Hi everyone! I'm starting with a dataset like this:     Question         Name          Unit          Score   ------------         --------          -----           -------   111                  John            1               2.5   112                  John            1               3   113                  John            1               2   111                  Sarah           2              2.5   112                  Sarah          2              3.5   113                  Sarah          2              1   111                  Jennifer       1              1.5   112                  Jennifer       1              4   113                  Jennifer       1              3.5   And I'm hoping, using iterative data step code, to generate a table that looks like this:     Question          Mean_Score          Median_Score          Min_Score          Max_Score          SEM_Score          Conf95_Score          Std_Dev_Score   ------------          -----------------          -------------------          --------------           ---------------          ----------------           ------------------           ---------------------   111                   2.16667                 2.5                            1.5                       2.5                       0.2722                   0.533                        0.4714   112                   3.5                         3.5                            3                          4                          0.2357                   0.462                        0.4082   113                   2.16667                 2                               1                          3.5                       0.5932                   1.163                        1.0274   Any assistance you can provide would be greatly appreciated!   Thank you! ... View more

I have multiple SAS datasets, all of which I need to combine into one, including each table's unique variables. For example:   Table 1: Yr/Mo         FName          LName          Amount 202102       Angela           Apple             $150.00 202102       Edward          Engles           $200.00   Table 2: Yr/Mo         FName          LName          DOB 202102       Angela           Apple             08/09/1970 202102       Charles         Carter             11/15/1945   Table 3: Yr/Mo         FName          LName         Gender 202102       Brian             Berry             Male 202102       Charles         Carter            Male 202102       Doris             Dixon             Female   I need the resultant table to look like this: Yr/Mo          FName          LName          Amount          DOB              Gender 202102        Angela           Apple             $150.00          08/09/1970 202102        Brian              Berry                                                          Male           202102        Charles          Carter                                   11/15/1945    Male 202102        Doris              Dixon                                                          Female 202102        Edward          Engles           $200.00   Any assistance you can provide would be most welcome. Thanks! ... View more

