I have a dataset as follows:
data have;
input;
ID Base Adverse Fixed$ Date RepricingFrequency
1 38 50 FIXED 2016 2
2 40 60 FLOATING 2017 3
3 20 20 FIXED 2016 2
4 ...
5
6
I am looking to build an array such that each ID has four vintage years 2017-2020, where the subsequent years are to be filled out with a piece of array code I have that works
like such
ID Vintage Base Adverse Fixed$ Date RepricingFrequency
1 2017 38 50 FIXED 2016 2
1 2018
1 2019
1 2020
In the beginning I just need to duplicate the dataset with the blanks,
The code I've tried so far is
data want;
set have;
do I=1 to 4;
output;
drop I;
run;
but of course that keeps the repeats of all the observations. So I tried an array.
data want;
set have;
array Base(2017:2020) Base2017-Base2020
array Vintage(2017:2020) Vintage2017-Vintage2020
But I'm not sure where to go from here on either accord.
The question is how do I extrapolate my data set for ID1-8 to a dataset where I have ID 1111-8888 where each ID is repeated 4 times with blanks.
Make a dummy dataset with all of the observations
data frame ;
set have(keep=id);
by id ;
if first.id then do date=2017 to 2020 ;
output;
end;
run;
and merge it back with the original.
data want ;
merge have frame ;
by id date ;
run;
Related
I've got a dataset that has id, start date and a claim value (in dollars) in each row - most ids have more than one row - some span over 50 rows. The earliest date for each ID/claim varies, and the claim values are mostly different.
I'd like to do a rolling sum of the value of IDs that have claims within 365 days of each other, to report each ID that has claims that have exceeded a limiting value across each period. So for an ID that had a claim date on 1 January, I'd sum all claims to 31 December (inclusive). Most IDs have several years of data so for the example above, I'd also need to check that if they had a claim on 1 May that they hadn't exceeded the limit by 30 April the following year and so on. I normally see this referred to as a 'rolling sum'. My site has many SAS products including base, stat, ets, and others.
I'm currently testing code on a small mock dataet and so far I've converted a thin file to a fat file with one column for each claim value and each date of the claim. The mock dataset is similar to the client dataset that I'll be using. Here's what I've done so far (noting that the mock data uses days rather than dates - I'm not at the stage where I want to test on real data yet).
data original_data;
input ppt $1. day claim;
datalines;
a 1 7
a 2 12
a 4 12
a 6 18
a 7 11
a 8 10
a 9 14
a 10 17
b 1 27
b 2 12
b 3 14
b 4 12
b 6 18
b 7 11
b 8 10
b 9 14
b 10 17
c 4 2
c 6 4
c 8 8
;
run;
proc sql;
create table ppt_counts as
select ppt, count(*) as ppts
from work.original_data
group by ppt;
select cats('value_', max(ppts) ) into :cats
from work.ppt_counts;
select cats('dates_',max(ppts)) into :cnts
from work.ppt_counts;
quit;
%put &cats;
%put &cnts;
data flipped;
set original_data;
by ppt;
array vars(*) value_1 -&cats.;
array dates(*) dates_1 - &cnts.;
array m_vars value_1 - &cats.;
array m_dates dates_1 - &cnts.;
if first.ppt then do;
i=1;
do over m_vars;
m_vars="";
end;
do over m_dates;
m_dates="";
end;
end;
if first.ppt then do:
i=1;
vars(i) = claim;
dates(i)=day;
if last.ppt then output;
i+1;
retain value_1 - &cats dates_1 - &cnts. 0.;
run;
data output;
set work.flipped;
max_date =max(of dates_1 - &cnts.);
max_value =max(of value_1 - &cats.);
run;
This doesn't give me even close to what I need - not sure how to structure code to make this correct.
What I need to end up with is one row per time that an ID exceeds the yearly limit of claim value (say in the mock data if a claim exceeds 75 across a seven day period), and to include the sum of the claims. So it's likely that there may be multiple lines per ID and the claims from one row may also be included in the claims for the same ID on another row.
type of output:
ID sum of claims
a $85
a $90
b $80
On separate rows.
Any help appreciated.
Thanks
If you need to perform a rolling sum, you can do this with proc expand. The code below will perform a rolling sum of 5 days for each group. First, expand your data to fill in any missing gaps:
proc expand data = original_data
out = original_data_expanded
from = day;
by ppt;
id day;
convert claim / method=none;
run;
Any days with gaps will have missing value of claim. Now we can calculate a moving sum and ignore those missing days when performing the moving sum:
proc expand data = original_data
out = want(where=(NOT missing(claim)));
by ppt;
id day;
convert claim = rolling_sum / transform=(movsum 5) method=none;
run;
Output:
ppt day rolling_sum claim
a 1 7 7
a 2 19 12
a 4 31 12
a 6 42 18
a 7 41 11
...
b 9 53 14
b 10 70 17
c 4 2 2
c 6 6 4
c 8 14 8
The reason we use two proc expand statements is because the rolling sum is calculated before the days are expanded. We need the rolling sum to occur after the expansion. You can test this by running the above code all in a single statement:
/* Performs moving sum, then expands */
proc expand data = original_data
out = test
from = day;
by ppt;
id day;
convert claim = rolling_sum / transform=(movsum 5) method=none;
run;
Use a SQL self join with the dates being within 365 days of itself. This is time/resource intensive if you have a very large data set.
