Consider the following example:
input group day month year number treatment NUM
1 1 2 2000 1 1 2
1 1 6 2000 2 0 .
1 1 9 2000 3 0 .
1 1 5 2001 4 0 .
1 1 1 2010 5 1 1
1 1 5 2010 6 0 .
2 1 1 2001 1 1 0
2 1 3 2002 2 1 0
end
gen date = mdy(month,day,year)
format date %td
drop day month year
For each group, I have a varying number of observations. Each observations refers to an event that is specified with a date. Variable number is the numbering within each group.
Now, I want to count the number of observations that occur one year starting from the date of each treatment observation (excluding itself) within this group. This means, I want to create the variable NUM that I have already put into my example above. I do not care about the number of observations with treatment = 0.
EDIT Begin: The following information was found to be missing but necessary to tackle this problem: The treatment variable will have a value of 1 if there is no observation within the same group in the last year. Thus it is also not possible that the variable NUM will have to consider observations with treatment = 1. In principal, it is possible that there are two observations within a group that have identical dates. EDIT End
I have looked into Stata tip 51: Events in intervals. It seems to work out however my dataset is huge (> 1 mio observations) such that it is really really inefficient - especially because I do not care about all treatment = 0 observations.
I was wondering if there is any alternative. My approach was to look for the observation with the latest date within each group that is still in the range of 1 year (and maybe store it in variable latestDate). Then I would simply subtract the value in variable number of the observation found from the value in count of the treatment = 0 variable.
Note: My "inefficient" code looks as follows
gsort -treatment
gen treatment_id = _n
replace treatment_id = . if treatment==0
gen count=.
sum treatment_id, meanonly
qui forval i = 1/`r(max)'{
count if inrange(date-date[`i'],1,365) & group == group[`i']
replace count = r(N) in `i'
}
sort group date
I am assuming that treatment can't occur within 1 year of the previous treatment (in the group). This is true in your example data, but may not be true in general. But, assuming that it is the case, then this should work. I'm using carryforward which is on SSC (ssc install carryforward). Like your latestDate thought, I determine one year after the most recent treatment and count the number of observations in that window.
sort group date
gen yrafter = (date + 365) if treatment == 1
by group: carryforward yrafter, replace
format yrafter %td
gen in_window = date <= yrafter & treatment == 0
egen answer = sum(in_window), by(group yrafter)
replace answer = . if treatment == 0
I can't promise this will be faster than a loop but I suspect that it will be.
The question is not completely clear.
Consider the following data with two different results, num2 and num3:
+-----------------------------------------+
| date2 group treat num2 num3 |
|-----------------------------------------|
| 01feb2000 1 1 3 2 |
| 01jun2000 1 0 . . |
| 01sep2000 1 0 . . |
| 01nov2000 1 1 0 0 |
| 01may2002 1 0 . . |
| 01jan2010 1 1 1 1 |
| 01may2010 1 0 . . |
|-----------------------------------------|
| 01jan2001 2 1 0 0 |
| 01mar2002 2 1 0 0 |
+-----------------------------------------+
The variable num2 is computed assuming you are interested in counting all observations that are within a one-year period after a treated observation (treat == 1), be those observations equal to 0 or 1 for treat. For example, after 01feb2000, there are three observations that comply with the time span condition; two have treat==0 and one has treat == 1, and they are all counted.
The variable num3 is also counting observations that are within a one-year period after a treated observation, but only the cases for which treat == 0.
num2 is computed with code in the spirit of the article you have cited. The use of in makes the run more efficient and there is no gsort (as in your code), which is quite slow. I have assumed that in each group there are no repeated dates:
clear
set more off
input ///
group str15 date count treat num
1 01.02.2000 1 1 2
1 01.06.2000 2 0 .
1 01.09.2000 3 0 .
1 01.11.2000 3 1 .
1 01.05.2002 4 0 .
1 01.01.2010 5 1 1
1 01.05.2010 6 0 .
