I am currently working on a dataset in SAS like this:
people - word - date - rank
A - bla - 01/01/2017 - 1
A - bla - 02/01/2017 - 2
A - test - 03/01/2017 - 3
B - bla - 01/01/2017 - 1
B - test - 09/01/2017 - 2
C - bla - 03/01/2017 - 1
C - test - 05/01/2017 - 2
C - test - 07/01/2017 - 3
C - sas - 08/01/2017 - 4
And I would like to transform it like this :
people - word - rank
A -------- bla ----- 1
A -------- test ----- 2
B -------- bla ----- 1
B -------- test ----- 2
C -------- bla ----- 1
C -------- test ----- 2
C -------- sas ----- 3
The rank is in function of the date, group by people.
I tried to use the lag function, but also syntaxes with case when (it works but I have to do this for every case and I have a maximum rank of 94... Not really easy !)
So I did not find a great way to have the last table.
Can you help me ?
Thanks a lot
Whilst posting your attempted code is good protocol on this site, I don't think it would help here as lag and case when are not the way to go.
Essentially you are trying to remove duplicate entries of word and rebase your rank column. You can achieve this in a single dataset, taking advantage of first. processing, which is available when a by statement is used.
For the rank, the easiest way is to completely rebuild it from scratch as the data step moves through the records.
data have;
input people $ word $ date :ddmmyy10. rank;
format date ddmmyy10.;
datalines;
A bla 01/01/2017 1
A bla 02/01/2017 2
A test 03/01/2017 3
B bla 01/01/2017 1
B test 09/01/2017 2
C bla 03/01/2017 1
C test 05/01/2017 2
C test 07/01/2017 3
C sas 08/01/2017 4
;
run;
data want;
set have (drop=rank date); /* remove rank as being rebuilt; date not required */
by people word notsorted; /* enable first. processing; notsorted option required as data not sorted by people and word */
if first.people then rank=0; /* reset rank when people value changes */
if first.word then do;
rank+1; /* increment rank by 1 for the first word (will ignore subsesquent duplicates) */
output; /* output row */
end;
run;
Related
I am working on an interaction diary data set. I got this data file sent to me cleaned, but the people that cleaned it did not add an index variable that I need for analyses. So:
Participants completed survey questionnaires for every interaction that they had over X number of days. This means that participants may have multiple records (interaction diaries) for each day. To do the analyses, I need 3 index variables - Person_id, DiaryDay, and InterOnDay. Like this:
Person_ID
DiaryDay
InterOnDay
2300
1
1
2300
1
2
2300
2
1
2300
2
2
2300
2
3
2300
3
1
I have the first two index variables, but am missing InterOnDay.
Of note, the number of diarydays vary by person, and the number of interactions recorded vary by person and diaryday.
I think I need to do DO loops, but I have just utterly failed at figuring out how. Does anyone have suggestions for how this code might look?
This is what I have so far, but I know this is not enough code.
DATA WORK.TEST;
SET WORK.DT;
by ID DATETODAY;
do IntOnDay = 1 to ;
output;
end;
run;
I think this is what you want.
data have;
input Person_ID DiaryDay;
datalines;
2300 1
2300 1
2300 2
2300 2
2300 2
2300 3
;
data want;
set have;
by Person_ID DiaryDay;
if first.DiaryDay then InterOnDay = 0;
InterOnDay + 1;
run;
I am working with crime data. Now, I have the following table crimes. Each row contains a specific crime (e.g. assault): the date it was committed (date) and a person-ID of the offender (person).
date person
------------------------------
02JAN2017 1
03FEB2017 1
04JAN2018 1 --> not to be counted (more than a year after 02JAN2017)
27NOV2017 2
28NOV2018 2 --> should not be counted (more than a year after 27NOV2017)
01MAY2017 3
24FEB2018 3
10OCT2017 4
I am interested in whether each person has committed (relapse=1) or not committed (relapse=0) another crime within 1 year after the first crime committed by the same person. Another condition is that the first crime has to be committed within a specific year (here 2017).