I am using an Oracle pass-thru in SAS to bring in the first 10 rows of a dataset that has twelve columns, ten of which contain identifying data and the remaining two ("REQUEST_PAYLOAD" and "RESPONSE_PAYLOAD"), each containing a separate JSON file.   Due to the lengths of these strings, I have had to break them into 3200 character chunks. (Each JSON file contains 5,700+ key/value pairs, and has a very complex structure.)   Here's how I'm bringing in the data: libname ora_test ORACLE USER=XXXXXX password=XXXXXXXX PATH=XXXXXX SCHEMA=XXXXXXXX; proc sql; connect using ora_test; create table work.qdata as select * from connection to ora_test (select qid,qrefid,created_date,updated_date,qtype,lob,srcsys,gwqid,brefid,pnum, dbms_lob.substr(REQUEST_PAYLOAD,3200,1) as ReqChunk1, dbms_lob.substr(REQUEST_PAYLOAD,3200,3201) as ReqChunk2, dbms_lob.substr(REQUEST_PAYLOAD,3200,6401) as ReqChunk3, dbms_lob.substr(REQUEST_PAYLOAD,3200,9601) as ReqChunk4, dbms_lob.substr(REQUEST_PAYLOAD,3200,12801) as ReqChunk5, dbms_lob.substr(REQUEST_PAYLOAD,3200,16001) as ReqChunk6, dbms_lob.substr(REQUEST_PAYLOAD,3200,19201) as ReqChunk7, dbms_lob.substr(REQUEST_PAYLOAD,3200,22401) as ReqChunk8, dbms_lob.substr(REQUEST_PAYLOAD,3200,25601) as ReqChunk9, dbms_lob.substr(REQUEST_PAYLOAD,3200,28801) as ReqChunk10, dbms_lob.substr(REQUEST_PAYLOAD,3200,32001) as ReqChunk11, dbms_lob.substr(REQUEST_PAYLOAD,3200,35201) as ReqChunk12, dbms_lob.substr(REQUEST_PAYLOAD,3200,38401) as ReqChunk13, dbms_lob.substr(REQUEST_PAYLOAD,3200,41601) as ReqChunk14, dbms_lob.substr(REQUEST_PAYLOAD,3200,44801) as ReqChunk15, dbms_lob.substr(REQUEST_PAYLOAD,3200,48001) as ReqChunk16, dbms_lob.substr(REQUEST_PAYLOAD,3200,51201) as ReqChunk17, dbms_lob.substr(REQUEST_PAYLOAD,3200,54401) as ReqChunk18, dbms_lob.substr(REQUEST_PAYLOAD,3200,57601) as ReqChunk19, dbms_lob.substr(REQUEST_PAYLOAD,3200,60801) as ReqChunk20, dbms_lob.substr(RESPONSE_PAYLOAD,3200,1) as RespChunk1, dbms_lob.substr(RESPONSE_PAYLOAD,3200,3201) as RespChunk2, dbms_lob.substr(RESPONSE_PAYLOAD,3200,6401) as RespChunk3, dbms_lob.substr(RESPONSE_PAYLOAD,3200,9601) as RespChunk4, dbms_lob.substr(RESPONSE_PAYLOAD,3200,12801) as RespChunk5, dbms_lob.substr(RESPONSE_PAYLOAD,3200,16001) as RespChunk6, dbms_lob.substr(RESPONSE_PAYLOAD,3200,19201) as RespChunk7, dbms_lob.substr(RESPONSE_PAYLOAD,3200,22401) as RespChunk8, dbms_lob.substr(RESPONSE_PAYLOAD,3200,25601) as RespChunk9, dbms_lob.substr(RESPONSE_PAYLOAD,3200,28801) as RespChunk10, dbms_lob.substr(RESPONSE_PAYLOAD,3200,32001) as RespChunk11, dbms_lob.substr(RESPONSE_PAYLOAD,3200,35201) as RespChunk12, dbms_lob.substr(RESPONSE_PAYLOAD,3200,38401) as RespChunk13, dbms_lob.substr(RESPONSE_PAYLOAD,3200,41601) as RespChunk14, dbms_lob.substr(RESPONSE_PAYLOAD,3200,44801) as RespChunk15, dbms_lob.substr(RESPONSE_PAYLOAD,3200,48001) as RespChunk16, dbms_lob.substr(RESPONSE_PAYLOAD,3200,51201) as RespChunk17, dbms_lob.substr(RESPONSE_PAYLOAD,3200,54401) as RespChunk18, dbms_lob.substr(RESPONSE_PAYLOAD,3200,57601) as RespChunk19, dbms_lob.substr(RESPONSE_PAYLOAD,3200,60801) as RespChunk20 from XXXXXX.INFO t fetch next 10 rows only); disconnect from ora_test; quit; REQUEST_PAYLOAD breaks into 12 pieces and totals around 38,000 characters. RESPONSE_PAYLOAD breaks into 15 pieces and totals around 47,000 characters. Due to the combined length of these strings, I can't concatenate either JSON into a single field to be parsed using the JSON libname engine.   I'm wondering if anyone can offer some insight into how I might go about doing this.   Note: I would love to post an example of the output but I cannot, as the file contains PII data. ... View more

I have a SAS EG 7.15 project with a PROC SQL statement that pulls data from a table based on a hard-coded date range condition: rundate between '01Aug2018:0:0:0'dt and '15Aug2018:0:0:0'dt This project gets run on the first & third Wednesdays of each month. On the first Wednesday of the month, the date range is changed to pull data from the 16th through the end of the previous month. On the third Wednesday of the month, the date range is again changed to pull data from the 1st - 15th of the current month.   I would like to be able to automate this process. However, I'm unsure how I can apply a condition to my condition; for example, how to tell SAS that if it's the first Wednesday of October, the above line needs to read: rundate between '16Sep2018:0:0:0'dt and '30Sep2018:0:0:0'dt and then, on the third Wednesday of October, it would read: rundate between '01Oct2018:0:0:0'dt and '15Oct2018:0:0:0'dt I've explored using the NWKDOM() function to no avail; it seems like this situation might be calling for a %macro, with which I have no experience.   Any help that anyone can provide will certainly be much appreciated! ... View more