2 01.01.2001 1 1 0
2 01.03.2002 2 1 0
end
list
gen date2 = date(date,"DMY")
format date2 %td
drop date count num
order date
list, sepby(group)
*----- what you want -----
gen num2 = .
isid group date, sort
forvalues j = 1/`=_N' {
count in `j'/L if inrange(date2 - date2[`j'], 1, 365) & group == group[`j']
replace num2 = r(N) in `j'
}
replace num2 = . if !treat
list, sepby(group)
num3 is computed with code similar in spirit (and results) as that posted by #jfeigenbaum:
<snip>
*----- what you want -----
isid group date, sort
by group: gen indicat = sum(treat)
sort group indicat, stable
by group indicat: egen num3 = total(inrange(date2 - date2[1], 1, 365))
replace num3 = . if !treat
list, sepby(group)
Even more than two interpretations are possible for your problem, but I'll leave it at that.
(Note that I have changed your example data to include cases that probably make the problem more realistic.)
Related
I am performing an event study, see reproducible example below. I only include one unit but this is enough for the question I'm asking.
input unit year treatment
1 2000 0
1 2001 0
1 2002 1
1 2003 0
1 2004 0
1 2005 1
1 2006 0
1 2007 0
end
I generate dif_year which should take the difference of years to the treatment:
sort unit year
bysort unit: gen year_nb = _n
bysort unit: gen year_target = year_nb if treatment == 1
by unit: egen target_distance = min(year_target)
drop year_target
gen dif_year = year_nb - target_distance
drop year_nb target_distance
It works well with one treatment by unit, but here I have two. Using the code snippet from above, I get the following result:
unit
year
treatment
dif_year
1
2000
0
-2
1
2001
0
-1
1
2002
1
0
1
2003
0
1
1
2004
0
2
1
2005
1
3
1
2006
0
4
1
2007
0
5
You can see that it is anchored to the first treatment (2002) but ignores the second one (2005). How can I adapt dif_year to make it work with multiple treatments (here, in 2005) ? The values for 2003 and before are correct, but I would expect to get the value -1 for 2004, 0 for 2005, -1 for 2006 and -2 for 2007.
This solution uses no loops. Evidently the problem hinges on looking backwards as well as forwards; hence reversing time temporarily is a device that can be used.
clear
input unit year treatment
1 2000 0
1 2001 0
1 2002 1
1 2003 0
1 2004 0
1 2005 1
1 2006 0
1 2007 0
end
bysort unit (year) : gen wanted1 = 0 if treatment
by unit: replace wanted1 = wanted1[_n-1] + 1 if missing(wanted1)
gen negyear = -year
bysort unit (negyear) : gen wanted2 = 0 if treatment
by unit: replace wanted2 = wanted2[_n-1] + 1 if missing(wanted2)
gen wanted = cond(abs(wanted2) < abs(wanted1), - wanted2, wanted1)
sort unit year
list , sep(0)
+---------------------------------------------------------------+
| unit year treatm~t wanted1 negyear wanted2 wanted |
|---------------------------------------------------------------|
1. | 1 2000 0 . -2000 2 -2 |
2. | 1 2001 0 . -2001 1 -1 |
3. | 1 2002 1 0 -2002 0 0 |
4. | 1 2003 0 1 -2003 2 1 |
5. | 1 2004 0 2 -2004 1 -1 |
6. | 1 2005 1 0 -2005 0 0 |
7. | 1 2006 0 1 -2006 . 1 |
8. | 1 2007 0 2 -2007 . 2 |
+---------------------------------------------------------------+
Here is a solution where the largest number of years does not need to be hardcoded.
clear
input unit year treatment
1 2000 0
1 2001 0
1 2002 1
1 2003 0
1 2004 0
1 2005 1
1 2006 0
1 2007 0
1 2008 0
1 2009 0
1 2010 1
end
sort unit year
*Set all treatment years to 0
gen diff_year = 0 if treatment == 1
*Initilize locals used in the loop
local stop "false"
local diff_distance = 0
while "`stop'" == "false" {
**Replace diff to one more than diff on row above if unit is the same,
* no diff for this row, and diff on row above is the diff distance
* for this iteration of the loop.
