Vincent Thorne. Removing the by groups doesn't solve the issue: the problem seems to come from the string variable to be counted. Trying the same command with an integer variable yields expected results, and no error occurs. Trying Carlo's code, I get the same error, i. Moreover, my colleagues on Windows do not experience this issue using Stata Any idea what is the issue here? Vincent: welcome to this forum. The first question in this case is: is your copy of Stata full updated?
This was a bug fixed within the lifetime of Stata update 03mar 6. This has been fixed. Carlo, thanks for the welcome and the advice. Should have thought about it earlier, obviously. Thank you Nick for the technical details. Kind regards, Vincent. Bianca Duelken. The third adjustment accounts for the differences between binary and decimal units. A thousand in decimal is 1,, of course. A thousand in binary a.
To get to the billions, we have to cube these numbers. Or more, if there are few enough variables. Or less, if there are more variables. Checkout Continue shopping.
Stata: Data Analysis and Statistical Software. Go Stata. Purchase Products Training Support Company. Allowing more than 2 billion observations was introduced in Stata See the latest version of huge datasets.
See the new features in Stata Upgrade now Order Stata. Stata New in Stata Why Stata? Order Stata. Company Contact us Customer service Announcements Search. You have to be careful with logical operators; notice the syntax in the third line. There are no individuals in the dataset who are older than 55 AND younger than We want to drop if older than 55 OR younger than Here is a list of operators in expressions. Another way in which you may need to make your dataset smaller is by dropping variables that are not useful to your research.
It may be that the information contained in a given variable is duplicated i. Clearly we will not learn anything from that variable, so we can drop it. The syntax for dropping variable is simple:. Where varlist is the list of variables you would like to drop.
Sometimes variables are not coded the way you want them to be. In this section we will look at two transformations you may need to do on some variables before using them: recode and destring. This is convenient because it will not affect calculations you might do using the data for example if you calculate an average.
However, many datasets use as a missing variable code, and that might be problematic. The syntax for this command is:. The CCHS dataset does not contain any string variable. We will then convert that variable back to a numerical format. Now, when we destring, we are replacing the string variable by its numerical counterpart. How you choose to do this in your own dataset depends on how you plan to use the variables. Will you still have any use for the string variable?
If so generate a new one when you destring. Do you just want that variable to not be in string format? Then replace it with the new one. Outliers deserve their own section because there is often confusion as to what exactly constitutes an outlier. An outlier is NOT an observation with an unusual but possible value for a variable [12] ; rare events do occur.
The outliers you should be concerned about are the ones that come from coding error. How do you tell which is which? Common sense goes a long way here. First, look at your data using the data editor browse. Outliers tend to jump at you. If you have a small dataset, you can also tabulate each of your variables:.
Tabulating a variable will give you a list of all the possible values that variable takes in the dataset.
Outliers will be the extreme values. Look at the order of magnitude. Are these values believable? If the dataset is very big, however, it may not be practical to stare at all the values a variable can take.
In fact, Stata will not tabulate if there are too many different values. In the CCHS dataset, caseid is the individual id, while hwtghtm is the height in meters.
The graph tells us there are no outliers in this dataset:. Another way to look for outliers is to summarize the observations for a variable, using the detailed option:. Clearly, there are no outliers. Is it plausible that there really was a 5. Look at the order of magnitude by which this observation would differ from the second largest.
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