I have the following dataset :
ID CODE
1 A
1 B
2 A
2 A
2 B
3 A
3 B
I would like to add a third column to this table which gives a sequence no. as given below :
ID CODE SEQ
1 A 1
1 B 2
2 A 1
2 A 1
2 B 2
3 A 1
3 B 2
How can I achieve this instead of coding A as 1 and B as 2 rather by a retain statement ?
You should look at by processing and first.. Something like this will work; basically, for each ID initialize seq to zero, and for each new code increment it by one.
data want;
set have;
by id code;
if first.id then seq=0;
if first.code then seq+1;
run;
Related
I would like to create a page break value that can help me break the page when I use proc report.
Now my data looks like this:
Group Value
a 1
a 2
a 3
...
b 1
b 2
...
c 1
c 2
c 3
And suppose I only want two lines per page, and break if the group changed.
So I need a dataset like this:
Group Value Page
a 1 1
a 2 1
a 3 2
...
b 1 3
b 2 3
...
c 1 4
c 2 4
c 3 5
Can anyone help me with this? Thanks!
Retain holds values across rows. Create a counter value that you can use to track the number of records per group. This allows you to split it into pages of N amount.
Use BY and FIRST to reset counter at the start of each group
Check if the you need to increment page
data have;
input Group $ Value;
cards;
a 1
a 2
a 3
b 1
b 2
c 1
c 2
c 3
;;;;
data want;
set have;
by group;
retain counter page;
if first.group then counter=0;
counter+1;
if mod(counter, 2) =1 or first.group then page+1;
run;
proc print data=want;
run;
Results:
Obs Group Value counter page
1 a 1 1 1
2 a 2 2 1
3 a 3 3 2
4 b 1 1 3
5 b 2 2 3
6 c 1 1 4
7 c 2 2 4
8 c 3 3 5
I'd like to ask help in this, as I am new to SAS, but a PROC SQL approach is usable as well.
My dataset has IDs, a time variable, and a flag. After I sort by id and time, I need to find the first flagged observation of the last flagged group/streak. As in:
ID TIME FLAG
1 2 1
1 3 1
1 4 1
1 5 0
1 6 1
1 7 0
1 8 1
1 9 1
1 10 1
2 2 0
2 3 1
2 4 1
2 5 1
2 6 1
2 7 1
Here I want my script to return the row where time is 8 for ID 1, as it is the first observation from the last "streak", or flagged group. For ID 2 it should be where time is 3.
Desired output:
ID TIME FLAG
1 8 1
2 3 1
I'm trying to wrap my head around using first. and last. here, but I suppose the problem here is that I view temporally displaced flagged groups/streaks as different groups, while SAS looks at them as they are only separated by flag, so a simple "take first. from last." is not sufficient.
I was also thinking of collapsing the flags to a string and using a regex lookahead, but I couldn't come up with either the method or the pattern.
I would just code a double DOW loop. The first will let you calculate the observation for this ID that you want to output and the second will read through the records again and output the selected observation.
You can use the NOTSORTED keyword on the BY statement to have SAS calculate the FIRST.FLAG variable.
data have;
input ID TIME FLAG;
cards;
1 2 1
1 3 1
1 4 1
1 5 0
1 6 1
1 7 0
1 8 1
1 9 1
1 10 1
2 2 0
2 3 1
2 4 1
2 5 1
2 6 1
2 7 1
;
data want;
do obs=1 by 1 until(last.id);
set have;
by id flag notsorted;
if first.flag then want=obs;
end;
do obs=1 to obs;
set have;
if obs=want then output;
end;
drop obs want;
run;
Loop through the dataset by id. Use the lag function to look at the current and previous value of flag. If the current value is 1 and the previous value is 0, or it's the first observation for that ID, write the value of time to a retained variable. Only output the last observation for each id. The retained variable should contain the time of the first flagged observation of the last flagged group:
data result;
set have;
by id;
retain firstflagged;
prevflag = lag(flag);
if first.id and flag = 1 then firstflagged = time;
else if first.id and flag = 0 then firstflagged = .;
else if flag = 1 and prevflag = 0 then firstflagged = time;
if last.id then output;
keep id firstflagged flag;
rename firstflagged = time;
run;
I have a data structure that looks like this:
DATA have ;
INPUT famid indid implicate imp_inc;
CARDS ;
1 1 1 40000
1 1 2 25000
1 1 3 34000
1 1 4 23555
1 1 5 49850
1 2 1 1000
1 2 2 2000
1 2 3 3000
1 2 4 4000
1 2 5 5000
1 3 1 .