replace diff_year = diff_year[_n-1] + 1 if unit == unit[_n-1] & missing(diff_year) & diff_year[_n-1] == `diff_distance'
**Replace diff to one less than diff on row below if unit is the same,
* no diff for this row, and diff on row above is the diff distance
* for this iteration of the loop.
replace diff_year = diff_year[_n+1] - 1 if unit == unit[_n+1] & missing(diff_year) & diff_year[_n+1] == `diff_distance' * -1
*Test if there are still missing values, and if so set stop local to true
count if missing(diff_year)
if `r(N)' == 0 local stop "true"
*Increment the diff distance by one for next loop
local diff_distance = `diff_distance' + 1
}
I found a quick fix to my own question.
I generate a variable that takes missing values if there is no treatment. I then loop over rows, replacing the row below and above each treatment year by its value, until there isn't any remaining missing values.
Here, three iterations are enough but I set the loop until i = 10 just to show that adding more loops doesn't change the outcome.
sort unit year
bysort unit: gen year_nb = _n
bysort unit: gen year_target = year_nb if treatment == 1
gen closest_treatment = year_target
forvalues i = 1(1)10 {
bysort unit: replace closest_treatment = closest_treatment[_n-`i'] if(year_target[_n-`i'] != . & closest_treatment[_n] == .)
bysort unit: replace closest_treatment = closest_treatment[_n+`i'] if(year_target[_n+`i'] != . & closest_treatment[_n] == .)
}
replace year_target = closest_treatment if year_target == .
drop closest_treatment
gen dif_year = year_nb - year_target
drop year_nb year_target
Edit: in my example, the number of rows between the two treatments is even. But this solution also works for odd values, as the last row to be iterated over would be exactly in between two treatments. It doesn't matter whether we assign the distance to the previous or next treatment, unless you are interested in the sign of the number, which I assume you want to take into consideration while doing event studies (e.g. if the distance to previous treatment would be +3 years, the distance to the next treatment would be -3). This code snippet assigns value to the previous treatment (positive sign). If you want the opposite, just swap the two lines inside the loop.
My data
I am working on a spell dataset in the following format:
cls
clear all
set more off
input id spellnr str7 bdate_str str7 edate_str employed
1 1 2008m1 2008m9 1
1 2 2008m12 2009m8 0
1 3 2009m11 2010m9 1
1 4 2010m10 2011m9 0
///
2 1 2007m4 2009m12 1
2 2 2010m4 2011m4 1
2 3 2011m6 2011m8 0
end
* translate to Stata monthly dates
gen bdate = monthly(bdate_str,"YM")
gen edate = monthly(edate_str,"YM")
drop *_str
format %tm bdate edate
list, sepby(id)
Corresponding to:
+---------------------------------------------+
| id spellnr employed bdate edate |
|---------------------------------------------|
1. | 1 1 1 2008m1 2008m9 |
2. | 1 2 0 2008m12 2009m8 |
3. | 1 3 1 2009m11 2010m9 |
4. | 1 4 0 2010m10 2011m9 |
|---------------------------------------------|
5. | 2 1 1 2007m4 2009m12 |
6. | 2 2 1 2010m4 2011m4 |
7. | 2 3 0 2011m6 2011m8 |
+---------------------------------------------+
Here a given person (id) can have multiple spells (spellnr) of two types (unempl: 1 for unemployment; 0 for employment). the start-end dates of each spell are definied by bdate and edate, respectively.
Imagine the data was already cleaned, and is such that no spells overlap with each other.
There might be "missing" periods in between any two spells though.
This is captured by the dummy dataset above.
My question:
For each unemployment spell, I need to compute the number of months spent in employment in the last 6 months, 12 months, and 24 months.
Note that, importantly, each id can go in and out from employment, and all past employment spells should be taken into account (not just the last one).
