I have a dataset with tree variables, three binary variables.
I wrote a proc tabulate
proc tabulate data=mydata;
class country var1 var2;
table Country, var1 var2;
run;
Var1 Var2
0 1 0 1
USA 40 50 40 50
AUS 50 20 50 20
IRE 60 40 60 40
DUB 70 50 70 50
Here I get the table with the totals of both var 1 var 2 for 0s and 1s.
However I want only the totals of 1s in this cross table. How can I do that.
If I use a where caluse as below, it shows only 1ns both..
proc tabulate data=mydata;
class country var1 var2;
table Country, var1 var2;
where var1=1 and var2=2;
run;
When I use the above it brings out only the 1s present in both at the sametime.
Which is not I am looking for.
So the dataset I want is as below.
Var1 Var2
1 1
USA 50 50
AUS 20 20
IRE 40 40
DUB 50 50
Is there any other way of doing this?
Change and to or.
Truth table for
Var1=1, Var2=1
Include?
Var1 Var2 AND OR
0 0 N N
0 1 N Y
1 0 N Y
1 1 Y Y
Since your variables are coded 0,1 you can ask for the SUM statistic to get the "count" of the number of ones.
proc tabulate data=mydata;
class country;
var var1 var2;
table Country, var1*sum var2*sum;
run;
Related
I want to transform my SAS table from data Have to data want.
I feel I need to use Proc transpose but could not figure it out how to do it.
data Have;
input Stat$ variable_1 variable_2 variable_3 variable_4;
datalines;
MAX 6 7 11 23
MIN 0 1 3 5
SUM 29 87 30 100
;
data Want;
input Variable $11.0 MAX MIN SUM;
datalines;
Variable_1 6 0 29
Variable_2 7 1 87
Variable_3 11 3 87
Variable_4 23 5 100
;
You are right, proc transpose is the solution
data Have;
input Stat$ variable_1 variable_2 variable_3 variable_4;
datalines;
MAX 6 7 11 23
MIN 0 1 3 5
SUM 29 87 30 100
;
/*sort it by the stat var*/
proc sort data=Have; by Stat; run;
/*id statement will keep the column names*/
proc transpose data=have out=want name=Variable;
id stat;
run;
proc print data=want; run;
Say I have a dataset like this
day product sales
1 a 1 48
2 a 2 55
3 a 3 88
4 b 2 33
5 b 3 87
6 c 1 97
7 c 2 95
On day "b" there were no sales for product 1, so there is no row where day = b and product = 1. Is there an easy way to add a row with day = b, product = 1 and sales = 0, and similar "missing" rows to get a dataset like this?
day product sales
1 a 1 48
2 a 2 55
3 a 3 88
4 b 1 0
5 b 2 33
6 b 3 87
7 c 1 97
8 c 2 95
9 c 3 0
In R you can do complete(df, day, product, fill = list(sales = 0)). I realize you can accomplish this with a self-join in proc sql, but I'm wondering if there is a procedure for this.
In this particular example you can also use the SPARSE option in PROC FREQ. It tells SAS to generate all the complete types with every value from DAY included with PRODUCT, so similar to a cross join between those elements. If you do not have the value in the table already it cannot add the value. You would need a different method in that case.
data have;
input n day $ product sales;
datalines;
1 a 1 48
2 a 2 55
3 a 3 88
4 b 2 33
5 b 3 87
6 c 1 97
7 c 2 95
;;;;
run;
proc freq data=have noprint;
table day*product / out=want sparse;
weight sales;
run;
proc print data=want;run;
There are, as usual in SAS, about a dozen ways to do this. Here's my favorite.
data have;
input n day $ product sales;
datalines;
1 a 1 48
2 a 2 55
3 a 3 88
4 b 2 33
5 b 3 87
6 c 1 97
7 c 2 95
;;;;
run;
proc means data=have completetypes;
class day product;
types day*product;
var sales;
output out=want sum=;
run;
completetypes tells SAS to put out rows for every class combination, including missing ones. You could then use proc stdize to get them to be 0's (if you need them to be 0). It's possible you might be able to do this in the first place with proc stdize, I'm not as familiar unfortunately with that proc.
You can do this with proc freq using the sparse option.
Code:
proc freq data=have noprint;
table day*product /sparse out=freq (drop=percent);
run;
Output:
day=a product=1 COUNT=1
day=a product=2 COUNT=1
day=a product=3 COUNT=1
day=b product=1 COUNT=0
day=b product=2 COUNT=1
day=b product=3 COUNT=1
day=c product=1 COUNT=1
day=c product=2 COUNT=1
day=c product=3 COUNT=0
I am new to SAS and I want to transpose the following table in SAS
From
ID Var1 Var2 Jul-09 Aug-09 Sep-09
1 10 15 200 300
2 5 17 -150 200
to
ID Var1 Var2 Date Transpose
1 10 15 Jul-09 200
1 10 15 Aug-09 300
2 5 17 Aug-09 -150
2 5 17 Sep-09 200
Can anyone help please?
