I have two datasets, one extract the extreme values from proc univariate. I would like to create a new variable and label them as 1 if the n in the original dataset equals the extracted line number in the univariate dataset. But I don't know how to program it not manually enter the line number.
There're a few ways to do this, but one easy way is to just add the rownum to the original dataset and merge on it.
Here's an example.
ods output extremeobs=extreme_test;
proc univariate data=sashelp.heart;
run;
ods output close;
data extreme_diastolic extreme_systolic; *just creating the extreme datasets;
set extreme_test;
if varname='Diastolic' then output extreme_diastolic;
else if varname='Systolic' then output extreme_systolic;
run;
data for_merge; *adding rownum on to the original dataset;
set sashelp.heart;
rownum = _n_;
run;
*now, sort the extreme datasets by the `highobs` and `lowobs` values respectively and save those as `rownum`, so they can be merged;
proc sort data=extreme_diastolic out=high_diastolic(keep=highobs rename=highobs=rownum);
by highobs;
run;
proc sort data=extreme_systolic out=high_systolic(keep=highobs rename=highobs=rownum);
by highobs;
run;
proc sort data=extreme_diastolic out=low_diastolic(keep=lowobs rename=lowobs=rownum);
by lowobs;
run;
proc sort data=extreme_systolic out=low_systolic(keep=lowobs rename=lowobs=rownum);
by lowobs;
run;
*now, merge those on using `in=` to identify which are matches.;
data heart_extremes;
merge for_merge high_diastolic(in=_highd) high_systolic(in=_highs) low_diastolic(in=_lowd) low_systolic(in=_lows);
by rownum;
if _highd then high_diastolic = 1;
if _highs then high_systolic = 1;
if _lowd then low_diastolic = 1;
if _lows then low_systolic = 1;
run;
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
Suppose I have these data read into SAS:
I would like to list each unique name and the number of months it appeared in the data above to give a data set like this:
I have looked into PROC FREQ, but I think I need to do this in a DATA step, because I would like to be able to create other variables within the new data set and otherwise be able to manipulate the new data.
Data step:
proc sort data=have;
by name month;
run;
data want;
set have;
by name month;
m=month(lag(month));
if first.id then months=1;
else if month(date)^=m then months+1;
if last.id then output;
keep name months;
run;
Pro Sql:
proc sql;
select distinct name,count(distinct(month(month))) as months from have group by name;
quit;
While it's possible to do this in a data step, you wouldn't; you'd use proc freq or similar. Almost every PROC can give you an output dataset (rather than just print to the screen).
PROC FREQ data=sashelp.class;
tables age/out=age_counts noprint;
run;
Then you can use this output dataset (age_counts) as a SET input to another data step to perform your further calculations.
You can also use proc sql to group the variable and count how many are in that group. It might be faster than proc freq depending on how large your data is.
proc sql noprint;
create table counts as
select AGE, count(*) as AGE_CT from sashelp.class
group by AGE;
quit;
If you want to do it in a data step, you can use a Hash Object to hold the counted values:
data have;
do i=1 to 100;
do V = 'a', 'b', 'c';
output;
end;
end;
run;
data _null_;
set have end=last;
if _n_ = 1 then do;
declare hash cnt();
rc = cnt.definekey('v');
rc = cnt.definedata('v','v_cnt');
rc = cnt.definedone();
call missing(v_cnt);
end;
rc = cnt.find();
if rc then do;
v_cnt = 1;
cnt.add();
end;
else do;
v_cnt = v_cnt + 1;
cnt.replace();
end;
if last then
rc = cnt.output(dataset: "want");
run;
This is very efficient as it is a single loop over the data. The WANT data set contains the key and count values.
I would like to use proq freq to count the number of food types that someone consumed on a specific day(fint variable). My data is in long format with repeated idno for the different food types and different number of interview dates. However SAS hangs and does not run the code. I have more than 300,000 datalines.Is there another way to do this?
proc freq;
tables idno*fint*foodtype / out=countft;
run;
I am a little unsure of your data structure, but proc means can also count.
Assuming that you have multiple dates for each person, and multiple food types for each date, you can use:
data dataset;
set dataset;
count=1;
run;
proc means data=dataset sum;
class idno fint foodtype;
var count;
output out=countft sum=counftpday;
run;
/* Usually you only want the lines with the largest _type_, so keep going here */
proc sql noprint;
select max(_type_) into :want from countft;
quit; /*This grabs the max _type_ from output file */
data countft;
set countft;
where _type_=&want.;
run;
Try a proc sql:
proc sql;
create table want as
select distinct idno, fint, foodtype, count(*) as count
from have
order by 1, 2, 3;
quit;
Worse case scenario, sort and count in a data step.
proc sort data=have;
by idno fint foodtype;
run;
data count;
set have;
by idno fint foodtype;
if first.foodtype then count=1;
else count+1;
if last.foodtype then output;
run;
I have the following problem:
I want to fill missing values with proc expand be simply taking the value from the next data row.
My data looks like this:
date;index;
29.Jun09;-1693
30.Jun09;-1692
01.Jul09;-1691
02.Jul09;-1690
03.Jul09;-1689
04.Jul09;.
