I have a dataset with ~4 million transactional records, grouped by Customer_No (consisting of 1 or more transactions per Customer_No, denoted by a sequential counter). Each transaction has a Type code and I am only interested in customers where a particular combination of transaction Types were used. Neither joining the table on itself or using EXISTS in Proc Sql is allowing me to efficiently evaluate the transaction Type criteria. I suspect a data step using retain and do-loops would process the dataset faster
The dataset:
Customer_No Tran_Seq Tran_Type
0001 1 05
0001 2 12
0002 1 07
0002 2 86
0002 3 04
0003 1 07
0003 2 84
0003 3 84
0003 4 84
The criteria I am trying to apply:
All Customer_No's Tran_Type's must only be in ('04','05','07','84','86'),
drop all transactions for that Customer_No if any other Tran_Type was used
Customer_No's Tran_Type's must include ('84' or '86') AND '04', drop all transactions for the Customer_No if this condition is not met
The output I want:
Customer_No Tran_Seq Tran_Type
0002 1 07
0002 2 86
0002 3 04
The DoW loop solution should be the most efficient if the data is sorted. If it's not sorted, it will either be the most efficient or similar in scale but slightly less efficient depending on the circumstances of the dataset.
I compared to Dom's solution with a 3e7 ID dataset, and got for the DoW a similar (slightly less) total length with less CPU for unsorted dataset, and about 50% faster for sorted. It is guaranteed to run in about the length of time the dataset takes to write out (maybe a bit more, but it shouldn't be much), plus sorting time if needed.
data want;
do _n_=1 by 1 until (last.customer_no);
set have;
by customer_no;
if tran_type in ('84','86')
then has_8486 = 1;
else if tran_type in ('04')
then has_04 = 1;
else if not (tran_type in ('04','05','07','84','86'))
then has_other = 1;
end;
do _n_= 1 by 1 until (last.customer_no);
set have;
by customer_no;
if has_8486 and has_04 and not has_other then output;
end;
run;
I don't think it's that complicated. Join to a subquery, group by Customer_No, and put your conditions in a having clause. A condition in a min function must be true for all rows, whereas a condition in a max function must be true for any one row:
proc sql;
create table want as
select
h.*
from
have h
inner join (
select
Customer_No
from
have
group by
Customer_No
having
min(Tran_Type in('04','05','07','84','86')) and
max(Tran_Type in('84','86')) and
max(Tran_Type eq '04')) h2
on h.Customer_No = h2.Customer_No
;
quit;
I must have made a join error. On re-writing, Proc Sql completed in less than 30 seconds (on the original 4.9 million record dataset). It's not particularly elegant code though, so I'd still appreciate any improvements or alternative methods.
data Have;
input Customer_No $ Tran_Seq $ Tran_Type:$2.;
cards;
0001 1 05
0001 2 12
0002 1 07
0002 2 86
0002 3 04
0003 1 07
0003 2 84
0003 3 84
0003 4 84
;
run;
Proc sql;
Create table Want as
select t1.* from Have t1
LEFT JOIN (select DISTINCT Customer_No from Have
where Tran_Type not in ('04','05','07','84','86')
) t2
ON(t1.Customer_No=t2.Customer_No)
INNER JOIN (select DISTINCT Customer_No from Have
where Tran_Type in ('84','86')
) t3
ON(t1.Customer_No=t3.Customer_No)
INNER JOIN (select DISTINCT Customer_No from Have
where Tran_Type in ('04')
) t4
ON(t1.Customer_No=t4.Customer_No)
Where t2.Customer_No is null
;Quit;
I would offer a slightly less complex SQL solution than #naed555 using the INTERSECT operator.
proc sql noprint;
create table to_keep as
(
select distinct customer_no
from have
where tran_type in ('84','86')
INTERSECT
select distinct customer_no
from have
where tran_type in ('04')
)
EXCEPT
select distinct customer_no
from have
where tran_type not in ('04','05','07','84','86')
;
create table want as
select a.*
from have as a
inner join
to_keep as b
on a.customer_no = b.customer_no;
quit;
Related
suppose to have the following:
ID HS REP YEAR
0001 A a 2015
0001 B a 2015
0001 B c 2015
0001 B d 2015
0002 A f 2015
0002 A g 2015
0002 B a 2015
...... .... ..... .....
