.6  42.8 
   23.0  93.9  43.6  24.3  73.0  39.3  24.8  15.1  42.3
   24.8  26.3  39.7  26.4  58.0  36.9  26.9  65.0  37.8 
   27.7  83.3  41.8  27.9  90.8  43.3  29.1  47.9  36.7
   29.5  89.4  43.0  30.1   6.1  43.6  30.8  12.1  42.8
   32.7  40.2  37.5  34.8   8.1  43.3  35.3  32.0  38.8
   37.0  70.3  39.2  38.2  77.9  40.7  38.9  23.3  40.5
   39.4  82.5  41.4  43.0   4.7  43.3  43.7   7.6  43.1
   46.4  84.1  41.5  46.7  10.6  42.6  49.9  22.1  40.7
   51.0  88.8  42.0  52.8  68.9  39.3  52.9  32.7  39.2
   55.5  92.9  42.2  56.0   1.6  42.7  60.6  75.2  40.1
   62.1  26.6  40.1  63.0  12.7  41.8  69.0  75.6  40.1
   70.5  83.7  40.9  70.9  11.0  41.7  71.5  29.5  39.8
   78.1  45.5  38.7  78.2   9.1  41.7  78.4  20.0  40.8
   80.5  55.9  38.7  81.1  51.0  38.6  83.8   7.9  41.6
   84.5  11.0  41.5  85.2  67.3  39.4  85.5  73.0  39.8 
   86.7  70.4  39.6  87.2  55.7  38.8  88.1   0.0  41.6
   88.4  12.1  41.3  88.4  99.6  41.2  88.8  82.9  40.5 
   88.9   6.2  41.5  90.6   7.0  41.5  90.7  49.6  38.9 
   91.5  55.4  39.0  92.9  46.8  39.1  93.4  70.9  39.7 
   55.8  50.5  38.1  96.2  84.3  40.3  98.2  58.2  39.5
;

title "Coal Seam Thickness";
title2 "Sequential Color Model";
proc sgplot data=thick;
scatter x=East y=North / colorresponse=Thick
   markerattrs=(symbol=CircleFilled size=14)
   filledoutlinedmarkers 
   colormodel=(CXFFFFB2 CXFED976 CXFEB24C CXFD8D3C CXFC4E2A CXE31A1C CXB10026);
xaxis grid; yaxis grid;
run;


title; footnote; ods graphics / pop;