05.Jul09;.
06.Jul09;-1688
07.Jul09;-1687
08.Jul09;-1686
09.Jul09;-1685
10.Jul09;-1684
11.Jul09;.
12.Jul09;.
13.Jul09;-1683
As you can see for some dates the index is missing. I want to achieve the following:
date;index;
29.Jun09;-1693
30.Jun09;-1692
01.Jul09;-1691
02.Jul09;-1690
03.Jul09;-1689
04.Jul09;-1688
05.Jul09;-1688
06.Jul09;-1688
07.Jul09;-1687
08.Jul09;-1686
09.Jul09;-1685
10.Jul09;-1684
11.Jul09;-1683
12.Jul09;-1683
13.Jul09;-1683
As you can see the values for the missing data where taken from the next row (11.Jul09 and 12Jul09 got the value from 13Jul09)
So proc expand seems to be the right approach and i started using this code:
PROC EXPAND DATA=DUMMY
OUT=WORK.DUMMY_TS
FROM = DAY
ALIGN = BEGINNING
METHOD = STEP
OBSERVED = (BEGINNING, BEGINNING);
ID date;
CONVERT index /;
RUN;
QUIT;
This filled the gaps but from the previous row and whatever I set for ALIGN, OBSERVED or even sorting the data descending I do not achieve the behavior I want.
If you know how to make it right it would be great if you could give me a hint. Good papers on proc expand are apprechiated as well.
Thanks for your help and kind regards
Stephan
I don't know about proc expand. But apparently this can be done with a few steps.
Read the dataset and create a new variable that will get the value of n.
data have;
set have;
pos = _n_;
run;
Sort this dataset by this new variable, in descending order.
proc sort data=have;
by descending pos;
run;
Use Lag or retain to fill the missing values from the "next" row (After sorting, the order will be reversed).
data want;
set have (rename=(index=index_old));
retain index;
if not missing(index_old) then index = index_old;
run;
Sort back if needed.
proc sort data=want;
by pos;
run;
I'm no PROC EXPAND expert but this is what I came up with. Create LEADS for the maximum gap run (2) then coalesce them into INDEX.
data index;
infile cards dsd dlm=';';
input date:date11. index;
format date date11.;
cards4;
29.Jun09;-1693
30.Jun09;-1692
01.Jul09;-1691
02.Jul09;-1690
03.Jul09;-1689
04.Jul09;.
05.Jul09;.
06.Jul09;-1688
07.Jul09;-1687
08.Jul09;-1686
09.Jul09;-1685
10.Jul09;-1684
11.Jul09;.
12.Jul09;.
13.Jul09;-1683
;;;;
run;
proc print;
run;
PROC EXPAND DATA=index OUT=index2 method=none;
ID date;
convert index=lead1 / transform=(lead 1);
CONVERT index=lead2 / transform=(lead 2);
RUN;
QUIT;
proc print;
run;
data index3;
set index2;
pocb = coalesce(index,lead1,lead2);
run;
proc print;
run;
Modified to work for any reasonable gap size.
data index;
infile cards dsd dlm=';';
input date:date11. index;
format date date11.;
cards4;
27.Jun09;
28.Jun09;
29.Jun09;-1693
30.Jun09;-1692
01.Jul09;-1691
02.Jul09;-1690
03.Jul09;-1689
04.Jul09;.
05.Jul09;.
06.Jul09;-1688
07.Jul09;-1687
08.Jul09;-1686
09.Jul09;-1685
10.Jul09;-1684
11.Jul09;.
12.Jul09;.
13.Jul09;-1683
14.Jul09;
15.Jul09;
16.Jul09;
17.Jul09;-1694
;;;;
run;
proc print;
run;
/* find the largest gap */
data gapsize(keep=n);
set index;
by index notsorted;
if missing(index) then do;
if first.index then n=0;
n+1;
if last.index then output;
end;
run;
proc summary data=gapsize;
output out=maxgap(drop=_:) max(n)=maxgap;
run;
/* Gen the convert statement for LEADs */
filename FT67F001 temp;
data _null_;
file FT67F001;
set maxgap;
do i = 1 to maxgap;
put 'Convert index=lead' i ' / transform=(lead ' i ');';
end;
stop;
run;
proc expand data=index out=index2 method=none;
id date;
%inc ft67f001;
run;
quit;
data index3;
set index2;
pocb = coalesce(index,of lead:);
drop lead:;
run;
proc print;
run;
I'm a beginner in SAS and I have the following problem.
I need to calculate counts and percents of several variables (A B C) from one dataset and save the results to another dataset.
my code is:
proc freq data=mydata;
tables A B C / out=data_out ; run;
the result of the procedure for each variable appears in the SAS output window, but data_out contains the results only for the last variable. How to save them all in data_out?
Any help is appreciated.
