How can I convert my SAS data set, into a data set that I can easily paste into the forum or hand over to someone to replicate my data. Ideally, I'd also like to be able to control the amount of records that are included.
Ie I have sashelp.class in the SASHELP library, but I want to provide it here so others can use it as the starting point for my question.
To do this, you can use a macro written by Mark Jordan at SAS, the code is stored in GitHub as well.
You need to provide the data set name, including library and the number of observations you want to output. It takes them in order. The code will then appear in your SAS log.
*data set you want to create demo data for;
%let dataSetName = sashelp.Class;
*number of observations you want to keep;
%let obsKeep = 5;
******************************************************
DO NOT CHANGE ANYTHING BELOW THIS LINE
******************************************************;
%let source_path = https://gist.githubusercontent.com/statgeek/bcc55940dd825a13b9c8ca40a904cba9/raw/865d2cf18f5150b8e887218dde0fc3951d0ff15b/data2datastep.sas;
filename reprex url "&source_path";
%include reprex;
filename reprex;
option linesize=max;
%data2datastep(dsn=&dataSetName, obs=&obsKeep);
This may not work if you do not have access to the github page, in that case, you can manually navigate to the page (same link) and copy/paste it into SAS. Then run the program and run only the last step, the %data2datastep(dsn=, obs=);
This topic came up recently on SAS Communities and I created a little more robust macro than the one Reeza linked. You can see it in Github: ds2post.sas
* Pull macro definition from GITHUB ;
filename ds2post url
'https://raw.githubusercontent.com/sasutils/macros/master/ds2post.sas'
;
%include ds2post ;
For example if you wanted to share the first 5 observations of SASHELP.CARS you would run this macro call:
%ds2post(sashelp.cars,obs=5)
Which would generate this code to the SAS log:
data work.cars (label='2004 Car Data');
infile datalines dsd dlm='|' truncover;
input Make :$13. Model :$40. Type :$8. Origin :$6. DriveTrain :$5.
MSRP Invoice EngineSize Cylinders Horsepower MPG_City MPG_Highway
Weight Wheelbase Length
;
format MSRP dollar8. Invoice dollar8. ;
label EngineSize='Engine Size (L)' MPG_City='MPG (City)'
MPG_Highway='MPG (Highway)' Weight='Weight (LBS)'
Wheelbase='Wheelbase (IN)' Length='Length (IN)'
;
datalines4;
Acura|MDX|SUV|Asia|All|36945|33337|3.5|6|265|17|23|4451|106|189
Acura|RSX Type S 2dr|Sedan|Asia|Front|23820|21761|2|4|200|24|31|2778|101|172
Acura|TSX 4dr|Sedan|Asia|Front|26990|24647|2.4|4|200|22|29|3230|105|183
Acura|TL 4dr|Sedan|Asia|Front|33195|30299|3.2|6|270|20|28|3575|108|186
Acura|3.5 RL 4dr|Sedan|Asia|Front|43755|39014|3.5|6|225|18|24|3880|115|197
;;;;
Try this little test to compare the two macros.
First make a sample dataset with a couple of issues.
data testit;
set sashelp.class (obs=5);
if _n_=1 then name='Le Bron';
if _n_=2 then age=.;
if _n_=3 then wt=.;
if _n_=4 then name='12;34';
run;
Then run both macros to dump code to the SAS log.
%ds2post(testit);
%data2datastep(dsn=testit,obs=20);
Copy the code from the log. Changing the name in the DATA statements to not overwrite the original dataset or each other. Run them and compare the result to the original.
proc compare data=testit compare=testit1; run;
proc compare data=testit compare=testit2; run;
Result using %DS2POST:
The COMPARE Procedure
Comparison of WORK.TESTIT with WORK.TESTIT1
(Method=EXACT)
Data Set Summary
Dataset Created Modified NVar NObs
WORK.TESTIT 02NOV18:17:09:40 02NOV18:17:09:40 6 5
WORK.TESTIT1 02NOV18:17:10:29 02NOV18:17:10:29 6 5
Variables Summary
Number of Variables in Common: 6.
Observation Summary
Observation Base Compare
First Obs 1 1
Last Obs 5 5
Number of Observations in Common: 5.
Total Number of Observations Read from WORK.TESTIT: 5.
Total Number of Observations Read from WORK.TESTIT1: 5.
Number of Observations with Some Compared Variables Unequal: 0.
Number of Observations with All Compared Variables Equal: 5.
Summary of results using %Data2DataStep:
Comparison of WORK.TESTIT with WORK.TESTIT2
(Method=EXACT)
Data Set Summary
Dataset Created Modified NVar NObs
WORK.TESTIT 02NOV18:17:09:40 02NOV18:17:09:40 6 5
WORK.TESTIT2 02NOV18:17:10:29 02NOV18:17:10:29 6 3
Variables Summary
Number of Variables in Common: 6.
Observation Summary
Observation Base Compare
First Obs 1 1
First Unequal 1 1
Last Unequal 3 3
Last Match 3 3
Last Obs 5 .
Number of Observations in Common: 3.
