I have 2 customers 2 months transaction date, now i need to extract only 1st transaction date for that month for that particular customer. Like that i need month wise. I didn't get proper idea on this on SAS. Can anyone help? Thanks in Advance.
Cust_name Vis_date
V 3/1/2016
V 8/1/2016
V 16/1/2016
V 18/1/2016
V 26/1/2016
V 27/1/2016
E 5/1/2016
E 8/1/2016
E 18/1/2016
E 19/1/2016
E 25/1/2016
E 26/1/2016
V 4/2/2016
V 8/2/2016
V 17/2/2016
V 25/2/2016
V 26/2/2016
V 27/2/2016
E 5/2/2016
E 8/2/2016
E 23/2/2016
E 24/2/2016
E 25/2/2016
E 28/2/2016
I would first get the month for each record and then sort. With that you can pull the observations out with a data step as follows:
data test.doc1;
set test.doc;
Month = month(__Vis_date);
run;
proc sort data=test.doc1;
by Cust_name Month __Vis_date;
run;
data test.doc2;
set test.doc1;
by Cust_name Month;
if first.Month then output;
run;
If you create a variable that keeps track of the month, this becomes pretty easy! Vis_date needs to be formatted as a date variable for this to work though.
data your_data2;
set your_data;
month = month(vis_date);
run;
proc sort data = your_data2;
by cust_name vis_date;
run;
proc sort nodupkey data = your_data2;
by cust_name month;
run;
You can do it in a single SQL statement :
proc sql ;
create table want as
select Cust_name,
put(Vis_date,yymmn6.) as Month,
min(Vis_Date) as First_Date format=date9.
from have
group by 1,2
order by 1,2 ;
quit ;
Related
proc sql;
create table abc as select distinct formatted_date ,Contract, late_days
from merged_dpd_raw_2602
group by 1,2
;quit;
this gives me the 3 variables I\m working with
they have the form
|ID|Date in YYMMs.10| number|
proc sql;
create table max_dpd_per_contract as select distinct contract, max(late_days) as DPD_for_contract
from sasa
group by 1
;quit;
this gives me the maximum number for the entire period but how do I go on to make it per period?
I'm guessing the timeseries procedure should be used here.
proc timeseries data=sasa
out=sasa2;
by contract;
id formatted_date interval=day ACCUMULATE=maximum ;
trend maximum ;
var late_days;
run;
but I am unsure how to continue.
I want to to find the maximum value of the variable "late days" per a given time period(month). So for contact A for the time period jan2018 the max late_days value is X.
how the data looks:https://imgur.com/iIufDAx
In SQL you will want to calculate your aggregate within a group that uses a computed month value.
Example:
data have;
call streaminit(2021);
length contract date days_late 8;
do contract = 1 to 10;
days_late = 0;
do date = '01jan2020'd to '31dec2020'd;
if days_late then
if rand('uniform') < .55 then
days_late + 1;
else
days_late = 0;
else
days_late + rand('uniform') < 0.25;
output;
end;
end;
format date date9.;
run;
options fmterr;
proc sql;
create table want as
select
contract
, intnx('month', date, 0) as month format = monyy7.
, max(days_late) as max_days_late
from
have
group by
contract, month
;
You will get the same results using Proc MEANS
proc means nway data=have noprint;
class contract date;
format date monyy7.;
output out=want_2 max(days_late) = max_days_late;
run;
Grateful for feedback, I'm still a notice programmer. I'm trying to code the below in SAS.
I have two data sets a) and b), containing the following variables:
a) Bene_ID, county_id_1, county_id_2, county_id_3 etc (it's 12 months)
b) county_ID, rural (yes/no)
What I would normally do is create an array in a data step:
Array country (12) county_ID_1- county_ID_12
and use by group processing on bene_ID, to output a long (normalized) data set like this:
bene_id, month 1, county_id
bene_id, month 2, county_id
bene_id, month 3, county_id
etc.
