Hello-,   I am trying to create a variable year from SAS date.     When I run this code on the date, I get the wrong answer: data want1; input Birthdate; cards;     5/27/2014 5/28/2014 5/29/2014 5/30/2014 5/31/2014 6/1/2014 6/2/2014 6/3/2014 6/4/2014 6/5/2014 6/6/2014 6/7/2014 6/8/2014 6/9/2014 6/10/2014 6/11/2014 6/12/2014 6/13/2014 6/14/2014 6/15/2014 6/16/2014 6/17/2014 6/18/2014 6/19/2014 6/20/2014 6/21/2014 6/22/2014 6/23/2014 6/24/2014 6/25/2014 6/26/2014 6/27/2014 6/28/2014 6/29/2014 6/30/2014 7/1/2014 7/2/2014 7/3/2014 7/4/2014 7/5/2014 7/6/2014 7/7/2014 7/8/2014 7/9/2014 7/10/2014 7/11/2014 7/12/2014 7/13/2014 7/14/2014 7/15/2014 7/16/2014 7/17/2014 7/18/2014 7/19/2014 7/20/2014 7/21/2014 7/22/2014 7/23/2014 7/24/2014 7/25/2014 7/26/2014 7/27/2014 7/28/2014 7/29/2014 7/30/2014 7/31/2014 8/1/2014 8/2/2014 8/3/2014 8/4/2014 8/5/2014 8/6/2014 8/7/2014 8/8/2014 8/9/2014 8/10/2014 8/11/2014 8/12/2014 8/13/2014 8/14/2014 8/15/2014 8/16/2014 8/17/2014 8/18/2014 8/19/2014 8/20/2014 8/21/2014 8/22/2014 8/23/2014 8/24/2014 8/25/2014 8/26/2014 8/27/2014 8/28/2014 8/29/2014 8/30/2014 8/31/2014 9/1/2014 9/2/2014 9/3/2014 9/4/2014 9/5/2014 9/6/2014 9/7/2014 9/8/2014 9/9/2014 9/10/2014 9/11/2014 9/12/2014 9/13/2014 9/14/2014 9/15/2014 9/16/2014 9/17/2014 9/18/2014 9/19/2014 9/20/2014 9/21/2014 9/22/2014 9/23/2014 9/24/2014 9/25/2014 9/26/2014 9/27/2014 9/28/2014 9/29/2014 9/30/2014 10/1/2014 10/2/2014 10/3/2014 10/4/2014 10/5/2014 10/6/2014 10/7/2014 10/8/2014 10/9/2014 10/10/2014 10/11/2014 10/12/2014 10/13/2014 10/14/2014 10/15/2014 10/16/2014 10/17/2014 10/18/2014 10/19/2014 10/20/2014 10/21/2014 10/22/2014 10/23/2014 10/24/2014 10/25/2014 10/26/2014 10/27/2014 10/28/2014 10/29/2014 10/30/2014 10/31/2014 11/1/2014 11/2/2014 11/3/2014 11/4/2014 11/5/2014 11/6/2014 11/7/2014 11/8/2014 11/9/2014 11/10/2014 11/11/2014 11/12/2014 11/13/2014 11/14/2014 11/15/2014 11/16/2014 11/17/2014 11/18/2014 11/19/2014 11/20/2014 11/21/2014 11/22/2014 11/23/2014 11/24/2014 11/25/2014 11/26/2014 11/27/2014 11/28/2014 11/29/2014 11/30/2014 12/1/2014 12/2/2014 12/3/2014 12/4/2014 12/5/2014 12/6/2014 12/7/2014 12/8/2014 12/9/2014 12/10/2014 12/11/2014 12/12/2014 12/13/2014 12/14/2014 12/15/2014 12/16/2014 12/17/2014 12/18/2014 12/19/2014 12/20/2014 12/21/2014 12/22/2014 12/23/2014 12/24/2014 12/25/2014 12/26/2014 12/27/2014 12/28/2014 12/29/2014 12/30/2014 12/31/2014 1/1/2015 1/2/2015 1/3/2015 1/4/2015 1/5/2015 1/6/2015 1/7/2015 1/8/2015 1/9/2015 1/10/2015 1/11/2015 1/12/2015 1/13/2015 1/14/2015 1/15/2015 1/16/2015 1/17/2015 1/18/2015 1/19/2015 1/20/2015 1/21/2015 1/22/2015 1/23/2015 1/24/2015 1/25/2015 1/26/2015 1/27/2015 1/28/2015 1/29/2015 1/30/2015 1/31/2015 2/1/2015 2/2/2015 2/3/2015 2/4/2015 2/5/2015 2/6/2015 