Number of Observations in WORK.TESTIT but not in WORK.TESTIT2: 2.
Total Number of Observations Read from WORK.TESTIT: 5.
Total Number of Observations Read from WORK.TESTIT2: 3.
Number of Observations with Some Compared Variables Unequal: 3.
Number of Observations with All Compared Variables Equal: 0.
Variable Values Summary
Values Comparison Summary
Number of Variables Compared with All Observations Equal: 1.
Number of Variables Compared with Some Observations Unequal: 5.
Number of Variables with Missing Value Differences: 4.
Total Number of Values which Compare Unequal: 12.
Maximum Difference: 0.
Variables with Unequal Values
Variable Type Len Ndif MaxDif MissDif
Name CHAR 8 1 0
Sex CHAR 1 3 3
Age NUM 8 2 0 2
Height NUM 8 3 0 3
Weight NUM 8 3 0 3
Note that I am sure there are values that will cause trouble for my macro also. But hopefully they are caused by data that is less likely to occur than spaces or semi-colons.
Related
EDIT!!!! GO TO BOTTOM FOR BETTER REPRODUCABLE CODE!
I have a data set with a quantitative variable that's missing 65 values that I need to impute. I used the ODS output and proc glm to simultaneously fit a model for this variable and predict values:
ODS output
predictedvalues=pred_val;
proc glm data=Six_min_miss;
class nyha_4_enroll;
model SIX_MIN_WALK_z= nyha_4_enroll kccq12sf_both_base /p solution;
run;
ODS output close;
However, I am missing 21 predicted values because 21 of my observations are missing either of the two independent predictors.
If SAS can't make a prediction because of this missingness, it leaves an underscore (not a period) to show that it didn't make a prediction.
For some reason, if it can't make a prediction, SAS also puts an underscore for the 'observed' value--even if an observed value is present (the value in the highlighted cell under 'observed' should be 181.0512):
The following code merges the ODS output data set with the observed and predicted values, and the original data. The second data step attempts to create a new 'imputed' version of the variable that will use the original observation if it's not missing, but uses the predicted value if it is missing:
data PT_INFO_6MIN_IMP_temp;
merge PT_INFO pred_val;
drop dependent observation biased residual;
run;
data PT_INFO_6MIN_IMP_temp2;
set PT_INFO_6MIN_IMP_temp;
if missing (SIX_MIN_WALK_z) then observed=predicted;
rename observed=SIX_MIN_WALK_z_IMPUTED;
run;
However, as you can see, SAS is putting an underscore in the imputed column, when there was an original value that should have been used:
In other words, because the original variable values is not missing (it's 181.0512) SAS should have taken that value and copied it to the imputed value column. Instead, it put an underscore.
I've also tried if SIX_MIN_WALK_z =. then observed=predicted
Please let me know what I'm doing wrong and/or how to fix. I hope this all makes sense.
Thanks
EDIT!!!!! EDIT!!!!! EDIT!!!!!
See below for a truncated data set so that one can reproduce what's in the pictures. I took only the first 30 rows of my data set. There are three missing observations for the dependent variable that I'm trying to impute (obs 8, 11, 26). There are one of each of the independent variables missing, such that it can't make a prediction (obs 8 & 24). You'll notice that the "_IMP" version of the dependent variable mirrors the original. When it gets to missing obs #8, it doesn't impute a value because it wasn't able to predict a value. When it gets to #11 and #26, it WAS able to predict a value, so it added the predicted value to "_IMP." HOWEVER, for obs #24, it was NOT able to predict a value, but I didn't need it to, because we already have an observed value in the original variable (181.0512). I expected SAS to put this value in the "_IMP" column, but instead, it put an underscore.
data test;
input Study_ID nyha_4_enroll kccq12sf_both_base SIX_MIN_WALK_z;
cards;
01-001 3 87.5 399.288
01-002 4 83.333333333 411.48
01-003 2 87.5 365.76
01-005 4 14.583333333 0
01-006 3 52.083333333 362.1024
01-008 3 52.083333333 160.3248
01-009 2 56.25 426.72
01-010 4 75 .
01-011 3 79.166666667 156.3624
01-012 3 27.083333333 0
01-013 4 45.833333333 0
01-014 4 54.166666667 .
01-015 2 68.75 317.2968
01-017 3 29.166666667 196.2912
01-019 4 100 141.732
01-020 4 33.333333333 0
01-021 2 83.333333333 222.504
01-022 4 20.833333333 389.8392
01-025 4 0 0
01-029 4 43.75 0
01-030 3 83.333333333 236.22
01-031 2 35.416666667 302.0568
01-032 4 64.583333333 0
01-033 4 33.333333333 0
01-034 . 100 181.0512
01-035 4 12.5 0
01-036 4 66.666666667 .
