I have a col Test_Name in which I have a total of 15000 records. In this col there are in total 14 distinct variables which have different counts. For eg Test name A has 347 counts B has 1500 C has 233 count D has 40 and E has 12 counts etc resp.
Now i want that where ever the count is >100 I should get random 100 records for a specific test or if I get the first 100 records for each test which has a count >100 would be just fine in either case.
how can i do that in SAS? An early response would be appreciated.
Here is away you can just get the data from 100th record in the keep dataset
proc sort data=test;
by test_Name;
run;
data new keep;
set test;
by Test_Name;
if first.Test_Name then n=0;
n+1;
if n=99 then output keep;
run;
Related
What I have:
Number Cost Amount
52 98 1
108 50 3
922 12 1
What I want:
Number Cost
52 98
108 50
109 50
110 50
922 12 1
My dataset has a variable Amount. If Amount is 2 for a certain row, I want to create a new row right beneath it with the same Cost and the Number equal to that of the row above + 1. If the Amount is 3, I want to create two new rows right beneath it, both with the same Cost and with the Numbers being Number from row above +1 and Number from row above +2, and so on.
My final step would be to delete the Amount column, which I can do with
data want (drop=Amount);
set have;
I am having problems implementing this, my thoughts have been to use proc sql insert into but I am having trouble combining this with an if condition that runs through the amount variable.
Code to reproduce table:
proc sql;
create table want
(Number num, Cost num, Amount num);
insert into want
values(52,98,1)
values(108,50,3)
values(922,12,1);
This can help you:
proc sort data=want out=want_s nodupkey;
by Number;
run;
data result;
keep Number Cost;
set want_s;
do i=1 to Amount;
output;
Number=Number+1;
end;
run;
You might need to take care that Number does not overlap with the next input row like below:
Number ; Amount
108 ; 10
110 ; 1
Use a DO loop to output the AMOUNT number of rows. You can code the index variable of the loop to increment the NUMBER
Example (untested)
data want(keep=number cost);
set have;
do number = number to number + amount-1;
output;
end;
However, you may not need to perform this expansion of data in some cases. Many SAS Procedures provide a WEIGHT or FREQ statement that allows a variable to perform that statistical or processing roles.
I have a dataset that looks like:
Month Cost_Center Account Actual Annual_Budget
June 53410 Postage 13 234
June 53420 Postage 0 432
June 53430 Postage 48 643
June 53440 Postage 0 917
June 53710 Postage 92 662
June 53410 Phone 73 267
June 53420 Phone 103 669
June 53430 Phone 90 763
...
I would like to first sum the Actual and Annual columns, respectively and then create a variable where it flags if the Actual extrapolated for the entire year is greater than than Annual column.
I have the following code:
Data Test;
set Combined;
%All_CC; /*MACRO TO INCLUDE ALL COST CENTERS*/
%Total_Other_Expenses;/*MACRO TO INCLUDE SPECIFIC Account Descriptions*/
Sum_Actual = sum(Actual);
Sum_Annual = sum(Annual_Budget);
Run_Rate = Sum_Actual*12;
if Run_Rate > Sum_Annual then Over_Budget_Alarm = 1;
run;
However, when I run this code, it does not sum by group, for example, this is the output I get:
Account_Description Sum_Actual Sum_Annual Run_Rate Over_Budget_Alarm
Postage 13 234 146
Postage 0 432 0
Postage 48 643 963 1
Postage 0 917 0
Postage 92 662 634 1
I'm looking for output where all the 'postage' are summed for Actual and Annual, leaving just one row of data.
Use PROC MEANS to summarize the data
Use a data step and IF/THEN statement to create your flags.
proc means data=have N SUM NWAY STACKODS;
class account;
var amount annual_budget;
ods output summary = summary_stats1;
output out = summary_stats2 N = SUM= / AUTONAME;
run;
data want;
set summary_stats;
if sum_actual > sum_annual_budget then flag=1;
else flag=0;
run;
SAS DATA step behavior is quite complex ("About DATA Step Execution" in SAS Language Reference: Concepts). The default behavior, that you're seeing, is: at the end of each iteration (i.e. for each input row) the row is written to the output data set, and the PDV - all data step variables - is reset.
You can't expect to write Base SAS "intuitively" without spending a few days learning it first, so I recommend using PROC SQL, unless you have a reason not to.
If you really want to aggregate in data step, you have to use something called BY groups processing: after ensuring the input data set is sorted by the BY vars, you can use something like the following:
data Test (keep = Month Account Sum_Actual Sum_Annual /*...your Run_Rate and Over_Budget_Alarm...*/);
set Combined; /* the input table */
by Month Account; /* must be sorted by these */
retain Sum_Actual Sum_Annual; /* don't clobber for each input row */
if first.account then do; /* instead do it manually for each group */
Sum_Actual = 0;
Sum_Annual = 0;
end;
/* accumulate the values from each row */
Sum_Actual = sum(Sum_Actual, Actual);
Sum_Annual = sum(Sum_Annual, Annual_Budget);
/* Note that Sum_Actual = Sum_Actual+Actual; will not work if any of the input values is 'missing'. */
if last.account then do;
/* The group has been processed.
