Based on the image, I would like to loop through the columns to find where there is a text mo. It updates mo with the results not the text mo. The challenge has been how to select the result in the next column different from where mo is.
Your answer to my comment above suggests to me that the question you ask reflects the wrong approach to the larger problem. Your description suggests that you have observations with a varying number of testname/testvalue pairs, such as
+----------------------------------------+
| id day test1 val1 test2 val2 |
|----------------------------------------|
| A 1 mo 11 . |
| A 2 mo 12 df 98.2 |
|----------------------------------------|
| B 1 df 98.3 mo 23 |
| B 2 mo 14 . |
+----------------------------------------+
and your objective is to produce observations that look like this
+----------------------+
| id day df mo |
|----------------------|
| A 1 . 11 |
| A 2 98.2 12 |
|----------------------|
| B 1 98.3 23 |
| B 2 . 14 |
+----------------------+
If that is the case, here is a reproducible example that you can copy, paste into Stata's Do-file Editor window, execute it, and examine the output to see how the technique avoids all the complexity you introduce by trying to use loops to accomplish the task. The reshape command is one of Stata's most powerful data management tools and it will benefit you to learn how to use it.
clear
input str8 id int day str8 test1 float val1 str8 test2 float val2
A 1 "mo" 11 "" .
A 2 "mo" 12 "df" 98.2
B 1 "df" 98.3 "mo" 23
B 2 "mo" 14 "" .
end
list, sepby(id) noobs
reshape long test val, i(id day) j(num)
drop if missing(test)
drop num
list, sepby(id) noobs
reshape wide val, i(id day) j(test) str
rename val* *
list, sepby(id) noobs
Related
I'm trying to scale one variable by another lagged variable.
(IB) scaled by the lagged total assets(AT) = ROA
I've tried this two methods below from here.
xtset companyid fyear, year
gen at1 = l.at
gen roa=ib/at1
and
xtset gvkey year
gen roa=(ib)/(at[_n-1])
The first one came back with all zeros for 1.ta
The second one seems to generate values on the previous entry, even if it's a different company. I think this is true because only the first row has a missing value. I would assume there should be a missing value for the first year of each company.
Additionally I've tried this code below but it said invalid syntax.
xtset gvkey year
foreach gvkey {
gen roa = (ib)/(at[_n-1]) }
I'm using compustat so it's similar to below:
gvkey|Year |Ticker | at | ib |
-------|-----|--------|------|------|
001111| 2006| abc |1000 |50 |
001111| 2007| abc |1100 |60 |
001111| 2008| abc |1200 |70 |
001111| 2009| abc |1300 |80 |
001112| 2008| www |28777 |1300 |
001112| 2009| www |26123 |870 |
001113| 2009| ttt |550 |-1000 |
001114| 2010| vvv |551 |-990 |
This is hard to follow. 1.ta may, or may not, be a typo for L.at.
Is gvkey string? At the Stata tag, there is really detailed advice about how to give Stata data examples, which you are not following.
In principle, your first approach is correct, so it is hard to know what went wrong, except that
The second one seems to generate values on the previous entry, even if
it's a different company.
That's exactly correct. The previous observation is the previous observation, and nothing in that command refers or alludes to the panel structure or xtset or tsset information.
Your foreach statement is just wild guessing and nothing to do with any form supported by foreach. foreach isn't needed here at all: the lag operator implies working within panels automatically.
I did this, which may help.
clear
input str6 gvkey Year str3 Ticker at ib
001111 2006 abc 1000 50
001111 2007 abc 1100 60
001111 2008 abc 1200 70
001111 2009 abc 1300 80
001112 2008 www 28777 1300
001112 2009 www 26123 870
001113 2009 ttt 550 -1000
001114 2010 vvv 551 -990
end
egen id = group(gvkey), label
xtset id Year
gen wanted = at/L.ib
list, sepby(gvkey)
+------------------------------------------------------------+
| gvkey Year Ticker at ib id wanted |
|------------------------------------------------------------|
1. | 001111 2006 abc 1000 50 001111 . |
2. | 001111 2007 abc 1100 60 001111 22 |
3. | 001111 2008 abc 1200 70 001111 20 |
4. | 001111 2009 abc 1300 80 001111 18.57143 |
|------------------------------------------------------------|
5. | 001112 2008 www 28777 1300 001112 . |
6. | 001112 2009 www 26123 870 001112 20.09462 |
|------------------------------------------------------------|
7. | 001113 2009 ttt 550 -1000 001113 . |
|------------------------------------------------------------|
8. | 001114 2010 vvv 551 -990 001114 . |
+------------------------------------------------------------+
I have a variable called Category that specifies the category of observations. The problem is that some observation have multiple categories. For example:
id Category
1 Economics
2 Biology
3 Psychology; Economics
4 Economics; Psychology
There is no meaning in the order of categories. They are always separated by ";". There are 250 categories, so creating dummy variables might be tricky. I have the complete list of categories in a separate Excel file if this might help.
