@Derek_Hu wrote: Thanks. That means ORDER=FORMATTED is equal to using the PUT function to convert the data into a specified format, instead of keeping the variable as numeric with the appearance of BEST12.. Please correct me if anything is wrong. I wouldn't say it is "equal". Using a corresponding character variable created by the PUT function would result in the same sort order. However, this character variable could not be used, say, in arithmetic calculations in a COMPUTE block -- unlike the existing numeric variable, formatted or not. ... View more

Hi @em1535,   As far as I see, your DATA step creating dataset ANALYSIS.COUNT works perfectly on survival data with a discrete time scale like year 1, 2, 3, 4, 5 with corresponding variables TSMK_1, ..., TSMK_5 describing the smoking status in each of those time periods. However, you apply this program to data with time measured on a continuous scale with values ranging at least from 0.1314 to 85.8809.   So, the failed attempt of retrieving the smoking status at time 48 from the five-element array TSMK_ raises the question: What are the five time intervals on your continuous scale that the variables TSMK_1, ..., TSMK_5 correspond to? ... View more

Hello @Discaboota, @Discaboota wrote: I am trying to select alternate columns from my dataset, starting from column number 1 and including every nth alternate column after that. For example, I want to select columns 1, 3, 5, 7, and so on up to column n. To distinguish between the first and the second "n", let's say we're going to select every kth variable (starting from the first) up to the nth. Examples of variable numbers to be selected: 1, 3, 5, 7, 9, 11 (k=2, n=11 or 12) 1, 4, 7, 10, 13 (k=3, n=13, 14 or 15)   Code: %let k=2; %let n=13; /* Create list of first, (k+1)-th, (2k+1)-th, ... variable name up to the nth */ proc sql noprint; select nliteral(name) into :varlist separated by ' ' from dictionary.columns where libname='YOURLIB' & memname='HAVE' & mod(varnum,&k)=1 & varnum<=&n; quit; /* Select variables from the prepared list */ data want; set yourlib.have(keep=&varlist); run; Replace "YOURLIB" and "HAVE" with your libref and dataset name (using upper case in the WHERE clause). Make sure that the length of the list of variable names doesn't exceed the maximum length specified in system option MVARSIZE (typically 65534 bytes).   ... View more

Hello @kk13,   Use the NEXTRVAL= option:  %let nTop = 3; ods select none; ods output ExtremeValues=extrval(keep=varname high); proc univariate data=example NExtrVal=&nTop; run; ods select all; proc transpose data=extrval out=want(drop=_:) prefix=Top; by varname; run; ... View more

@Patrick wrote: There is something "weird" with the t modifier. I'm not sure if I'm just missing something or if there is something not working as it should when using the t modifier with a blank separated string. ...if feels as if the t modifier also removes the blank as defined word separator.  The t modifier does indeed trim the third argument (cf. https://communities.sas.com/t5/SAS-Procedures/FINDW-Function/td-p/171304). The simplest workaround might be to add an "odd" second delimiter to the character list which doesn't occur anywhere else: findwRes = findw("&allowedTeams",team,' §','eit'); Trimming the second argument with a function is, of course, the cleaner approach. ... View more

What I like to do in such cases is to create a list or cross-tabulation of all possible combinations, where the "possibilities" only go as far as the IF/ELSE conditions distinguish them. According to your code, the four new variables are derived independently of each other, each depending on just two existing variables, so cross-tabulations are feasible.   Cross-tabulations for MA_group (analogous for TM_group) and ADRD_group (similar for non_ADRD_group): MA_group | ADM_MA_FLAG_YR cohort_flag | 3 | ne 3 ------------+-------+-------- 0 | . | . ------------+-------+-------- 1 | 1 | 0 ------------+-------+-------- ^in (0,1) | 0 | 0 ----------------------------- ADRD_group | HLT_ALZDEM cohort_flag | 1 | 2 | ^in (1,2) ------------+-------+-------+----------- 0 | . | . | . ------------+-------+-------+----------- 1 | 1 | 0 | . ------------+-------+-------+----------- ^in (0,1) | 0 | 0 | . ----------------------------------------   I derived these cross-tabulations from your code. You would better use the specifications as a basis for them and then start writing the code according to the cross-tabulations.   If the above cross-tabulations and those for TM_group (with ADM_FFS_FLAG_YR replacing ADM_MA_FLAG_YR) and non_ADRD_group (with "1 | 0" switched to "0 | 1") are in accordance with the specifications, the code could be simplified to: data want(drop=_c); set have; if cohort_flag ne 0 then do; _c=(cohort_flag=1); MA_group=(_c & ADM_MA_FLAG_YR =3); TM_group=(_c & ADM_FFS_FLAG_YR=3); if HLT_ALZDEM in (1,2) then do; ADRD_group=(_c & HLT_ALZDEM=1); non_ADRD_group=(_c & HLT_ALZDEM=2); end; end; run; Important: This assumes that the new variables are not contained in dataset HAVE. Otherwise, they must be dropped from HAVE with a DROP= dataset option (or initialized to missing after the SET statement). Also, all variables involved are assumed to be numeric.   The derived variables are either set to 1 (for TRUE) or 0 (for FALSE) resulting from a Boolean expression such as _c & HLT_ALZDEM=1 (i.e., cohort_flag=1 & HLT_ALZDEM=1) or they are left missing if an IF condition prevents the execution of the assignment statement.   If you are in doubt about the content of a numeric variable, use the HEX16. format.   Example log: 378 data _null_; 379 have=9.9/3.3; 380 want=3; 381 put (have want)(=hex16./); 382 run; have=4008000000000001 want=4008000000000000   ... View more

