Hi all,     I'm trying to do the same procedure for a set of known variables, say proc freq for {andy, jane, may, ali}. There are two ways I can think of: 1. brute force   PROC FREQ; TABLE andy; TABLE jane; TABLE may; TABLE ali; RUN; 2. brute force(macro version)   %MACRO ForFun(Variable); PROC FREQ; TABLE &Variable; RUN; %MEND; %ForFun(andy); %ForFun(jane); %ForFun(may); %ForFun(ali); Sort of want to hear any better solution about this, thanks guys! ... View more

Hi guys,    I've encountered a problem that I couldn't understand why couldn't be solved. Please take a look at the following dataset: (ID and Level are what I have, while Sn is what I want to generate) ID    Level    Sn 1         L        1 1         L        1             .             .             . 1         M        2 1         M        2             .             .             . 2         H        3 2         H        3             .             .             . 2         L        4 2         L        4             .             .             . 2         M        5 2         M        5             .             .             . 2         H        6 2         H        6             .             .             .   General rule is that every subject has several records on three levels, and what I want to do is to generate serial number according to different levels for each subject. I know it can be achieved by the combination of "do loop" and "by" statement. However, the work just couldn't be done. (still trying) Therefore, I post it to ask you guys for some help!   Thanks in advance!   Best regards,  Bill ... View more

Hi there,    When I was processing dataset this morning, I faced a problem. Let's take a look at my data first:   30210027甲三郎30210397乙先天冰30210069丙小姐30210493丁阿旺 30210044物阿梅30210021己阿有30240052更有言30210346心一? ...   Basic it's in the pattern of "8-digit number" + "Chinese name of a person", and it is stored in the text file. My problem is: I don't know how to divide this seemingly random variable into the way I want?   As I said, I want to divide each row into "ID1" + "Name1" + "ID2" + "Name2" + "ID3" + "Name3" + "ID4" + "Name4" As a whole, I wish the data looked like ID1               Name1   ID2               Name2      ID3              Name3    ID4              Name4   30210027    甲三郎    30210397    乙先天冰    30210069    丙小姐    30210493    丁阿旺 30210044    物阿梅    30210021    己阿有        30240052   更有言     30210346    心一? ...   I've tried three ways #1 Import It leads to a result that I wouldn't conclude it as a failure, but it's definitely not a success:     #2 Infile Some variables are wrong because of the random length of the Chinese name, plus there's no delimiter between each variable.   #3 Adding delimiter, such as ",", by my hand. For this method, I added a comma between each variable. It works actually. However, considering the total of around 10000 variables awaits me to process, It may not work that well as I thought...   Therefore, I'm here to ask for your kindly help. Any suggestions would be helpful. Thanks in advance!    ... View more