In my example, this would lead to the following desired output:
+--------------------------------------------------------------+
| id spellnr employed bdate edate m6 m24 m48 |
|--------------------------------------------------------------|
1. | 1 1 1 2008m1 2008m9 . . . |
2. | 1 2 0 2008m12 2009m8 4 9 9 |
3. | 1 3 1 2009m11 2010m9 . . . |
4. | 1 4 0 2010m10 2011m9 6 11 20 |
|--------------------------------------------------------------|
5. | 2 1 1 2007m4 2009m12 . . . |
6. | 2 2 1 2010m4 2011m4 . . . |
7. | 2 3 0 2011m6 2011m8 5 20 44 |
+--------------------------------------------------------------+
My (working) attempt:
The following code returns the desired result.
* expand each spell to one observation per time unit (here "months"; works also for days)
expand edate-bdate+1
bysort id spellnr: gen spell_date = bdate + _n - 1
format %tm spell_date
list, sepby(id spellnr)
* fill-in empty months (not covered by spells)
xtset id spell_date, monthly
tsfill
* compute cumulative time spent in employment and lagged values
bysort id (spell_date): gen cum_empl = sum(employed) if employed==1
bysort id (spell_date): replace cum_empl = cum_empl[_n-1] if cum_empl==.
bysort id (spell_date): gen lag_7 = L7.cum_empl if employed==0
bysort id (spell_date): gen lag_24 = L25.cum_empl if employed==0
bysort id (spell_date): gen lag_48 = L49.cum_empl if employed==0
qui replace lag_7=0 if lag_7==. & employed==0 // fix computation for first spell of each "id" (if not enough time to go back with "L.")
qui replace lag_24=0 if lag_24==. & employed==0
qui replace lag_48=0 if lag_48==. & employed==0
* compute time spent in employment in the last 6, 24, 48 months, at the beginning of each unemployment spell
bysort id (spell_date): gen m6 = cum_empl - lag_7 if employed==0
bysort id (spell_date): gen m24 = cum_empl - lag_24 if employed==0
bysort id (spell_date): gen m48 = cum_empl - lag_48 if employed==0
qui drop if (spellnr==.)
qui bysort id spellnr (spell_date): keep if _n == 1
drop spell_date cum_empl lag_*
list
This works fine, but becomes quite inefficient when using (several millions of) daily data. Can you suggest any alternative approach that does not involve expanding the dataset?
In words what I do above is:
I expand data to have one row per month;
I fill-in the "gaps" in between the spells with -tsfill-
I Compute the running time spent in employment, and use lag operators to get the three quantities of interest.
This is in the vein of what done here, in a past question that I posted. However the working example there was unnecessarily complicated and with some mistakes.
SOLUTIONS PERFORMANCE
I tried different approaches suggested in the accepted answer below (including using joinby as suggested in an earlier version of the answer). In order to create a larger dataset I used:
expand 500000
bysort id spellnr: gen new_id = _n
drop id
rename new_id id
which creates a dataset with 500,000 id's (for a total of 3,500,000 spells).
The first solution largely dominates the ones that use joinby or rangejoin (see also the comments to the accepted answer below).
Below code might save some running time.
bys id (employed): gen tag = _n if !employed
sum tag, meanonly
local maxtag = `r(max)'
foreach i in 6 24 48 {
gen m`i' = .
forval d = 1/`maxtag' {
by id: gen x = 1 + min(bdate[`d'],edate) - max(bdate[`d']-`i',bdate) if employed
egen y = total(x*(x>0)), by(id)
replace m`i' = y if tag == `d'
drop x y
}
}
sort id bdate
The same logic, along with -rangejoin- (ssc) should also deserve a try. Please kindly provide some feedback after testing with your (large) actual data.
preserve
keep if employed
replace employed = 0
tempfile em
save `em'
restore
foreach i in 6 24 48 {
gen _bd = bdate - `i'
rangejoin edate _bd bdate using `em', by(id employed) p(_)
egen m`i' = total(_edate - max(_bd,_bdate)+1) if !employed, by(id bdate)
bys id bdate: keep if _n==1
drop _*
}
Imagine the following Stata data structure:
input x y
1 3
1 .