I would like to get the count of "rep" per "HS" (for each HS) and also the count of "Ids" per "REP" for each "HS" (no matter if the same ID appears in more than two HS and hence it will be recorded two times). Desired output:
Year HS REP TotIDs
2015 A 3 2
2015 B 4 2
It means: HS "A" has 3 REPs and 2 IDs found (overall without distinguish by REP) corresponding to HS "A". The same for "B".
I need also some summary statistics like the mean, median etc. I there a way to do this with proc means or univariate or freq (maybe?) in one shot?
Thank you in advance
SQL is a little more versatile when you want distinct value counting within a group.
SQL does not have an aggregate function for mode statistic.
If the grouping is what appears to be year, hs the following is an example:
data have; input
ID HS $ REP $ YEAR x;
id2=id;
datalines;
0001 A a 2015 12
0001 B a 2015 2
0001 B c 2015 3
0001 B d 2015 5
0002 A f 2015 13
0002 A g 2015 14
0002 B a 2015 6
;
proc sql;
create table want as
select
year
, hs
, count(*) as rep
, count(distinct id) as id_n_unq
, sum (x) as x_sum
, mean (x) as x_mean
, median (x) as x_median
/* , mode(x) as x_mode -- SQL does not have aggregate function for mode*/
from have
group year, hs
;
is there a way in SAS to order columns (variables) of a data set based on the order of another data set? The names are perfectly equal.
And is there also a way to append them (vertically) based on the same column names?
Thank you in advance
ID YEAR DAYS WORK DATASET
0001 2020 32 234 1
0002 2019 31 232 1
0003 2015 3 22 1
0004 2003 15 60 1
0005 2021 32 98 1
0006 2000 31 56 1
DATASET DAYS WORK ID YEAR
2 56 23 0001 2010
2 34 123 0002 2011
2 432 3 0003 2013
2 45 543 0004 2022
2 76 765 0005 2000
2 43 8 0006 1999
I just need to sort the second data set based on the first and append the second to the first.
Can anyone help me please?
This should work:
data have1;
input ID YEAR DAYS WORK DATASET;
format ID z4.;
datalines;
0001 2020 32 234 1
0002 2019 31 232 1
0003 2015 3 22 1
0004 2003 15 60 1
0005 2021 32 98 1
0006 2000 31 56 1
;
run;
data have2;
input DATASET DAYS WORK ID YEAR;
format ID z4.;
datalines;
2 56 23 0001 2010
2 34 123 0002 2011
2 432 3 0003 2013
2 45 543 0004 2022
2 76 765 0005 2000
2 43 8 0006 1999
;
run;
First we create a new table by copying our first table. Then we just insert into it variables from the second table. No need to change the column order of the original second table.
proc sql;
create table want as
select *
from have1
;
insert into want(ID, YEAR, DAYS, WORK, DATASET)
select ID, YEAR, DAYS, WORK, DATASET
from have2
;
quit;
I have no idea how you could sort based on something that is not there.
But appending is trivial. You can just set them together.
data want;
set one two;
run;
And if both dataset are already sorted by some key variables (year perhaps in your example?) then you could interleave the observations instead. Just add a BY statement.
data want;
set one two;
by year;
run;
And if you want to make a new version of the second dataset with the variable order modified to match the variable order in the first dataset (something that really has nothing to with sorting the data) you could use the OBS= dataset option. So code like this will order the variables based on the order they have in ONE but not actually use any of the data from that dataset.
data want;
set one(obs=0) two;
run;
I have large dataset of a few million patient encounters that include a diagnosis, timestamp, patientID, and demographic information.
We have found that a particular type of disease is frequently comorbid with a common condition.
I would like to count the number of this type of disease that each patient has, and then create a histogram showing how many people have 1,2,3,4, etc. additional diseases.
This is the format of the data.
PatientID Diagnosis Date Gender Age
1 282.1 1/2/10 F 25
1 282.1 1/2/10 F 87
1 232.1 1/2/10 F 87
1 250.02 1/2/10 F 41
1 125.1 1/2/10 F 46
1 90.1 1/2/10 F 58
2 140 12/15/13 M 57
2 282.1 12/15/13 M 41
2 232.1 12/15/13 M 66
3 601.1 11/19/13 F 58
3 231.1 11/19/13 F 76
3 123.1 11/19/13 F 29
4 601.1 12/30/14 F 81
4 130.1 12/30/14 F 86
5 230.1 1/22/14 M 60
5 282.1 1/22/14 M 46
5 250.02 1/22/14 M 53
Generally, I was thinking of a DO loop, but I'm not sure where to start because there are duplicates in the dataset, like with patient 1 (282.1 is listed twice). I'm not sure how to account for that. Any thoughts?