2/7/2015 2/8/2015 2/9/2015 2/10/2015 2/11/2015 2/12/2015 2/13/2015 2/14/2015 2/15/2015 2/16/2015 2/17/2015 2/18/2015 2/19/2015 2/20/2015 2/21/2015 2/22/2015 2/23/2015 2/24/2015 2/25/2015 2/26/2015 2/27/2015 2/28/2015 3/1/2015 3/2/2015 3/3/2015 3/4/2015 3/5/2015 3/6/2015 3/7/2015 3/8/2015 3/9/2015 3/10/2015 3/11/2015 3/12/2015 3/13/2015 3/14/2015 3/15/2015 3/16/2015 3/17/2015 3/18/2015 3/19/2015 3/20/2015 3/21/2015 3/22/2015 3/23/2015 3/24/2015 3/25/2015 3/26/2015 3/27/2015 3/28/2015 3/29/2015 3/30/2015 3/31/2015 4/1/2015 4/2/2015 4/3/2015 4/4/2015 4/5/2015 4/6/2015 4/7/2015 4/8/2015 4/9/2015 4/10/2015 4/11/2015 4/12/2015 4/13/2015 4/14/2015 4/15/2015 4/16/2015 4/17/2015 4/18/2015 4/19/2015 4/20/2015 4/21/2015 4/22/2015 4/23/2015 4/24/2015 4/25/2015 4/26/2015 4/27/2015 4/28/2015 4/29/2015 4/30/2015 5/1/2015 5/2/2015 5/3/2015 5/4/2015 5/5/2015 5/6/2015 5/7/2015 5/8/2015 5/9/2015 5/10/2015 5/11/2015 5/12/2015 5/13/2015 5/14/2015 5/15/2015 5/16/2015 5/17/2015 5/18/2015 5/19/2015 5/20/2015 5/21/2015 5/22/2015 5/23/2015 5/24/2015 5/25/2015 5/26/2015 5/27/2015 5/28/2015 5/29/2015 5/30/2015 5/31/2015 6/1/2015 6/2/2015 6/3/2015 6/4/2015 6/5/2015 6/6/2015 6/7/2015 6/8/2015 6/9/2015 6/10/2015 6/11/2015 6/12/2015 6/13/2015 6/14/2015 6/15/2015 6/16/2015 6/17/2015 6/18/2015 6/19/2015 6/20/2015 6/21/2015 6/22/2015 6/23/2015 6/24/2015 6/25/2015 6/26/2015 6/27/2015 6/28/2015 6/29/2015 6/30/2015 7/1/2015 7/2/2015 7/3/2015 7/4/2015 7/5/2015 7/6/2015 7/7/2015 7/8/2015 7/9/2015 7/10/2015 7/11/2015 7/12/2015 7/13/2015 7/14/2015 7/15/2015 7/16/2015 7/17/2015 7/18/2015 7/19/2015 7/20/2015 7/21/2015 7/22/2015 7/23/2015 7/24/2015 7/25/2015 7/26/2015 7/27/2015 7/28/2015 7/29/2015 7/30/2015 7/31/2015 8/1/2015 8/2/2015 8/3/2015 8/4/2015 8/5/2015 8/6/2015 8/7/2015 8/8/2015 8/9/2015 8/10/2015 8/11/2015 8/12/2015 8/13/2015 8/14/2015 8/15/2015 8/16/2015 8/17/2015 8/18/2015 8/19/2015 8/20/2015 8/21/2015 8/22/2015 8/23/2015 8/24/2015 8/25/2015 8/26/2015 8/27/2015 8/28/2015 8/29/2015 8/30/2015 8/31/2015 9/1/2015 9/2/2015 9/3/2015 9/4/2015 9/5/2015 9/6/2015 9/7/2015 9/8/2015 9/9/2015 9/10/2015 9/11/2015 9/12/2015 9/13/2015 9/14/2015 9/15/2015 9/16/2015 9/17/2015 9/18/2015 9/19/2015 9/20/2015 9/21/2015 9/22/2015 9/23/2015 9/24/2015 9/25/2015 9/26/2015 9/27/2015 9/28/2015 9/29/2015 9/30/2015 10/1/2015 10/2/2015 10/3/2015 10/4/2015 10/5/2015 10/6/2015 10/7/2015 10/8/2015 10/9/2015 10/10/2015 10/11/2015 10/12/2015 10/13/2015 10/14/2015 10/15/2015 10/16/2015 10/17/2015 10/18/2015 10/19/2015 10/20/2015 10/21/2015 10/22/2015 10/23/2015 10/24/2015 10/25/2015 10/26/2015 10/27/2015 10/28/2015 10/29/2015 10/30/2015 10/31/2015 11/1/2015 11/2/2015 11/3/2015 11/4/2015 11/5/2015 11/6/2015 11/7/2015 11/8/2015 11/9/2015 11/10/2015 11/11/2015 11/12/2015 