value labels to values

[Rebecca Ivester]

I am working on modifying an existing data set (IPUMS - HigherEd). This data set contains a number of variables that are numeric and that have value labels--e.g., race_eth == 1 has a value label of white, race_eth == 2 has a value label of black, etc. For our purposes, it would be better to have the value labels as the actual values. I imagine there is some more efficient way of achieving this than to convert everything manually. Any ideas?

[Rich Goldstein]

see

help decode

[Nick Cox]

That's what decode does.

On the whole, however. a numeric variable with value labels has far more uses than the corresponding string variable. For example, string variables can't in general be used in models and it's harder to graph them. Further, string variables sort alphabetically, which is good for dictionaries but usually poor for statistics.

Hence what are your purposes and why should string variables be better?

[Marcos Almeida]

You already have it, I mean, what you see is the label.

Please look at the example below:

. sysuse auto
(1978 Automobile Data)

. tab foreign

   Car type |      Freq.     Percent        Cum.
------------+-----------------------------------
   Domestic |         52       70.27       70.27
    Foreign |         22       29.73      100.00
------------+-----------------------------------
      Total |         74      100.00

. codebook foreign

-------------------------------------------------------------------------------------------------------------
foreign                                                                                              Car type
-------------------------------------------------------------------------------------------------------------

                  type:  numeric (byte)
                 label:  origin

                 range:  [0,1]                        units:  1
         unique values:  2                        missing .:  0/74

            tabulation:  Freq.   Numeric  Label
                            52         0  Domestic
                            22         1  Foreign

. regress mpg i.foreign

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(1, 72)        =     13.18
       Model |  378.153515         1  378.153515   Prob > F        =    0.0005
    Residual |  2065.30594        72  28.6848048   R-squared       =    0.1548
-------------+----------------------------------   Adj R-squared   =    0.1430
       Total |  2443.45946        73  33.4720474   Root MSE        =    5.3558

------------------------------------------------------------------------------
         mpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     foreign |
    Foreign  |   4.945804   1.362162     3.63   0.001     2.230384    7.661225
       _cons |   19.82692   .7427186    26.70   0.000     18.34634    21.30751
------------------------------------------------------------------------------

. tab foreign, sum(mpg)

            |      Summary of Mileage (mpg)
   Car type |        Mean   Std. Dev.       Freq.
------------+------------------------------------
   Domestic |   19.826923   4.7432972          52
    Foreign |   24.772727   6.6111869          22
------------+------------------------------------
      Total |   21.297297   5.7855032          74

Hope that helps.

P.S.: crossed with Nick's insightful comment, always faster on the trigger.