1 .
2 3
2 .
2 .
. 3
end
I want to fill the missing values using the corresponding match of pairs for other observations. However, if there is ambiguity (in the example, 3 corresponding to both 1 and 2), the code should not copy. In my example, the final data structure should look like this:
1 3
1 3
1 3
2 3
2 3
2 3
. 3
Note that both 1 and 2 are filled, as they are unambiguously 3.
My data is only numeric, and the number of unique values of variables x and y is large, so I am looking for a general rule that works in every case.
I am thinking on using the user-written command carryforward, running something like
bysort x: carryforward y if x != . , replace dynamic_condition(x[_n-1] == x[_n]) strict
bysort y: carryforward x if y != . , replace dynamic_condition(y[_n-1] == y[_n]) strict
Yet, this does not work when there are double matches.
UPDATE: the solution proposed by Nick does not work for every example. I updated the example to reflect this. The reason why the proposed solution does not work is because the function tag puts a 1 only at one instance of each value. Thus, when a value (3) is related to two values (1, 2), the tag will appear only in one of them. Hence, the copying occurs for one. In the example above, Nick's code and results are:
egen tagy = tag(y) if !missing(y)
egen tagx = tag(x) if !missing(x)
egen ny = total(tagy), by(x)
egen nx = total(tagx), by(y)
bysort x (y) : replace y = y[1] if ny == 1
bysort y (x) : replace x = x[1] if nx == 1
list, sep(0)
+-------------------------------+
| x y tagy tagx ny nx |
|-------------------------------|
1. | 1 3 0 0 1 0 |
2. | 1 3 0 0 1 0 |
3. | 1 3 1 1 1 2 |
4. | 2 3 0 1 0 2 |
5. | . 3 0 0 0 2 |
6. | 2 . 0 0 0 0 |
7. | 2 . 0 0 0 0 |
+-------------------------------+
As seen, the code works for filling x=1 and not filling y=3 (line 5). Yet, it does not fill lines 6 and 7 because tagy=1 only appears once (x=1).
This is a bit clunky, but it should work:
bysort x: egen temp=sd(x) if x!=.
bysort x (y): replace y=y[1] if temp==0
drop temp
Since the standard deviation of a constant is zero, temp=0 if non-missing x's are all the same.
sort x, y
replace y = y[_n-1] if missing(y) & x[_n-1] == x[_n]
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.
I'm relatively new to Stata and am trying to count the number of active cases an employee has open over time in my dataset (see link below for example). I tried writing a loop using forvalues based on an example I found online, but keep getting
invalid syntax
For each EmpID I want to count the number of cases that employee had open when a new case was added to the queue. So if a case is added with an OpenDate of 03/15/2015 and the EmpID has two other cases open at the time, the code would assign a value of 2 to NumActiveWhenOpened field. A case is considered active if (1) its OpenDate is less then the new case's OpenDate & (2) its CloseDate is greater than the new case's OpenDate.
The link below provides an example. I'm trying to write a loop that creates the NumActiveWhenOpened column. Any help would be greatly appreciated. Thanks!
http://i.stack.imgur.com/z4iyR.jpg
EDIT
Here is the code that is not working. I'm sure there are several things wrong with it and I'm not sure how to store the count in the [NumActiveWhenOpen] field.
by EmpID: generate CaseNum = _n
egen group = group(EmpID)
su group, meanonly
gen NumActiveWhenOpen = 0
forvalues i = 1/ 'r(max)' {
forvalues x = 1/CaseNum if group == `i'{
count if OpenDate[_n] > OpenDate[_n-x] & CloseDate[_n-x] > OpenDate[_n]
}
}
This sounds like a problem discussed in http://www.stata-journal.com/article.html?article=dm0068 but let's try to be self-contained. I am not sure that I understand the definitions, but this may help.