The result should therefore look like this:
date person relapse
------------------------------
02JAN2017 1 1
03FEB2017 1 1
04JAN2018 1 1
27NOV2017 2 0
28NOV2018 2 0
01MAY2017 3 1
24FEB2018 3 1
10OCT2017 4 0
Can anyone please give me a hint on how to do this in SAS?
Obviously, the real data are much larger, so I cannot do it manually.
One approach is to use DATA step by group processing.
The BY <var> statement sets up binary variables first.<var> and last.<var> that flag the first row in a group and the last row in a group.
You appear to be assigning the computed relapse flag over the entire group, and that kind of computation can be done with what SAS coders call a DOW loop -- a loop with the SET statement inside loop, with a follow up loop that assigns the computation to each row in the group.
The INTCK function can compute the number of years between two dates.
For example:
data want(keep=person date relapse);
* DOW loop computes assertion that relapse occurred;
relapse = 0;
do _n_ = 1 by 1 until (last.person);
set crimes; * <-------------- CRIMES;
by person date;
* check if persons first crime was in 2017;
if _n_ = 1 and year(date) = 2017 then _first = date;
* check if persons second crime was within 1 year of first;
if _n_ = 2 and _first then relapse = intck('year', _first, date, 'C') < 1;
end;
* at this point the relapse flag has been computed, and its value
* will be repeated for each row output;
* serial loop over same number of rows in the group, but
* read in through a second SET statement;
do _n_ = 1 to _n_;
set crimes; * <-------------- CRIMES;
output;
end;
run;
The process would be more complex, with more bookkeeping variables, if the actual process is to classify different time frames of a person as either relapsed or reformed based on rules more nuanced than "1st in 2017 and next within 1 year".
I started using sas relatively recent - I'm not by any means attempting to create perfect code here.
I'd sort the data by id/person and date first (date should be numeric), and then use retain statements check against the date of the first crime. It's not perfect, but if your data is good (no missing dates), it'll work, and it is easy to follow imho.
This only works if the first record and act of crime is supposed to happen in 2017. If you have crimes happening in 2016, and want to check whether 'a crime' is committed in 2017 and then check the relapse, then this code is not going to work - but I think that is covered in the comments beneath your question.
data test;
input tmp_year $ 1-9 person;
datalines;
02JAN2017 1
03FEB2017 1
04JAN2018 1
27NOV2017 2
28NOV2018 2
01MAY2017 3
24FEB2018 3
10OCT2017 4
;
run;
data test2;
set test;
crime_date = input(tmp_year, date9.);
act_year = year(crime_date);
run;
proc sort data=test2;
by person crime_date ;
run;
data want;
set test2;
by person crime_date;
retain date_of_crime;
if first.person and act_year = 2017 then date_of_crime = crime_date;
else if first.person then call missing(date_of_crime);
if intck('YEAR', date_of_crime, crime_date) =< 1 and not first.person
then relapse = 1;
else relapse = 0;
run;
The above code flags the act of crimes committed one year after an act of crime in 2017. You can then retrieve the unique persons with a proc sql statement, and join them with whatever dataset you have.
I have data that's tracking a certain eye phenomena. Some patients have it in both eyes, and some patients have it in a single eye. This is what some of the data looks like:
EyeID PatientID STATUS Gender
1 1 1 M
2 1 0 M
3 2 1 M
4 3 0 M
5 3 1 M
6 4 1 M
7 4 0 M
8 5 1 F
9 6 1 F
10 6 0 F
11 7 1 F
12 8 1 F
13 8 0 F
14 9 1 F
As you can see from the data above, there are 9 patients total and all of them have the particular phenomena in one eye.
I need the count the number of patients with this eye phenomena.