11/13/2015 11/14/2015 11/15/2015 11/16/2015 11/17/2015 11/18/2015 11/19/2015 11/20/2015 11/21/2015 11/22/2015 11/23/2015 11/24/2015 11/25/2015 11/26/2015 11/27/2015 11/28/2015 11/29/2015 11/30/2015 12/1/2015 12/2/2015 12/3/2015 12/4/2015 12/5/2015 12/6/2015 12/7/2015 12/8/2015 12/9/2015 12/10/2015 12/11/2015 12/12/2015 12/13/2015 12/14/2015 12/15/2015 12/16/2015 12/17/2015 12/18/2015 12/19/2015 12/20/2015 12/21/2015 12/22/2015 12/23/2015 12/24/2015 12/25/2015 12/26/2015 12/27/2015 12/28/2015 12/29/2015 12/30/2015 12/31/2015 1/1/2016 1/2/2016 1/3/2016 1/4/2016 1/5/2016 1/6/2016 1/7/2016 1/8/2016 1/9/2016 1/10/2016 1/11/2016 1/12/2016 1/13/2016 1/14/2016 1/15/2016 1/16/2016 1/17/2016 1/18/2016 1/19/2016 1/20/2016 1/21/2016 1/22/2016 1/23/2016 1/24/2016 1/25/2016 1/26/2016 1/27/2016 1/28/2016 1/29/2016 1/30/2016 1/31/2016 2/1/2016 2/2/2016 2/3/2016 2/4/2016 2/5/2016 2/6/2016 2/7/2016 2/8/2016 2/9/2016 2/10/2016 2/11/2016 2/12/2016 2/13/2016 2/14/2016 2/15/2016 2/16/2016 2/17/2016 2/18/2016 2/19/2016 2/20/2016 2/21/2016 2/22/2016 2/23/2016 2/24/2016 2/25/2016 2/26/2016 2/27/2016 2/28/2016 2/29/2016 3/1/2016 3/2/2016 3/3/2016 3/4/2016 3/5/2016 3/6/2016 3/7/2016 3/8/2016 3/9/2016 3/10/2016 3/11/2016 3/12/2016 3/13/2016 3/14/2016 3/15/2016 3/16/2016 3/17/2016 3/18/2016 3/19/2016 3/20/2016 3/21/2016 3/22/2016 3/23/2016 3/24/2016 3/25/2016 3/26/2016 3/27/2016 3/28/2016 3/29/2016 3/30/2016 3/31/2016 4/1/2016 4/2/2016 4/3/2016 4/4/2016 4/5/2016 4/6/2016 4/7/2016 4/8/2016 4/9/2016 4/10/2016 4/11/2016 4/12/2016 4/13/2016 4/14/2016 4/15/2016 4/16/2016 4/17/2016 4/18/2016 4/19/2016 4/20/2016 4/21/2016 4/22/2016 4/23/2016 4/24/2016 4/25/2016 4/26/2016 4/27/2016 4/28/2016 4/29/2016 4/30/2016 5/1/2016 5/2/2016 5/3/2016 5/4/2016 5/5/2016 5/6/2016 5/7/2016 5/8/2016 5/9/2016 5/10/2016 5/11/2016 5/12/2016 5/13/2016 5/14/2016 5/15/2016 5/16/2016 5/17/2016 5/18/2016 5/19/2016 5/20/2016 5/21/2016 5/22/2016 5/23/2016 5/24/2016 5/25/2016 5/26/2016 5/27/2016 5/28/2016 5/29/2016 5/30/2016 5/31/2016 6/1/2016 6/2/2016 6/3/2016 6/4/2016 6/5/2016 6/6/2016 6/7/2016 6/8/2016 6/9/2016 6/10/2016 6/11/2016 6/12/2016 6/13/2016 6/14/2016 6/15/2016 6/16/2016 6/17/2016 6/18/2016 6/19/2016 6/20/2016 6/21/2016 6/22/2016 6/23/2016 6/24/2016 6/25/2016 6/26/2016 6/27/2016 6/28/2016 6/29/2016 6/30/2016 7/1/2016 7/2/2016 7/3/2016 7/4/2016 7/5/2016 7/6/2016 7/7/2016 7/8/2016 7/9/2016 7/10/2016 7/11/2016 7/12/2016 7/13/2016 7/14/2016 7/15/2016 7/16/2016 7/17/2016 7/18/2016 7/19/2016 7/20/2016 7/21/2016 7/22/2016 7/23/2016 7/24/2016 7/25/2016 7/26/2016 7/27/2016 7/28/2016 7/29/2016 7/30/2016 7/31/2016 8/1/2016 8/2/2016 8/3/2016 8/4/2016 8/5/2016 8/6/2016 8/7/2016 8/8/2016 8/9/2016 8/10/2016 8/11/2016 8/12/2016 8/13/2016 8/14/2016 8/15/2016 