What I want is simply to summarize my dataset by unique categories such as Economics (3), Psychology (2), Biology (1) (so the sum of all can be superior to the number of observations).
tabsplit from the tab_chi package on SSC will do this for you.
clear
input id str42 Category
1 "Economics"
2 "Biology"
3 "Psychology; Economics"
4 "Economics; Psychology"
end
capture ssc install tab_chi
tabsplit Category, p(;)
Category | Freq. Percent Cum.
------------+-----------------------------------
Biology | 1 16.67 16.67
Economics | 3 50.00 66.67
Psychology | 2 33.33 100.00
------------+-----------------------------------
Total | 6 100.00
Note: You can count semi-colons and thus phrases like this.
gen count = 1 + length(category) - length(subinstr(category, ";", "", .))
The logic is that you measure the length of the string and its length should semi-colons ; be replaced by empty strings (namely, removed). The difference is the number of semi-colons, to which you add 1.
EDIT: How to get to a different data structure, starting with the data example above.
. split Category, p(;)
variables created as string:
Category1 Category2
. drop Category
. reshape long Category, i(id) j(mention)
(note: j = 1 2)
Data wide -> long
-----------------------------------------------------------------------------
Number of obs. 4 -> 8
Number of variables 3 -> 3
j variable (2 values) -> mention
xij variables:
Category1 Category2 -> Category
-----------------------------------------------------------------------------
. drop if missing(Category)
(2 observations deleted)
. list, sepby(id)
+----------------------------+
| id mention Category |
|----------------------------|
1. | 1 1 Economics |
|----------------------------|
2. | 2 1 Biology |
|----------------------------|
3. | 3 1 Psychology |
4. | 3 2 Economics |
|----------------------------|
5. | 4 1 Economics |
6. | 4 2 Psychology |
+----------------------------+
How can I delete duplicates which occur in column x but not in column y?
My dataset is as follows:
+-------+---+---+
| year | x | y |
+-------+---+---+
| 2001 | 1 | 2 |
| 2001 | 2 | 3 |
| 2001 | 2 | 3 |
| 2001 | 4 | 6 |
| 2001 | 5 | 9 |
| 2001 | 4 | 2 |
| 2001 | 4 | 9 |
+-------+---+---+
What I want is to remove the entries which occur in column y from the ones in column x.
My result would be: 1,4,5
I am currently learning Stata and I would love to know a good source for all possible commands, if this exists? So I can learn better on my own. Currently I have trouble to find good sources.
In Stata what you call columns are always called variables.
See http://www.statalist.org/forums/help#stata for general advice on how to present data examples in Stata questions. (The comments on CODE delimiters don't apply here.)
This may help. I didn't understand the role of year in your problem.
clear
input year x y
2001 1 2
2001 2 3
2001 2 3
2001 4 6
2001 5 9
2001 4 2
2001 4 9
end
rename x Datax
rename y Datay
gen long obs = _n
reshape long Data, i(obs) j(which) string
bysort Data (which) : drop if which[_N] == "y"
list
+---------------------------+
| obs which year Data |
|---------------------------|
1. | 1 x 2001 1 |
2. | 4 x 2001 4 |
3. | 7 x 2001 4 |
4. | 6 x 2001 4 |
5. | 5 x 2001 5 |
+---------------------------+
All possible commands aren't documented in a single place. Someone could write new commands all the time and they would not be documented anywhere except their help files. Did you mean that? Nor are all existing commands documented in one place: many are user-written and most of those are just documented by their help files.
Most of the official commands in Stata as supplied by StataCorp are documented in the manuals. Literally, there are also undocumented commands (I am not inventing this: see help undocumented) and there are also nondocumented commands that exist, known about because StataCorp mention them in talks or emails. To be as positive as possible: start with the manuals, bundled with your copy of Stata as .pdf files.
I am pretty new to Stata programming.
My question: I need to reorder/reshape a dataset through (I guess) a macro.
I have a dataset of individuals, with a variable birthyear' (year of birth) and variables each containing weight at a given CALENDAR year: e.g.