Just to add the explanation why your approach doesn't work and you should use a different procedure, as others have already suggested:   The PCTL function (unlike the MEDIAN function) is not among the PROC SQL summary functions. Hence, it is not applied to the variable (column) var, but to the single value of var in each observation (row). But for any non-missing numeric value the trivial equality pctl(75, var)=var holds. So, your output dataset MT contains variable cou and basically a copy of variable var, named var_p75, from the input dataset CHECK_1. In particular, it is not aggregated at all, as you have noticed.   You must have received a warning in the log about the absence of a summary function: WARNING: A GROUP BY clause has been transformed into an ORDER BY clause because neither the SELECT clause nor the optional HAVING clause of the associated table-expression referenced a summary function. ... View more

Again, SPAM (copied word by word from a 4-year old Reddit post).   Reported as inappropriate content. ... View more

In an earlier post in this thread you wrote: I am not addition another variable or row after this. And now you are requesting just that: @CathyVI wrote: if you can add an ID variable to the data and some will have 2 records, this will be nice.  You have also opened a second thread find the count of variable by group where, as far as I see, the same problem is being discussed.   In this situation I am not going to give another precise shot at a moving target, sorry, but suggest a general four-step approach: Aggregate the data to one observation per ID. Classify the aggregated observations into the categories of interest. Count the observations per category. Create a report from the counts. Example solutions for steps 2 (before aggregation, though) through 4 have been presented in this thread. Whether a DATA step, PROC SQL, PROC SUMMARY or other techniques are most appropriate for step 1 depends on the data structure and on the aggregation rules, neither of which we have seen entirely. For instance, if two records provide different pieces of information about forms that have been filled, their (e.g. chronological) order may or may not be relevant. ... View more

@PaigeMiller wrote: This seems like one of those spam messages we have been receiving, ... I had the same thought when I only saw the subject line, but didn't find a similar post elsewhere either (via internet search engines). Interestingly, now it can be found, as usual, on Reddit (https://www.reddit.com/r/GraphicsProgramming/comments/1chi2qm/what_is_the_most_low_level_language_for_graphics/) and there it says it was posted 7 hours ago, which predates the posting here. Probably the search engines needed some time to crawl the internet. ... View more

@Patrick wrote: Your column CT_value is likely still of type numeric (Proc Contents would tell you). The value 4.00000000001E-3 is likely just scientific display of a numerical value. I'm rather sure that it is character because the values appear to be left-aligned in the Viewtable and also "4.0000000000000001E-3" (15 zeros) is impossible as a value of a numeric variable in SAS (under Windows). The same holds for "7.0000000000000007E-2" in NT_value.   While 4.0000000000000001E-3 means 0.004 + 1E-19 mathematically, the least significant bit in the internal representation of 0.004 has a place value of 2**-60=8.67...E-19. So, those 1E-19 (and even 4E-19, still less than half that place value) are rounded off, which means that an import as a numeric value should result in the clean value 0.004 automatically.   It's a bit surprising, though, that the numeric literal 7.0000000000000007E-2 (mathematically: 0.07+7E-18) in SAS is equal to 0.07 (on my Windows workstation, regardless of the DECIMALCONV system option setting) and not rounded up, although 7E-18 is greater than half the place value 2**-56=1.38...E-17 of the least significant bit. However, SAS correctly rounds the sum 0.07+7E-18 up and the sum 0.07+6E-18 down.   Your example 4.00000000001E-3 (0.004+1E-14) and the number 4.00000000000001E-3 (0.004+1E-17) mentioned by the OP are far enough apart from 0.004 so that, as you rightly suggest, explicit rounding would be required to obtain 0.004. ... View more