8/16/2016 8/17/2016 8/18/2016 8/19/2016 8/20/2016 8/21/2016 8/22/2016 8/23/2016 8/24/2016 8/25/2016 8/26/2016 8/27/2016 8/28/2016 8/29/2016 8/30/2016 8/31/2016 9/1/2016 9/2/2016 9/3/2016 9/4/2016 9/5/2016 9/6/2016 9/7/2016 9/8/2016 9/9/2016 9/10/2016 9/11/2016 9/12/2016 9/13/2016 9/14/2016 9/15/2016 9/16/2016 9/17/2016 9/18/2016 9/19/2016 9/20/2016 9/21/2016 9/22/2016 9/23/2016 9/24/2016 9/25/2016 9/26/2016 9/27/2016 9/28/2016 9/29/2016 9/30/2016 10/1/2016 10/2/2016 10/3/2016 10/4/2016 10/5/2016 10/6/2016 10/7/2016 10/8/2016 10/9/2016 10/10/2016 10/11/2016 10/12/2016 10/13/2016 10/14/2016 10/15/2016 10/16/2016 10/17/2016 10/18/2016 10/19/2016 10/20/2016 10/21/2016 10/22/2016 10/23/2016 10/24/2016 10/25/2016 10/26/2016 10/27/2016 10/28/2016 10/29/2016 10/30/2016 10/31/2016 11/1/2016 11/2/2016 11/3/2016 11/4/2016 11/5/2016 11/6/2016 11/7/2016 11/8/2016 11/9/2016 11/10/2016 11/11/2016 11/12/2016 11/13/2016 11/14/2016 11/15/2016 11/16/2016 11/17/2016 11/18/2016 11/19/2016 11/20/2016 11/21/2016 11/22/2016 11/23/2016 11/24/2016 11/25/2016 11/26/2016 11/27/2016 11/28/2016 11/29/2016 11/30/2016 12/1/2016 12/2/2016 12/3/2016 12/4/2016 12/5/2016 12/6/2016 12/7/2016 12/8/2016 12/9/2016 12/10/2016 12/11/2016 12/12/2016 12/13/2016 12/14/2016 12/15/2016 12/16/2016 12/17/2016 12/18/2016 12/19/2016 12/20/2016 12/21/2016 12/22/2016 12/23/2016 12/24/2016 12/25/2016 12/26/2016 12/27/2016 12/28/2016 12/29/2016 12/30/2016 12/31/2016 1/1/2017 1/2/2017 1/3/2017 1/4/2017 1/5/2017 1/6/2017 1/7/2017 1/8/2017 1/9/2017 1/10/2017 1/11/2017 1/12/2017 1/13/2017 1/14/2017 1/15/2017 1/16/2017 1/17/2017 1/18/2017 1/19/2017 1/20/2017 1/21/2017 1/22/2017 1/23/2017 1/24/2017 1/25/2017 1/26/2017 1/27/2017 1/28/2017 1/29/2017 1/30/2017 1/31/2017 2/1/2017 2/2/2017 2/3/2017 2/4/2017 2/5/2017 2/6/2017 2/7/2017 2/8/2017 2/9/2017 2/10/2017 2/11/2017 2/12/2017 2/13/2017 2/14/2017 2/15/2017 2/16/2017 2/17/2017 2/18/2017 2/19/2017 2/20/2017 2/21/2017 2/22/2017 2/23/2017 2/24/2017 2/25/2017 2/26/2017 2/27/2017 2/28/2017 3/1/2017 3/2/2017 3/3/2017 3/4/2017 3/5/2017 3/6/2017 3/7/2017 3/8/2017 3/9/2017 3/10/2017 3/11/2017 3/12/2017 3/13/2017 3/14/2017 3/15/2017 3/16/2017 3/17/2017 3/18/2017 3/19/2017 3/20/2017 3/21/2017 3/22/2017 3/23/2017 3/24/2017 3/25/2017 3/26/2017 3/27/2017 3/28/2017 3/29/2017 3/30/2017 3/31/2017 4/1/2017 4/2/2017 4/3/2017 4/4/2017 4/5/2017 4/6/2017 4/7/2017 4/8/2017 4/9/2017 4/10/2017 4/11/2017 4/12/2017 4/13/2017 4/14/2017 4/15/2017 4/16/2017 4/17/2017 4/18/2017 4/19/2017 4/20/2017 4/21/2017 4/22/2017 4/23/2017 4/24/2017 4/25/2017 4/26/2017 4/27/2017 4/28/2017 4/29/2017 4/30/2017 5/1/2017 5/2/2017 5/3/2017 5/4/2017 5/5/2017 5/6/2017 5/7/2017 5/8/2017 5/9/2017 5/10/2017 5/11/2017 5/12/2017 5/13/2017 5/14/2017 5/15/2017 