BIRTHYEAR | W_1990 | W_1991 | W_1992 | ... | w_2000
1989 | 7.2 | 9.3 | 10.2 | ... | 35.2
1981 | 33.2 | 35.3 | ...
I would like to obtain new variables containing weight at different ages, e.g. Weight_age_1, Weight_age_2, etc.: this means take for instance first obs of example, leave Weight_age_1 blank, put 7.2 in Weight_age_2, and so on.
I have tried something like...
forvalues i = 1/10{
capture drop weight_age_`i'
capture drop birth`i
gen birth_`i'=birthyear-1+`i'
tostring birth_`i', replace
gen weight_age_`i'= w_birth_`i'
}
.. but it doesn't work.
Can you please help me?
Experienced Stata users wouldn't try to write a self-contained program here: they would see that the heart of the problem is a reshape.
clear
input birthyear w_1990 w_1991 w_1992
1989 7.2 9.3 10.2
1981 33.2 35.3 37.6
end
gen id = _n
reshape long w_, i(id)
rename _j year
gen age = year - birthyear
l, sepby(id)
+-----------------------------------+
| id year birthy~r w_ age |
|-----------------------------------|
1. | 1 1990 1989 7.2 1 |
2. | 1 1991 1989 9.3 2 |
3. | 1 1992 1989 10.2 3 |
|-----------------------------------|
4. | 2 1990 1981 33.2 9 |
5. | 2 1991 1981 35.3 10 |
6. | 2 1992 1981 37.6 11 |
+-----------------------------------+
To get the variables you say you want, you could reshape wide, but this long structure is by far the more convenient way to store these data for future Stata work.
P.S. The heart of your programming problem is that you are getting confused between the names of variables and their contents.
But this is a "look-up" approach made to work:
clear
input birthyear w_1990 w_1991 w_1992
1989 7.2 9.3 10.2
1981 33.2 35.3 37.6
end
quietly forval j = 1/10 {
gen weight_`j' = .
forval k = 1990/1992 {
replace weight_`j' = w_`k' if (`k' - birthyear) == `j'
}
}
The essential trick is to do name manipulation using local macros. In Stata, variables are mainly for holding data; single-valued constants are better held in local macros and scalars. (Your sense of the word "macro" as meaning script or program is not how the term is used in Stata.)
As above: this is the data structure you ask for, but it is likely to be more problematic than that produced by reshape long.
I have some data on diseases and age of diagnosis. Each participant was asked what diseases they have had and at what age that disease was diagnosed.
There are a set of variables disease1-28 with a numeric code for each disease and another set age1-28 with the age at diagnosis in years. The diseases are placed in successive variables in the order recalled; the age of diagnosis is placed in the appropriate age variable.
I would like to generate a new variable for each of several diseases giving the age of diagnosis of that disease: e.g. asthma_age_at_diagnosis
Can I do this without having 28 replace statements?
Example of the data:
+-------------+----------+----------+----------+------+------+------+
| Participant | Disease1 | Disease2 | Disease3 | Age1 | Age2 | Age3 |
+-------------+----------+----------+----------+------+------+------+
| 1 | 123 | 3 | . | 30 | 2 | . |
| 2 | 122 | 123 | 5 | 23 | 51 | 44 |
| 3 | 5 | . | . | 50 | . | . |
+-------------+----------+----------+----------+------+------+------+
I give a general heads-up that a question of this form without any code of your own is often considered off-topic for Stack Overflow. Still, the Stata users around here are the people answering Stata questions (surprise) and we usually indulge questions like this if interesting and well-posed.
I'd advise a different data structure, period. With your example data
clear
input Patient Disease1 Disease2 Disease3 Age1 Age2 Age3
1 123 3 . 30 2 .
2 122 123 5 23 51 44
3 5 . . 50 . .
end
You can reshape
reshape long Disease Age, i(Patient) j(Order)
drop if missing(Disease)
list, sep(0)
+--------------------------------+
| Patient Order Disease Age |
|--------------------------------|
1. | 1 1 123 30 |
2. | 1 2 3 2 |
3. | 2 1 122 23 |
4. | 2 2 123 51 |
5. | 2 3 5 44 |
6. | 3 1 5 50 |
+--------------------------------+
With the data in this form you can now answer lots of questions easily. I don't see that a whole bunch of new variables would make many analyses easier. Another way to see this is that you have hinted that the order in which diseases are coded is arbitrary; that being so, wiring that into the data structure is ill-advised. Even if order is important, it is still accessible as part of the dataset (variable Order).
Hint: If you still want separate variables for some purposes, look at separate.