1 3 2 .
1 3 3 .
1 3 4 .
1 3 5 .
2 1 1 40000
2 1 2 45000
2 1 3 50000
2 1 4 34000
2 1 5 23500
2 2 1 .
2 2 2 .
2 2 3 .
2 2 4 .
2 2 5 .
2 3 1 41000
2 3 2 39000
2 3 3 24000
2 3 4 32000
2 3 5 53000
RUN ;
So, we have family id, individual id, implicate number and imputed income for each implicate.
What i need is to replicate the results of the first individual in each family (all of the five implicates) for the remaining individuals within each family, replacing whatever values we previously had on those cells, like this:
DATA want ;
INPUT famid indid implicate imp_inc;
CARDS ;
1 1 1 40000
1 1 2 25000
1 1 3 34000
1 1 4 23555
1 1 5 49850
1 2 1 40000
1 2 2 25000
1 2 3 34000
1 2 4 23555
1 2 5 49850
1 3 1 40000
1 3 2 25000
1 3 3 34000
1 3 4 23555
1 3 5 49850
2 1 1 40000
2 1 2 45000
2 1 3 50000
2 1 4 34000
2 1 5 23500
2 2 1 40000
2 2 2 45000
2 2 3 50000
2 2 4 34000
2 2 5 23500
2 3 1 40000
2 3 2 45000
2 3 3 50000
2 3 4 34000
2 3 5 23500
RUN ;
In this example I'm trying to replicate only one variable but in my project I will have to do this for dozens of variables.
So far, I came up with this solution:
%let implist_1=imp_inc;
%macro copyv1(list);
%let nwords=%sysfunc(countw(&list));
%do i=1 %to &nwords;
%let varl=%scan(&list, &i);
proc means data=have max noprint;
var &varl;
by famid implicate;
where indid=1;
OUTPUT OUT=copy max=max_&varl;
run;
data want;
set have;
drop &varl;
run;
data want (drop=_TYPE_ _FREQ_);
merge want copy;
by famid implicate;
rename max_&varl=&varl;
run;
%end;
%mend;
%copyv1(&imp_list1);
This works well for one or two variables. However it is tremendously slow once you do it for 400 variables in a data-set with the size of 1.5 GB.
I'm pretty sure there is a faster way to do this with some form of proc sql or first.var etc., but i'm relatively new to SAS and so far I couldn't come up with a better solution.
Thank you very much for your support.
Best regards
Yes, this can be done in DATA step using a first. reference made available via the by statement.
data want;
set have (keep=famid indid implicate imp_inc /* other vars */);
by famid indid implicate; /* by implicate is so step logs an error (at run-time) if data not sorted */
if first.famid then if indid ne 1 then abort;
array across imp_inc /* other vars */;
array hold [1,5] _temporary_; /* or [<n>,5] where <n> means the number of variables in the across array */
if indid = 1 then do; /* hold data for 1st individuals implicate across data */
do _n_ = 1 to dim(across);
hold[_n_,implicate] = across[_n_]; /* store info of each implicate of first individual */
end;
end;
else do;
do _n_ = 1 to dim(across);
across[_n_] = hold[_n_,implicate]; /* apply 1st persons info to subsequent persons */
end;
end;
run;
The DATA step could be significantly faster due to single pass through data, however there is an internal processing cost associated with calculating all those pesky [] array addresses at run; time, and that cost could become impactful at some <n>
SQL is simpler syntax, clearer understanding and works if have data set is unsorted or has some peculiar sequencing in the by group.
This is fairly straightforward with a bit of SQL:
proc sql;
create table want as
select a.famid, a.indid, a.implicate, b.* from
have a
left join (
select * from have
group by famid
having indid = min(indid)
) b
on
a.famid = b.famid
and a.implicate = b.implicate
order by a.famid, a.indid, a.implicate
;
quit;
The idea is to join the table to a subset of itself containing only the rows corresponding to the first individual within each family.
It is set up to pick the lowest numbered individual within each family, so it will work even if there is no row with indid = 1. If you are sure that there will always be such a row, you can use a slightly simpler query:
proc sql;
create table want as
select a.famid, a.indid, a.implicate, b.* from
have(sortedby = famid) a
left join have(where = (indid = 1)) b
on
a.famid = b.famid
and a.implicate = b.implicate
order by a.famid, a.indid, a.implicate
;
quit;
Specifying sortedby = famid provides a hint to the query optimiser that it can skip one of the initial sorts required for the join, which may improve performance a bit.