5/16/2017 5/17/2017 5/18/2017 5/19/2017 5/20/2017 5/21/2017 5/22/2017 5/23/2017 5/24/2017 5/25/2017 5/26/2017 5/27/2017 5/28/2017 5/29/2017 5/30/2017 5/31/2017 6/1/2017 6/2/2017 6/3/2017 6/4/2017 6/5/2017 6/6/2017 6/7/2017 6/8/2017 6/9/2017 6/10/2017 6/11/2017 6/12/2017 6/13/2017 6/14/2017 6/15/2017 6/16/2017 6/17/2017 6/18/2017 6/19/2017 6/20/2017 6/21/2017 6/22/2017 6/23/2017 6/24/2017 6/25/2017 6/26/2017 6/27/2017 6/28/2017 6/29/2017 6/30/2017 7/1/2017 7/2/2017 7/3/2017 7/4/2017 7/5/2017 7/6/2017 7/7/2017 7/8/2017 7/9/2017 7/10/2017 7/11/2017 7/12/2017 7/13/2017 7/14/2017 7/15/2017 7/16/2017 7/17/2017 7/18/2017 7/19/2017 7/20/2017 7/21/2017 7/22/2017 7/23/2017 7/24/2017 7/25/2017 7/26/2017 7/27/2017 7/28/2017 7/29/2017 7/30/2017 7/31/2017 8/1/2017 8/2/2017 8/3/2017 8/4/2017 8/5/2017 8/6/2017 8/7/2017 8/8/2017 8/9/2017 8/10/2017 8/11/2017 8/12/2017 8/13/2017 8/14/2017 8/15/2017 8/16/2017 8/17/2017 8/18/2017 8/19/2017 8/20/2017 8/21/2017 8/22/2017 8/23/2017 8/24/2017 8/25/2017 8/26/2017 8/27/2017 8/28/2017 8/29/2017 8/30/2017 8/31/2017 9/1/2017 9/2/2017 9/3/2017 9/4/2017 9/5/2017 9/6/2017 9/7/2017 9/8/2017 9/9/2017 9/10/2017 9/11/2017 9/12/2017 9/13/2017 9/14/2017 9/15/2017 9/16/2017 9/17/2017 9/18/2017 9/19/2017 9/20/2017 9/21/2017 9/22/2017 9/23/2017 9/24/2017 9/25/2017 9/26/2017 9/27/2017 9/28/2017 9/29/2017 9/30/2017 10/1/2017 10/2/2017 10/3/2017 10/4/2017 10/5/2017 10/6/2017 10/7/2017 10/8/2017 10/9/2017 10/10/2017 10/11/2017 10/12/2017 10/13/2017 10/14/2017 10/15/2017 10/16/2017 10/17/2017 10/18/2017 10/19/2017 10/20/2017 10/21/2017 10/22/2017 10/23/2017 10/24/2017 10/25/2017 10/26/2017 10/27/2017 10/28/2017 10/29/2017 10/30/2017 10/31/2017 11/1/2017 11/2/2017 11/3/2017 11/4/2017 11/5/2017 11/6/2017 11/7/2017 11/8/2017 11/9/2017 11/10/2017 11/11/2017 11/12/2017 11/13/2017 11/14/2017 11/15/2017 11/16/2017 11/17/2017 11/18/2017 11/19/2017 11/20/2017 11/21/2017 11/22/2017 11/23/2017 11/24/2017 11/25/2017 11/26/2017 11/27/2017 ; run; if 05/27/2014<=BirthDate<05/27/2015 then year=1; else if 05/27/2015<=BirthDate<05/27/2016 then year=2; else if 05/27/2016<=BirthDate<05/27/2017 then year=3; else if BirthDate>=05/27/2017 then year=4; run;   proc freq data=want1; table year; run;   Wrong results:   SAS Output year Frequency Percent Cumulative Frequency Cumulative Percent 4 20207 100.00 20207 100.00   data:       SAS Output   ... 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Hello -,   I have this data: birthdate     Age 6/22/99       18.9 7/6/99          18.9 .                    . .                    . .                      .    9/15/05       12.6         run;   This are my question.   I want to create data sets where the different in age from the youngest and oldest is not more that 3.5 years using the birthdate. You can use the first birthday as first date   Thank you, DA     ... View more