Assuming you have a date variable, the intnx is probably the better way to calculate the date interval than 365 depending on how you want to account for leap years.
If you have a claim id to group on, that would also be better than using the group by clause in this example.
data have;
input ppt $1. day claim;
datalines;
a 1 7
a 2 12
a 4 12
a 6 18
a 7 11
a 8 10
a 9 14
a 10 17
b 1 27
b 2 12
b 3 14
b 4 12
b 6 18
b 7 11
b 8 10
b 9 14
b 10 17
c 4 2
c 6 4
c 8 8
;
run;
proc sql;
create table want as
select a.*, sum(b.claim) as total_claim
from have as a
left join have as b
on a.ppt=b.ppt and
b.day between a.day and a.day+365
group by 1, 2, 3;
/*b.day between a.day and intnx('year', a.day, 1, 's')*/;
quit;
Assuming that you have only one claim per day you could just use a circular array to keep track of the pervious N days of claims to generate the rolling sum. By circular array I mean one where the indexes wrap around back to the beginning when you increment past the end. You can use the MOD() function to convert any integer into an index into the array.
Then to get the running sum just add all of the elements in the array.
Add an extra DO loop to zero out the days skipped when there are days with no claims.
%let N=5;
data want;
set original_data;
by ppt ;
array claims[0:%eval(&n-1)] _temporary_;
lagday=lag(day);
if first.ppt then call missing(of lagday claims[*]);
do index=max(sum(lagday,1),day-&n+1) to day-1;
claims[mod(index,&n)]=0;
end;
claims[mod(day,&n)]=claim;
running_sum=sum(of claims[*]);
drop index lagday ;
run;
Results:
running_
OBS ppt day claim sum
1 a 1 7 7
2 a 2 12 19
3 a 4 12 31
4 a 6 18 42
5 a 7 11 41
6 a 8 10 51
7 a 9 14 53
8 a 10 17 70
9 b 1 27 27
10 b 2 12 39
11 b 3 14 53
12 b 4 12 65
13 b 6 18 56
14 b 7 11 55
15 b 8 10 51
16 b 9 14 53
17 b 10 17 70
18 c 4 2 2
19 c 6 4 6
20 c 8 8 14
Working in a known domain of date integers, you can use a single large array to store the claims at each date and slice out the 365 days to be summed. The bookkeeping needed for the modular approach is not needed.
Example:
data have;
call streaminit(20230202);
do id = 1 to 10;
do date = '01jan2012'd to '02feb2023'd;
date + rand('integer', 25);
claim = rand('integer', 5, 100);
output;
end;
end;
format date yymmdd10.;
run;
options fullstimer;
data want;
set have;
by id;
array claims(100000) _temporary_;
array slice (365) _temporary_;
if first.id then call missing(of claims(*));
claims(date) = claim;
call pokelong(
peekclong(
addrlong (claims(date-365))
, 8*365)
,
addrlong(slice(1))
);
rolling_sum_365 = sum(of slice(*));
if dif1(claim) < 365 then
claims_out_365 = lag(claim) - dif1(rolling_sum_365);
if first.id then claims_out_365 = .;
run;
Note: SAS Date 100,000 is 16OCT2233
I have the following that contains dates, the visit number, and a specific variable of interest. I would like to retain the last five visits that are available in SAS by person. I am familiar with retaining the first and last visits. The data for a single subject is listed below:
Person Date VisitNumber VariableOfInterest
001 10/10/2001 1 6
001 11/12/2001 3 8
001 01/05/2002 5 12
001 03/10/2002 6 5
001 05/03/2002 8 3
001 07/29/2002 10 11
Any insight would be appreciated.
