Data is setup with a bunch of information corresponding to an ID, which can show-up more than once.
ID Data
1 X
1 Y
2 A
2 B
2 Z
3 X
I want a loop that signifies which instance of the ID I am looking at. Is it the first time, second time, etc? I want it as a string in the form _# so I have to go beyond the simple _n function in Stata, to my knowledge. If someone knows a way to do what I want without the loop let me know, but I would still like the answer.
I have the following loop in Stata
by ID: gen count_one = _n
gen count_two = ""
quietly forval j = 1/3 {
replace count_two = "_`j'" if count_one == `j'
}
The output now looks like this:
ID Data count_one count_two
1 X 1 _1
1 Y 2 _2
2 A 1 _1
2 B 2 _2
2 Z 3 _3
3 X 1 _1
The question is how can I replace the 16 above with to tell Stata to take the max of the count_one column because I need to run this weekly and that max will change and I want to reduce errors.
It's hard to understand why you want this, but it is one line whether you want numeric or string:
bysort ID : gen nummax = _N
bysort ID : gen strmax = "_" + string(_N)
Note that the sort order within ID is irrelevant to the number of observations for each.
Some parts of your question aren't clear ("...replace the 16 above with to tell Stata...") but:
Why don't you just use _n with tostring?
gsort +ID +data
bys ID: g count_one=_n
tostring count_one, gen(count_two)
replace count_two="_"+count_two
Then to generate the max (answering the partial question at the end there) -- although note this value will be repeated across instances of each ID value:
bys ID: egen maxcount1=max(count_one)
or more elegantly:
bys ID: g maxcount2=_N
Related
I am working on injury severity scores (ISS) and my dataset has these four columns: ID, High_AIS, Dxcode (diagnosis code), ISS_bodyregion. Each ID/case has several values for "dxcode" and respective High_AIS and ISS_bodyregion - which means each ID/case has multiple injuries in different body regions. The rule to calculate ISS specifies that we have to select AIS values of three different ISS body regions
For some IDs, we have only one value (of course when a person only has single injury and one associated dxcode and AIS). My goal is to calculate ISS (ranges from 0-75) and in order to do this, I want to tell SAS the following things:
Select three largest AIS values by ID (of course when ID has more than 3 values for AIS), take their squares and add them to get ISS.
If ID has only one injury and that has the AIS = 6, the ISS will automatically be equal to 75 (regardless of the injuries elsewhere).
If ID has less than 3 AIS values (for example, 5th ID has only two AIS values: 0 and 1), then consider only two, square them and add them, as we do not have third severely ISS body region for this ID.
If ID has only 3 AIS (for example, 1,0,0) then consider only three, square them and add them even if it is ISS=1.
If ID has all the injuries and AIS values equal to 0 (for example: 0,0) then ISS will equal to 0.
If ID has multiple injuries, and AIS values are: 2,2,1,1,1 and ISS_bodyregion = 5,5,6,6,6. Then we see that ISS_bodyregion repeats itself, the instructions suggest that we only select highest AIS value of ISS body region only once, because it has to be from DIFFERENT ISS body regions. So, in such situation, I want to tell SAS that if ISS_bodyregion repeats itself, only select the one with highest AIS value and leave the rest.
I am so confused as I am telling SAS to keep account of all these aforementioned considerations and I cannot seem to put them all in a single code. Thank you so much in advance. I have already sorted my data by ID descending high_AIS.
So if you are trying to implement this algorithm https://aci.health.nsw.gov.au/networks/institute-of-trauma-and-injury-management/data/injury-scoring/injury_severity_score then you need data like this:
data have;
input id region :$20. ais ;
cards;
1 HEAD/NECK 4
1 HEAD/NECK 3
1 FACE 1
1 CHEST 2
1 ABDOMEN 2
1 EXTREMITIES 3
1 EXTERNAL 1
2 ABDOMEN 3
3 FACE 1
3 CHEST 2
4 HEAD/NECK 6
;
So first find the max per id per region. For example by using PROC SUMMARY.
