I have a question about transposing data without using PROC Transpose.
0 a b c
1 dog cat camel
2 9 7 2534
Without using PROC TRANSPOSE, how can I get a resulting dataset of:
Animals Weight
1 dog 9
2 cat 7
3 camel 2534
This is a bit of a curious request. This example code is hard coded for your 3 variables. You will have to generalize this if needed.
data temp;
input a $ b $ c $;
datalines;
dog cat camel
9 7 2534
;
run;
data animal_weight;
set temp end=last;
format animal animals1-animals3 $8.;
format weight weights1-weights3 best. ;
retain animals: weights:;
array animals[3];
array weights[3];
if _n_ = 1 then do;
animals[1] = a;
animals[2] = b;
animals[3] = c;
end;
else if _n_ = 2 then do;
weights[1] = input(a,best.);
weights[2] = input(b,best.);
weights[3] = input(c,best.);
end;
if last then do;
do i=1 to 3;
animal = animals[i];
weight = weights[i];
output;
end;
end;
drop i animals: weights: a b c;
run;
Read the values into 2 arrays, converting the weights from strings into numbers. Use the _N_ variable to figure out which array to populate. At the end of the data set, output the values in the arrays.
I wouldn't give this as an answer to a homework problem that I actually wanted to get a good grade on (because it's far too advanced, so it's obvious you asked for help); but the hash solution is almost certainly the most flexible and what I'd hope someone doing this in the real world would do (assuming there is a 'don't use proc transpose' real world reason, such as available resources). The problem is somewhat undefined, so this is only moderately fault-tolerant.
data have;
input a $ b $ c $;
datalines;
dog cat camel
9 7 2534
;;;;
run;
data _null_;
set have end=eof;
array charvars _character_;
if _n_ = 1 then do;
length animal $15 weight 8;
declare hash h();
h.defineKey('row');
h.defineData('animal','weight');
h.defineDone();
end;
animal=' ';
weight=.;
do row = 1 to dim(charvars);
rc_f = h.find();
if rc_f ne 0 then do;
animal=charvars[row];
rc_a = h.add();
animal=' ';
end;
else if rc_f eq 0 then do;
weight=input(charvars[row],best12.);
rc_r = h.replace();
end;
end;
if eof then rc_o = h.output(dataset:'want');
run;
Do you always have just two rows or is that the no of columns and the rows are dynamic?
If you have a dynamic no of rows and columns, then the ideal way will be to use open function, get the no of columns to a macro variable. This will be the no of rows in your new dataset. Then take the no of rows in your original dataset which will be the no of columns in your new dataset. This must happen before the actual Transpose method. Post this you can read it in to an array and using the macro variables as the dimensions output the values in to the new dataset.
Having said all this, why would you want to re-invent the wheel when you already have the SAS provided ready made transpose function?
Related
I can't find a way to summarize the same variable using different weights.
I try to explain it with an example (of 3 records):
data pippo;
a=10;
wgt1=0.5;
wgt2=1;
wgt3=0;
output;
a=3;
wgt1=0;
wgt2=0;
wgt3=1;
output;
a=8.9;
wgt1=1.2;
wgt2=0.3;
wgt3=0.1;
output;
run;
I tried the following:
proc summary data=pippo missing nway;
var a /weight=wgt1;
var a /weight=wgt2;
var a /weight=wgt3;
output out=pluto (drop=_freq_ _type_) sum()=;
run;
Obviously it gives me a warning because I used the same variable "a" (I can't rename it!).
I've to save a huge amount of data and not so much physical space and I should construct like 120 field (a0-a6,b0-b6 etc) that are the same variables just with fixed weight (wgt0-wgt5).
I want to store a dataset with 20 columns (a,b,c..) and 6 weight (wgt0-wgt5) and, on demand, processing a "summary" without an intermediate datastep that oblige me to create 120 fields.
Due to the huge amount of data (more or less 55Gb every month) I'd like also not to use proc sql statement:
proc sql;
create table pluto
as select sum(db.a * wgt1) as a0, sum(db.a * wgt1) as a1 , etc.
quit;
There is a "Super proc summary" that can summarize the same field with different weights?
Thanks in advance,
Paolo
I think there are a few options. One is the data step view that data_null_ mentions. Another is just running the proc summary however many times you have weights, and either using ods output with the persist=proc or 20 output datasets and then setting them together.
