Here is the data I have, I use proc tabulate to present it how it is presented in excel, and to make the visualization easier. The goal is to make sure groups strictly below the diagonal (i know it's a rectangle, the (1,1) (2,2)...(7,7) "diagonal") to roll up the column until it hits the diagonal or makes a group size of at least 75.
1 2 3 4 5 6 7 (month variable)
(age)
1 80 90 100 110 122 141 88
2 80 90 100 110 56 14 88
3 80 90 87 45 12 41 88
4 24 90 100 110 22 141 88
5 0 1 0 0 0 0 2
6 0 1 0 0 0 0 6
7 0 1 0 0 0 0 2
8 0 1 0 0 0 0 11
Ive already used if/thens to regroup certain data values, but I need a general way to do it for other sets.
Thanks in advance
desired results
1 2 3 4 5 6 7 (month variable)
(age)
1 80 90 100 110 122 141 88
2 80 90 100 110 56 14 88
3 104 90 87 45 12 41 88
4 0 94 100 110 22 141 88
5 0 0 0 0 0 0 2
6 0 0 0 0 0 0 6
7 0 0 0 0 0 0 13
8 0 0 0 0 0 0 0
Mock up some categorical data for some patients who have to be counted
data mock;
do patient_id = 1 to 2500;
month = ceil(7*ranuni(123));
age = ceil(8*ranuni(123));
output;
end;
stop;
run;
Create a tabulation of counts (N) similar to the one shown in the question:
options missing='0';
proc tabulate data=mock;
class month age;
table age,month*n=''/nocellmerge;
run;
For each month get the sub-diagonal patient count
proc sql;
/* create table subdiagonal_column as */
select month, count(*) as subdiag_col_freq
from mock
where age > month
group by month;
For each row get the pre-diagonal patient count
/* create table prediagonal_row as */
select age, count(*) as prediag_row_freq
from mock
where age > month
group by age;
other sets can be tricky if the categorical values are not +1 monotonic. To do a similar process for non-montonic categorical values you will need to create surrogate variables that are +1 monotonic. For example:
data mock;
do item_id = 1 to 2500;
pet = scan ('cat dog snake rabbit hamster', ceil(5*ranuni(123)));
place = scan ('farm home condo apt tower wild', ceil(6*ranuni(123)));
output;
end;
run;
proc tabulate data=mock;
class pet place;
table pet,place*n=''/nocellmerge;
run;
proc sql;
create table unq_pets as select distinct pet from mock;
create table unq_places as select distinct place from mock;
data pets;
set unq_pets;
pet_num = _n_;
run;
data places;
set unq_places;
place_num = _n_;
run;
proc sql;
select distinct place_num, mock.place, count(*) as subdiag_col_freq
from mock
join pets on pets.pet = mock.pet
join places on places.place = mock.place
where pet_num > place_num
group by place_num
order by place_num
;
Related
My goal is to combine these tables into one without having to manually run my macro each time for each column.
The code I currently have with me is the following:
%macro task_Oct(set,col_name);
data _type_;
set &set;
call symputx('col_type', vtype(&col_name));
run;
proc sql;
create table work.oct27 as
select "&col_name" as variable,
"&col_type" as type,
nmiss(&col_name) as missing_val,
count(distinct &col_name) as distinct_val
from &set;
quit;
%mend task_Oct;
%task_Oct(sashelp.cars,Origin)
The above code gives me the following output:
|Var |Type |missing_val|distinct_val|
|Origin|Character|0 | 3 |
But the sashelp.cars data sheet has 15 columns and so I would like to output a new data sheet which has 15 rows with 4 columns.
I would like to get the following combined table as the output of my code:
|Var |Type |missing_val|distinct_val|
|Make |Character|0 | 38 |
|Model |Character|0 | 425 |
|Type |Character|0 | 6 |
|Origin|Character|0 | 3 |
...
...
...
Since I'm using a macro, I could run my code 15 different times by manually entering the names of the columns and then merging the tables into 1; and it wouldn't be a problem. But what if I have a table with 100s of columns? I could use some loop statement but I'm not sure how to go about that in this case. Help would be appreciated. Thank you.