Hello,  I have this data that I want to sort by SID New_dt and NIPR. For each subject I want just the minimum NPIR for that day. I also want to delete all observations with missing NPIR For example this data should be reduced to Data A below: Obs SID New_dt NPiR 1 10155 . . 2 101004 10/06/15 . 3 101004 10/14/15 4.2 4 101004 10/16/15 4.5 5 101004 10/19/15 4.1 6 101004 10/19/15 4.3 7 101004 10/19/15 4.6 8 101023 11/03/15 . 9 101023 11/05/15 4.2 10 101023 11/06/15 1.3 11 101023 11/07/15 3.8 12 101023 11/07/15 2.4   Should be reduced TO  Data A :   3 101004 10/14/15 4.2 4 101004 10/16/15 4.5 5 101004 10/19/15 4.1 9 101023 11/05/15 4.2 10 101023 11/06/15 1.3 12 101023 11/07/15 2.4         ... View more

I have this repeated measure with varying duplicate observation. I wish to make sure for each id, there is just one timepoint. Below is the data and corrected version (bold). I just one observation for each timepoint by sid   SID NPiL SizeL timepoint 200001 3.8 . 1 200001 3.8 . 1 200001 3.8 . 1 200001 3.8 . 1 200001 3.8 . 1 200001 3.8 4.99 1 200001 3.8 4.89 2 200001 3.8 4.89 2 200001 3.8 4.89 2 200001 3.8 4.89 2 200001 3.8 4.89 2 200001 3.8 4.89 2 200001 4.3 4.22 3 200001 4.3 4.22 3 200001 4.3 4.22 3 200001 4.3 4.22 3 200001 4.3 4.22 3 200001 4.3 4.22 3 200001 4.2 4.25 4 200001 4.2 4.25 4 200001 4.2 4.25 4 200001 4.2 4.25 4 200001 4.2 4.25 4 200001 4.2 4.25 4 200001 4.1 5.71 5 200001 4.1 5.71 5 200001 4.1 5.71 5 200001 4.1 5.71 5 200001 4.1 5.71 5 200001 4.1 5.71 5 200001 3.9 4.96 6 200001 3.9 4.96 6 200001 3.9 4.96 6 200001 3.9 4.96 6 200001 3.9 4.96 6 200001 3.9 4.96 6 200001 4.1 5.17 7 200001 4.1 5.17 7 200001 4.1 5.17 7 200001 4.1 5.17 7 200001 4.1 5.17 7 200001 4.1 5.17 7 200001 3.5 4.71 8 200001 3.5 4.71 8 200001 3.5 4.71 8 200001 3.5 4.71 8 200001 3.5 4.71 8 200001 3.5 4.71 8 200001 3.1 5.36 9 200001 3.1 5.36 9 200001 3.1 5.36 9 200001 3.1 5.36 9 200001 3.1 5.36 9 200001 3.1 5.36 9 200001 2.4 5.81 10 200001 2.4 5.81 10 200001 2.4 5.81 10 200001 2.4 5.81 10 200001 2.4 5.81 10 200001 2.4 5.81 10 200001 3.1 5.78 11 200001 3.1 5.78 11 200001 3.1 5.78 11 200001 3.1 5.78 11 200001 3.1 5.78 11 200001 3.1 5.78 11 200001 2.9 5.5 12 200001 2.9 5.5 12 200001 2.9 5.5 12 200001 2.9 5.5 12 200001 2.9 5.5 12 200001 2.9 5.5 12 200001 2.4 6.04 13 200001 2.4 6.04 13 200001 2.4 6.04 13 200001 2.4 6.04 13 200001 2.4 6.04 13 200001 2.4 6.04 13 200001 2.6 6.24 14 200001 2.6 6.24 14 200001 2.6 6.24 14 200001 2.6 6.24 14 200001 2.6 6.24 14 200001 2.6 6.24 14 200001 3 5.42 15 200001 3 5.42 15 200001 3 5.42 15 200001 3 5.42 15 200001 3 5.42 15 200001 3 5.42 15 200001 4.2 3.58 16 200001 4.2 3.58 16 200001 4.2 3.58 16 200001 4.2 3.58 16 200001 4.2 3.58 16 200001 4.2 3.58 16 200001 2.8 6.08 17 200001 2.8 6.08 17 200001 2.8 6.08 17 200001 2.8 6.08 17 200001 2.8 6.08 17                 corrected data                     SID NPiL SizeL timepoint 200001 3.8 . 