A double DOW loop will let you measure the group in the first loop and select from the group based on your desired per-group criteria in the second loop. This is useful when have is large and pre-sorted, and you want to avoid additional sorting.
data want;
* measure the group size;
do _n_ = 1 by 1 until (last.person);
set have;
by person visitnumber; * visitnumber in by only to enforce expectation of orderness;
end;
_i_ = _n_;
* apply the criteria "last 5 rows in group";
do _n_ = 1 to _n_;
set have;
if _i_ - _n_ < 5 then output;
end;
run;
It is easier if you sort by descending VisitNumber so that the problem becomes take the first 5 observations for a person. Then just generate a counter of which observation this is for the person and subset on that.
data want;
set have ;
by person descending visitnumber;
if first.person then rowno=0;
rowno+1;
if rowno <= 5;
run;
I am trying to transpose a sequence of ranges from an excel file into SAS. The excel file looks something like this:
31 Dec 01Jan 02Jan 03Jan 04Jan
Book id1 23 24 35 43 98
Book id2 3 4 5 4 1
(few blank rows in between)
05Jan 06Jan 07Jan 08Jan 09Jan
Book id1 14 100 30 23 58
Book id2 2 7 3 8 6
(and it repeats..)
My final output should have a first column for the date and then two additional columns for the book Ids:
Date Book id1 Book id2
31 Dec 23 3
01Jan 24 4
02Jan 35 5
03Jan 43 4
04Jan 98 1
05Jan 14 2
06Jan 100 7
07Jan 30 3
08Jan 23 8
09Jan 58 6
In this particular case I am asking for a simpler method to:
Either import and transpose each range of data and replacing the data range with macro variables to separately import and transpose each individual range
Or to import the whole datafile first and then to create a loop that
transposes each range of data
Code I used for a simple import and transpose of a specific data range:
proc import datafile="&input./have.xlsx"
out=want
dbms=xlsx replace;
range="Data$A3:F5" ;
run;
proc transpose data=want
out=want_transposed
name=date;
id A;
run;
Thanks!
A data row that is split over several segments or blocks of rows in an Excel file can be imported raw into SAS and then processed into a categorical form using a DATA Step.
In this example sample data is put into a text file and imported such that the column names are generic VAR-1 ... VAR-n. The generic import is then processed across each row, outputting one SAS data set row per import cell.
The column names in each segment are retained within a temporary array an updated whenever a blank book id is encountered.
* mock data;
filename demo "%sysfunc(pathname(WORK))\demo.txt";
data _null_;
input;
file demo;
put _infile_;
datalines;
., 31Dec, 01Jan, 02Jan, 03Jan, 04Jan
Book_id1, 23 , 24 , 35 , 43 , 98
Book_id2, 3 , 4 , 5 , 4 , 1
., 05Jan, 06Jan, 07Jan, 08Jan, 09Jan
Book_id1, 14 , 100 , 30 , 23 , 58
Book_id2, 2 , 7 , 3 , 8 , 6
run;
* mock import;
proc import replace out=work.haveraw file=demo dbms=csv;
getnames = no;
datarow = 1;
run;
ods listing;
proc print data=haveraw;
run;
When Excel import is be made to look like this:
Obs VAR1 VAR2 VAR3 VAR4 VAR5 VAR6
1 31Dec 01Jan 02Jan 03Jan 04Jan
2 Book_id1 23 24 35 43 98
3 Book_id2 3 4 5 4 1
4
5 05Jan 06Jan 07Jan 08Jan 09Jan
6 Book_id1 14 100 30 23 58
7 Book_id2 2 7 3 8 6
It can be processed in a transposing way, outputting only the name value pairs corresponding to a original cell.
data have (keep=bookid date value);
set haveraw;
array dates(1000) $12 _temporary_ ;
array vars var:;
if missing(var1) then do;
do index = 2 by 1 while (index <= dim(vars));
if not missing(vars(index)) then
dates(index) = put(index-1,z3.) || '_' || vars(index); * adjust as you see fit;
else
dates(index) = '';
end;
end;
else do;
bookid = var1;
do index = 2 by 1 while (index <= dim(vars));
date = dates(index);
value = input(vars(index),??best12.);
output;
end;
end;
run;
I have the following example data set with an ID and the contract status in six months (01/2017 - 06/2017).
Example data:
ID Month1 Month2 Month3 Month4 Month5 Month6**
12 5 5 5 5 5 5
34 5 5 6 6 5 5
56 6 6 6 -7 -7 -7
78 6 6 5 5 5 5
12 5 5 5 5 6 -7
If the status is 5 the ID is active, if 6 it's canceled and -7 is "not able to reactivate".
I want to check two kind of changes:
1) IDs which change from status 5 to 6
2) IDs which change from 6 to 5
When the status changes from 5 to 6 I want a new variable "churn" containing the month in which the status changes to 6.
For the second group, I want a new variable "reactivation" containing the month in which the status changes to 5.
If an ID is in both groups (from 5 to 6 to 5) both variables should be filled.
What I have so far is an array, which shows me how many status matches occur in one row, but I do not get the next step. Here is the code:
data want (drop= i j);
set have (obs=100);
array stat_check {*} month1-month6;
sum=0;
do i=1 to dim(stat_check)-1;
do j=i+1 to dim(stat_check);
sum=sum(sum,stat_check(i) eq stat_check(j));
end;
end;
run;
Thanks in advance!