The main output you appear to want can be generated directly by PROC FREQ with the NLEVELS option. If you want to add in the variable type then just merge it with the output of PROC CONTENTS.
ods exclude all;
ods output nlevels=nlevels;
proc freq data=sashelp.cars nlevels;
tables _all_ / noprint ;
run;
ods select all;
proc contents data=sashelp.cars noprint out=contents;
run;
proc sql;
create table want(drop=table:) as
select c.varnum,c.name,c.type,n.*
from contents c inner join nlevels n
on c.name=n.TableVar
order by varnum
;
quit;
Result
NNon
NMiss Miss
Obs VARNUM NAME TYPE NLevels Levels Levels
1 1 Make 2 38 0 38
2 2 Model 2 425 0 425
3 3 Type 2 6 0 6
4 4 Origin 2 3 0 3
5 5 DriveTrain 2 3 0 3
6 6 MSRP 1 410 0 410
7 7 Invoice 1 425 0 425
8 8 EngineSize 1 43 0 43
9 9 Cylinders 1 8 1 7
10 10 Horsepower 1 110 0 110
11 11 MPG_City 1 28 0 28
12 12 MPG_Highway 1 33 0 33
13 13 Weight 1 348 0 348
14 14 Wheelbase 1 40 0 40
15 15 Length 1 67 0 67
The NMissLevels variable counts the number of distinct types of missing values.
If instead you want to count the number of observations with (any) missing value you will need to use code generation. So use the CONTENTS data to generate an SQL query to generate all of the counts you want into a single observation with only one pass through the data. You can then transpose that to make it usable for re-merging with the CONTENTS data.
filename code temp;
data _null_;
set contents end=eof;
length nliteral $65 dsname $80;
nliteral=nliteral(name);
dsname = catx('.',libname,nliteral(memname));
file code;
if _n_=1 then put 'create table counts as select ' / ' ' # ;
else put ',' #;
put 'nmiss(' nliteral ') as missing_' varnum
/',count(distinct ' nliteral ') as distinct_' varnum
;
if eof then put 'from ' dsname ';';
run;
proc sql;
%include code /source2;
quit;
proc transpose data=counts out=count2 name=name ;
run;
proc sql ;
create table want as
select c.varnum, c.name, c.type
, m.col1 as nmissing
, d.col1 as ndistinct
from contents c
left join count2 m on m.name like 'missing%' and c.varnum=input(scan(m.name,-1,'_'),32.)
left join count2 d on d.name like 'distinct%' and c.varnum=input(scan(d.name,-1,'_'),32.)
order by varnum
;
quit;
Result
Obs VARNUM NAME TYPE nmissing ndistinct
1 1 Make 2 0 38
2 2 Model 2 0 425
3 3 Type 2 0 6
4 4 Origin 2 0 3
5 5 DriveTrain 2 0 3
6 6 MSRP 1 0 410
7 7 Invoice 1 0 425
8 8 EngineSize 1 0 43
9 9 Cylinders 1 2 7
10 10 Horsepower 1 0 110
11 11 MPG_City 1 0 28
12 12 MPG_Highway 1 0 33
13 13 Weight 1 0 348
14 14 Wheelbase 1 0 40
15 15 Length 1 0 67
I have this data
data have;
input cust_id pmt months;
datalines;
AA 100 0
AA 50 1
AA 200 2
AA 350 3
AA 150 4
AA 700 5
BB 500 0
BB 300 1
BB 1000 2
BB 800 3
run;
and I'd like to generate an output that looks like this
data want;
input cust_id pmt months i;
datalines;
AA 100 0 0
AA 50 0 1
AA 200 0 2
AA 350 0 3
AA 150 0 4
AA 700 0 5
AA 50 1 0
AA 200 1 1
AA 350 1 2
AA 150 1 3
AA 700 1 4
AA 200 2 0
AA 350 2 1
AA 150 2 2
AA 700 2 3
AA 350 3 0
AA 150 3 1
AA 700 3 2
AA 150 4 0
AA 700 4 1
AA 700 5 0
BB 500 0 0
BB 300 0 1
BB 1000 0 2
BB 800 0 3
BB 300 1 0
BB 1000 1 1
BB 800 1 2
BB 1000 2 0
BB 800 2 1
BB 800 3 0
run;
There are few thousand rows with different cust_ID and different months length. I tried joining tables but it couldn't get me the sequence of 100 50 200 350 150 700 (for cust_ID AA). I could only replicated 100 if my months are 0, 50 if months are 1 & so on. I created a maxval which is the maximum month value. My code is something like this
data temp1;
set have;
do i = 0 to maxval;
if (months <=maxval) then output;
end;
i thought of creating a uniquekey to join my have data and temp1 data but it could only give me
AA 100 0 0
AA 50 0 1
AA 200 0 2
AA 350 0 3
AA 150 0 4
AA 700 0 5
AA 100 1 0
AA 50 1 1
AA 200 1 2
AA 350 1 3
AA 150 1 4
AA 100 2 0
AA 50 2 1
AA 200 2 2
AA 350 2 3
AA 100 3 0
AA 50 3 1
AA 200 3 2
AA 100 4 0
AA 50 4 1
AA 100 5 0
Any thoughts or different approach on how to generate my want table? Thank you!