proc summary data=have nway;
class id region;
var ais;
output out=bodysys max=ais;
run;
Now order by ID and AIS
proc sort data=bodysys ;
by id ais ;
run;
Now you can process by ID and accumulate the AIS scores into an array. You can use MOD() function to cycle through the array so that the last three observations per ID will be the values left in the array (skips the need to first subset to three observations per ID).
data want;
do count=0 by 1 until(last.id);
set bodysys;
by id;
array x[3] ais1-ais3 ;
x[1+mod(count,3)] = ais;
end;
iss=0;
if ais>5 then iss=75;
else do count=1 to 3 ;
iss + x[count]**2;
end;
keep id ais1-ais3 iss ;
run;
Result:
Obs id ais1 ais2 ais3 iss
1 1 2 3 4 29
2 2 3 . . 9
3 3 1 2 . 5
4 4 6 . . 75
I have a dataset as such:
Case #|DateA |Drug.1|Drug.2|Drug.3|DateB.1 |DateB.2 |DateB.3 |IV.1|IV.2|IV.3
------|------|------|------|------|--------|---------|--------|----|----|----
1 |DateA1| X | Y | X |DateB1.1|DateB1.2 |DateB1.3| 1 | 0 | 1
2 |DateA2| X | Y | X |DateB2.1|DateB2.2 |DateB2.3| 1 | 0 | 1
3 |DateA3| Y | Z | X |DateB3.1|DateB3.2 |DateB3.3| 0 | 0 | 1
4 |DateA4| Z | Z | Z |DateB4.1|DateB4.2 |DateB4.3| 0 | 0 | 0
For each case, there are linked variables i.e. Drug.1 is linked with DateB.1 and IV.1 (Indicator Variable.1); Drug.2 is linked with DateB.2 and IV.2, etc.
The variable IV.1 only = 1 if Drug.1 is the case that I want to analyze (in this example, I want to analyze each receipt of Drug "X"), and so on for the other IV variables. Otherwise, IV = 0 if the drug for that scenario is not "X".
I want to calculate the difference between DateA and DateB for each instance where Drug "X" is received.
e.g. In the example above I want to calculate a new variable:
DateDiffA1_B1.1 = DateA1 - DateB1.1
DateDiffA1_B2.1 = DateA1 - DateB2.1
DateDiffA1_B1.3 = DateA1 - DateB1.3
DateDiffA1_B2.3 = DateA1 - DateB2.3
DateDiffA1_B3.3 = DateA1 - DateB3.3
I'm not sure if this new variable would need to be linked to each instance of Drug "X" as for the other variables, or if it could be a single variable that COUNTS all the instances for each case.
The end goal is to COUNT how many times each case had a date difference of <= 2 weeks when they received Drug "X". If they did not receive Drug "X", I do not want to COUNT the date difference.
I will eventually want to compare those who did receive Drug "X" with a date difference <= 2 weeks to those who did not, so having another indicator variable to help separate out these specific patients would be beneficial.
I am unsure about the best way to go about this; I suspect it will require a combination of IF and REPEAT functions using the IV variable, but I am relatively new with SPSS and syntax and am not sure how this should be coded to avoid errors.
Thanks for your help!
EDIT: It seems like I may need to use IV as a vector variable to loop through the linked variables in each case. I've tried the syntax below to no avail:
DATASET ACTIVATE DataSet1.
vector IV = IV.1 to IV.3.
loop #i = .1 to .3.
do repeat DateB = DateB.1 to DateB.3
/ DrugDateDiff = DateDiff.1 to DateDiff.3.
if IV(#i) = 1
/ DrugDateDiff = datediff(DateA, DateB, "days").
end repeat.
end loop.
execute.
Actually there is no need to add the vector and the loop, all you need can be done within one DO REPEAT:
compute N2W=0.
do repeat DateB = DateB.1 to DateB.3 /IV=IV.1 to IV.3 .
if IV=1 and datediff(DateA, DateB, "days")<=14 N2W = N2W + 1.
end repeat.
execute.
This syntax will first put a zero in the count variable N2W. Then it will loop through all the dates, and only if the matching IV is 1, the syntax will compare them to dateA, and add 1 to the count if the difference is <=2 weeks.
if you prefer to keep the count variable as missing when none of the IV are 1, instead of compute N2W=0. start the syntax with:
If any(1, IV.1 to IV.3) N2W=0.