Do any additional processing for the group as a whole, e.g.
calculate Over_Budget_Alarm. */
output; /* write one output row per group */
end;
run;
Proc SQL can be very effective for understanding aggregate data examination. With out seeing what the macros do, I would say perform the run rate checks after outputting data set test.
You don't show rows for other months, but I must presume the annual_budget values are constant across all months -- if so, I don't see a reason to ever sum annual_budget; comparing anything to sum(annual_budget) is probably at the incorrect time scale and not useful.
From the show data its hard to tell if you want to know any of these
which (or if some) months had a run_rate that exceeded the annual_budget
which (or if some) months run_rate exceeded the balance of annual_budget (i.e. the annual_budget less the prior months expenditure)
Presume each row in test is for a single year/month/costCenter/account -- if not the underlying data would have to be aggregated to that level.
Proc SQL;
* retrieve presumed constant annual_budget values from data;
* this information might (should) already exist in another table;
* presume constant annual budget value at each cost center | account combination;
* distinct because there are multiple months with the same info;
create table annual_budgets as
select distinct Cost_Center, Account, Annual_Budget
from test;
create table account_budgets as
select account, sum(annual_budget) as annual_budget
from annual_budgets
group by account;
* flag for some run rate condition;
create table annual_budget_mon_runrate_check as
select
2019 as year,
account,
sum(actual) as yr_actual, /* across all month/cost center */
min (
select annual_budget from account_budgets as inner
where inner.account = outer.account
) as account_budget,
max (
case when actual * 12 > annual_budget then 1 else 0 end
) as
excessive_runrate_flag label="At least one month had a cost center run rate that would exceed its annual_budget")
from
test as outer
group by
year, account;
You can add a where clause to restrict the accounts processed.
Changing the max to sum in the flag computation would return the number of cost center months with excessive run rates.
I have a SAS data set that contains a column of numbers ranging from -2000 to 4000.
I want to select 37 random samples based on the following conditions.
If num between -2000 to -1000, randomly select 10 samples from this range,
if num between -1000 to 0, randomly select 15 sample from this range,
if num between 0 to 1000, randomly select 12 samples from this range,
I've tried the following:
proc surveyselect data=save.table
method=srs n=37 out=save.table_sample seed=1953;
run;
But this would give me random 37 samples from the whole population. I want to randomly select according the data range.
Please help with SAS code, thanks so much in advance!
Create a grouping variable in your data set that you can use to group analysis.
data output;
set save.table;
if number < -1000 then group=1;
else if number < 0 then group=2;
else if number < 1000 then group=3;
run;
Use PROC SURVEYSELECT with either a data set that has the same variable, GROUP, as well as the sample size or list the sample size in the PROC SURVEYSELECT.
proc surveyselect data=output
method=srs n=37 out=save.table_sample seed=1953 sampsize=(37 15 12);
strata group;
run;
Couldn't test because no sample data was provided, so here's an example using SASHELP.HEART
proc sort data=sashelp.heart out=heart; by chol_status; run;
proc surveyselect data=heart (where=(not missing(chol_status))) method=srs sampsize=(5 10 15) out=want;
strata chol_status;
run;
If you want to continue to use proc surveyselect, then a simple way to do this is:
data set1 set2 set3;
set save.table;
if number < -1000 then output set1;
else if number < 0 then output set2;
else if number < 1000 then output set3;
run;
Then call proc surveyselect thrice with different n values on these 3 datasets.
I created this fakedata as an example:
data fakedata;
length name $5;
infile datalines;
input name count percent;
return;
datalines;
Ania 1 17
Basia 1 3
Ola 1 10
Basia 1 52
Basia 1 2
Basia 1 16
;
run;
The result I want to have is:
---> summed counts and percents for Basia
I would like to have summed count and percent for Basia as she was only once in the table with count 4 and percent 83. I tried exchanging name into a number to do GROUP BY in proc sql but it changes into order by (I had such an error). Suppose that it isn't so difficult, but I can't find the solution. I also tried some arrays without any success. Any help appreciated!
It sounds like proc sql does what you want:
proc sql;
select name, count(*) as cnt, sum(percent) as sum_percent
from fakedata
group by name;
You can add a where clause to get the results just for one name.
Hm, actually I got an answer.
proc summary data=fakedata;
by name;
var count percent;
output out=wynik (drop = _FREQ_ _TYPE_) sum(count)=count sum(percent)=percent;
run;
You can go back a step and use PROC FREQ most likely to generate this output in a single step. Based on counts the percents are not correct, but I'm not sure they're intended to be, right now they add up to over 100%. If you already have some summaries, then use the WEIGHT statement to account for the counts.
proc freq data=fakedata;
table name;
weight count;
run;
The question might be quite vague but I could not come up with a decent concise title.