You can use proc transpose to tranform data.
options validvarname=any;
data a;
infile datalines missover;
input ID Var1 Var2 "Jul-09"n "Aug-09"n "Sep-09"n;
datalines;
1 10 15 200 300
2 5 17 -150 200
;
run;
proc transpose data=a out=b(rename=(_NAME_=Date COL1=Transpose));
var "Jul-09"n--"Sep-09"n;
by ID Var1-Var2;
run;
data a;
input ID Var1 Var2 Jul_09 Aug_09;
CARDS;
1 10 15 200 300
2 5 17 -150 200
;
DATA b(drop=i jul_09 aug_09);
array dates_{*} jul_09 aug_09;
set a;
do i=1 to dim(dates_);
this_value=dates_{i};
this_date=input(compress(vname(dates_{i}),'_'),MONYY5.);
output;
end;
format this_date monyy5.;
run;
Say I have two data sets A and B that have identical variables and want to rank values in B based on values in A, not B itself (as "PROC RANK data=B" does.)
Here's a simplified example of data sets A, B and want (the desired output):
A:
obs_A VAR1 VAR2 VAR3
1 10 100 2000
2 20 300 1000
3 30 200 4000
4 40 500 3000
5 50 400 5000
B:
obs_B VAR1 VAR2 VAR3
1 15 150 2234
2 14 352 1555
3 36 251 1000
4 41 350 2011
5 60 553 5012
want:
obs VAR1 VAR2 VAR3
1 2 2 3
2 2 4 2
3 4 3 1
4 5 4 3
5 6 6 6
I come up with a macro loop that involves PROC RANK and PROC APPEND like below:
%macro MyRank(A,B);
data AB; set &A &B; run;
%do i=1 %to 5;
proc rank data=AB(where=(obs_A ne . OR obs_B=&i) out=tmp;
var VAR1-3;
run;
proc append base=want data=tmp(where=(obs_B=&i) rename=(obs_B=obs)); run;
%end;
%mend;
This is ok when the number of observations in B is small. But when it comes to very large number, it takes so long and thus wouldn't be a good solution.
Thanks in advance for suggestions.
I would create formats to do this. What you're really doing is defining ranges via A that you want to apply to B. Formats are very fast - here assuming "A" is relatively small, "B" can be as big as you like and it's always going to take just as long as it takes to read and write out the B dataset once, plus a couple read/writes of A.
First, reading in the A dataset:
data ranking_vals;
input obs_A VAR1 VAR2 VAR3;
datalines;
1 10 100 2000
2 20 300 1000
3 30 200 4000
4 40 500 3000
5 50 400 5000
;;;;
run;
Then transposing it to vertical, as this will be the easiest way to rank them (just plain old sorting, no need for proc rank).
data for_ranking;
set ranking_vals;
array var[3];
do _i = 1 to dim(var);
var_name = vname(var[_i]);
var_value = var[_i];
output;
end;
run;
proc sort data=for_ranking;
by var_name var_value;
run;
Then we create a format input dataset, and use the rank as the label. The range is (previous value -> current value), and label is the rank. I leave it to you how you want to handle ties.
data for_fmt;
set for_ranking;
by var_name var_value;
retain prev_value;
if first.var_name then do; *initialize things for a new varname;
rank=0;
prev_value=.;
hlo='l'; *first record has 'minimum' as starting point;
end;
rank+1;
fmtname=cats(var_name,'F');
start=prev_value;
end=var_value;
label=rank;
output;
if last.var_name then do; *For last record, some special stuff;
start=var_value;
end=.;
hlo='h';
label=rank+1;
output; * Output that 'high' record;
start=.;
end=.;
label=.;
hlo='o';
output; * And a "invalid" record, though this should never happen;
end;
prev_value=var_value; * Store the value for next row.;
run;
proc format cntlin=for_fmt;
quit;
And then we test it out.
data test_b;
input obs_B VAR1 VAR2 VAR3;
var1r=put(var1,var1f.);
var2r=put(var2,var2f.);
var3r=put(var3,var3f.);
datalines;
1 15 150 2234
2 14 352 1555
3 36 251 1000
4 41 350 2011
5 60 553 5012
;;;;
run;
One way that you can rank by a variable from a separate dataset is by using proc sql's correlated subqueries. Essentially you counts the number of lower values in the lookup dataset for each value in the data to be ranked.
proc sql;
create table want as
select
B.obs_B,
(
select count(distinct A.Var1) + 1
from A
where A.var1 <= B.var1.
) as var1
from B;
quit;
Which can be wrapped in a macro. Below, a macro loop is used to write each of the subqueries. It looks through the list of variable and parametrises the subquery as required.