1 200001 3.8 4.89 2 200001 4.3 4.22 3 200001 4.2 4.25 4 200001 4.1 5.71 5 200001 3.9 4.96 6 200001 4.1 5.17 7 200001 3.5 4.71 8 200001 3.1 5.36 9 200001 2.4 5.81 10 200001 3.1 5.78 11 200001 2.9 5.5 12 200001 2.4 6.04 13 200001 2.6 6.24 14 200001 3 5.42 15 200001 4.2 3.58 16 200001 2.8 6.08 17 ... View more

I have this data set A with N=100. I want to create a data set B with N=75 through simple random sampling without replacement and the remaining 25 should be data set C.   Help! ... View more

   Hello Madam/Sir,   I have this data set with variables Meds1-Meds10. I have to create 10 other variables Medi1-Medi10 such that Medi1-Medi10 should have only the name of the medication, that is the first character variable. For example in obs 1,  Meds1,  the first data point is LIPITOR 10MG TABLET. This will be Lipitor. That is 10MG TABLET deleted. The case like in obs 3, Meds2, the BUDESONIDE will stay BUDESONIDE in the new name Medi2.   SAS DATA Obs Meds1 Meds2 Meds3 Meds4 Meds5 Meds6 Meds7 Meds8 Meds9 Meds10 1 LIPITOR 10 MG TABLET BUDESONIDE COREG 12.5 MG TABLET NEXIUM 20 MG CAPSULE,DELAYED RELEASE FLUTICASONE 50 MCG/ACTUATION NASAL SPRAY,SUSPENSION FLUTICASONE 50 MCG/ACTUATION NASAL SPRAY,SUSPENSION ISOSORBIDE MONONITRATE ER 60 MG TABLET,EXTENDED RELEASE 24 HR LEVALBUTEROL HFA 45 MCG/ACTUATION AEROSOL INHALER LEVALBUTEROL 0.63 MG/3 ML SOLUTION FOR NEBULIZATION LISINOPRIL 20 MG 2 ALBUTEROL SULFATE HFA 90 MCG/ACTUATION AEROSOL INHALER AMLODIPINE 10 MG TABLET ATORVASTATIN 10 MG TABLET LIPITOR 20 MG TABLET BUDESONIDE COREG 25 MG TABLET NEXIUM 20 MG CAPSULE,DELAYED RELEASE FLUTICASONE 50 MCG/ACTUATION NASAL SPRAY,SUSPENSION LISINOPRIL 20 MG LORATADINE 10 MG TABLET 3 LIPITOR 10 MG TABLET BUDESONIDE ESOMEPRAZOLE MAGNESIUM 20 MG CAPSULE,DELAYED RELEASE FLUTICASONE 50 MCG/ACTUATION NASAL SPRAY,SUSPENSION ISOSORBIDE MONONITRATE ER 60 MG TABLET,EXTENDED RELEASE 24 HR LEVALBUTEROL HFA 45 MCG/ACTUATION AEROSOL INHALER LISINOPRIL 20 MG LORATADINE 10 MG TABLET METOPROLOL TARTRATE 25 MG TABLET VERAPAMIL ER (SR) 240 MG TABLET,EXTENDED RELEASE 4 ALBUTEROL SULFATE HFA 90 MCG/ACTUATION AEROSOL INHALER AMLODIPINE 10 MG TABLET ATORVASTATIN 10 MG TABLET LIPITOR 20 MG TABLET BUDESONIDE CARVEDILOL 25 MG TABLET NEXIUM 20 MG CAPSULE,DELAYED RELEASE FLUTICASONE 50 MCG/ACTUATION NASAL SPRAY,SUSPENSION ISOSORBIDE MONONITRATE ER 60 MG TABLET,EXTENDED RELEASE 24 HR LISINOPRIL 20 MG 5 LIPITOR 10 MG TABLET BUDESONIDE ESOMEPRAZOLE MAGNESIUM 20 MG CAPSULE,DELAYED RELEASE FLUTICASONE 50 