Hi, I have some character variables in my dataset named var1_1, var1_2, var1_3, var2_1, var2_2 etc. The text in each column starts with the numerical suffix of the variable name but the text that follows is different. For example: var1_1 = 1. This is apple. var1_1 = 1. That is a car. Var1_2 = 2. I like to read. Var 1_2= 2. I am happy. And so on.   I want to recode these character variables into numeric variables so that re_var1_1=1 if there is any text in this column. re_var1_2=2 if there is any text in this column. re_var1_3=3 if there is any text in this column. re_var2_1=1 if there is any text in this column. and so on. Any suggestions on how to get this done? Thank you, Angi   proc sql; create table want(year float, community varchar(1), var1_1 varchar(157), var1_2 varchar(290), var1_3 varchar(318), var2_1 varchar(82), var2_2 varchar(46), var2_3 varchar(78)); Insert into want(year, community, var1_1, var1_2, var1_3, var2_1, var2_2, var2_3) Values(2014, 'A', '1. It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife.', '', '', '', '', '3. Every summer Lin Kong returned to Goose Village to divorce his wife, Shuyu.'); Insert into want(year, community, var1_1, var1_2, var1_3, var2_1, var2_2, var2_3) Values(2014, 'B', '', '2. Happy families are all alike; every unhappy family is unhappy in its own way.', '', '1. Through the fence, between the curling flower spaces, I could see them hitting.', '', ''); Insert into want(year, community, var1_1, var1_2, var1_3, var2_1, var2_2, var2_3) Values(2014, 'C', '', '', '3. You are about to begin reading Italo Calvino''s new novel, If on a winter''s night a traveler.', '', '2. I am a sick man . . . I am a spiteful man.', ''); Insert into want(year, community, var1_1, var1_2, var1_3, var2_1, var2_2, var2_3) Values(2014, 'D', '', '', '3. If you really want to hear about it, the first thing you''ll probably want to know is where I was born, and what my lousy childhood was like, and how my parents were occupied and all before they had me, and all that David Copperfield kind of crap, but I don''t feel like going into it, if you want to know the truth.', '', '', '3. Mrs. Dalloway said she would buy the flowers herself.'); Insert into want(year, community, var1_1, var1_2, var1_3, var2_1, var2_2, var2_3) Values(2014, 'B', '1. Many years later, as he faced the firing squad, Colonel Aureliano Buendía was to remember that distant afternoon when his father took him to discover ice.', '', '', '', '', ''); Insert into want(year, community, var1_1, var1_2, var1_3, var2_1, var2_2, var2_3) Values(2014, 'E', '', '2. It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair.', '', '', '', ''); Insert into want(year, community, var1_1, var1_2, var1_3, var2_1, var2_2, var2_3) Values(2014, 'M', '1. You don''t know about me without you have read a book by the name of The Adventures of Tom Sawyer; but that ain''t no matter.', '', '', '1. The moment one learns English, complications set in.', '', '');   proc print data=want; run; ... View more

Hi, I have a dataset with the following variables: school name, employee name, job title, and employment year. I need to calculate annual turnover rates for a) all employees within a school, and b) teachers within a school from 2009 to 2010. I’d appreciate any help you can provide in this matter. Job title: 1 = principal, 2 = teacher, 3 = other employees.   I'm using SAS 9.4.   