IF ELSE STATA HOW TO

You can use wildcards - The same rowmean command can be written like this: Gen mean_score = (math_score + physics_score + chemistry_score) / 3ДЎ. You might ask, what's the difference between the rowmean() and simply using the gen command: If you want the mean score (across students) of the mean score (across subjects), you need to first do mean and then rowmean (or vice versa). If you want the mean score in class for any of the subjects (mean score across students), you should use the mean() function instead of the rowmean(). Take the previous example: There's no meaning to do add a by(class_id) option to the egen command when using the rowmean function. Note that the fact that it computes it separately for each of the observations makes the by option irrelevant. This example will simply create a new variable - mean_score - which will hold the mean of math, physics and chemistry score for each of the students. Suppose you had a dataset of students and their scores. The function rowmean also compute means, but instead of computing means of a variable across observations, it compute the mean across variables for each of the observations.Д®gen mean_score = rowmean(math_score physics_score chemistry_score) This will put, for each observation, the mean wage of all other observatios with the same firm_id and occupation_id. One can omit the by option - this will put the mean of the original variable for all observations in the dataset.Д®gen mean_firm_occupation_wage = mean(wage), by(firm_id occupation_id) See the figure under rowmean() for a graphic illustration. This example will create a variable in which, for each observation, the value will be the mean price of all observations that have the same store_id.

IF ELSE STATA HOW TO

These examples will hopefully clarify how to use the different functions and how can they help us.Д®gen store_mean_price = mean(price), by(store_id) There are many of them, all described in help egen, and the following sectios of this step will describe the use of the most commonly used functions. The functions actually determine what the egen command will do. So this is actually the next phase of data manipulation. Things that in other statistical programs might take a lot of commands are possible to do with a couple of egen commands. The above command states that within each stockid group (that has been sorted by ascending order of year), all ticker observations except the first are to be replaced with the value from the first year.What's so special, really, about the egen (extensions to genereate) command? The answer is that it lets you do lots of things to the data. We can automate the process using by and _n.Д«ysort stockid (year): replace ticker=ticker if _n>1 To fill in rows 6 through 10 with 'AMZN', you could do this:Д¬learly, with multiple observations to replace, replacing values line-by-line this way becomes cumbersome and prone to human error. To fill in row 6 of ticker with the appropriate value ('AMZN'), you could do this: We will fill in the blanks in the ticker variable (this assumes that the ticker symbols for these stocks did not change over time).ДЇirst, make sure that the data are sorted by stockid and year. Now that you have a sense of what _n and _N do, let's use _n in combination with by to perform a concrete task.