This problem is a little tricky because you have things going in three directions
The number of group repetitions descends from group count. Within each repetition:
The payments item start index ascends and terminates at group count
The months (as I) item start index is 1 and termination descends from group count
SQL
One SQL approach is a three-way reflexive join with-in group. The months values act as a within group index and must be monotonic by 1 from 0 for this to work.
proc sql;
create table want as
select X.cust_id, Z.pmt, X.months, Y.months as i
from have as X
join have as Y on X.cust_id = Y.cust_id
join have as Z on Y.cust_id = Z.cust_id
where
X.months + Y.months = Z.months
order by
X.cust_id, X.months, Z.months
;
quit;
DATA Step
A DOW loop is used to count the group size. 2-deep looping crosses the combinations and three point= values are computed (finagled) to retrieve the relevant values.
data want2;
if 0 then set have; * prep pdv to match have;
retain point_end ;
point_start = sum(point_end,0);
do group_count = 1 by 1 until (last.cust_id);
set have(keep=cust_id);
by cust_id;
end;
do index1 = 1 to group_count;
point1 = point_start + index1;
set have (keep=months) point = point1;
do index2 = 0 to group_count - index1 ;
point2 = point_start + index1 + index2;
set have (keep=pmt) point=point2;
point3 = point_start + index2 + 1;
set have (keep=months rename=months=i) point=point3;
output;
end;
end;
point_end = point1;
keep cust_id pmt months i;
run;
Try the following:
data want(drop = start_obs limit j);
retain start_obs 1;
/* read by cust_id group */
do until(last.cust_id);
set have end = last_obs;
by cust_id;
end;
limit = months;
do j = 0 to limit;
i = 0;
do obs_num = start_obs + j to start_obs + limit;
/* read specific observations using direct access */
set have point = obs_num;
months = j;
output;
i = i + 1;
end;
end;
/* prepare for next direct access read */
start_obs = limit + 2;
if last_obs then
stop;
run;
data test;
input ID month d_month;
datalines;
1 59 0
1 70 11
1 80 21
2 10 0
2 11 1
2 13 3
3 5 0
3 9 4
4 8 0
;
run;
I have two columns of data ID and Month. Column 1 is the ID, the same ID may have multiple rows (1-5). The second column is the enrolled month. I want to create the third column. It calculates the different between the current month and the initial month for each ID.
you can do it like that.
data test;
input ID month d_month;
datalines;
1 59 0
1 70 11
1 80 21
2 10 0
2 11 1
2 13 3
3 5 0
3 9 4
4 8 0
;
run;
data calc;
set test;
by id;
retain current_month;
if first.id then do;
current_month=month;
calc_month=0;
end;
if ^first.id then do;
calc_month = month - current_month ;
end;
run;
Krs
I want an data-step or SQL statement to do the following.
Consider this table:
(Before)
id div dlenfol repurch rlenfol
1 0 145 1 25
2 0 114 0 114
2 0 114 0 114
3 0 189 1 53
3 0 189 0 189
3 1 149 0 189
4 1 14 0 182
4 0 182 1 46
4 0 182 0 182
Grouping by id, how do I convert all the values of dlenfol to the minimum value in the dlenfol column, and all the values of rlenfol to the minimum value in the rlenfol column?
Meanwhile I also want to create a variable called choice that:
=1 if a certain id EVER had a div=1;
=0 if a certain id EVER had a repurch=1 (but never had a div=1);
=1 if a certain id EVER had a div=1 AND EVER had a repurch=1;
and =. if the certain id never had a div=1 nor repurch=1.
i.e. Like this:
(After)
id div dlenfol repurch rlenfol choice
1 0 145 1 25 0
2 0 114 0 114 .
2 0 114 0 114 .
