I have a dataset like this:
string_var | var1 | var2 | var3
var2 | 8 | 8 | 4
var3 | 7 | 5 | 7
var2 | 10 | 10 | 5
I need to test whether var1 is equal to either var2 or var3, depending on the string that is contained in string_var. My problem is to convert these strings into variable names to do something like:
gen test=1 if var1==string_var
where, after the ==, I need some kind of conversion function to let Stata read the string e.g. var2 as the following:
gen test=1 if var1==var2
With just two possibilities, you can branch on the choice. (With several possibilities, I think you'd need a loop.)
clear
input str4 string_var var1 var2 var3
var2 8 8 4
var3 7 5 7
var2 10 10 5
end
gen test = cond(string_var == "var2", var1 == var2, var1 == var3)
list
+--------------------------------------+
| string~r var1 var2 var3 test |
|--------------------------------------|
1. | var2 8 8 4 1 |
2. | var3 7 5 7 1 |
3. | var2 10 10 5 1 |
+--------------------------------------+
EDIT:
Here is a more general solution. (If anyone else thinks of a neater solution, be sure to post.)
gen test = .
levelsof string_var, local(names)
quietly foreach name of local names {
replace test = var1 == `name' if string_var == "`name'"
}
Related
Suppose I have 100 variables named ID, var1, var2, ..., var99. I have 1000 rows. I want to browse all the rows and columns that contain a 0.
I wanted to just do this:
browse ID, var* if var* == 0
but it doesn't work. I don't want to hardcode all 99 variables obviously.
I wanted to essentially write an if like this:
gen has0 = 0
forvalues n = 1/99 {
if var`n' does not contain 0 {
drop v
} // pseudocode I know doesn't work
has0 = has0 | var`n' == 0
}
browse if has0 == 1
but obviously that doesn't work.
Do I just need to reshape the data so it has 2 columns ID, var with 100,000 rows total?
My dear colleague #NickCox forces me to reply to this (duplicate) question because he is claiming that downloading, installing and running a new command is better than using built-in ones when you "need to select from 99 variables".
Consider the following toy example:
clear
input var1 var2 var3 var4 var5
1 4 9 5 0
1 8 6 3 7
0 6 5 6 8
4 5 1 8 3
2 1 0 2 1
4 6 7 1 9
end
list
+----------------------------------+
| var1 var2 var3 var4 var5 |
|----------------------------------|
1. | 1 4 9 5 0 |
2. | 1 8 6 3 7 |
3. | 0 6 5 6 8 |
4. | 4 5 1 8 3 |
5. | 2 1 0 2 1 |
6. | 4 6 7 1 9 |
+----------------------------------+
Actually you don't have to download anything:
preserve
generate obsno = _n
reshape long var, i(obsno)
rename var value
generate var = "var" + string(_j)
list var obsno value if value == 0, noobs
+----------------------+
| var obsno value |
|----------------------|
| var5 1 0 |
| var1 3 0 |
| var3 5 0 |
+----------------------+
levelsof var if value == 0, local(selectedvars) clean
display "`selectedvars'"
var1 var3 var5
restore
This is the approach i recommended in the linked question for identifying negative values. Using levelsof one can do the same thing with findname using a built-in command.
This solution can also be adapted for browse:
preserve
generate obsno = _n
reshape long var, i(obsno)
rename var value
generate var = "var" + string(_j)
browse var obsno value if value == 0
levelsof var if value == 0, local(selectedvars) clean
display "`selectedvars'"
pause
restore
Although i do not see why one would want to browse the results when can simply list them.
