I would like to add a new column to a dataset but I am not sure how to do so. My dataset has a variable called KEYVAR (character variable) with three different values. A participant can appear multiple times in my dataset, with each row containing a similar or different value for KEYVAR. What I want to do is create a new variable call NEWVAR that counts how many times a participant has a specific value for KEYVAR; when a participant does not have an observation for that specific value, I want NEWVAR to have a result of zero.
Here's an example of the dataset I would like (in this example, I want to count every instance of "Y" per participants as newvar):
have
PARTICIPANT KEYVAR
A Y
A N
B Y
B Y
B Y
C W
C N
C W
D Y
D N
D N
D Y
D W
want
PARTICIPANT KEYVAR NEWVAR
A Y 1
A N 1
B Y 3
B Y 3
B Y 3
C W 0
C N 0
C W 0
D Y 2
D N 2
D N 2
D Y 2
D W 2
You can use Proc SQL to compute an aggregate result over a group meeting a criteria, and have that aggregate value automatically merged into the result set.
-OR-
Use a MEANS, TRANSPOSE, MERGE approach
Sample Code (SQL)
data have;
input ID $ value $; datalines;
A Y
A N
B Y
B Y
B Y
C W
C N
C W
D Y
D N
D N
D Y
D W
E X
;
proc sql;
create table want as
select ID, value
, sum(value='Y') as Y_COUNT /* relies on logic eval 'math' 0 false, 1 true */
, sum(value='N') as N_COUNT
, sum(value='W') as W_COUNT
from have
group by ID
;
Sample Code (PROC and MERGE)
* format for PRELOADFMT and COMPLETETYPES;
proc format;
value $eachvalue
'Y' = 'Y'
'N' = 'N'
'W' = 'W'
other = '-';
;
run;
* Count how many per combination ID/VALUE;
proc means noprint data=have nway completetypes;
class ID ;
class value / preloadfmt;
format value $eachvalue.;
output out=freqs(keep=id value _freq_);
run;
* TRANSPOSE reshapes to wide (across) data layout, one row per ID;
proc transpose data=freqs suffix=_count out=counts_across(drop=_name_);
by id;
id value;
var _freq_;
where put(value,$eachvalue.) ne '-';
run;
* MERGE;
data want_way_2;
merge have counts_across;
by id;
run;
Related
I have the following SAS dataset.
correlation
policynum
risknum
A
X
Y
A
X
Y
A
X
Y
B
X
Y
B
X
Y
B
X
Y
B
X
L
B
X
L
B
X
L
C
Z
M
C
Z
M
C
Z
M
D
Z
M
D
Z
M
D
Z
M
In SAS, I want to filter the above dataset so I get my final output as:
correlation
policynum
risknum
B
X
Y
B
X
Y
B
X
Y
B
X
L
B
X
L
B
X
L
D
Z
M
D
Z
M
D
Z
M
i.e. for each group of policynum and risknum, if multiple values exist for correlation, I want to keep the second value and get rid of the first value.
If only a single value of correlation exists for a group of policynum and risknum, I want to retain that group in my final output too.
What would be the best way to do this? It might be something simple as I am relatively new to SAS.
Thanks in advance!
If the order of the correlation values, in sort order, is the same ordering as they appear row-wise in the data set you can use SQL. Otherwise, SQL, being based on set theory, which does not have implicit row numbers, can not be used. A DATA step with DOW loop can be used.
Example:
FYI, one common situation in which SAS coders use the phrase 'DOW loop' is when SET & BY statements occur inside a DO loop.
data have;
input correlation $ policynum $ risknum $;
datalines;
A X Y
A X Y
A X Y
B X Y
B X Y
B X Y
B X L
B X L
B X L
C Z M
C Z M
C Z M
D Z M
D Z M
D Z M
;
/* keep last group of a nested group */
* SQL can be used only if correlation wanted is ALWAYS highest valued correlation;
proc sql;
create table want as
select * from have
group by policynum, risknum
having correlation = max(correlation)
;
* DATA Step DOW loops can be used when correlation wanted is last occurring correlation within by group;
data want;
do _n_ = 1 by 1 until (last.policynum);
set have;
by policynum risknum notsorted; /* presume at least contiguous */
end;
_want_correlation = correlation;
do _n_ = 1 to _n_;
set have;
if _want_correlation = correlation then OUTPUT;
end;
run;
I have a SAS dataset like this:
Name MgrName Dept.
A B
B C
C D
X Y
I need to fill in the Dept. using a recursive logic. I know D is the head of 'Payroll', so I fill in:
Name MgrName Dept.
A B
B C
C D Payroll
X Y
But using some kind of recursion,everybody that is in D's reporting chain (A, B,C) also needs to be assigned 'Payroll'. How can I do that in SAS?
Here is a hash based approach in the context of a Proc DS2 program.
Each node (name) has only one parent (mgrname) so a hash can key with name and have mgrname as data. A loop over find method will seek the ancestral parent for a node or not find one.