New to SAS and would appreciate advice and help on how best to handle this data mangement situation.
I have a dataset in which each observation represents a client. Each client has a "description" variable which could include either a comprehensive assessment, treatment or discharge. I have created 3 new variables to flag each observation if they contain one of these.
So for example:
treat_yes = 1 if description contains "tx", "treatment"
dc_yes = 1 if description contains "dc", "d/c" or "discharge"
ca_yes = 1 if desciption contains "comprehensive assessment" or "ca" or "comprehensive ax"
My end goal is to have a new dataset of clients that have gone through a Comprehensive Assessment, Treatment and Discharge.
I'm a little stumped as to what my next move should be here. I have all my variables flagged for clients. But there could be duplicate observations just because a client could have come in many times. So for example:
Client_id treatment_yes ca_yes dc_yes
1234 0 1 1
1234 1 0 0
1234 1 0 1
All I really care about is if for a particular client the variables treatment_yes, ca_yes and dc_yes DO NOT equal 0 (i.e., they each have at least one "1". They could have more than one "1" but as long as they are flagged at least once).
I was thinking my next step might be to collapse the data (how do you do this?) for each unique client ID and sum treatment_yes, dc_yes and ca_yes for each client.
Does that work?
If so, how the heck do I accomplish this? Where do I start?
thanks everyone!
I think the easiest thing to do at this point is to use a proc sql step to find the max value of each of your three variables, aggregated by client_id:
data temp;
input Client_id $ treatment_yes ca_yes dc_yes;
datalines;
1234 0 1 1
1234 1 0 0
1234 1 0 1
;
run;
proc sql;
create table temp_collapse as select distinct
client_id, max(treatment_yes) as treatment_yes,
max(ca_yes) as ca_yes, max(dc_yes) as dc_yes
from temp
group by client_id;
quit;
A better overall approach would be to use the dataset you used to create the _yes variables and do something like max(case when desc = "tx" then 1 else 0 end) as treatment_yes etc., but since you're still new to SAS and understand what you've done so far, I think the above approach is totally sufficient.
The following code allows you to preserve other variables from your original dataset. I have added two variables (var1 and var2) for illustrative purposes:
data temp;
input Client_id $ treatment_yes ca_yes dc_yes var1 var2 $;
datalines;
1234 0 1 1 10 A
1234 1 0 0 11 B
1234 1 0 1 12 C
;
run;
Join the dataset with itself so that each row of a client_id in the original dataset is merged with its corresponding row in an aggregated dataset constructed in a subquery.
proc sql;
create table want as
select *
from temp as a
left join (select client_id,
max(treatment_yes) as max_treat,
max(ca_yes) as max_ca,
max(dc_yes) as max_dc
from temp
group by client_id) as b
on a.client_id=b.client_id;
quit;
I have a struct mpc with the following structure:
num type col3 col4 ...
mpc.bus = 1 2 ... ...
2 2 ... ...
3 1 ... ...
4 3 ... ...
5 1 ... ...
10 2 ... ...
99 1 ... ...
to from col3 col4 ...
mpc.branch = 1 2 ... ...
1 3 ... ...
2 4 ... ...
10 5 ... ...
10 99 ... ...
What I need to do is:
1: Re-order the rows of mpc.bus, such that all rows of type 1 are first, followed by 2 and at last, 3. There is only one element of type 3, and no other types (4 / 5 etc.).
2: Make the numbering (column 1 of mpc.bus, consecutive, starting at 1.
3: Change the numbers in the to-from columns of mpc.branch, to correspond to the new numbering in mpc.bus.
4: After running simulations, reverse the steps above to turn up with the same order and numbering as above.
It is easy to update mpc.bus using find.
type_1 = find(mpc.bus(:,2) == 1);
type_2 = find(mpc.bus(:,2) == 2);
type_3 = find(mpc.bus(:,2) == 3);
mpc.bus(:,:) = mpc.bus([type1; type2; type3],:);
mpc.bus(:,1) = 1:nb % Where nb is the number of rows of mpc.bus
The numbers in the to/from columns in mpc.branch corresponds to the numbers in column 1 in mpc.bus.