EDIT:
Here's an example more closely resembling the OP's dataset:
clear
set seed 12345
set obs 1000
generate id = int((_n - 1) / 300) + 1
forvalues i = 1 / 100 {
generate var`i' = rnormal(0, 150)
}
ds var*
foreach var in `r(varlist)' {
generate rr = runiform()
replace `var' = 0 if rr < 0.0001
drop rr
}
Applying the above solution yields:
display "`selectedvars'"
var13 var19 var35 var36 var42 var86 var88 var90
list id var obsno value if value == 0, noobs sepby(id)
+----------------------------+
| id var obsno value |
|----------------------------|
| 1 var86 18 0 |
| 1 var19 167 0 |
| 1 var13 226 0 |
|----------------------------|
| 2 var88 351 0 |
| 2 var36 361 0 |
| 2 var35 401 0 |
|----------------------------|
| 3 var42 628 0 |
| 3 var90 643 0 |
+----------------------------+
Short answer: wildcards for bunches of variables can't be inserted in if qualifiers. (The if command is different from the if qualifier.)
Your question is contradictory on what you want. At one point your pseudocode has you dropping variables! drop has a clear, destructive meaning to Stata programmers: it doesn't mean "ignore".
But let's stick to the emphasis on browse.
findname, any(# == 0)
finds variables for which any value is 0. search findname, sj to find the latest downloadable version.
Note also that
findname, type(numeric)
will return the numeric variables in r(varlist) (and also a local macro if you so specify).
Then several egen functions compete for finding 0s in each observation for a specified varlist: the command findname evidently helps you identify which varlist.
Let's create a small sandbox to show technique:
clear
set obs 5
gen ID = _n
forval j = 1/5 {
gen var`j' = 1
}
replace var2 = 0 in 2
replace var3 = 0 in 3
list
findname var*, any(# == 0) local(which)
egen zero = anymatch(`which'), value(0)
list `which' if zero
+-------------+
| var2 var3 |
|-------------|
2. | 0 1 |
3. | 1 0 |
+-------------+
So, the problem is split into two: finding the observations with any zeros and finding the observations with any zeros, and then putting the information together.
Naturally, the use of findname is dispensable as you can just write your own loop to identify the variables of interest:
local wanted
quietly foreach v of var var* {
count if `v' == 0
if r(N) > 0 local wanted `wanted' `v'
}
Equally naturally, you can browse as well as list: the difference is just in the command name.
I am using a dataset with about 100 variables and 1000 rows, similar to the one below:
. var1 var2 var3 var4
AL 10 11 12 13
AK -1 0 0 18
AZ 5 -5 -2 22
VA 15 16 0 0
How can I list the variables / observations that have a negative value?
For example, I would like to list that AK has negative var1 and AZ has negative var2 and var3.
Here's an example of how you can create a marker variable for each of your var variables:
clear
input str2 state var1 var2 var3 var4
AL 10 11 12 13
AK -1 0 0 18
AZ 5 -5 -2 22
VA 15 16 0 0
end
foreach var in var1 var2 var3 var4 {
generate tag_`var' = `var' < 0
}
list
+-------------------------------------------------------------------------------+
| state var1 var2 var3 var4 tag_var1 tag_var2 tag_var3 tag_var4 |
|-------------------------------------------------------------------------------|
1. | AL 10 11 12 13 0 0 0 0 |
2. | AK -1 0 0 18 1 0 0 0 |
3. | AZ 5 -5 -2 22 0 1 1 0 |
4. | VA 15 16 0 0 0 0 0 0 |
+-------------------------------------------------------------------------------+
You can then do the following:
list state var1 if tag_var1 == 1
+--------------+
| state var1 |
|--------------|
2. | AK -1 |
+--------------+
or
list state var* if tag_var1 == 1 | tag_var2 == 1 | tag_var3 == 1 | tag_var4 == 1
+-----------------------------------+
| state var1 var2 var3 var4 |
|-----------------------------------|
2. | AK -1 0 0 18 |
3. | AZ 5 -5 -2 22 |
+-----------------------------------+
If you do not need the extra flexibility of a marker variable you can simply do:
list state var1 if var1 < 0
EDIT:
Alternatively you could do the following:
preserve
generate obsno = _n
reshape long var, i(obsno)
rename var value
generate var = "var" + string(_j)
list state var obsno value if value < 0, noobs sepby(state)
+------------------------------+
| state var obsno value |
|------------------------------|
| AK var1 2 -1 |
|------------------------------|
| AZ var2 3 -5 |
| AZ var3 3 -2 |
+------------------------------+
restore
There are two other techniques that can be mentioned. One is to calculate the minimum in each observation (row) and then list if and only if that minimum is negative. That way, you get any zeros, positives and missings too in the same observations.