Example (name is id and namemgr is pid):
data have;
input id $ pid $;datalines;
A B
B C
C D
F D
G F
H F
P H
Q H
R H
X Y
run;
proc ds2;
data want / overwrite=yes;
declare package hash links();
declare char _seek_pid;
declare char _seek_id;
declare char dept;
keep id pid dept;
* populate hash;
method init();
links.definekey('id');
links.definedata('pid');
links.dataset('{select pid, id from have {options locktable=share}}');
links.multidata('yes');
links.definedone();
end;
* seek ancestor from which value should be applied;
method apply(char rootid, char value, char id);
declare int limit;
limit = 0;
_seek_id = id;
do while (
links.find([_seek_id], [_seek_pid]) = 0 and
limit < 100 and
rootid ne _seek_pid
);
limit+1;
_seek_id = _seek_pid;
end;
if rootid = _seek_pid then dept = value;
end;
* apply some values to some nodes and children thereof;
method run();
set have (locktable=share);
apply ('D','payroll', id);
apply ('F','shadow$', id);
end;
enddata;
run;
quit;
%let syslast = want;
There is probably a smarter way, but here is a hash object approach
data have;
input Name $ MgrName $;
datalines;
A B
B C
C D
X Y
;
data want(drop=rc);
declare hash h1(dataset:'have');
h1.definekey('MgrName');
h1.definedata('Name');
h1.definedone();
declare hash h2();
h2.definekey('Name');
h2.definedone();
length Name $ 100 MgrName $ 100;
do rc=h1.find(key:'D') by 0 while (rc=0);
h2.replace();
rc=h1.find(key:Name);
end;
do until (lr);
set have end=lr;
Dept=ifc(h2.check()=0, 'Payroll', '');
output;
end;
run;
Assume I have a data-set D1 as follows:
ID ATR1 ATR2 ATR3
1 A R W
2 B T X
1 A S Y
2 C T E
3 D U I
1 T R W
2 C X X
I want to create a data-set D2 from this as follows
ID ATR1 ATR2 ATR3
1 A R W
2 C T X
3 D U I
In other words, Data-set D2 consists of unique IDs from D1. For each ID in D2, the values of ATR1-ATR3 are selected as the most frequent (of the respective variable) among the records in D1 with the same ID. For example ID = 1 in D2 has ATR1 = A (most frequent).
I have one solution which is very clumsy. I simply sort copies of the data set `D1' three times (by ID and ATR1 e.g) and remove duplicates. I later merge the three data-sets to get what I want. However, I think there might be an elegant way to do this. I have about 20 such variables in the original data-set.
Thanks
/*
read and restructure so we end up with:
id attr_id value
1 1 A
1 2 R
1 3 W
etc.
*/
data a(keep=id attr_id value);
length value $1;
array attrs_{*} $ 1 attr_1 - attr_3;
infile cards;
input id attr_1 - attr_3;
do attr_id=1 to dim(attrs_);
value = attrs_{attr_id};
output;
end;
cards;
1 A R W
2 B T X
1 A S Y
2 C T E
3 D U I
1 T R W
2 C X X
;
run;
/* calculate frequencies of values per id and attr_id */
proc freq data=a noprint;
tables id*attr_id*value / out=freqs(keep=id attr_id value count);
run;
/* sort so the most frequent value per id and attr_id ends up at the bottom of the group.
if there are ties then it's a matter of luck which value we get */
proc sort data = freqs;
by id attr_id count;
run;
/* read and recreate the original structure. */
data b(keep=id attr_1 - attr_3);
retain attr_1 - attr_3;
array attrs_{*} $ 1 attr_1 - attr_3;
set freqs;
by id attr_id;
if first.id then do;
do i=1 to dim(attrs_);
attrs_{i} = ' ';
end;
end;
if last.attr_id then do;
attrs_{attr_id} = value;
end;
if last.id then do;
output;
end;
run;
I have the following data set:
data data_one;
length X 3
Y $ 20;
input x y ;
datalines;
1 test
2 test
3 test1
4 test1
5 test
6 test
7 test1
run;
data data_two;
length Z 3
A $ 20;
input Z A;
datalines;
1 test
2 test1
3 test2
run;
What I would like to have is a data set which tells me how often column Y in data_one contains the same string of column A in data_two. The result should look like this one:
Obs test test1 test2
1 4 3 0
Thanks in advance!
First we need the counts for those values of Y present in data_one.
Then we create a sorted (for the next merge) list of the values present in data_two.
The data_one Y counts from 1. are merged with the list from 2.
The Y values present in data_two but not in data_one (b and not a) are assigned count=0, the Y values not present in data_two are discarded (if b).
The last passage transposes the vertical list of counts in an horizontal set of variables.
proc freq data=data_one noprint;
table y / out=count_one (keep=y count);
run;
proc sort data=data_two out=list_two (keep=a rename=(a=y)) nodupkey;
by a;
run;
data count_all;
merge count_one (in=a) list_two (in=b);
by y;
if (b and not a) then count=0;
if b;
run;
proc transpose data=count_all out=final (drop=_name_ _label_);
id y;
run;
The first 3 steps can be replaced with one proc SQL:
proc sql;
create table count_all as
select distinct
coalesce(t1.y,t2.a) as y,
case
when missing(t1.y) then 0
else count(t1.y)
end as N
from data_one as t1
right join data_two as t2
on t1.y=t2.a
group by 1
order by 1;
quit;
proc transpose data=count_all out=final (drop=_name_);
id y;
run;
I have below two datasets and need the third dataset as an output.
ONE TWO
---------- ----------
ID FLAG NUMB
1 N 2
2 Y 3
3 Y 9
4 N 2
5 N 3
9 Y 9
10 Y
OUTPUT
-------
ID FLAG NEW
1 N N
2 Y Y
3 Y Y
4 N N
5 N N
9 Y Y
10 Y N
If ONE.ID is found in TWO.NUMB and it's ONE.FLAG = Y then the new variable NEW = Y
else NEW = N
I was able to do this using PROC SQL as below.
proc sql;
create table output as
(
select distinct id, flag, case when numb is null then 'N' else 'Y' end as NEW
from one
left join
two
on id = numb
and flag = 'Y'
);
quit;
Could this be done in DATA step/MERGE?
since you have a sql step attempt here's an improvement on that
--this sql step does not require a merge--
proc sql noprint;
create table output as
select distinct *, case
when id in (select distinct numb from two) then "Y"
else "N"
end as new
from one
;
quit;