For an array approach, sounds like you need to compare each variable in the array to the variable immediately before it. You don't need two passes through the array, only one. You want to compare month2 to month1, month3 to month2 ... month6 to month5.
I would try something like (untested):
data want (drop= month);
set have (obs=100);
array stat_check {*} month1-month6;
sum=0;
do month=2 to dim(stat_check);
if stat_check{month}-stat_check{month-1} = 1 then Churn=month;
else if stat_check{month}-stat_check{month-1} = -1 then Reactivation =month;
end;
run;
If you could have multiple churns or multiple reactivations for the same ID, that would capture the latest churn or reactivation.
But honestly, I would transpose the data to have one row per ID-month. That would avoid the need for an array, and would allow you to capture multiple churns/reactivations. Generally it is easier to work with tall skinny data rather than short wide data. For example, it would be easy to count the number of months each ID was active.
You can try this one. vname function is used to get the variable name (month)
data two (drop= i j);
set one;
array stat_check {*} m1-m6;
sum=0;
do i=1 to dim(stat_check)-1;
do j=i+1 to dim(stat_check);
sum=stat_check(i)-stat_check(j);
if sum=1 then churn=vname(stat_check(i));
if sum=-1 then reactivation=vname(stat_check(i));
end;
end;
run;
I am trying to summarize my data set using the proc sql, but I have repeated values in the output, a simple version of my code is:
PROC SQL;
CREATE TABLE perm.rx_4 AS
SELECT patid,ndc,fill_mon,
COUNT(dea) AS n_dea,
sum(DEDUCT) AS tot_DEDUCT
FROM perm.rx
GROUP BY patid,ndc,fill_mon;
QUIT;
Some sample output is:
Obs Patid Ndc FILL_mon n_dea DEDUCT
3815 33003605204 00054465029 2000-05 2 0
3816 33003605204 00054465029 2000-05 2 0
12257 33004361450 00406035701 2000-06 2 0
16564 33004744098 00603128458 2000-05 2 0
16565 33004744098 00603128458 2000-05 2 0
16566 33004744098 00603128458 2000-06 2 0
16567 33004744098 00603128458 2000-06 2 0
46380 33008165116 00406035705 2000-06 2 0
85179 33013674758 00406035801 2000-05 2 0
89248 33014228307 00054465029 2000-05 2 0
107514 33016949900 00406035805 2000-06 2 0
135047 33056226897 63481062370 2000-05 2 0
213691 33065594501 00472141916 2000-05 2 0
215192 33065657835 63481062370 2000-06 2 0
242848 33066899581 60432024516 2000-06 2 0
As you can see there are repeated out put, for example obs 3815,3816. I have saw some people had similar problem, but the answers didn't work for me.
The content of the dataset is this:
The SAS System 5
17:01 Thursday, December 3, 2015
The CONTENTS Procedure
Engine/Host Dependent Information
Data Set Page Size 65536
Number of Data Set Pages 210
First Data Page 1
Max Obs per Page 1360
Obs in First Data Page 1310
Number of Data Set Repairs 0
Filename /home/zahram/optum/rx_4.sas7bdat
Release Created 9.0401M2
Host Created Linux
Inode Number 424673574
Access Permission rw-r-----
Owner Name zahram
File Size (bytes) 13828096
The SAS System 6
17:01 Thursday, December 3, 2015
The CONTENTS Procedure
Alphabetic List of Variables and Attributes
# Variable Type Len Format Informat Label
3 FILL_mon Num 8 YYMMD. Fill month
2 Ndc Char 11 $11. $20. Ndc
1 Patid Num 8 19. Patid
4 n_dea Num 8
5 tot_DEDUCT Num 8
Sort Information
Sortedby Patid Ndc FILL_mon
Validated YES
Character Set ASCII
The SAS System 7
17:01 Thursday, December 3, 2015
The CONTENTS Procedure
Sort Information
Sort Option NODUPKEY
NOTE: PROCEDURE CONTENTS used (Total process time):
real time 0.08 seconds
cpu time 0.01 seconds
I'll guess that you have a format on a variable, most likely the date. Proc SQL does not aggregate over formatted values but will use the underlying values but still shows them as formatted, so they appear as duplicates. Your proc contents confirms this. You can get around this by converting this the variable to a character variable.
PROC SQL;
CREATE TABLE perm.rx_4 AS
SELECT patid,ndc, put(fill_mon, yymmd.) as fill_month,
COUNT(dea) AS n_dea,
sum(DEDUCT) AS tot_DEDUCT
FROM perm.rx
GROUP BY patid,ndc, calculated fill_month;
QUIT;