%macro rankBy(
inScore /*Dataset containing data to be ranked*/,
inLookup /*Dataset containing data against which to rank*/,
varID /*Variable by which to identify an observation*/,
varsRank /*Space separated list of variable names to be ranked*/,
outData /*Output dataset name*/);
/* Rank variables in one dataset by identically named variables in another */
proc sql;
create table &outData. as
select
scr.&varID.
/* Loop through each variable to be ranked */
%do i = 1 %to %sysfunc(countw(&varsRank., %str( )));
/* Store the variable name in a macro variable */
%let var = %scan(&varsRank., &i., %str( ));
/* Rank: count all the rows with lower value in lookup */
, (
select count(distinct lkp&i..&var.) + 1
from &inLookup. as lkp&i.
where lkp&i..&var. <= scr.&var.
) as &var.
%end;
from &inScore. as scr;
quit;
%mend rankBy;
%rankBy(
inScore = B,
inLookup = A,
varID = obs_B,
varsRank = VAR1 VAR2 VAR3,
outData = want);
Regarding speed, this will be slow if your A is large, but should be okay for large B and small A.
In rough testing on a slow PC I saw:
A: 1e1 B: 1e6 time: ~1s
A: 1e2 B: 1e6 time: ~2s
A: 1e3 B: 1e6 time: ~5s
A: 1e1 B: 1e7 time: ~10s
A: 1e2 B: 1e7 time: ~12s
A: 1e4 B: 1e6 time: ~30s
Edit:
As Joe points out below the length of time the query takes depends not just on the number of observations in the dataset, but how many unique values exist within the data. Apparently SAS performs optimisations to reduce the comparisons to only the distinct values in B, thereby reducing the number of times the elements in A need to be counted. This means that if the dataset B contains a large number of unique values (in the ranking variables) the process will take significantly longer then the times shown. This is more likely to happen if your data is not integers as Joe demonstrates.
Edit:
Runtime test rig:
data A;
input obs_A VAR1 VAR2 VAR3;
datalines;
1 10 100 2000
2 20 300 1000
3 30 200 4000
4 40 500 3000
5 50 400 5000
;
run;
data B;
do obs_B = 1 to 1e7;
VAR1 = ceil(rand("uniform")* 60);
VAR2 = ceil(rand("uniform")* 500);
VAR3 = ceil(rand("uniform")* 6000);
output;
end;
run;
%let start = %sysfunc(time());
%rankBy(
inScore = B,
inLookup = A,
varID = obs_B,
varsRank = VAR1 VAR2 VAR3,
outData = want);
%let time = %sysfunc(putn(%sysevalf(%sysfunc(time()) - &start.), time12.2));
%put &time.;
Output:
0:00:12.41
I have a table with some variables, say var1 and var2 and an identifier, and for some reasons, some identifiers have 2 observations.
I would like to know if there is a simple way to put back the second observation of the same identifier into the first one, that is
instead of having two observations, each with var1 var2 variables for the same identifier value
ID var1 var2
------------------
A1 12 13
A1 43 53
having just one, but with something like var1 var2 var1_2 var2_2.
ID var1 var2 var1_2 var2_2
--------------------------------------
A1 12 13 43 53
I can probably do that with renaming all my variables, then merging the table with the renamed one and dropping duplicates, but I assume there must be a simpler version.
Actually, your suggestion of merging the values back is probably the best.
This works if you have, at most, 1 duplicate for any given ID.
data first dups;
set have;
by id;
if first.id then output first;
else output dups;
run;
proc sql noprint;
create table want as
select a.id,
a.var1,
a.var2,
b.var1 as var1_2,
b.var2 as var2_2
from first as a
left join
dups as b
on a.id=b.id;
quit;
Another method makes use of PROC TRANSPOSE and a data-step merge:
/* You can experiment by adding more data to this datalines step */
data have;
infile datalines;
input ID : $2. var1 var2;
datalines;
A1 12 13
A1 43 53
;
run;
/* This step puts the var1 values onto one line */
proc transpose data=tab out=new1 (drop=_NAME_) prefix=var1_;
by id;
var var1;
run;
/* This does the same for the var2 values */
proc transpose data=tab out=new2 (drop=_NAME_) prefix=var2_;
by id;
var var2;
run;
/* The two transposed datasets are then merged together to give one line */
data want;
merge new1 new2;
by id;
run;
As an example:
data tab;
infile datalines;
input ID : $2. var1 var2;
datalines;
A1 12 13
A1 43 53
A2 199 342
A2 1132 111
A2 91913 199191
B1 1212 43214
;
run;
Gives:
ID var1_1 var1_2 var1_3 var2_1 var2_2 var2_3
---------------------------------------------------
A1 12 43 . 13 53 .
A2 199 1132 91913 342 111 199191
B1 1212 . . 43214 . .
There's a very simple way of doing this, using the IDGROUP function within PROC SUMMARY.
data have;
input ID $ var1 $ var2 $;
datalines;
A1 12 13
A1 43 53
;
run;
proc summary data=have nway;
class id;
output out=want (drop=_:)
idgroup(out[2] (var1 var2)=);
run;