Thank you, Angi   data WORK.EMPLOYEE_DATA_SAMPLE(label='employee_data_sample dataset written by Stat/Transfer Ver. 11.2.2106.0521       '); infile datalines dsd truncover; input School:$7. Name:$17. Job:32. Year:32.; datalines; SchoolA Smith, John 1 2009 SchoolA Doe, Jane 2 2009 SchoolA Cooper, Laura 3 2009 SchoolA Adams, Charlie 2 2009 SchoolA Anderson, Joyce 2 2009 SchoolA Bell, Anne 2 2009 SchoolA Abbott, Sharon 3 2009 SchoolB Lane, Gary 1 2009 SchoolB Nelson, Elizabeth 2 2009 SchoolB Bumpus, Toby 3 2009 SchoolB Doe, John 3 2009 SchoolB Smith, Jane 2 2009 SchoolB Richards, Simon 2 2009 SchoolB Tate, Lori 2 2009 SchoolB Bates, Linda 2 2009 SchoolB Pitt, Joel 3 2009 SchoolA Smith, John 1 2010 SchoolA Doe, Jane 2 2010 SchoolA Hill, Claire 3 2010 SchoolA Adams, Charlie 2 2010 SchoolA Arnold, Gayle 2 2010 SchoolA Bell, Anne 2 2010 SchoolA Abbott, Sharon 3 2010 SchoolA Bond, Connie 2 2010 SchoolB Lane, Gary 1 2010 SchoolB Nelson, Elizabeth 2 2010 SchoolB Bumpus, Toby 3 2010 SchoolB Doe, John 3 2010 SchoolB Anderson, Gilbert 2 2010 SchoolB Gately, Ronan 2 2010 SchoolB Tate, Lori 2 2010 SchoolB Bates, Linda 2 2010 SchoolB Pitt, Joel 3 2010 SchoolB Jeffords, Jim 2 2010 ;;;; ... View more

Hello, I have a program implementation dataset with program ID, cohort number to which the participants belong, total number of participants, and participant count for demographic sub-categories such as age, gender, and ethnicity.   Variables for sub-category Age = Age04 Age511 Age1214 Age1517 Age1820 Age2124 Age2544 Age4564 Age65Plus AgeUnk Variables for sub-category Gender = Male Female GenderUnk Variables for sub-category Ethnicity = NotHispanic MexicanChicano PuertoRican Cuban OtherHisp HispUnk   I am trying to calculate demographic proportions by cohort. For example, in the gender category, I'd like to calculate the total number and proportion of males, females, and unknown for cohort 1, cohort 2, cohort 3. I'd appreciate any advice on how to do this.     data WORK.PARTICIPANTS(label='participants dataset written by Stat/Transfer Ver. 11.2.2106.0521       '); infile datalines dsd truncover;   input ProgramID_:32. TotalParticipants:32. Age04:32. Age511:32. Age1214:32. Age1517:32. Age1820:32. Age2124:32. Age2544:32. Age4564:32. Age65Plus:32. AgeUnk:32. Male:32. Female:32. GenderUnk:32. NotHispanic:32. MexicanChicano:32. PuertoRican:32. Cuban:32. OtherHisp:32. HispUnk:32. cohort:32.; datalines4;   1 22 0 13 9 0 0 0 0 0 0 0 12 10 0 19 0 0 0 0 3 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 4 6 0 0 0 5 1 0 0 0 0 0 2 4 0 5 0 0 0 1 0 1 5 0 3 2 0 0 1 0 2 0 0 10 4 4 10 5 0 0 0 13 0 2 6 0 2 1 0 0 4 0 1 1 0 0 5 4 0 7 2 0 0 0 0 3 7 0 0 0 0 0 2 5 10 5 0 0 10 12 0 21 1 0 0 0 0 3 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 12 2 0 0 0 0 0 0 0 2 0 0 0 0 2 2 0 0 0 0 0 2 13 25 0 0 5 20 0 0 0 0 0 0 5 20 0 22 0 0 0 3 0 2 14 24 0 0 5 19 0 0 0 0 0 0 12 12 0 23 0 0 0 1 0 2 15 0 34 54 17 5 0 10 40 20 0 0 90 90 0 170 10 0 0 0 0 2 16 0 0 5 20 40 10 10 30 30 10 0 75 80 0 150 5 0 0 0 0 1 17 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 18 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 19 9 0 0 9 0 0 0 0 0 0 0 0 9 0 6 3 0 0 0 0 1 20 150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ;;;;   I am using SAS 9.4. Thank you, ... View more

Hi, I have a dataset with program ID, total number of participants, and participant count for demographic sub-categories such as age, gender, and ethnicity. Total participants should equal to the total for each sub-categories but this is not always the case.   I would like to do the following: Create a new variable called new_TotalParticipants with the following rules: If TotalParticipants => total count for age and total count for gender and total count for ethnicity, then new_TotalParticipants=TotalParticipants If TotalParticipants<total count for age or total count for gender or total count for ethnicity, then new_TotalParticipants=whichever total count for demographic subcategory is the highest.   