This MWE produces too many invalid data note s due to some occasional null s as follows. data irx; infile 'https://query1.finance.yahoo.com/v7/finance/download/^irx ?period1=-9999999999&period2=9999999999' url firstobs=2 dsd truncover; input date yymmdd10. +1 open hi lo close adj vol; if adj>. then output; run; and the file is Date,Open,High,Low,Close,Adj Close,Volume 1960-01-04,4.520000,4.520000,4.520000,4.520000,4.520000,0 1960-01-05,4.550000,4.550000,4.550000,4.550000,4.550000,0 1960-01-06,4.680000,4.680000,4.680000,4.680000,4.680000,0 1960-01-07,4.630000,4.630000,4.630000,4.630000,4.630000,0 1960-01-08,4.590000,4.590000,4.590000,4.590000,4.590000,0 1960-01-10,null,null,null,null,null,null 1960-01-11,4.540000,4.540000,4.540000,4.540000,4.540000,0 1960-01-12,4.540000,4.540000,4.540000,4.540000,4.540000,0 1960-01-13,4.560000,4.560000,4.560000,4.560000,4.560000,0 1960-01-14,4.510000,4.510000,4.510000,4.510000,4.510000,0 1960-01-15,4.490000,4.490000,4.490000,4.490000,4.490000,0 1960-01-17,null,null,null,null,null,null 1960-01-18,4.370000,4.370000,4.370000,4.370000,4.370000,0 1960-01-19,4.310000,4.310000,4.310000,4.310000,4.310000,0 1960-01-20,4.300000,4.300000,4.300000,4.300000,4.300000,0 1960-01-21,4.270000,4.270000,4.270000,4.270000,4.270000,0 1960-01-22,4.120000,4.120000,4.120000,4.120000,4.120000,0 1960-01-24,null,null,null,null,null,null 1960-01-25,4.120000,4.120000,4.120000,4.120000,4.120000,0 1960-01-26,4.040000,4.040000,4.040000,4.040000,4.040000,0 1960-01-27,3.990000,3.990000,3.990000,3.990000,3.990000,0 1960-01-28,3.920000,3.920000,3.920000,3.920000,3.920000,0 1960-01-29,3.990000,3.990000,3.990000,3.990000,3.990000,0 1960-01-31,null,null,null,null,null,null Can I skip these unnecessary observations, instead of reading all and delete them? ... View more

Updated: The following MWE better shows my situation. data have; input id :$16. @@; f=compress("https://fred.stlouisfed.org/graph/fredgraph.csv?id="||id); infile _ url filevar=f firstobs=2 dsd truncover end=e; do until(e); input date yymmdd10. +1 value; if value>. then output; end; cards; will5000indfc indpro sp500 unrate dexuseu cpiaucsl gdp dgs10 fedfunds ; data sganno(drop=v); display="fill"; fillcolor="black"; filltransparency=0.8; x1space="datavalue"; y1space="wallpercent"; function="polygon "; infile "https://fred.stlouisfed.org/graph/fredgraph.csv?id=usrecm" url firstobs=2 dsd truncover; input x1 yymmdd10. +1 v; if v then do; y1=0; output; function="polycont"; y1+100; output; x1=intnx("month",x1,1); output; y1+-100; output; end; run; ods html close; ods results=off; ods listing gpath="!userprofile\desktop\"; ods graphics/reset; proc sgplot data=have sganno=sganno; by notsorted id; series x=date y=value; xaxis valuesformat=year4.; yaxis valuesformat=best8. type=log; run; I cannot simply adjust the SGANNO data set. How can I avoid the overwhelming SGANNO shades automatically? Original: This MWE draws a shaded SGPLOT SERIES. data data; do t=1 to 100; x+rannor(1); output; end; run; data sganno; display="fill"; fillcolor="black"; filltransparency=0.8; x1space="datavalue"; y1space="wallpercent"; do x1=-10 to 90 by 10; function="polygon "; y1=0; output; function="polycont"; y1=100; output; x1+10; output; y1=0; output; end; run; ods html close; ods results=off; ods listing gpath="!userprofile\desktop\"; ods graphics/reset imagename="sgplot"; proc sgplot data=data sganno=sganno; series x=t y=x; run; SGANNO has six uniform shades starting at X1=-10, 10, 30, 50, 70, 90, so SGPLOT locates the first shade outside the box. I have manually used PROC SGPLOT DATA=DATA SGANNO=SGANNO(WHERE=(X1>=0)); to avoid this, but this requires too many micromanagements. How can I automatically limit SGANNO not to overwhelm the box? ... View more

The following have has 50000 observations. The data step delete s the second half of have , so the last sql returns 25000. data have; do i=1 to 50000; output; end; run; data have; set have; if i>25000 then delete; run; proc sql; select nobs from dictionary.tables where libname="WORK" and memname="HAVE"; quit; The following have identically has 50000 observations. The second sql identically delete s the second half of have , but the third sql still returns 50000. data have; do i=1 to 50000; output; end; run; proc sql; delete from have where i>25000; quit; proc sql; select nobs from dictionary.tables where libname="WORK" and memname="HAVE"; quit; I have never thought about the second possibility because I don't delete observations using sql . I found this issue from the advanced programming sample questions—the nobs and nlobs of dictionary.columns are different. How are the data delete and sql delete different? Why does SAS distinguish these two delete s? ... View more