Target diagnoses to count would be 282.1, 232.1, 250.02. In this example, patient 1 would have a count of 3, patient 2 would have 2, etc.
Edit:
This is what I have used, but the output is showing each PatientID on multiple lines in the output.
PROC SQL;
create table want as
select age, gender, patientID,
count(distinct diagnosis_description) as count
from dz_prev
where diagnosis in (282.1, 232.1)
group by patientID;
quit;
This is what the output table looks like. Why is this patientID showing up so many times?
Obs AGE GENDER PATIENTID count
1 55 Male 107828695 1
2 54 Male 107828695 1
3 54 Male 107828695 1
4 54 Male 107828695 1
5 54 Male 107828695 1
If you include variables that are neither grouping variables or summary statistics then SAS will happily re-merge your summary statistics back with all of the source records. That is why you are getting multiple records. AGE can usually vary if your dataset covers many years. And GENDER can also vary if your data is messy. So for a quick analysis you might try something like this.
create table want as
select patientID
, min(age) as age_at_onset
, min(gender) as gender
, count(distinct diagnosis_description) as count
from dz_prev
where diagnosis in (282.1, 232.1)
group by patientID
;
I think you can get what you want with an SQL statement
PROC SQL NOPRINT;
create table want as
select PatientID,
count(distinct Diagnosis) as count
from have
where Diagnosis in (282.1, 232.1, 250.02)
group by PatientID;
quit;
This filters to only the diagnoses you are interested in, counts the distinct times they are seen, by the PatientID, and saves the results to a new table.
I have a data set that has ID, datetime + bunch of value fields.
The idea is that the records are within one hour of each other are one session. There can only be one session every 24 hours. (Time is measured from the start of the first record)
The day() approach does not work as one record can be 23:55 PM and the next one could be 12:01 AM the next day and it would be the same session.
I've added rowid and ran the following:
data testing;
set testing;
by subscriber_no;
prev_dt = lag(record_ts);
prev_row = lag(rowid);
time_from_last = intck("Second",record_ts,prev_dt);
if intck("Second",record_ts,prev_dt) > -60*60 and intck("Second",record_ts,prev_dt) < 0 then
same_session = 'yes';
else same_session = 'no';
if intck("Second",record_ts,prev_dt) > -60*60 and intck("Second",record_ts,prev_dt) < 0 then
rowid = prev_row;
else rowid = rowid;
format prev_dt datetime19.;
output;
run;
Input
ID record_TS rowid
52 17MAY2017:06:24:28 4
52 17MAY2017:07:16:12 5
91 05APR2017:07:04:55 6
91 05APR2017:07:23:37 7
91 05APR2017:08:04:52 8
91 05MAY2017:08:56:23 9
input file is sorted by ID and record TS.
The output was
ID record_TS rowid prev_dt prev_row time_from_last same_session
52 17MAY2017:06:24:28 4 28APR2017:08:51:25 3 -1632783 no
52 17MAY2017:07:16:12 4 17MAY2017:06:24:28 4 -3104 yes
91 05APR2017:07:04:55 6 17MAY2017:07:16:12 5 3629477 no
91 05APR2017:07:23:37 6 05APR2017:07:04:55 6 -1122 yes
91 05APR2017:08:04:52 7 05APR2017:07:23:37 7 -2475 yes This needs to be 6
91 05MAY2017:08:56:23 9 05APR2017:08:04:52 8 -2595091 no
Second row from the bottom - rowid comes out 7, while I need it to come be 6.
Basically I need to change to the current rowid saved before the script moves to assess the next one.
Thank you
Ben
I've achieved what I needed with
proc sql;
create table testing2 as
select distinct t1.*, min(t2.record_TS) format datetime19. as from_time, max(t2.record_TS) format datetime19. as to_time
from testing t1
join testing t2 on t1.id_val= t2.id_val
and intck("Second",t1.record_ts,t2.record_ts) between -3600 and 3600
group by t1.id_val, t1.record_ts
order by t1.id_val, t1.record_ts
;
quit;
But I'm still wondering if there is a way to commit changes to current row before moving to assess the next row.