Hi guys,   What I'm trying to do is to merge several data sets and then calculate length with first variable variables. For example, data set 1:   Obs MRN DM_Med_Date DM_Med 1 CGMHLK0001 2014/06/18 1 2 CGMHLK0002 2014/07/09 12 3 CGMHLK0003 2014/06/24 1 4 CGMHLK0004 2014/07/09 12     data set 2: Obs MRN NSAID_Date NSAID 1 CGMHLK0001 2014/06/18 1 2 CGMHLK0002 2014/07/09 1 3 CGMHLK0003 2014/06/24 1 4 CGMHLK0004 2014/07/09 1 data set 3: NSAID_ Obs MRN NSAID_freq_Date freq 1 CGMHLK0001 2014/06/18 1 2 CGMHLK0002 2014/07/09 2 3 CGMHLK0003 2014/06/24 1 4 CGMHLK0004 2014/07/09 1 What I want: Obs MRN DM_Med_Date DM_Med NSAID_Date NSAID DateLength_DM_Med_VS_NSAID NSAID_freq_Date NSAID_freq DateLength_DM_Med_VS_NSAID_freq 1 CGMHLK0001 2014/6/18 1 2014/6/18 1 0 2014/6/18 1 0 2 CGMHLK0001 2014/6/18 1 2014/6/18 1 0 2014/7/9 2 21 3 CGMHLK0001 2014/6/18 1 2014/6/18 1 0 2014/6/24 1 6 4 CGMHLK0001 2014/6/18 1 2014/6/18 1 0 2014/7/9 1 21 5 CGMHLK0001 2014/6/18 1 2014/7/9 1 21 2014/6/18 1 0 6 CGMHLK0001 2014/6/18 1 2014/7/9 1 21 2014/7/9 2 21 7 CGMHLK0001 2014/6/18 1 2014/7/9 1 21 2014/6/24 1 6 8 CGMHLK0001 2014/6/18 1 2014/7/9 1 21 2014/7/9 1 21 9 CGMHLK0001 2014/6/18 1 2014/6/24 1 6 2014/6/18 1 0 10 CGMHLK0001 2014/6/18 1 2014/6/24 1 6 2014/7/9 2 21 11 CGMHLK0001 2014/6/18 1 2014/6/24 1 6 2014/6/24 1 6 12 CGMHLK0001 2014/6/18 1 2014/6/24 1 6 2014/7/9 1 21 13 CGMHLK0001 2014/6/18 1 2014/7/9 1 21 2014/6/18 1 0 14 CGMHLK0001 2014/6/18 1 2014/7/9 1 21 2014/7/9 2 21 15 CGMHLK0001 2014/6/18 1 2014/7/9 1 21 2014/6/24 1 6 16 CGMHLK0001 2014/6/18 1 2014/7/9 1 21 2014/7/9 1 21 17 CGMHLK0002 2014/7/9 12 2014/6/18 1 21 2014/6/18 1 21 18 CGMHLK0002 2014/7/9 12 2014/6/18 1 21 2014/7/9 2 0 19 CGMHLK0002 2014/7/9 12 2014/6/18 1 21 2014/6/24 1 15 20 CGMHLK0002 2014/7/9 12 2014/6/18 1 21 2014/7/9 1 0 21 CGMHLK0002 2014/7/9 12 2014/7/9 1 0 2014/6/18 1 21 22 CGMHLK0002 2014/7/9 12 2014/7/9 1 0 2014/7/9 2 0 23 CGMHLK0002 2014/7/9 12 2014/7/9 1 0 2014/6/24 1 15 24 CGMHLK0002 2014/7/9 12 2014/7/9 1 0 2014/7/9 1 0 25 CGMHLK0002 2014/7/9 12 2014/6/24 1 15 2014/6/18 1 21 26 CGMHLK0002 2014/7/9 12 2014/6/24 1 15 2014/7/9 2 0 27 CGMHLK0002 2014/7/9 12 2014/6/24 1 15 2014/6/24 1 15 28 CGMHLK0002 2014/7/9 12 2014/6/24 1 15 2014/7/9 1 0 29 CGMHLK0002 2014/7/9 12 2014/7/9 1 0 2014/6/18 1 21 30 CGMHLK0002 2014/7/9 12 2014/7/9 1 0 2014/7/9 2 0 31 CGMHLK0002 2014/7/9 12 2014/7/9 1 0 2014/6/24 1 15 32 CGMHLK0002 2014/7/9 12 2014/7/9 1 0 2014/7/9 1 0 33 CGMHLK0003 2014/6/24 1 2014/6/18 1 6 2014/6/18 1 6 34 CGMHLK0003 2014/6/24 1 2014/6/18 1 6 2014/7/9 2 15 35 CGMHLK0003 2014/6/24 1 2014/6/18 1 6 2014/6/24 1 0 36 CGMHLK0003 2014/6/24 1 2014/6/18 1 6 2014/7/9 1 15 37 CGMHLK0003 2014/6/24 1 2014/7/9 1 15 2014/6/18 1 6 38 CGMHLK0003 2014/6/24 1 2014/7/9 1 15 2014/7/9 2 15 39 CGMHLK0003 2014/6/24 1 2014/7/9 1 15 2014/6/24 1 0 40 CGMHLK0003 2014/6/24 1 2014/7/9 1 15 2014/7/9 1 15 41 CGMHLK0003 2014/6/24 1 2014/6/24 1 0 2014/6/18 1 6 42 CGMHLK0003 2014/6/24 1 2014/6/24 1 0 2014/7/9 2 15 43 CGMHLK0003 2014/6/24 1 2014/6/24 1 0 2014/6/24 1 0 44 CGMHLK0003 2014/6/24 1 2014/6/24 1 0 2014/7/9 1 15 45 CGMHLK0003 2014/6/24 1 2014/7/9 1 15 2014/6/18 1 6 46 CGMHLK0003 2014/6/24 1 2014/7/9 1 15 2014/7/9 2 15 47 CGMHLK0003 2014/6/24 1 2014/7/9 1 15 2014/6/24 1 0 48 CGMHLK0003 2014/6/24 1 2014/7/9 1 15 2014/7/9 1 15 49 CGMHLK0004 2014/7/9 12 2014/6/18 1 21 2014/6/18 1 21 50 CGMHLK0004 2014/7/9 12 2014/6/18 1 21 2014/7/9 2 0 51 CGMHLK0004 2014/7/9 12 2014/6/18 1 21 2014/6/24 1 15 52 CGMHLK0004 2014/7/9 12 2014/6/18 1 21 2014/7/9 1 0 53 CGMHLK0004 2014/7/9 12 2014/7/9 1 0 2014/6/18 1 21 54 CGMHLK0004 2014/7/9 12 2014/7/9 1 0 2014/7/9 2 0 55 CGMHLK0004 2014/7/9 12 2014/7/9 1 0 2014/6/24 1 15 56 CGMHLK0004 2014/7/9 12 2014/7/9 1 0 2014/7/9 1 0 57 CGMHLK0004 2014/7/9 12 2014/6/24 1 15 2014/6/18 1 21 58 CGMHLK0004 2014/7/9 12 2014/6/24 1 15 2014/7/9 2 0 59 CGMHLK0004 2014/7/9 12 2014/6/24 1 15 2014/6/24 1 15 60 CGMHLK0004 2014/7/9 12 2014/6/24 1 15 2014/7/9 1 0 61 CGMHLK0004 2014/7/9 12 2014/7/9 1 0 2014/6/18 1 21 62 CGMHLK0004 2014/7/9 12 2014/7/9 1 0 2014/7/9 2 0 63 CGMHLK0004 2014/7/9 12 2014/7/9 1 0 2014/6/24 1 15 64 CGMHLK0004 2014/7/9 12 2014/7/9 1 0 2014/7/9 1 0   In this example, I set DM_Med as the first variable, so for each data set, I'll need to calculate the length difference with DM_Med_date. While I'm still trying how to achieve this, very welcom if you have any idea! Thanks in advance!                       ... View more