I am trying to create a copy of a dataset (123,005 observations, 469 variables - including long, double, and string variables).
I want the new copy to include all the variables of the original dataset as well as the full range of unique values and their labels.
However, I don't want the new copy of the dataset to contain any of the original observations (participants) - so I want all observations to be fabricated/random.
The reason for this is that I cannot work on the 'real' data remotely due to data regulations. I could work remotely on a version of the dataset that contained the full range possible values and metadata but none of the original observations.
I have tried dropping all observations "drop in 1/123005" which keeps colulmn names but because there are no observations there are no values.
From what I can tell, the only way to do this would be to create enough dummy observations so that the full range of possible values existed. But doing this manually for 469 columns with various ranges and labels is not feasible.
Any help or advice in the right direction from anyone who has done anything of this sort before would be very much apreciated!
I want the new copy to include all the variables of the original dataset as well as the full range of unique values and their labels.
However, I don't want the new copy of the dataset to contain any of the original observations (participants) - so I want all observations to be fabricated/random.
The reason for this is that I cannot work on the 'real' data remotely due to data regulations. I could work remotely on a version of the dataset that contained the full range possible values and metadata but none of the original observations.
I have tried dropping all observations "drop in 1/123005" which keeps colulmn names but because there are no observations there are no values.
From what I can tell, the only way to do this would be to create enough dummy observations so that the full range of possible values existed. But doing this manually for 469 columns with various ranges and labels is not feasible.
Any help or advice in the right direction from anyone who has done anything of this sort before would be very much apreciated!
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