To get the number of total patients in the dataset, I used:
PROC FREQ data=new nlevels;
tables PatientID;
run;
To count the number of patients with this eye phenomena, I used:
PROC SORT data=new out=new1 nodupkey;
by Patientid Status;
run;
proc freq data=new1 nlevels;
tables Status;
run;
However, it gave the correct number of patients with the phenomena (9), but not the correct number without (0).
I now need to calculate the gender distribution of this phenomena. I used:
proc freq data=new1;
tables gender*Status/chisq;
run;
However, in the cross table, it has the correct number of patients who have the phenomena (9), but not the correct number without (0). Does anyone have any thoughts on how to do this chi-square, where if the has this phenomena in at least 1 eye, then they are positive for this phenomena?
Thanks!
PROC FREQ is doing what you told it to: counting the status=0 cases.
In general here you are using sort of blunt tools to accomplish what you're trying to accomplish, when you probably should use a more precise tool. PROC SORT NODUPKEY is sort of overkill for example, and it doesn't really do what you want anyway.
To set up a dataset of has/doesn't have, for example, let's do a few things. First I add one more row - someone who actually doesn't have - so we see that working.
data have;
input eyeID patientID status gender $;
datalines;
1 1 1 M
2 1 0 M
3 2 1 M
4 3 0 M
5 3 1 M
6 4 1 M
7 4 0 M
8 5 1 F
9 6 1 F
10 6 0 F
11 7 1 F
12 8 1 F
13 8 0 F
14 9 1 F
15 10 0 M
;;;;
run;
Now we use the data step. We want a patient-level dataset at the end, where we have eye-level now. So we create a new patient-level status.
data patient_level;
set have;
by patientID;
retain patient_status;
if first.patientID then patient_status =0;
patient_status = (patient_Status or status);
if last.patientID then output;
keep patientID patient_Status gender;
run;
Now, we can run your second proc freq. Also note you have a nice dataset of patients.
title "Patients with/without condition in any eye";
proc freq data=patient_level;
tables patient_status;
run;
title;
You also may be able to do your chi-square analysis, though I'm not a statistician and won't dip my toe into whether this is an appropriate analysis. It's likely better than your first, anyway - as it correctly identifies has/doesn't have status in at least one eye. You may need a different indicator, if you need to know number of eyes.
title "Crosstab of gender by patient having/not having condition";
proc freq data=patient_level;
tables gender*patient_Status/chisq;
run;
title;
If your actual data has every single patient having the condition, of course, it's unlikely a chi-square analysis is appropriate.
data jul11.merge11;
input month sales ;
datalines ;
1 3123
1 1234
2 7482
2 8912
3 1284
;
run;
data jul11.merge22;
input month goal ;
datalines;
1 4444
1 5555
1 8989
2 9099
2 8888
3 8989
;
run;
data jul11.merge1;
merge jul11.merge11 jul11.merge22 ;
by month;
difference =goal - sales ;
run;
proc print data=jul11.merge1 noobs;
run;
output:
month sales goal difference
1 3123 4444 1321
1 1234 5555 4321
1 1234 8989 7755
2 7482 9099 1617
2 8912 8888 -24
3 1284 8989 7705
Why it didn't match all observation in table 1 with in table 2 for common months ?
pdv retains data of observation to seek if any more observation are left for that particular by group before it reinitialises it , in that case it should have done cartesian product .
Gives perfect cartesian product for one to many merging but not for many to many .
This is because of how SAS processes the data step. A merge is never a true cartesian product (ie, all records are searched and matched up against all other records, like a SQL comma join might ); what SAS does (in the case of two datasets) is it follows down one dataset (the one on the left) and advances to the next particular by-group value; then it looks over on the right dataset, and advances until it gets to that by group value. If there are other records in between, it processes those singly. If there are not, but there is a match, then it matches up those records.