I have data where there are id ,date, amountA and AmtB as my variables. The task is to pick the dates that are within 10 days of each other and then see if their amountA are within 20% and if they are then pick the one with highest amountB. I have used to this code to achieve this
id date amountA amountB
1 1/15/2014 1000 79
1 1/16/2014 1100 81
1 1/30/2014 700 50
1 2/05/2014 710 80
1 2/25/2014 720 50
This is what I need
id date amountA amountB
1 1/16/2014 1100 81
1 1/30/2014 700 50
1 2/25/2014 720 50
I wrote this code but the problem with this code is its not automatic and has to be done on a case to case basis.I need a way to loop it so that it automatically outputs the results.I am no pro at looping and hence am stuck.Any help is greatly appreciated
data test2;
set test1;
diff_days=abs(intck('days',first_dt,date));
if diff_days<=10 then flag=1;
else if diff_days>10 then flag=0;
run;
data test3 rem_test3;
set test2;
if flag=1 then output test3;
else output rem_test3;
run;
proc sort data=test3;
by id amountA;
run;
data all_within;
set test3;
by id amountA;
amtA_lag=lag1(amountA);
if first.id then
do;
counter=1;
flag1=1;
end;
if first.id=0 then
do;
counter+1;
diff=abs(amountA-amtA_lag);
if diff<(10/100*amountA) then flag1+1;
else flag1=0;
end;
if last.stay and flag1=counter then output all_within;
run;
If I understand the problem correctly, you want to group all records together that have (no skip of 10+ days) and (amt A w/in 20%)?
Looping isn't your problem - no explicitly coded loop is needed to do this (or at least, the way I think of it). SAS does the data step loop for you.
What you want to do is:
Identify groups. A group is the consecutive records that you want to, among them, collapse to one row. It's not perfectly clear to me how amountA has to behave here - does the whole group need to have less than a maximum difference of 10%, or a record to next record difference of < 10%, or a (current highest amtB of group) < 10% - but you can easily identify all of these rules. Use a RETAINed variable to keep track of the previous amountA, previous date, highest amountB, date associated with the highest amountB, amountA associated with highest amountB.
When you find a record that doesn't fit in the current group, output a record with the values of the previous group.
You shouldn't need two steps for this, although you can if you want to see it more easily - this may be helpful for debugging your rules. Set it so that you have a GroupNum variable, which you RETAIN, and you increment that any time you see a record that causes a new group to start.
I had trouble figuring out the rules...but here is some code that checks each record against the previous for the criteria I think you want.
Data HAVE;
input id date :mmddyy10. amountA amountB ;
format date mmddyy10.;
datalines;
1 1/15/2014 1000 79
1 1/16/2014 1100 81
1 1/30/2014 700 50
1 2/05/2014 710 80
1 2/25/2014 720 50
;
Proc Sort data=HAVE;
by id date;
Run;
Data WANT(drop=Prev_:);
Set HAVE;
Prev_Date=lag(date);
Prev_amounta=lag(amounta);
Prev_amountb=lag(amountb);
If not missing(prev_date);
If date-prev_date<=10 then do;
If (amounta-prev_amounta)/amounta<=.1 then;
If amountb<prev_amountb then do;
Date=prev_date;
AmountA=prev_amounta;
AmountB=prev_amountb;
end;
end;
Else delete;
Run;
Here is a method that I think should work. The basic approach is:
Find all the pairs of sufficiently close observations
Join the pairs with themselves to get all connected ids
Reduce the groups
Join to the original data and get the desired values
data have;
input
id
date :mmddyy10.
amountA
amountB;
format date mmddyy10.;
datalines;
1 1/15/2014 1000 79
2 1/16/2014 1100 81
3 1/30/2014 700 50
4 2/05/2014 710 80
5 2/25/2014 720 50
;
run;
/* Count the observations */
%let dsid = %sysfunc(open(have));
%let nobs = %sysfunc(attrn(&dsid., nobs));
%let rc = %sysfunc(close(&dsid.));
/* Output any connected pairs */
data map;
array vals[3, &nobs.] _temporary_;
set have;
/* Put all the values in an array for comparison */
vals[1, _N_] = id;
vals[2, _N_] = date;
vals[3, _N_] = amountA;
/* Output all pairs of ids which form an acceptable pair */
do i = 1 to _N_;
if
abs(vals[2, i] - date) < 10 and
abs((vals[3, i] - amountA) / amountA) < 0.2
then do;
id2 = vals[1, i];
output;
end;
end;
keep id id2;
run;
proc sql;
/* Reduce the connections into groups */
create table groups as
select
a.id,
min(min(a.id, a.id2, b.id)) as group
from map as a
left join map as b
on a.id = b.id2
group by a.id;
/* Get the final output */
create table lookup (where = (amountB = maxB)) as
select
have.*,
groups.group,
max(have.amountB) as maxB
from have
left join groups
on have.id = groups.id
group by groups.group;
quit;
The code works for the example data. However, the group reduction is insufficient for more complicated data. Fortunately, approaches for finding all the subgraphs given a set of edges can be found here, here, here or here (using SAS/OR).