BUT, how do I access the other data set b) within a data step? to pull in the rural variable? This is what I want:
bene_id, month 1, county_id, if rural = "yes"
bene_id, month 2, county_id, if rural = "yes"
bene_id, month 3, county_id, if rural = "yes"
I tried looking for other similar questions on this bulletin board but I wasn't even sure of the correct terms to search for. The reason I don't want to do a full merge is: how to filter on an array value? e.g. when rural = "no"?
Thanks everyone,
Lori
This is an example where using a FORMAT would help. You can use your second dataset to create a format
data formats;
retain fmtname 'rural';
set b;
rename county_id=start rural=label;
run;
proc format cntlin=formats ;
run;
and then use the format when processing the first dataset.
data want ;
set A;
array county_id_ [12];
do month=1 to dim(county_id_);
county=county_id_[month];
rural = put(county,rural3.);
output;
end;
drop county_id_: ;
run;
You are transforming the data structure from wide (array form) to tall (categorical form). This is generally known as a pivot or transpose. The transformation turns the information stored in each array element name (columns) into data that becomes accessible at the row-level.
You can merge the transpose with the counties to select rural ones.
* 80% of counties are rural;
data counties;
do countyId = 1 to 50;
if ranuni(123) < 0.80 then rural='Yes'; else rural='No';
output;
end;
run;
* for 10 people track with county they are in each month;
data have;
do personId = 1 to 10;
array countyId (12);
countyId(1) = ceil(50*ranuni(123));
do _n_ = 2 to dim(countyId);
if ranuni(123) < 0.15 then
countyId(_n_) = ceil(50*ranuni(123)); * simulate 15% chance of moving;
else
countyId(_n_) = countyId(_n_-1) ;
end;
output;
end;
run;
proc transpose data=have out=have_transpose(rename=(col1=countyId)) ;
by personId;
var countyId:;
run;
proc sort data=have_transpose;
by countyId personId;
run;
data want_rural;
merge have_transpose(in=tracking) counties;
by countyId;
if tracking and rural='Yes';
month = input(substr(_name_, length('countyId')+1), 8.);
drop _name_;
run;
If your wide data also has an additional a set of 12 columns, for say an array of amounts disbursed in each month, the best approach is to do 'DATA Step' transpose like #Tom showed, with an additional assignment inside the loop
amount = amount_[month];
Sample Data
I was wondering if it is possible to use data instead of proc to count the number of categorical variables on a row as shown in 'count' example above. This will allow me to further use the data e.g COUNT=1 or COUNT > 1 to check morbidity.
Also will it be possible to then count the number of each diagnosis in the entire data set per patient while accounting for duplicates if there is any? For example there are 3 CB's and 2 AA's in this data set but CB should be 2 because patient 2 had it recorded twice.
Thank you for your time and have a lovely new year.
Your question is not clear but your could manage your diag using union all and count distinct
selec patient count(distinct diag )
from (
select patient, diag1 as diag
from my_table
uniona all
select patient, diag2
from my_table
uniona all
select patient, diag3
from my_table
uniona all
select patient, diag4
from my_table
) t
group by patient
or simply union and count
selec patient count(diag )
from (
select patient, diag1 as diag
from my_table
uniona
select patient, diag2
from my_table
uniona
select patient, diag3
from my_table
uniona
select patient, diag4
from my_table
) t
group by patient
The image indicates that for each row you want a count of the number of columns with non-missing values. Additionally, you apparently have some way to do this using a PROC step, but would like to know how using a DATA step.
In DATA step you can count the number of non-missing values indirectly using CMISS, or directly using COUNTC against a constructed value:
data have;
attrib pid length=8 diag1-diag4 length=$5;
input pid & diag1-diag4;
datalines;
1 AA J9 HH6 .
2 CB . . CB
3 J10 AA CB J10
4 B B . F90 .
5 J10 . . .
6 . . . .
run;
data have_with_count;
set have;
count = 4 - cmiss (of diag1-diag4);
count_way2 = countc(catx('~', of diag1-diag4, 'SENTINEL'), '~');
run;
In order to work again MySQL data source you will also need a libref that connects you to that remote data server.