ODS OUTPUT is your answer. You can't output directly using the OUT=, but you can output them like so:
ods output OneWayFreqs=freqs;
proc freq data=sashelp.class;
tables age height weight;
run;
ods output close;
OneWayFreqs is the one-way tables, (n>1)-way tables are CrossTabFreqs:
ods output CrossTabFreqs=freqs;
ods trace on;
proc freq data=sashelp.class;
tables age*height*weight;
run;
ods output close;
You can find out the correct name by running ods trace on; and then running your initial proc whatever (to the screen); it will tell you the names of the output in the log. (ods trace off; when you get tired of seeing it.)
Lots of good basic sas stuff to learn here
1) Run three proc freq statements (one for each variable a b c) with a different output dataset name so the datasets are not over written.
2) use a rename option on the out = statement to change the count and percent variables for when you combine the datasets
3) sort by category and merge all datasets together
(I'm assuming there are values that appear in in multiple variables, if not you could just stack the data sets)
data mydata;
input a $ b $ c$;
datalines;
r r g
g r b
b b r
r r r
g g b
b r r
;
run;
proc freq noprint data = mydata;
tables a / out = data_a
(rename = (a = category count = count_a percent = percent_a));
run;
proc freq noprint data = mydata;
tables b / out = data_b
(rename = (b = category count = count_b percent = percent_b));
run;
proc freq noprint data = mydata;
tables c / out = data_c
(rename = (c = category count = count_c percent = percent_c));
run;
proc sort data = data_a; by category; run;
proc sort data = data_b; by category; run;
proc sort data = data_c; by category; run;
data data_out;
merge data_a data_b data_c;
by category;
run;
As ever, there are lots of different ways of doing this sort of thing in SAS. Here are a couple of other options:
1. Use proc summary rather than proc freq:
proc summary data = sashelp.class;
class age height weight;
ways 1;
output out = freqs;
run;
2. Use multiple table statements in a single proc freq
This is more efficient than running 3 separate proc freq statements, as SAS only has to read the input dataset once rather than 3 times:
proc freq data = sashelp.class noprint;
table age /out = freq_age;
table height /out = freq_height;
table weight /out = freq_weight;
run;
data freqs;
informat age height weight count percent;
set freq_age freq_height freq_weight;
run;
This is a question I've dealt with many times and I WISH SAS had a better way of doing this.
My solution has been a macro that is generalized, provide your input data, your list of variables and the name of your output dataset. I take into consideration the format/type/label of the variable which you would have to do
Hope it helps:
https://gist.github.com/statgeek/c099e294e2a8c8b5580a
/*
Description: Creates a One-Way Freq table of variables including percent/count
Parameters:
dsetin - inputdataset
varlist - list of variables to be analyzed separated by spaces
dsetout - name of dataset to be created
Author: F.Khurshed
Date: November 2011
*/
%macro one_way_summary(dsetin, varlist, dsetout);
proc datasets nodetails nolist;
delete &dsetout;
quit;
*loop through variable list;
%let i=1;
%do %while (%scan(&varlist, &i, " ") ^=%str());
%let var=%scan(&varlist, &i, " ");
%put &i &var;
*Cross tab;
proc freq data=&dsetin noprint;
table &var/ out=temp1;
run;
*Get variable label as name;
data _null_;
set &dsetin (obs=1);
call symput('var_name', vlabel(&var.));
run;
%put &var_name;
*Add in Variable name and store the levels as a text field;
data temp2;
keep variable value count percent;
Variable = "&var_name";
set temp1;
value=input(&var, $50.);
percent=percent/100; * I like to store these as decimals instead of numbers;
format percent percent8.1;
drop &var.;
run;
%put &var_name;
*Append datasets;
proc append data=temp2 base=&dsetout force;
run;
/*drop temp tables so theres no accidents*/
proc datasets nodetails nolist;
delete temp1 temp2;
quit;
*Increment counter;
%let i=%eval(&i+1);
%end;
%mend;
%one_way_summary(sashelp.class, sex age, summary1);
proc report data=summary1 nowd;
column variable value count percent;
define variable/ order 'Variable';
define value / format=$8. 'Value';
define count/'N';
define percent/'Percentage %';
run;
EDIT (2022):
Better way of doing this is to use the ODS Tables:
/*This code is an example of how to generate a table with
Variable Name, Variable Value, Frequency, Percent, Cumulative Freq and Cum Pct
No macro's are required
Use Proc Freq to generate the list, list variables in a table statement if only specific variables are desired
Use ODS Table to capture the output and then format the output into a printable table.
*/
*Run frequency for tables;
ods table onewayfreqs=temp;
proc freq data=sashelp.class;
table sex age;
run;
*Format output;
data want;
length variable $32. variable_value $50.;
set temp;
Variable=scan(table, 2);
Variable_Value=strip(trim(vvaluex(variable)));
keep variable variable_value frequency percent cum:;
label variable='Variable'
variable_value='Variable Value';
run;
*Display;
proc print data=want(obs=20) label;
run;
The option STACKODS(OUTPUT) added to PROC MEANS in 9.3 makes this a much simpler task.
proc means data=have n nmiss stackods;
ods output summary=want;
run;
| Variable | N | NMiss |
| ------ | ----- | ----- |
| a | 4 | 3 |
| b | 7 | 0 |
| c | 6 | 1 |