Variables for sub-category Age = Age04 Age511 Age1214 Age1517 Age1820 Age2124 Age2544 Age4564 Age65Plus AgeUnk Variables for sub-category Gender = Male Female GenderUnk Variables for sub-category Ethnicity = NotHispanic MexicanChicano PuertoRican Cuban OtherHisp HispUnk   Data data y.PARTICIPANTS(label='participants dataset written by Stat/Transfer Ver. 11.2.2106.0521       ');   infile datalines dsd truncover;   input ProgramID_:32. TotalParticipants:32. Age04:32. Age511:32. Age1214:32. Age1517:32. Age1820:32. Age2124:32. Age2544:32. Age4564:32. Age65Plus:32. AgeUnk:32. Male:32. Female:32. GenderUnk:32. NotHispanic:32. MexicanChicano:32. PuertoRican:32. Cuban:32. OtherHisp:32. HispUnk:32.; datalines4; 1,22,0,13,9,0,0,0,0,0,0,0,12,10,0,19,0,0,0,0,3 2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 4,6,0,0,0,5,1,0,0,0,0,0,2,4,0,5,0,0,0,1,0 5,0,3,2,0,0,1,0,2,0,0,10,4,4,10,5,0,0,0,13,0 6,0,2,1,0,0,4,0,1,1,0,0,5,4,0,7,2,0,0,0,0 7,0,0,0,0,0,2,5,10,5,0,0,10,12,0,21,1,0,0,0,0 8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 12,2,0,0,0,0,0,0,0,2,0,0,0,0,2,2,0,0,0,0,0 13,25,0,0,5,20,0,0,0,0,0,0,5,20,0,22,0,0,0,3,0 14,24,0,0,5,19,0,0,0,0,0,0,12,12,0,23,0,0,0,1,0 15,0,34,54,17,5,0,10,40,20,0,0,90,90,0,170,10,0,0,0,0 16,0,0,5,20,40,10,10,30,30,10,0,75,80,0,150,5,0,0,0,0 17,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 18,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 19,9,0,0,9,0,0,0,0,0,0,0,0,9,0,6,3,0,0,0,0 20,150,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ;;;;   I am using SAS 9.4.    Thank you! ... View more

Hi, I have a dataset with program ID, total number of participants, and participant count for demographic sub-categories such as age, gender, and ethnicity. Total participants should equal to the total for each sub-categories. Could you please let me know how I can check this using SAS 9.4? Is there a way to identify programs for which total participant number does not match the total of sub-categories?   Variables for sub-category Age = Age04 Age511 Age1214 Age1517 Age1820 Age2124 Age2544 Age4564 Age65Plus AgeUnk Variables for sub-category Gender = Male Female GenderUnk Variables for sub-category Ethnicity = NotHispanic MexicanChicano PuertoRican Cuban OtherHisp HispUnk   Data data y.PARTICIPANTS(label='participants dataset written by Stat/Transfer Ver. 11.2.2106.0521       ');   infile datalines dsd truncover;   input ProgramID_:32. TotalParticipants:32. Age04:32. Age511:32. Age1214:32. Age1517:32. Age1820:32. Age2124:32. Age2544:32. Age4564:32. Age65Plus:32. AgeUnk:32. Male:32. Female:32. GenderUnk:32. NotHispanic:32. MexicanChicano:32. PuertoRican:32. Cuban:32. OtherHisp:32. HispUnk:32.; datalines4; 1,22,0,13,9,0,0,0,0,0,0,0,12,10,0,19,0,0,0,0,3 2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 4,6,0,0,0,5,1,0,0,0,0,0,2,4,0,5,0,0,0,1,0 5,0,3,2,0,0,1,0,2,0,0,10,4,4,10,5,0,0,0,13,0 6,0,2,1,0,0,4,0,1,1,0,0,5,4,0,7,2,0,0,0,0 7,0,0,0,0,0,2,5,10,5,0,0,10,12,0,21,1,0,0,0,0 8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 12,2,0,0,0,0,0,0,0,2,0,0,0,0,2,2,0,0,0,0,0 13,25,0,0,5,20,0,0,0,0,0,0,5,20,0,22,0,0,0,3,0 14,24,0,0,5,19,0,0,0,0,0,0,12,12,0,23,0,0,0,1,0 15,0,34,54,17,5,0,10,40,20,0,0,90,90,0,170,10,0,0,0,0 16,0,0,5,20,40,10,10,30,30,10,0,75,80,0,150,5,0,0,0,0 17,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 18,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 19,9,0,0,9,0,0,0,0,0,0,0,0,9,0,6,3,0,0,0,0 20,150,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ;;;;   Thank you! ... View more