A third option, though, is to roll your own summarization. This is advantageous in that it only sees the data once - so it's faster. It's disadvantageous in that there's a bit of work involved and it's more complicated.
Here's an example of doing this with sashelp.baseball. In your actual case you'll want to use code to generate the array reference for the variables, and possibly for the weights, if they're not easily creatable using a variable list or similar. This assumes you have no CLASS variable, but it's easy to add that into the key if you do have a single (set of) class variable(s) that you want NWAY combinations of only.
data test;
set sashelp.baseball;
array w[5];
do _i = 1 to dim(w);
w[_i] = rand('Uniform')*100+50;
end;
output;
run;
data want;
set test end=eof;
i = .;
length varname $32;
sumval = 0 ;
sum=0;
if _n_ eq 1 then do;
declare hash h_summary(suminc:'sumval',keysum:'sum',ordered:'a');;
h_summary.defineKey('i','varname'); *also would use any CLASS variable in the key;
h_summary.defineData('i','varname'); *also would include any CLASS variable in the key;
h_summary.defineDone();
end;
array w[5]; *if weights are not named in easy fashion like this generate this with code;
array vars[*] nHits nHome nRuns; *generate this with code for the real dataset;
do i = 1 to dim(w);
do j = 1 to dim(vars);
varname = vname(vars[j]);
sumval = vars[j]*w[i];
rc = h_summary.ref();
if i=1 then put varname= sumval= vars[j]= w[i]=;
end;
end;
if eof then do;
rc = h_summary.output(dataset:'summary_output');
end;
run;
One other thing to mention though... if you're doing this because you're doing something like jackknife variance estimation or that sort of thing, or anything that uses replicate weights, consider using PROC SURVEYMEANS which can handle replicate weights for you.
You can SCORE your data set using a customized SCORE data set that you can generate
with a data step.
options center=0;
data pippo;
retain a 10 b 1.75 c 5 d 3 e 32;
run;
data score;
if 0 then set pippo;
array v[*] _numeric_;
retain _TYPE_ 'SCORE';
length _name_ $32;
array wt[3] _temporary_ (.5 1 .333);
do i = 1 to dim(v);
call missing(of v[*]);
do j = 1 to dim(wt);
_name_ = catx('_',vname(v[i]),'WGT',j);
v[i] = wt[j];
output;
end;
end;
drop i j;
run;
proc print;[enter image description here][1]
run;
proc score data=pippo score=score;
id a--e;
var a--e;
run;
proc print;
run;
proc means stackods sum;
ods exclude summary;
ods output summary=summary;
run;
proc print;
run;
enter image description here
I need some help in trying to execute a comparison of rows within different ID variable groups, all in a single dataset.
That is, if there is any duplicate observation within two or more ID groups, then I'd like to delete the observation entirely.
I want to identify any duplicates between rows of different groups and delete the observation entirely.
For example:
ID Value
1 A
1 B
1 C
1 D
1 D
2 A
2 C
3 A
3 Z
3 B
The output I desire is:
ID Value
1 D
3 Z
I have looked online extensively, and tried a few things. I thought I could mark the duplicates with a flag and then delete based off that flag.
The flagging code is:
data have;
set want;
flag = first.ID ne last.ID;
run;
This worked for some cases, but I also got duplicates within the same value group flagged.
Therefore the first observation got deleted:
ID Value
3 Z
I also tried:
data have;
set want;
flag = first.ID ne last.ID and first.value ne last.value;
run;
but that didn't mark any duplicates at all.
I would appreciate any help.
Please let me know if any other information is required.
Thanks.
Here's a fairly simple way to do it: sort and deduplicate by value + ID, then keep only rows with values that occur only for a single ID.
data have;
input ID Value $;
cards;
1 A
1 B
1 C
1 D
1 D
2 A
2 C
3 A
3 Z
3 B
;
run;
proc sort data = have nodupkey;
by value ID;
run;
data want;
set have;
by value;
if first.value and last.value;
run;
proc sql version:
proc sql;
create table want as
select distinct ID, value from have
group by value
having count(distinct id) =1
order by id
;
quit;
This is my interpretation of the requirements.