I have the following dataset
data input;
input Row$ A B;
datalines;
1 1 2
2 1 2
3 1 1
4 1 1
5 2 3
6 2 3
7 2 3
8 2 2
9 2 2
10 2 1
;
run;
My goal is only to keep records of the first group of data for the variable A. For example I only want records where A=1 and B=2 (lines 1 and 2) and for the next group where A=2 and B=3 and so on...
I tried the following code
data input (rename= (count=rank_b));
set input;
count + 1;
by A descending B;
if first.B then count = 1;
run;
which just gives the number of observations in A (1 to 4) and B (1 to 6). What I would like is
A B rank_b rank_b_desired
1 2 1 1
1 2 2 1
1 1 1 2
1 1 2 2
2 3 1 1
2 3 2 1
2 2 1 2
2 2 2 2
2 1 1 3
So that I can then eliminate all obs where rank_b_desired does not equal 1.
Set a flag to 1 when you encounter a new value of A, then set it to 0 if B changes. retain will preserve the value of the flag when a new line is read from the input.
data want;
set input;
by A descending B;
retain flag;
if first.B then flag = 0;
if first.A then flag = 1;
run;
The desired result can also be achieved via proc sql, with the added benefit that it does not depend on the data being pre sorted.
proc sql;
create table want as
select *
from input
group by A
having B = max(B)
order by Row;
quit;
Or to match user234821's output:
proc sql;
create table want as
select
*,
ifn(B = max(B), 1, 0) as flag
from input
group by A
order by Row;
quit;
I have a dataset that consists of a series of readings made by different people/instruments, of a bunch of different dimensions. It looks like this:
SUBJECT DIM1_1 DIM1_2 DIM1_3 DIM1_4 DIM1_5 DIM2_1 DIM2_2 DIM2_3 DIM3_1 DIM3_2
1 1 . 1 1 2 3 3 3 2 .
2 1 1 . 1 1 2 2 3 1 1
3 2 2 2 . . 1 . . 5 5
... ... ... ... ... ... ... ... ... ... ...
My real dataset contains around 190 dimensions, with up to 5 measures in each one
I have to obey a set of rules to create a new variable for each dimension:
If there are 2 different values in the same dimension (missings excluded), the new variable is a missing.
If all values are the same (missings excluded), the new variable assumes the same value.
My new variables should look like this:
SUBJECT ... DIM1_X DIM2_X DIM3_X
1 ... . 3 2
2 ... 1 . 1
3 ... 2 1 5
The problem here is that i don't have the same number of measures for each dimension. Also, i could only come up with a lot of IF's (and I mean a LOT, as more measures in a given dimension increases the number of comparisons), so I wonder if there is some easier way to handle this particular problem.
Any help would be apreciated.
Thanks in advance.
Easiest way is to transpose it to vertical (one row per DIMx_y), summarize, then set the ones you want missing to missing, then retranspose (and if needed merge back on).
data have;
input SUBJECT DIM1_1 DIM1_2 DIM1_3 DIM1_4 DIM1_5 DIM2_1 DIM2_2 DIM2_3 DIM3_1 DIM3_2;
datalines;
1 1 . 1 1 2 3 3 3 2 .
2 1 1 . 1 1 2 2 3 1 1
3 2 2 2 . . 1 . . 5 5
;;;;
run;
data have_pret;
set have;
array dim_data DIM:;
do _t = 1 to dim(dim_Data); *dim function is not related to the name - it gives # of vars in array;
dim_Group = scan(vname(dim_data[_t]),1,'_');
dim_num = input(scan(vname(dim_data[_t]),2,'_'),BEST12.);
dim_val=dim_data[_t];
output;
end;
keep dim_group dim_num subject dim_val;
run;
proc freq data=have_pret noprint;
by subject dim_group;
tables dim_val/out=want_pret(where=(not missing(dim_val)));
run;
data want_pret2;
set want_pret;
by subject dim_Group;
if percent ne 100 then dim_val=.;
idval = cats(dim_Group,'_X');
if last.dim_Group;
run;
proc transpose data=want_pret2 out=want;
by subject;
id idval;
var dim_val;
run;