I think your logic is just:
Grab record_TS datetime of the first record for each ID
For subsequent records, if their record_TS is within an hour of the first record's, recode it to be the same rowID as first record.
If that's the case, you can use RETAIN to keep track of the first record_TS and rowID for each ID. This should be easier than lag(), and allows there to be multiple records in a single session. Below seems to work:
data have;
input ID record_TS datetime. rowid;
format record_TS datetime.;
cards;
52 17MAY2017:06:24:28 4
52 17MAY2017:07:16:12 5
91 05APR2017:07:04:55 6
91 05APR2017:07:23:37 7
91 05APR2017:08:04:52 8
91 05MAY2017:08:56:23 9
;
run;
data want;
set have;
by ID Record_TS;
retain SessionStart SessionRowID;
if first.ID then do;
SessionStart=Record_TS;
SessionRowID=RowID;
end;
else if (record_TS-SessionStart)<(60*60) then RowID=SessionRowID;
drop SessionStart SessionRowID;
run;
Outputs:
ID record_TS rowid
52 17MAY17:06:24:28 4
52 17MAY17:07:16:12 4
91 05APR17:07:04:55 6
91 05APR17:07:23:37 6
91 05APR17:08:04:52 6
91 05MAY17:08:56:23 9
Sorry I'm new to a lot of the features of SAS - I've only been using for a couple months, mostly for survey data analysis but now I'm working with a dataset which has individual level data for a cross-over study. It's in the form: ID treatment period measure1 measure2 ....
What I want to do is be able to group these individuals by their treatment group and then output a variable with a group average for measure 1 and measure 2 and another variable with the count of observations in each group.
ie
ID trt per m1 m2
1 1 1 101 75
1 2 2 135 89
2 1 1 103 77
2 2 2 140 87
3 2 1 134 79
3 1 2 140 80
4 2 1 156 98
4 1 2 104 78
what I want is the data in the form:
group a = where trt=1 & per=1
group b = where trt=2 & per=2
group c = where trt=2 & per=1
group d = where trt=1 & per=2
trtgrp avg_m1 avg_m2 n
A 102 76 2
B ... ... ...
C
D
Thank you for the help.
/Creating Sample dataset/
data test;
infile datalines dlm=" ";
input ID : 8.
trt : 8.
per : 8.
m1 : 8.
m2 : 8.;
put ID=;
datalines;
1 1 1 101 75
1 2 2 135 89
2 1 1 103 77
2 2 2 140 87
3 2 1 134 79
3 1 2 140 80
4 2 1 156 98
4 1 2 104 78
;
run;
/Using proc summary to summarize trt and per/
Variables(dimensions) on which you want to summarize would go into class
Variables(measures) for which you want to have average would go into var
Since you want to have produce average so you will have to write mean as the desired statistics.
Read more about proc summary here
http://support.sas.com/documentation/cdl/en/proc/61895/HTML/default/viewer.htm#a002473735.htm
and here
http://web.utk.edu/sas/OnlineTutor/1.2/en/60476/m41/m41_19.htm
proc summary data=test nway;
class trt per;
var m1 m2;
output out=final(drop= _type_)
mean=;
run;
The alternative method uses PROC SQL, the advantage being that it makes use of plain-English syntax, so the concept of a group in your question is maintained in the syntax:
PROC SQL;
CREATE TABLE final AS
SELECT
trt,
per,
avg(m1) AS avg_m1,
avg(m2) AS avg_m2,
count(*) AS n
FROM
test
GROUP BY trt, per;
QUIT;
You can even add your own group headings by applying conditional CASE logic as you did in your question:
PROC SQL;
CREATE TABLE final AS
SELECT
CASE
WHEN trt=1 AND per=1 THEN 'A'
WHEN trt=2 AND per=2 THEN 'B'
WHEN trt=2 AND per=1 THEN 'C'
WHEN trt=1 AND per=2 THEN 'D'
END AS group
avg(m1) AS avg_m1,
avg(m2) AS avg_m2,
count(*) AS n
FROM
test
GROUP BY group;
QUIT;
COUNT(*) simply counts the number of rows found within the group. The AVG function calculates the average for the given column.
In each example, you can replace the explicitly named columns in the GROUP BY clause with a number representing column position in the SELECT clause.
GROUP BY 1,2
However, take care with this method, as adding columns to the SELECT clause later can cause problems.