I have a panel data set over three years 87 90 93 for 50 different states. For my variable of interest, exec, I want to drop all entries for the state where exec == 0 in each of the years and only if it equals zero in each of the years.
I've been trying to write some sort of for loop but have been unsuccessful so far.
No loop is needed. No extra variable is needed.
bysort state (exec) : drop if exec[1] == 0 & exec[_N] == 0
will drop observations for a state if and only if all values of exec are 0.
Something like this will work assuming exec can't be negative:
webuse airacc
bysort airline (time): egen tot = total(inprog)
drop if tot==0
This will drop airlines where the sum of inprog across time for each state is zero, treating missings as zeros.
egen and egenmore will save you from getting loopy.
Related
I have some data in Stata which look like the first two columns of:
group_id var_to_rank desired_rank
____________________________________
1 10 1
1 20 2
1 30 3
1 40 4
2 10 1
2 20 2
2 20 2
2 30 3
I'd like to create a rank of each observation within group (group_id) according to one variable (var_to_rank). Usually, for this purpose I used:
gen id = _n
However some of my observations (group_id = 2 in my small example) have the same values of ranking variable and this approach doesn't work.
I have also tried using:
egen rank
command with different options, but cannot make my rank variables make to look like desired_rank.
Could you point me to a solution to this problem?
The following works for me:
bysort group_id: egen desired_rank=rank(var_to_rank)
I'd say this question is posed the wrong way round for best understanding. The aim is to group observations, those with the lowest value all being assigned a grade 1, the next lowest being all assigned 2 and so forth. This isn't ranking in most senses that I have seen discussed, but Stata's egen, rank() does get you part of the way.
But the direct way, which was mentioned in the Statalist thread cited elewhere in this thread (start here) is simpler in spirit than any solution quoted:
bysort group_id (var_to_rank): gen desired_rank = sum(var_to_rank != var_to_rank[_n-1])
Once data are sorted on var_to_rank then when values differ from previous values at the start of each block of distinct values a value of 1 is the result of var_to_rank != var_to_rank[_n-1]; otherwise 0 is the result. Summing those 1s and 0s cumulatively gives the desired variable. The prefix command bysort does the sorting required and ensures that this is all done separately within the groups defined by group_id. No need for egen at all (a command that many people who only use Stata occasionally often find bizarre).
Declaration of interest: The Statalist thread cited shows that when asked a similar question I too did not think of this solution in one.
Stumbled upon such solution on the Statalist:
bysort group_id (var_to_rank) : gen rank = var_to_rank != var_to_rank[_n-1]
by group_id : replace rank = sum(rank)
Seems to sort out this issue.
#radek: you surely got it sorted out in the meantime ... but this would have been an easy (though not very elegant) solution:
bysort group_id: egen desired_rank_HELP =rank(var_to_rank), field
egen desired_rank =group(grup_id desired_rank_HELP)
drop desired_rank_HELP
Way too much work. Easy and elegant. Try this one.
gen desired_rank=int(var_to_rank/10)
try this command, it works for me so well: egen newid=group(oldid)
I have a data set that looks like this:
id firm earnings A
1 A 100 0
1 A 200 0
2 B 50 1
2 B 70 1
3 C 900 0
bys id firm, I want to keep only the first observation if A==0 and want to keep all the observations if A==1.
I've tried the following code:
if A==0{
bys id firm: keep if _n==1
}
However, this code drops all the _n>1 observations no matter what the A value is.
The if (conditional) {do something} syntax is used in control flow rather than in defining variables. As you have it now Stata is only testing if A==1 in the first row. Try adding additional conditions using and (&) or or (|) statements. Try this:
bys id firm: keep if (_n==1 & A==0) | A==1
I have a dataset in Stata and want to count by group (loc_ID) and year. I used the following two lines of code:
egen count_obsv = tag(loc_ID year)
This adds a counter to my dataset (count_obsv) which is 1 (and 0 for every element that has the same combination of loc_ID and year) for every new combination.
Then I use:
collapse (sum) count_obsv, by(loc_ID year)
according to various Stata forum posts this should result in eg.:
loc_ID year count_obsv
1 2000 342
1 2001 23
2 2008 23
...
But my output is:
loc_ID year count_obsv
1 2000 1
1 2001 1
2 2008 1
...