MCG/ACTUATION NASAL SPRAY,SUSPENSION ISOSORBIDE MONONITRATE ER 60 MG TABLET,EXTENDED RELEASE 24 HR LEVALBUTEROL HFA 45 MCG/ACTUATION AEROSOL INHALER LISINOPRIL 20 MG LORATADINE 10 MG TABLET METOPROLOL TARTRATE 25 MG TABLET VERAPAMIL ER (SR) 240 MG TABLET,EXTENDED RELEASE 6 ALBUTEROL 90 MCG/ACTUATION AEROSOL INHALER EXFORGE HCT 10 MG CALCIUM 600 + D(3) 600 MG (1,500 MG) VITAMIN D2 50,000 UNIT CAPSULE CENTRUM ORAL GABAPENTIN 100 MG CAPSULE FISH OIL 1,000 MG CAPSULE FLUTICASONE 110 MCG/ACTUATION HFA AEROSOL INHALER FAMOTIDINE 20 MG TABLET GABAPENTIN 100 MG CAPSULE 7 EXFORGE HCT 10 MG CALCIUM 600 + D(3) 600 MG (1,500 MG) CARVEDILOL 3.125 MG TABLET FAMOTIDINE 20 MG TABLET FLUTICASONE 50 MCG/ACTUATION NASAL SPRAY,SUSPENSION CENTRUM ORAL FUROSEMIDE 20 MG TABLET GABAPENTIN 100 MG CAPSULE GABAPENTIN 100 MG CAPSULE LEVOCETIRIZINE 5 MG TABLET 8 AMLODIPINE 10 MG TABLET EXFORGE HCT 10 MG CALCIUM 600 + D(3) 600 MG (1,500 MG) CARVEDILOL 3.125 MG TABLET FAMOTIDINE 20 MG TABLET FLUTICASONE 50 MCG/ACTUATION NASAL SPRAY,SUSPENSION CENTRUM ORAL GABAPENTIN 100 MG CAPSULE GABAPENTIN 100 MG CAPSULE CLARITIN 10 MG TABLET 9 PROVENTIL HFA 90 MCG/ACTUATION AEROSOL INHALER ALBUTEROL 90 MCG/ACTUATION AEROSOL INHALER EXFORGE HCT 10 MG CALCIUM 600 + D(3) 600 MG (1,500 MG) FAMOTIDINE 20 MG TABLET CENTRUM ORAL GABAPENTIN 100 MG CAPSULE FISH OIL 1,000 MG CAPSULE FLUTICASONE 50 MCG/ACTUATION NASAL SPRAY,SUSPENSION EXFORGE HCT 10 MG 10 ALBUTEROL SULFATE HFA 90 MCG/ACTUATION AEROSOL INHALER ALBUTEROL 90 MCG/ACTUATION AEROSOL INHALER EXFORGE HCT 10 MG EXFORGE HCT 10 MG FAMOTIDINE 20 MG TABLET GABAPENTIN 100 MG CAPSULE CLONIDINE HCL 0.1 MG TABLET CENTRUM ORAL FISH OIL 1,000 MG CAPSULE CALCIUM 600 + D(3) 600 MG (1,500 MG) ... View more

Dear Sir/Madam,   I have data set with variable CodedDCList as below. I want to separate it and create many different variables such that it should look like table 2 below. Please Help me with the code       SAS Output Obs CodedDxList 1 E78.5 -- Other and unspecified hyperlipidemia; R06.09, R09.89 -- Other dyspnea and respiratory abnormality; I10 -- Unspecified essential hypertension; J44.9 -- Chronic airway obstruction, not elsewhere classified; K21.9 -- Esophageal reflux; M17.10 -- Osteoarthrosis, unspecified whether generalized or localized, lower leg; K44.9 -- Diaphragmatic hernia without mention of obstruction or gangrene 2 I10 -- Unspecified essential hypertension; E04.2 -- Nontoxic multinodular goiter; E78.5 -- Other and unspecified hyperlipidemia       Table 2 Code1             Code2              Code 3           Code4        Code5       Code6            Code 1)  E78.5          R06.09, R09.89         I10               J44.9        K21.9            M17.10               K44.              ... View more