Then it looks on the left to see if there are any more in that by group, and if so, advances to the next. It does the same on the right. If only one of these has a match then it will only bring in those values; hence if it has 1 element on the left and 5 on the right, it will do 1x5 or 5 rows. However, if there are 2 on the left and 3 on the right, it won't do 2x3=6; it does 1:1, 2:2, and 2:3, because it's advancing record pointers sequentially.
The following example is a good way to see how this works. If you really want to see it in action, throw in the data step debugger and play around with it interactively.
data test1;
input x row1;
datalines;
1 1
1 2
1 3
1 4
2 1
2 2
2 3
3 1
;;;;
run;
data test2;
input x row2;
datalines;
1 1
1 2
1 3
2 1
3 1
3 2
3 3
;;;;
run;
data test_merge;
merge test1 test2;
by x;
put x= row1= row2=;
run;
If you do want to do a cartesian join in SAS datastep, you have to do nested SET statements.
data want;
set test1;
do _n_ = 1 to nobs_2;
set test2 point=_n_ nobs=nobs_2;
output;
end;
run;
That's the true cartesian, you can then test for by group equality; but that's messy, really. You could also use a hash table lookup, which works better with BY groups. There are a few different options discussed here.
SAS doesn't handle many-to-many merges very well within the datastep. You need to use a PROC SQL if you want to do a many-to-many merge.
I have three different questions about modifying a dataset in SAS. My data contains: the day and the specific number belonging to the tag which was registred by an antenna on a specific day.
I have three separate questions:
1) The tag numbers are continuous and range from 1 to 560. Can I easily add numbers within this range which have not been registred on a specific day. So, if 160-280 is not registered for 23-May and 40-190 for 24-May to add these non-registered numbers only for that specific day? (The non registered numbers are much more scattered and for a dataset encompassing a few weeks to much to do by hand).
2) Furthermore, I want to make a new variable saying a tag has been registered (1) or not (0). Would it work to make this variable and set it to 1, then add the missing variables and (assuming the new variable is not set for the new number) set the missing values to 0.
3) the last question would be in regard to the format of the registered numbers which is along the line of 528 000000000400 and 000 000000000054. I am only interested in the last three digits of the number and want to remove the others. If I could add the missing numbers I could make a new variable after the data has been sorted by date and the original transponder code but otherwise what would you suggest?
I would love some suggestions and thank you in advance.
I am inventing some data here, I hope I got your questions right.
data chickens;
do tag=1 to 560;
output;
end;
run;
data registered;
input date mmddyy8. antenna tag;
format date date7.;
datalines;
01012014 1 1
01012014 1 2
01012014 1 6
01012014 1 8
01022014 1 1
01022014 1 2
01022014 1 7
01022014 1 9
01012014 2 2
01012014 2 3
01012014 2 4
01012014 2 7
01022014 2 4
01022014 2 5
01022014 2 8
01022014 2 9
;
run;
proc sql;
create table dates as
select distinct date, antenna
from registered;
create table DatesChickens as
select date, antenna, tag
from dates, chickens
order by date, antenna, tag;
quit;
proc sort data=registered;
by date antenna tag;
run;
data registered;
merge registered(in=INR) DatesChickens;
by date antenna tag;
Registered=INR;
run;
data registeredNumbers;
input Numbers $16.;
datalines;
528 000000000400
000 000000000054
;
run;
data registeredNumbers;
set registeredNumbers;
NewNumbers=substr(Numbers,14);
run;
I do not know SAS, but here is how I would do it in SQL - may give you an idea of how to start.
1 - Birds that have not registered through pophole that day
SELECT b.BirdId
FROM Birds b
WHERE NOT EXISTS
(SELECT 1 FROM Pophole_Visits p WHERE b.BirdId = p.BirdId AND p.date = ????)
2 - Birds registered through pophole
If you have a dataset with pophole data you can query that to find if a bird has been through. What would you flag be doing - finding a bird that has never been through any popholes? Looking for dodgy sensor tags or dead birds?
3 - Data code
You might have more joy with the SUBSTRING function
Good luck