I'll steal part of Roberto Ferrer's sandbox.
clear
set more off
input ///
caseid str15(open close) empid
1 "1/1/2010" "3/1/2010" 1
2 "2/5/2010" "" 1
3 "2/15/2010" "4/7/2010" 1
4 "3/5/2010" "" 1
5 "3/15/2010" "6/15/2010" 1
6 "3/24/2010" "3/24/2010" 1
1 "1/1/2010" "3/1/2010" 2
2 "2/5/2010" "" 2
3 "2/15/2010" "4/7/2010" 2
4 "3/5/2010" "" 2
5 "3/15/2010" "6/15/2010" 2
end
gen d1 = date(open, "MDY")
gen d2 = date(close, "MDY")
format %td d1 d2
drop open close
reshape long d, i(empid caseid) j(status)
replace status = -1 if status == 2
replace status = . if missing(d)
bysort empid (d) : gen nopen = sum(status)
bysort empid d : replace nopen = nopen[_N]
l
The idea is to reshape so that each pair of dates becomes two observations. Then if we code each opening by 1 and each closing by -1 the total number of active cases is their cumulative sum. That's all. Here are the results:
. l, sepby(empid)
+---------------------------------------------+
| empid caseid status d nopen |
|---------------------------------------------|
1. | 1 1 1 01jan2010 1 |
2. | 1 2 1 05feb2010 2 |
3. | 1 3 1 15feb2010 3 |
4. | 1 1 -1 01mar2010 2 |
5. | 1 4 1 05mar2010 3 |
6. | 1 5 1 15mar2010 4 |
7. | 1 6 1 24mar2010 4 |
8. | 1 6 -1 24mar2010 4 |
9. | 1 3 -1 07apr2010 3 |
10. | 1 5 -1 15jun2010 2 |
11. | 1 2 . . 2 |
12. | 1 4 . . 2 |
|---------------------------------------------|
13. | 2 1 1 01jan2010 1 |
14. | 2 2 1 05feb2010 2 |
15. | 2 3 1 15feb2010 3 |
16. | 2 1 -1 01mar2010 2 |
17. | 2 4 1 05mar2010 3 |
18. | 2 5 1 15mar2010 4 |
19. | 2 3 -1 07apr2010 3 |
20. | 2 5 -1 15jun2010 2 |
21. | 2 4 . . 2 |
22. | 2 2 . . 2 |
+---------------------------------------------+
The bottom line is no loops needed, but by: helps mightily. A detail useful here is that the cumulative sum function sum() ignores missings.
Try something along the lines of
clear
set more off
*----- example data -----
input ///
caseid str15(open close) empid numact
1 "1/1/2010" "3/1/2010" 1 0
2 "2/5/2010" "" 1 1
3 "2/15/2010" "4/7/2010" 1 2
4 "3/5/2010" "" 1 2
5 "3/15/2010" "6/15/2010" 1 3
6 "3/24/2010" "3/24/2010" 1 .
1 "1/1/2010" "3/1/2010" 2 0
2 "2/5/2010" "" 2 1
3 "2/15/2010" "4/7/2010" 2 2
4 "3/5/2010" "" 2 2
5 "3/15/2010" "6/15/2010" 2 3
end
gen opend = date(open, "MDY")
gen closed = date(close, "MDY")
format %td opend closed
drop open close
order empid
list, sepby(empid)
*----- what you want -----
gen numact2 = .
sort empid caseid
forvalues i = 1/`=_N' {
count if empid[`i'] == empid & /// a different count for each employee
opend[`i'] <= closed /// the date condition
in 1/`i' // no need to look at cases that have not yet occurred
replace numact2 = r(N) - 1 in `i'
}
list, sepby(empid)
This is resource intensive so if you have a large data set, it will take some time. The reason is it loops over observations checking conditions. See help stored results and help return for an explanation of r(N).
A good read is
Stata tip 51: Events in intervals, The Stata Journal, by Nicholas J. Cox.
Note how I provided an example data set within the code (see help input). That is how I recommend you do it for future questions. This will save other people's time and increase the probabilities of you getting an answer.