Find levels of value that occur in only 1 ID.
data have;
input ID Value:$1.;
cards;
1 A
1 B
1 C
1 D
1 D
2 A
2 C
3 A
3 Z
3 B
;;;;
proc print;
proc summary nway; /*Dedup*/
class id value;
output out=dedup(drop=_type_ rename=(_freq_=occr));
run;
proc print;
run;
proc summary nway;
class value;
output out=want(drop=_type_) idgroup(out[1](id)=) sum(occr)=;
run;
proc print;
where _freq_ eq 1;
run;
proc print;
run;
A slightly different approach can use a hash object to track the unique values belonging to a single group.
data have; input
ID Value:& $1.; datalines;
1 A
1 B
1 C
1 D
1 D
2 A
2 C
3 A
3 Z
3 B
run;
proc delete data=want;
proc ds2;
data _null_;
declare package hash values();
declare package hash discards();
declare double idhave;
method init();
values.keys([value]);
values.data([value ID]);
values.defineDone();
discards.keys([value]);
discards.defineDone();
end;
method run();
set have;
if discards.find() ne 0 then do;
idhave = id;
if values.find() eq 0 and id ne idhave then do;
values.remove();
discards.add();
end;
else
values.add();
end;
end;
method term();
values.output('want');
end;
enddata;
run;
quit;
%let syslast = want;
I think what you should do is:
data want;
set have;
by ID value;
if not first.value then flag = 1;
else flag = 0;
run;
This basically flags all occurrences of a value except the first for a given ID.
Also I changed want and have assuming you create what you want from what you have. Also I assume have is sorted by ID value order.
Also this will only flag 1 D above. Not 3 Z
Additional Inputs
Can't you just do a sort to get rid of the duplicates:
proc sort data = have out = want nodupkey dupout = not_wanted;
by ID value;
run;
So if you process the observations by VALUE levels (instead of by ID levels) then you just need keep track of whether any ID is ever different than the first one.
data want ;
do until (last.value);
set have ;
by value ;
if first.value then first_id=id;
else if id ne first_id then remapped=1;
end;
if not remapped;
keep value id;
run;
I would like to turn the following long dataset:
data test;
input Id Injury $;
datalines;
1 Ankle
1 Shoulder
2 Ankle
2 Head
3 Head
3 Shoulder
;
run;
Into a wide dataset that looks like this:
ID Ankle Shoulder Head
1 1 1 0
2 1 0 1
3 0 1 1'
This answer seemed the most relevant but was falling over at the proc freq stage (my real dataset is around 1 million records, and has around 30 injury types):
Creating dummy variables from multiple strings in the same row
Additional help: https://communities.sas.com/t5/SAS-Statistical-Procedures/Possible-to-create-dummy-variables-with-proc-transpose/td-p/235140
Thanks for the help!
Here's a basic method that should work easily, even with several million records.
First you sort the data, then add in a count to create the 1 variable. Next you use PROC TRANSPOSE to flip the data from long to wide. Then fill in the missing values with a 0. This is a fully dynamic method, it doesn't matter how many different Injury types you have or how many records per person. There are other methods that are probably shorter code, but I think this is simple and easy to understand and modify if required.
data test;
input Id Injury $;
datalines;
1 Ankle
1 Shoulder
2 Ankle
2 Head
3 Head
3 Shoulder
;
run;
proc sort data=test;
by id injury;
run;
data test2;
set test;
count=1;
run;
proc transpose data=test2 out=want prefix=Injury_;
by id;
var count;
id injury;
idlabel injury;
run;
data want;
set want;
array inj(*) injury_:;
do i=1 to dim(inj);
if inj(i)=. then inj(i) = 0;
end;
drop _name_ i;
run;
Here's a solution involving only two steps... Just make sure your data is sorted by id first (the injury column doesn't need to be sorted).
First, create a macro variable containing the list of injuries
proc sql noprint;
select distinct injury
into :injuries separated by " "
from have
order by injury;
quit;
Then, let RETAIN do the magic -- no transposition needed!
data want(drop=i injury);
set have;
by id;
format &injuries 1.;
retain &injuries;
array injuries(*) &injuries;
if first.id then do i = 1 to dim(injuries);
injuries(i) = 0;
end;
do i = 1 to dim(injuries);
if injury = scan("&injuries",i) then injuries(i) = 1;
end;
if last.id then output;
run;
EDIT
Following OP's question in the comments, here's how we could use codes and labels for injuries. It could be done directly in the last data step with a label statement, but to minimize hard-coding, I'll assume the labels are entered into a sas dataset.