What am I summarizing wrong?
When you call up the tag() function of the egen command, you assign the value 1 to just one of any number of observations with the same distinct values for the specified variables, and 0 to all the others. Then when you ask for the sum of those values in the same groups of observations, you get the group sums of one 1 and any number of 0s, and each sum is thus necessarily 1.
Your question is probably abstracted from some other calculations that worked as you expected, but if all you wanted was a dataset with frequencies, then
contract loc_ID year
would do that for you. If you wanted a dataset with summaries of other variables too, you would need something more like
collapse (count) count=foo (mean) mean=foo (sd) sd=foo, by(loc_ID year)
I doubt that any Statalist posts state otherwise. (I wrote tag() in 1999, and I am not aware of this as a misunderstanding.) There is a related but so to speak distinct problem where tag() comes in useful, which is counting distinct values (often called unique values).
sysuse auto, clear
egen tag = tag(foreign rep78)
egen distinct = total(tag), by(foreign)
tabdisp foreign, c(distinct)
would be a way to get at the number of distinct values of rep78 within categories of foreign.
I have a dataset of the top management teams of US banks from 2005 - 2015.
Now I want to generate a change-variable if a TMT composition changed between 2006 and 2009.
So first I used:
drop if Year > 2009
drop if Year < 2006
by id (id), sort: gen changed = (DirectorID[1] != DirectorID[_N])
and afterwards I used
by id (id), sort: gen changed = (DirectorID[1] != DirectorID[_N]) if Year < 2010 & Year > 2005
However there is a difference in output between two variables:
247 cases of "No change" and 853 cases of "Change" in the first and 116 cases of "No change" and the rest as "Changed" in the second variable
Could anyone clarify what the differences between these two commands are in Stata?
There are a couple reasons you may be seeing a different count of changes to the dataset. The data is most likely sorted differently for these two calls. The (id) parts have no effect here because you are already sorting by id. What you likely want to do is residually sort by year. So, bysort id (Year) - this way the dataset will be in the same order for each command you type. In the second command, the if clause is going to set the variable changed to missing for observations outside of the year range, but those observations are still being included in the calculation. You could create a new variable to flag the years of interest, and then add that new variable to the bysort call.
Lastly, you need to decide whether you only want to look at changes year-over-year (the value of the changed could vary by year within id), or have the value of changed reflect whether there were any changes in DirectorID over the entire time frame of interest (the value of changed would be constant within id).
Here's a toy example illustrating the difference. Essentially, when you drop the data, the last and the first observation could be the same, but in general you will have less data to compare the first and last observation since much of the data will be gone. When you use if, then the data is still there, even though the calculation is restricted to the middle observation by the if:
. clear
. input id year director_id
id year directo~d
1. 1 2016 10
2. 1 2017 20
3. 1 2018 30
4. end
.
. bys id (year): gen changed = (director_id[1] != director_id[_N]) if year < 2018 & year > 2016
(2 missing values generated)
. list, clean noobs
id year direct~d changed
1 2016 10 .
1 2017 20 1
1 2018 30 .
.
. drop if inlist(year, 2016,2018)
(2 observations deleted)
. bys id (year): gen changed2 = (director_id[1] != director_id[_N]) if year < 2018 & year > 2016
. list, clean noobs
id year direct~d changed changed2
1 2017 20 1 0
I added a sort by year since that seems in the spirit of your exercise.
In a panel data set I have 3 variables: name, week, and income.
I would like to make an indicator variable that indicates initial weeks where income is 0. So say a person X has 0 income in the first 13 weeks, the indicator takes the value 1 the first 13 weeks, and is otherwise 0. The same procedure for person Y and so on.
I have tried using by groups, but I can't get it to work.
Any suggestions?
One solution is
bysort name (week) : gen no_income = sum(income) == 0
The function sum() yields cumulative or running sum. So, as long as income is 0, its cumulative sum remains 0 too. As soon as a person earns something, the cumulative sum becomes positive. The code is based on the presumption that cumulative income can not cross zero again because in a given week, income is negative. To exclude that possibility use an appropriate extra condition, such as
bysort name (week) : gen no_income = sum(income) == 0 & income == 0
For a problem with very similar flavour, see this FAQ. A meta-lesson is to look at the StataCorp FAQs as one of several resources.