The other is just to loop over the variables and list separately.
clear
input str2 state var1 var2 var3 var4
AL 10 11 12 13
AK -1 0 0 18
AZ 5 -5 -2 22
VA 15 16 0 0
end
egen min = rowmin(var*)
list if min < 0
+-----------------------------------------+
| state var1 var2 var3 var4 min |
|-----------------------------------------|
2. | AK -1 0 0 18 -1 |
3. | AZ 5 -5 -2 22 -5 |
+-----------------------------------------+
foreach v of var var* {
quietly count if `v' < 0
if r(N) list `v' if `v' < 0
}
+------+
| var1 |
|------|
2. | -1 |
+------+
+------+
| var2 |
|------|
3. | -5 |
+------+
+------+
| var3 |
|------|
3. | -2 |
+------+
I am trying to "combine" two categorical variables in Stata (say var1 and var2) into a new (also categorical) variable (say res).
The example below illustrates what I am trying to achieve:
var1 var2 res
1 1 A
1 2 A
2 1 A
3 3 B
4 2 A
5 4 D
What this example does is to combine all categories of var1 and var2 that "overlap".
Here, the pair var1 == 1 and var2 == 1 initially form a group (res== A). All other pairs containing var1 == 1 or var2 == 1 should belong to the same group (hence res== A in rows 2 and 3). Because in row 2 we have var2==2, any pair with containing var2==2 should belong to the same group. That's why in row 4 res== A.
Another way to look at this problem is using the following matrix:
| 1 2 3 4
-----------------------
1 | 1 1
2 | 1
3 | 1
4 | 1
5 | 1
Because the element [1,1] is not empty (or zero), all elements in row 1 and column 1 must belong to the same group. Because [1,2] is not empty, the same is true for row 1, column 2. And so on and so forth. It does not matter which row/column you decide to start from.
egen group alone doesn't cut it.
Any ideas?
Sounds like you want to further group var1 if the values of var2 are the same. If that's the case, then you can use a program I wrote called group_id that's available from SSC. To install it, type in Stata's Command window:
ssc install group_id
Here's an example of how you would use it:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(var1 var2) str1 res
1 1 "A"
1 2 "A"
2 1 "A"
3 3 "B"
4 2 "A"
5 4 "D"
end
gen long wanted = var1
group_id wanted, matchby(var2)
list, sep(0)
and the results:
. list, sep(0)
+----------------------------+
| var1 var2 res wanted |
|----------------------------|
1. | 1 1 A 1 |
2. | 1 2 A 1 |
3. | 2 1 A 1 |
4. | 3 3 B 3 |
5. | 4 2 A 1 |
6. | 5 4 D 5 |
+----------------------------+
I conducted a phone survey and here is the prototype of my dataset:
var1 var2
6666 1
6666 2
7676 2
7676 1
8876 1
8876 2
89898 1
89898 2
9999 1
9999 2
5656 1
5656 2
2323 1
2323 2
9876 1
7654 1
var1 is the unique identifier for each case in my survey (in this case, phone numbers).
var2 is the outcome of the survey: 1 (successful), 2 (not successful).
I want keep the observations for each var1 whose var2 == 1, yet retaining the observations for each var1 whosevar2 == 2 if there is no another case where var2 == 1.