It's OK to update the numbers on the to, from columns of mpc.branch as well.
However, I'm not able to find a non-messy way of retracing my steps. Can I update the numbering using some simple commands?
For the record: I have deliberately not included my code for re-numbering mpc.branch, since I'm sure someone has a smarter, simpler solution (that will make it easier to redo when the simulations are finished).
Edit: It might be easier to create normal arrays (to avoid woriking with structs):
bus = mpc.bus;
branch = mpc.branch;
Edit #2: The order of things:
Re-order and re-number.
Columns (3:end) of bus and branch are changed. (Not part of this question)
Restore original order and indices.
Thanks!
I'm proposing this solution. It generates a n x 2 matrix, where n corresponds to the number of rows in mpc.bus and a temporary copy of mpc.branch:
function [mpc_1, mpc_2, mpc_3] = minimal_example
mpc.bus = [ 1 2;...
2 2;...
3 1;...
4 3;...
5 1;...
10 2;...
99 1];
mpc.branch = [ 1 2;...
1 3;...
2 4;...
10 5;...
10 99];
mpc.bus = sortrows(mpc.bus,2);
mpc_1 = mpc;
mpc_tmp = mpc.branch;
for I=1:size(mpc.bus,1)
PAIRS(I,1) = I;
PAIRS(I,2) = mpc.bus(I,1);
mpc.branch(mpc_tmp(:,1:2)==mpc.bus(I,1)) = I;
mpc.bus(I,1) = I;
end
mpc_2 = mpc;
% (a) the following mpc_tmp is only needed if you want to truly reverse the operation
mpc_tmp = mpc.branch;
%
% do some stuff
%
for I=1:size(mpc.bus,1)
% (b) you can decide not to use the following line, then comment the line below (a)
mpc.branch(mpc_tmp(:,1:2)==mpc.bus(I,1)) = PAIRS(I,2);
mpc.bus(I,1) = PAIRS(I,2);
end
% uncomment the following line, if you commented (a) and (b) above:
% mpc.branch = mpc_tmp;
mpc.bus = sortrows(mpc.bus,1);
mpc_3 = mpc;
The minimal example above can be executed as is. The three outputs (mpc_1, mpc_2 & mpc_3) are just in place to demonstrate the workings of the code but are otherwise not necessary.
1.) mpc.bus is ordered using sortrows, simplifying the approach and not using find three times. It targets the second column of mpc.bus and sorts the remaining matrix accordingly.
2.) The original contents of mpc.branch are stored.
3.) A loop is used to replace the entries in the first column of mpc.bus with ascending numbers while at the same time replacing them correspondingly in mpc.branch. Here, the reference to mpc_tmp is necessary so ensure a correct replacement of the elements.
4.) Afterwards, mpc.branch can be reverted analogously to (3.) - here, one might argue, that if the original mpc.branch was stored earlier on, one could just copy the matrix. Also, the original values of mpc.bus are re-assigned.
5.) Now, sortrows is applied to mpc.bus again, this time with the first column as reference to restore the original format.
I have the following lookup grid
x A B C D
A 0 2 1 1
B 2 0 1 1
C 1 1 0 1
D 1 1 1 0
Think of this similar to the travelling salesman with point to point, although the algorithm isn't relevant to this problem. It is More like a lookup from A->B
What would be the best way to store in a database, since the time is the same both directions. A to B is 2, and B to A is 2
Start End Time
A B 2
A C 1
B A 2
etc
Doing this seems like it will be duplicating all the data which wouldn't be a good design.
Any thoughts which would be the best way implement this?
Don't store the duplicate rows. Just do a select like this:
select *
from LookupTable
where (Start = 'A' and End = 'B')
or (Start = 'B' and End = 'A')
Agree with OrbMan. You may adopt a convention to store either the upper triangle or the lower triangle. and after loading that triangle from the database just mirror it. Doing this in the db streamer, and loader should encapsulate/localize the behavior in one place.
Oh, another thing, you should probably use a matrix implementation which is similar so that a[i,j] returns a[j,i] if i>j, 0 if i==j. You get the point... Then just have to save and load the items where i<j.