Here's another suggestion: data; set isbn; s=0; do j=1 to 9; s+j*input(char(isbn,j),1.); end; m=mod(s,11); c=put(ifn(m=j,.X,m),1.)=char(isbn,j); put isbn c; run; The report is only suitable for internal purposes as it contains just the ISBNs and a "correctness flag" c, i.e., c=0 indicates fake ISBNs, c=1 correct ones: 0434488665 0 2018166516 0 9405643837 0 6032522768 1 4394205952 0 2353276079 0 6493135591 1 6776994355 0 2227835451 1 8018735913 1 0841779538 1 8730652341 0 1594122350 1 224320418X 0 6857923406 0 3967006111 0 9537581977 1 1283514257 1 566485052X 0 6994588902 0 9236137644 1 7649918275 0 458574645X 0 7601111520 1 1911465988 1 266671036X 1 4287030303 1 2561407012 1 6933010252 1 4278675463 0 351049735X 1 0566910450 0 9802436077 0 195432331X 1 8421179217 1 0979272564 1 3584795834 1 0713565068 0 9074601407 0 0168786583 1 I used a bit of algebra to simplify the formulas.   Of course, the code could be shortened further. For example: At the cost of an even uglier report (but now including the book titles) one could replace "isbn c" by "_all_" in the final PUT statement. An ugly log could be achieved by omitting the INPUT function in the sum statement. The name of the input dataset could also be omitted if the code is submitted directly after the DATA step creating dataset ISBN (risky!). Sacrificing code readability, one could write all statements on the same line, without separating blanks, where possible. However, the resulting DATA step (containing 108 characters) violates so many coding standards that it must be hidden behind a spoiler. data;set;s=0;do j=1to 9;s+j*char(isbn,j);end;m=mod(s,11);c=put(ifn(m=j,.X,m),1.)=char(isbn,j);put _all_;run; ... View more

Hello @stataq,   The case of m1+5; is indeed a bit tricky to look up in an alphabetical list of language elements as it is one of the rare examples where the name of the statement is not part of the syntax. It is called sum statement, so you find it under the letter "S" in the Dictionary of SAS DATA Step Statements. ... View more

@pavank wrote: Hi @ballardw , Thank you very much for your solution  I thought COALESCE function works here Hi @pavank,   Interesting that you ask about the COALESCE function because I used that yesterday for an alternative to JOL's solution. But then I saw that ballardw had suggested a quite similar DATA step in the meantime and so I didn't post mine.   Here it is: data want(drop=_); set sc; by group; if id=0 then _+0; else if first.group then _=.; else id=coalesce(_,id); run; (Note that the underscore is a valid variable name -- one that is rather unlikely to collide with a name of one of your existing variables.)     ... View more

The ODS table name is SenSpec (case-insensitive) and can be found on the page "ODS Table Names" of the "Details" section of the PROC FREQ documentation.   This is very similar to other (statistical) procedures: In virtually all cases, clicking on "Details" in the menu under the procedure name ("The XXXXXX Procedure") in the documentation will open a drop-down list with "ODS Table Names" and (if applicable) "ODS Graphics" as the last two entries. ... View more

Hello @mt88,   @ballardw wrote: And here is an example where the result is incorrect for two-digit value data test; input a b c d; Numzero = count(cats(a,b,c,d),'11'); cards; 1 0 9 1 1 1 0 0 0 9 11 1 0 0 9 1 ; run; The incorrect result for line 1 is because the CATS result for the line looks like "1100" and so there are two 1's next to each other and the Count function treats them as such. This problem can be avoided by introducing a delimiter: data want; set test; newvar=count(catx('||',.,of a--d,.),'|0|'); run; (where 0 could be replaced by 11 or any other number whose character representation is to be counted -- or even by a non-numeric character string). ... View more

@xxformat_com wrote: I like the two set statements. Is there a specific reason for preferring a view over a dataset? I thought using a view is conceptually closer to your intention of just printing an existing dataset as opposed to creating a new one with selected or reordered variables. ... View more

@karu05 wrote: (...) could you kindly elaborate the parameters in your solution [rand('extrvalue',s,s/a,1/a)] ... Put m=s, s=s/a and x=1/a into the formula of the RAND 'EXTRVALUE' function documentation, simplify the result (which is easy) and you'll get   F(x) = exp(−(x/s) −a )   for x > 0   as the cumulative distribution function (CDF). This matches exactly the formula from Wikipedia for m = 0.   (Also, I had checked the results of a simulation of ten million random numbers using my suggested code [with a=3 and s=2] before posting it: computed three probabilities, the mean and standard deviation and created a histogram. Everything was in agreement with the results computed from the Wikipedia formulas and the density curve shown there, respectively.)   @karu05 wrote: Would it be incorrect to just directly use the location, scale and shape parameters in the formula ... Note that the SAS documentation and the Wikipedia article use different parameterizations of that CDF: Each of the three parameters -- location, scale and shape -- is defined differently in the two sources and there might be even more variants of the formula in the literature. So, before using "the" location, scale and shape parameters 75000, 300000 and 1.3 in either formula, you must check very carefully what parameterization is appropriate for these numbers. Otherwise, you could obtain totally wrong results. ... View more

Hi @karu05 and welcome to the SAS Support Communities!   Using the parameterization from the Wikipedia article "Fréchet distribution" -- with shape parameter a, scale parameter s and location parameter m=0 -- you can use: rand('extrvalue',s,s/a,1/a) if the parameter values meet the criteria stated in the RAND function documentation Extreme Value Distribution. ... View more