3 0 149 1 53 1
3 0 149 0 53 1
3 1 149 0 53 1
4 1 14 0 46 1
4 0 14 1 46 1
4 0 14 0 46 1
The code I've been trying is not working:
data comb2d;
set comb;
do;
set comb;
by id;
dmin = min(dlenfol, dmin);
rmin = min(rlenfol, rmin);
if dlenfol=dmin and rlenfol^=rmin then CHOICE=1;
else if dlenfol^=dmin and rlenfol=rmin then CHOICE=0;
else if dlenfol=dmin and rlenfol=rmin then CHOICE=1;
else CHOICE=.;
/* if DIV=1 and REPURCH=0 then CHOICE=1;
else if DIV=0 and REPURCH=1 then CHOICE=0;
else if DIV=1 and REPURCH=1 then CHOICE=1;
else CHOICE=.; */
end;
dlenfol = dmin;
rlenfol = rmin;
/* drop dmin;
drop rmin; */
run;
The following SQL code seems to solve the minimum value issue but it creates 2 variables (dmin and rmin) that I don't really need:
proc sql;
create table comb3 as
select *, min(dlenfol) as dmin, min(rlenfol) as rmin
from comb
group by comb.id;
quit;
proc sort data=before out=sort1;
by id dlenfol;
run;
data sort1;
drop temp;
set sort1;
retain temp;
by id;
if first.id then temp = dlenfol;
else dlenfol = temp;
run;
then do the same thing for rlenfol
How about something like:
proc sql;
create table comb3 as
select id, div , repurch , min(dlenfol) as dlenfol, min(rlenfol) as relenfol
from comb
group by comb.id;
quit;
The following code seems to be working now:
proc sql;
create table comb3 as
select *, min(dlenfol) as dmin, min(rlenfol) as rmin, max(choice) as choicemax
from comb
group by comb.gvkey
order by comb.gvkey, comb.fyear;
quit;
This is a good example of a problem that can be handled by a double DOW loop. In the first loop find the minimums and check if the flag variables are ever true.
You then have the information needed to define the new CHOICE variable and set the variables to their minimum.
data want ;
do until(last.id);
set have ;
by id ;
mind=min(mind,dlenfol);
minr=min(minr,rlenfol);
anydiv=anydiv or div;
anyrep=anyrep or repurch;
end;
if anydiv then choice=1;
else if anyrep then choice=0;
else choice=.;
do until(last.id);
set have;
by id;
dlenfol=mind;
rlenfol=minr;
output;
end;
drop mind minr anydiv anyrep;
run;
I have the following two sas datasets:
data have ;
input a b;
cards;
1 15
2 10
3 40
4 200
1 25
2 15
3 10
4 75
1 1
2 99
3 30
4 100
;
data ref ;
input x y;
cards;
1 10
2 20
3 30
4 100
;
I would like to have the following dataset:
data want ;
input a b outcome ;
cards;
1 15 0
2 10 1
3 40 0
4 200 0
1 25 0
2 15 1
3 10 1
4 75 1
1 1 1
2 99 0
3 30 1
4 100 1
;
I would like to create a variable 'outcome' which is produced by an if statement upon conditions of variables a, b, x and y. As in reality the 'have' dataset is extremely large I would like to avoid a sort and merging the two datasets together (where a = x).
I am trying to use macro variables with the following code:
data _null_ ;
set ref ;
call symput('listx', x) ;
call symput('listy', y) ;
run ;
data want ;
set have ;
if a=&listx and b le &listy then outcome = 1 ; else outcome = 0 ;
run ;
which does not however produce the desired result:
data want ;
input a b outcome ;
cards;
1 15 0
2 10 1
3 40 0
4 200 0
1 25 0
2 15 1
3 10 1
4 75 1
1 1 1
2 99 0
3 30 1
4 100 1
;
redone my solution using hash tables. Below my approach
data ref2(rename=(x=a));
set ref ;
run;
data want;
declare Hash Plan ();
rc = plan.DefineKey ('a'); /*x originally*/
rc = plan.DefineData('a', 'y');
rc = plan.DefineDone();
do until (eof1);
set ref2 end=eof1;
rc = plan.add(); /*add each record from ref2 to plan (hash table)*/
end;
do until (eof2);
set have end=eof2;
call missing(y);
rc = plan.find();
outcome = (rc =0 and b<y);
output;
end;
stop;
run;
hope it helps