1 - Define Labels:
data myLabels;
infile datalines dlm="|" truncover;
informat injury $12. labl $24.;
input injury labl;
datalines;
S460|Acute meniscal tear, medial
S520|Head trauma
;
2 - Add a new query to the existing proc sql step to prepare the label assignment.
proc sql noprint;
/* Existing query */
select distinct injury
into :injuries separated by " "
from have
order by injury;
/* New query */
select catx("=",injury,quote(trim(labl)))
into :labls separated by " "
from myLabels;
quit;
3 - Then, at the end of the data want step, just add a label statement.
data want(drop=i injury);
set have;
by id;
/* ...same as before... */
* Add labels;
label &labls;
run;
And that should do it!
I have a dataset looks like the following:
Name Number
a 1
b 2
c 9
d 6
e 5.5
Total ???
I want to calculate the sum of variable Number and record the sum in the last row (corresponding with Name = 'total'). I know I can do this using proc means then merge the output backto this file. But this seems not very efficient. Can anyone tell me whether there is any better way please.
you can do the following in a dataset:
data test2;
drop sum;
set test end = last;
retain sum;
if _n_ = 1 then sum = 0;
sum = sum + number;
output;
if last then do;
NAME = 'TOTAL';
number = sum;
output;
end;
run;
it takes just one pass through the dataset
It is easy to get by report procedure.
data have;
input Name $ Number ;
cards;
a 1
b 2
c 9
d 6
e 5.5
;
proc report data=have out=want(drop=_:);
rbreak after/ summarize ;
compute after;
name='Total';
endcomp;
run;
The following code uses the DOW-Loop (DO-Whitlock) to achieve the result by reading through the observations once, outputting each one, then lastly outputting the total:
data want(drop=tot);
do until(lastrec);
set have end=lastrec;
tot+number;
output;
end;
name='Total';
number=tot;
output;
run;
For all of the data step solutions offered, it is important to keep in mind the 'Length' factor. Make sure it will accommodate both 'Total' and original values.
proc sql;
select max(5,length) into :len trimmed from dictionary.columns WHERE LIBNAME='WORK' AND MEMNAME='TEST' AND UPCASE(NAME)='NAME';
QUIT;
data test2;
length name $ &len;
set test end=last;
...
run;
I have a data set with 3 observations, 1 2 3
4 5 6
7 8 9 , now i have to interchange 1 2 3 and 7 8 9.
How can do this in base sas?
If you just want to sort your dataset by a variable in descending order, use proc sort:
data example;
input number;
datalines;
123
456
789
;
run;
proc sort data = example;
by descending number;
run;
If you want to re-order a dataset in a more complex way, create a new variable containing the position that you want each row to be in, and then sort it by that variable.
If you want to swap the contents of the first and last observations while leaving the rest of the dataset in place, you could do something like this.
data class;
set sashelp.class;
run;
data firstobs;
i = 1;
set sashelp.class(obs = 1);
run;
data lastobs;
i = nobs;
set sashelp.class nobs = nobs point = nobs;
output;
stop;
run;
data transaction;
set lastobs firstobs;
/*Swap the values of i for first and last obs*/
retain _i;
if _n_ = 1 then do;
_i = i;
i = 1;
end;
if _n_ = 2 then i = _i;
drop _i;
run;
data class;
set transaction(keep = i);
modify class point = i;
set transaction;
run;
This modifies just the first and last observations, which should be quite a bit faster than sorting or replacing a large dataset. You can do a similar thing with the update statement, but that only works if your dataset is already sorted / indexed by a unique key.
By Sandeep Sharma:sandeep.sharmas091#gmail.com
data testy;
input a;
datalines;
1
2
3
4
5
6
7
8
9
;
run;
data ghj;
drop y;
do i=nobs-2 to nobs;
set testy point=i nobs=nobs;
output;
end;
do n=4 to nobs-3;
set testy point=n;
output;
end;
do y=1 to 3;
set testy;
output;
end;
stop;
run;