I have a dataset (example below) with several variables and information separated by sex (total, female and male). I would like to create new variables with information separated by total, male and female. Therefore I need 3 new columns with information for "total", "male" and "female" for each variable in the dataset. Can you help me with this?
Thank you very mcuh in advance.
Thank you very mcuh in advance.
Code:
* Example generated by -dataex-. For more info, type help dataex clear input str28 country str6 sex int year float(fam1 fam10a) str4 cou float fam9c "Argentina" "TOTAL" 1990 3 . "ARG" . "Argentina" "TOTAL" 1991 2.97 . "ARG" . "Argentina" "TOTAL" 1992 2.93 . "ARG" . "Argentina" "TOTAL" 1993 2.88 . "ARG" . "Argentina" "TOTAL" 1994 2.83 . "ARG" . "Argentina" "TOTAL" 1995 2.77 . "ARG" . "Argentina" "TOTAL" 1996 2.72 . "ARG" . "Argentina" "TOTAL" 1997 2.67 . "ARG" . "Argentina" "TOTAL" 1998 2.62 . "ARG" . "Argentina" "TOTAL" 1999 2.58 . "ARG" . "Argentina" "TOTAL" 2000 2.54 . "ARG" . "Argentina" "TOTAL" 2001 2.51 . "ARG" . "Argentina" "TOTAL" 2002 2.49 . "ARG" . "Argentina" "TOTAL" 2003 2.46 . "ARG" . "Argentina" "TOTAL" 2004 2.44 . "ARG" . "Argentina" "TOTAL" 2005 2.42 . "ARG" . "Argentina" "TOTAL" 2006 2.4 . "ARG" . "Argentina" "TOTAL" 2007 2.38 . "ARG" . "Argentina" "TOTAL" 2008 2.37 . "ARG" . "Argentina" "TOTAL" 2009 2.36 . "ARG" . "Argentina" "TOTAL" 2010 2.35 . "ARG" . "Argentina" "TOTAL" 2011 2.34 . "ARG" . "Argentina" "TOTAL" 2012 2.33 . "ARG" . "Argentina" "TOTAL" 2013 2.32 . "ARG" . "Argentina" "TOTAL" 2014 2.31 . "ARG" . "Argentina" "TOTAL" 2015 2.3 . "ARG" . "Argentina" "TOTAL" 2016 2.29 . "ARG" . "Argentina" "TOTAL" 2017 2.28 . "ARG" . "Argentina" "FEMALE" 2018 . . "ARG" . "Argentina" "MALE" 2018 . . "ARG" . "Argentina" "TOTAL" 2018 2.26 . "ARG" . "Argentina" "TOTAL" 2019 2.25 . "ARG" . "Argentina" "TOTAL" 2020 2.23 . "ARG" . "Australia" "TOTAL" 1990 1.9 . "AUS" . "Australia" "TOTAL" 1991 1.85 . "AUS" . "Australia" "TOTAL" 1992 1.89 . "AUS" . "Australia" "TOTAL" 1993 1.86 . "AUS" . "Australia" "TOTAL" 1994 1.84 . "AUS" . "Australia" "TOTAL" 1995 1.82 . "AUS" . "Australia" "TOTAL" 1996 1.8 . "AUS" . "Australia" "TOTAL" 1997 1.78 . "AUS" . "Australia" "TOTAL" 1998 1.76 . "AUS" . "Australia" "TOTAL" 1999 1.76 . "AUS" . "Australia" "FEMALE" 2000 . . "AUS" . "Australia" "MALE" 2000 . . "AUS" . "Australia" "TOTAL" 2000 1.76 . "AUS" . "Australia" "FEMALE" 2001 . . "AUS" . "Australia" "MALE" 2001 . . "AUS" . "Australia" "TOTAL" 2001 1.73 . "AUS" . "Australia" "FEMALE" 2002 . . "AUS" . "Australia" "MALE" 2002 . . "AUS" . "Australia" "TOTAL" 2002 1.77 . "AUS" . "Australia" "FEMALE" 2003 . . "AUS" . "Australia" "MALE" 2003 . . "AUS" . "Australia" "TOTAL" 2003 1.77 . "AUS" . "Australia" "FEMALE" 2004 . . "AUS" . "Australia" "MALE" 2004 . . "AUS" . "Australia" "TOTAL" 2004 1.78 19.6 "AUS" 16.6 "Australia" "FEMALE" 2005 . . "AUS" . "Australia" "MALE" 2005 . . "AUS" . "Australia" "TOTAL" 2005 1.85 19.8 "AUS" 14.8 "Australia" "FEMALE" 2006 . . "AUS" . "Australia" "MALE" 2006 . . "AUS" . "Australia" "TOTAL" 2006 1.88 19.4 "AUS" 14.3 "Australia" "FEMALE" 2007 . . "AUS" . "Australia" "MALE" 2007 . . "AUS" . "Australia" "TOTAL" 2007 1.99 22.5 "AUS" 13.5 "Australia" "FEMALE" 2008 . . "AUS" . "Australia" "MALE" 2008 . . "AUS" . "Australia" "TOTAL" 2008 2.02 27.1 "AUS" 12.5 "Australia" "FEMALE" 2009 . . "AUS" . "Australia" "MALE" 2009 . . "AUS" . "Australia" "TOTAL" 2009 1.97 22.1 "AUS" 15.1 "Australia" "FEMALE" 2010 . . "AUS" . "Australia" "MALE" 2010 . . "AUS" . "Australia" "TOTAL" 2010 1.95 23.3 "AUS" 13.9 "Australia" "FEMALE" 2011 . . "AUS" . "Australia" "MALE" 2011 . . "AUS" . "Australia" "TOTAL" 2011 1.92 25.2 "AUS" 14 "Australia" "FEMALE" 2012 . . "AUS" . "Australia" "MALE" 2012 . . "AUS" . "Australia" "TOTAL" 2012 1.93 . "AUS" . "Australia" "FEMALE" 2013 . . "AUS" . "Australia" "MALE" 2013 . . "AUS" . "Australia" "TOTAL" 2013 1.88 . "AUS" . "Australia" "FEMALE" 2014 . . "AUS" . "Australia" "MALE" 2014 . . "AUS" . "Australia" "TOTAL" 2014 1.79 . "AUS" . "Australia" "FEMALE" 2015 . . "AUS" . "Australia" "MALE" 2015 . . "AUS" . "Australia" "TOTAL" 2015 1.79 . "AUS" . "Australia" "FEMALE" 2016 . . "AUS" . "Australia" "MALE" 2016 . . "AUS" . "Australia" "TOTAL" 2016 1.79 . "AUS" . "Australia" "FEMALE" 2017 . . "AUS" . "Australia" "MALE" 2017 . . "AUS" . "Australia" "TOTAL" 2017 1.74 . "AUS" . "Australia" "FEMALE" 2018 . . "AUS" . "Australia" "MALE" 2018 . . "AUS" . "Australia" "TOTAL" 2018 1.74 . "AUS" . end
Comment