Added
Counting distinct values across a row can be accomplished using a hash or sortc. Consider this example that sorts a copy of the row data (as an array) and counts the unique values within:
data want;
set have;
array diag diag1-diag4;
array v(4) $5 _temporary_;
do _n_ = 1 to dim(diag);
v(_n_) = diag(_n_);
end;
call sortc(of v(*));
uniq = 0;
do _n_ = 1 to dim(v);
if missing(v(_n_)) then continue;
if uniq = 0 then
uniq + 1;
else
uniq + ( v(_n_) ne v(_n_-1) );
end;
run;
With Richard's dummy data to count number of diagnosis and unique number of diagnosis:
data want;
set have;
array var diag:;
length temp $30.;
call missing(diag_num);
do over var;
if not missing(var) then do;
diag_num+1;
temp=ifc(whichc(var, temp),temp,catx(' ',temp,var));
end;
end;
unique_diag=countw(temp);
drop temp;
run;
I would like to merge 2 datasets with 2 different dimensions.
TABLE1: people
gender name
M raa
F chico
M july
F sergio
TABLE2: serial_numbers
gender serial
M 4
F 5
I want the result to be
result
gender name serial
M raa 4
F chico 5
M july 4
F sergio 5
I'm creating here the datasets to illustrate how to merge both datasets:
data people;
infile cards;
length gender $1
name $10;
input gender name;
cards;
M raa
F chico
M july
F sergio
;
run;
data serial_numbers;
length gender $1
serial 8;
infile cards;
input gender serial;
cards;
M 4
F 5
;
run;
Solution 1: use a proc sql to perform the join.
proc sql;
create table result as
select a.gender, a.name, b.serial
from people a LEFT JOIN serial_numbers b
on a.gender=b.gender;
quit;
proc print data=result;
run;
Solution 2: use a data step to merge both datasets. This requires the datasets to be sorted:
proc sort data=people;
by gender;
run;
proc sort data=serial_numbers;
by gender;
run;
data result;
merge people serial_numbers;
by gender;
run;
proc print data=result;
run;
I have a data set with daily data in SAS. I would like to convert this to monthly form by taking differences from the previous month's value by id. For example:
thedate, id, val
2012-01-01, 1, 10
2012-01-01, 2, 14
2012-01-02, 1, 11
2012-01-02, 2, 12
...
2012-02-01, 1, 20
2012-02-01, 2, 15
I would like to output:
thedate, id, val
2012-02-01, 1, 10
2012-02-01, 2, 1
Here is one way. If you license SAS-ETS, there might be a better way to do it with PROC EXPAND.
*Setting up the dataset initially;
data have;
informat thedate YYMMDD10.;
input thedate id val;
datalines;
2012-01-01 1 10
2012-01-01 2 14
2012-01-02 1 11
2012-01-02 2 12
2012-02-01 1 20
2012-02-01 2 15
;;;;
run;
*Sorting by ID and DATE so it is in the right order;
proc sort data=have;
by id thedate;
run;
data want;
set have;
retain lastval; *This is retained from record to record, so the value carries down;
by id thedate;
if (first.id) or (last.id) or (day(thedate)=1); *The only records of interest - the first record, the last record, and any record that is the first of a month.;
* To do END: if (first.id) or (last.id) or (thedate=intnx('MONTH',thedate,0,'E'));
if first.id then call missing(lastval); *Each time ID changes, reset lastval to missing;
if missing(lastval) then output; *This will be true for the first record of each ID only - put that record out without changes;
else do;
val = val-lastval; *set val to the new value (current value minus retained value);
output; *put the record out;
end;
lastval=sum(val,lastval); *this value is for the next record;
run;
You could achieve this using a PROC SQL, and the intnx function to bring last months date forward a month...
proc sql ;
create table lag as
select b.thedate, b.id, (b.val - a.val) as val
from mydata b
left join
mydata a on b.date = intnx('month',a.date,1,'s')
and b.id = a.id
order by b.date, b.id ;
quit ;
This may need tweaking to handle scenarios where the previous month doesn't exist or months which have a different number of days to the previous month.