I have tried
duplicates drop var1 if var2 == 2, force
but I am not getting the desired output
The question is wrongly titled: you don't want to overwrite anything.
Your syntax doesn't work as you wish because it is not what you want. You are asking whether there are duplicates of var1 if var2 == 2 and that command pays no attention whatsoever to observations for which var2 == 1.
Your example includes no observations for which var2 == 2 but there is no corresponding observation with var2 == 1. I have added one such.
Here's one way of meeting your goal. I show in passing that the duplicates command you have does nothing for this example; nor would it be expected to do anything.
. clear
. input var1 var2
var1 var2
1. 6666 1
2. 6666 2
3. 7676 2
4. 7676 1
5. 8876 1
6. 8876 2
7. 89898 1
8. 89898 2
9. 9999 1
10. 9999 2
11. 5656 1
12. 5656 2
13. 2323 1
14. 2323 2
15. 9876 1
16. 7654 1
17. 42 2
18. end
. duplicates list var1 if var2 == 2
Duplicates in terms of var1
(0 observations are duplicates)
. bysort var1 (var2) : assert _N == 1 | _N == 2
. by var1 : drop if _n == 2 & var2[2] == 2
(7 observations deleted)
. list, sepby(var1)
+--------------+
| var1 var2 |
|--------------|
1. | 42 2 |
|--------------|
2. | 2323 1 |
|--------------|
3. | 5656 1 |
|--------------|
4. | 6666 1 |
|--------------|
5. | 7654 1 |
|--------------|
6. | 7676 1 |
|--------------|
7. | 8876 1 |
|--------------|
8. | 9876 1 |
|--------------|
9. | 9999 1 |
|--------------|
10. | 89898 1 |
+--------------+
Another way to do it would be
. bysort var1 (var2) : keep if _n == 1 & var2[2] == 2
In fact
. bysort var1 (var2): keep if _n == 1
keeps observations with var2 == 1 if there are any and otherwise will also keep singletons with var2 == 2.
The hidden assumptions seem to include at most two observations for each distinct var1. Note the use of assert for checking assumptions about the dataset.
I have a dataset in long form that lists observations by month. I want to identify if consecutive rows for a variable can cancel out (in other words, have the same absolute value). And if so, I want to change both observations to zero. In addition, I want to have an additional dummy variable that tells me if I've changed anything for that row. How can I structure the code?
For example,
Date Var1 Var 2
Jan2010 5 6
Feb2010 6 0
Mar2010 -6 1
In the above example, I want to make the dataset into below
Date Var1 Var 2 Dummy
Jan2010 5 6 0
Feb2010 0 0 1
Mar2010 0 0 1
This (seemingly) meets the criteria described, but other considerations may come into play if there are other factors not explicitly mentioned (e.g., do you need to consider whether Var2 "cancels out"? What if Apr2010 is 6? etc.).
clear
input str7 Date Var1 Var2
"Jan2010" 5 6
"Feb2010" 6 0
"Mar2010" -6 1
end
gen Dummy = Var1 == Var1[_n+1] * -1 | Var1 == Var1[_n-1] * -1
replace Var1 = 0 if Dummy
replace Var2 = 0 if Dummy
li , noobs
yielding
+-------------------------------+
| Date Var1 Var2 Dummy |
|-------------------------------|
| Jan2010 5 6 0 |
| Feb2010 0 0 1 |
| Mar2010 0 0 1 |
+-------------------------------+
Or perhaps more correctly, Dummy should be generated with respect to actual months and not observations:
gen Month = monthly(Date, "MY")
format Month %tm
tsset Month , monthly
gen Dummy = Var1 == Var1[_n+1] * -1 | Var1 == Var1[_n-1] * -1
Edit: As Roberto rightly points out, the previous code (using abs()) was written based on the example posted, but multiplying by -1 is more robust and yields the same result (for the sample data posted). And the suggestion to preserve the original variables is of course a generally good idea.