Let’s use a file called hsb1 from our
web site and use that as an example.

use https://stats.idre.ucla.edu/stat/stata/notes/hsb1 , clear
(highschool and beyond (200 cases))

To make this simpler, let’s just keep the 25
observations with id less than or equal to 25.

keep if id <= 25
(175 observations deleted)

Now let’s say we want to export the
variables id gender race read write and science.  You can
see the data for those variables below.

list id gender race read write science
            id     gender          race       read      write    science 
  1.        11       male      hispanic         34         46         39  
  2.        20       male      hispanic         60         52         61  
  3.        12       male      hispanic         37         44         39  
  4.        16       male      hispanic         47         31         36  
  5.         7       male      hispanic         57         54         47  
  6.        21       male      hispanic         44         44         50  
  7.        15       male      hispanic         39         39         26  
  8.        22       male      hispanic         42         39         56  
  9.         9       male      hispanic         48         49         44  
 10.        18       male      hispanic         50         33         44  
 11.         5       male      hispanic         47         40          .  
 12.        14       male      hispanic         47         41         42  
 13.         3       male      hispanic         63         65         63  
 14.        24       male         asian         52         62         47  
 15.         8     female      hispanic         39         44         44  
 16.         1     female      hispanic         34         44         39  
 17.         4     female      hispanic         44         50         39  
 18.         2     female      hispanic         39         41         42  
 19.        19     female      hispanic         28         46         44  
 20.        17     female      hispanic         47         57         44  
 21.         6     female      hispanic         47         41         40  
 22.        10     female      hispanic         47         54         53  
 23.        13     female      hispanic         47         46         47  
 24.        23     female         asian         65         65         58  
 25.        25     female         asian         47         44         42  

Let’s write these variables out to a comma
separated file using the outsheet command.  After outsheet
we specify the names of the variables we want to write (if we omit this, it
will write all of the variables).  We use the comma option (placed
after a , ) to indicate we want a comma separated file (by default it will
make a tab separated file).

outsheet id gender race read write science using smauto1.csv , comma 

We use the type command to see how
the file looks.  It looks great.

type smauto1.csv
id,gender,race,read,write,science
11,"male","hispanic",34,46,39
20,"male","hispanic",60,52,61
12,"male","hispanic",37,44,39
16,"male","hispanic",47,31,36
7,"male","hispanic",57,54,47
21,"male","hispanic",44,44,50
15,"male","hispanic",39,39,26
22,"male","hispanic",42,39,56
9,"male","hispanic",48,49,44
18,"male","hispanic",50,33,44
5,"male","hispanic",47,40,
14,"male","hispanic",47,41,42
3,"male","hispanic",63,65,63
24,"male","asian",52,62,47
8,"female","hispanic",39,44,44
1,"female","hispanic",34,44,39
4,"female","hispanic",44,50,39
2,"female","hispanic",39,41,42
19,"female","hispanic",28,46,44
17,"female","hispanic",47,57,44
6,"female","hispanic",47,41,40
10,"female","hispanic",47,54,53
13,"female","hispanic",47,46,47
23,"female","asian",65,65,58
25,"female","asian",47,44,42

The variables gender and race
are really numeric variables that have value labels.  For example, gender
is really coded 1 and 2 with 1 representing male and 2 representing female.
Perhaps we want the numbers, not the labels, for gender and race.  If so,
we can use the nolabel option and Stata will output the
numeric values, not the labels, as shown below.

outsheet id gender race read write science using smauto2.csv , comma nolabel 

We can use the type command below and see that now the numeric
values of race and gender are output to the file.

type smauto2.csv
id,gender,race,read,write,science
11,1,1,34,46,39
20,1,1,60,52,61
12,1,1,37,44,39
16,1,1,47,31,36
7,1,1,57,54,47
21,1,1,44,44,50
15,1,1,39,39,26
22,1,1,42,39,56
9,1,1,48,49,44
18,1,1,50,33,44
5,1,1,47,40,
14,1,1,47,41,42
3,1,1,63,65,63
24,1,2,52,62,47
8,2,1,39,44,44
1,2,1,34,44,39
4,2,1,44,50,39
2,2,1,39,41,42
19,2,1,28,46,44
17,2,1,47,57,44
6,2,1,47,41,40
10,2,1,47,54,53
13,2,1,47,46,47
23,2,2,65,65,58
25,2,2,47,44,42

You can read this kind of file into any
program that knows how to read a comma separated file. For example, Excel or
SPSS
can read this file. In Excel, you would choose file then open
and then for files of type select comma separated
file (Excel expects those files to have a .csv extension).  You
can then click the file and open it in Excel.

You can learn more about this by seeing the
Stata help file for outsheet.