01-041 4 75 0
01-042 4 43.75 0
01-043 4 72.916666667 0
;
run;
data test2;
set test;
drop Study_ID;
run;
ODS output
predictedvalues=pred_val;
proc glm data=test2;
class nyha_4_enroll;
model SIX_MIN_WALK_z= nyha_4_enroll kccq12sf_both_base /p solution;
run;
ODS output close;
data combine;
merge test2 pred_val;
drop dependent observation biased residual;
run;
data combine_imp;
set combine;
if missing (SIX_MIN_WALK_z) then observed=predicted;
rename observed=SIX_MIN_WALK_z_IMPUTED;
run;
The special missing values (._) mark the observations excluded from the model because of missing values of the independent variables.
Try a simple example:
data class;
set sashelp.class(obs=10) ;
keep name sex age height;
if _n_=3 then age=.;
if _n_=4 then height=.;
run;
ods output predictedvalues=pred_val;
proc glm data=class;
class sex;
model height = sex age /p solution;
run; quit;
proc print data=pred_val; run;
Since for observation #3 the value of the independent variable AGE was missing in the predicted result dataset the values of observed, predicted and residual are set to ._.
Obs Dependent Observation Biased Observed Predicted Residual
1 Height 1 0 69.00000000 64.77538462 4.22461538
2 Height 2 0 56.50000000 58.76153846 -2.26153846
3 Height 3 1 _ _ _
4 Height 4 1 . 61.27692308 .
5 Height 5 0 63.50000000 64.77538462 -1.27538462
6 Height 6 0 57.30000000 59.74461538 -2.44461538
7 Height 7 0 59.80000000 56.24615385 3.55384615
8 Height 8 0 62.50000000 63.79230769 -1.29230769
9 Height 9 0 62.50000000 62.26000000 0.24000000
10 Height 10 0 59.00000000 59.74461538 -0.74461538
If you really want to just replace the values of OBSERVED or PREDICTED in the output with the values of the original variable that is pretty easy to do. Just re-combine with the source dataset. You can use the ID statement of PROC GLM to have it include any variables you want into the output. Like
id name sex age height;
Now you can use a dataset step to make any adjustments. For example to make a new height variable that is either the original or predicted value you could use:
data want ;
set pred_val ;
NEW_HEIGHT = coalesce(height,predicted);
run;
proc print data=want width=min;
var name height age predicted new_height ;
run;
Results:
NEW_
Obs Name Height Age Predicted HEIGHT
1 Alfred 69.0 14 64.77538462 69.0000
2 Alice 56.5 13 58.76153846 56.5000
3 Barbara 65.3 . _ 65.3000
4 Carol . 14 61.27692308 61.2769
5 Henry 63.5 14 64.77538462 63.5000
6 James 57.3 12 59.74461538 57.3000
7 Jane 59.8 12 56.24615385 59.8000
8 Janet 62.5 15 63.79230769 62.5000
9 Jeffrey 62.5 13 62.26000000 62.5000
10 John 59.0 12 59.74461538 59.0000
I want converted my data from long to wide format using data step. The problem is that due to missing values the values are not placed in the correct cells. I think to solve the problem I have to include placeholder for missing values.
The problem is I don't know how to do. Can someone please give me tip on how to go about it.
data tic;
input id country$ month math;
datalines;
1 uk 1 10
1 uk 2 15
1 uk 3 24
2 us 2 15
2 us 4 12
3 fl 1 15
3 fl 2 16
3 fl 3 17
3 fl 4 15
;
run;
proc sort data=tic;
by id;
run;
data tot(drop=month math);
retain month1-month4 math1-math4;
array tat{4} month1-month4;
array kat{4} math1-math4;
set tic;
by id;
if first.id then do;
i=1;
do j=1 to 4;
tat{j}=.;
kat{j}=.;
end;
end;
tat(i)=month;
kat(i)=math;
if last.id then output;
i+1;
run;
Edit
I finally figured out what the problem is:
changed this lines of code
tat(i)=month;
kat(i)=math;
to:
tat(month)=month;
kat(month)=math;
and it fixed the problem.
Data transformations from tall and skinny to short and wide often mean that categorical data ends up as column names. This is a process of moving data to metadata, which can be a problem later on for dealing with BY or CLASS groups.
SAS has Proc TABULATE and Proc REPORT for creating pivoted output. Proc TRANSPOSE is also a good standard way of creating pivoted data.
I did notice that you are pivoting two columns at once. TRANSPOSE can't multi-pivot. The DATA Step approach you showed is a typical way for doing a transpose transform when the indices lie within known ranges. In your case the array declaration must be such that 'direct-addressing' via index can to handle the minimal and maximal month values that occur over all the data.
I am trying to find a quick way to replace missing values with the average of the two nearest non-missing values. Example:
Id Amount
1 10
2 .
3 20
4 30
5 .
6 .
7 40
Desired output
Id Amount
1 10
2 **15**
3 20
4 30
5 **35**
6 **35**
7 40
Any suggestions? I tried using the retain function, but I can only figure out how to retain last non-missing value.
I thinks what you are looking for might be more like interpolation. While this is not mean of two closest values, it might be useful.
There is a nifty little tool for interpolating in datasets called proc expand. (It should do extrapolation as well, but I haven't tried that yet.) It's very handy when making series of of dates and cumulative calculations.
data have;
input Id Amount;
datalines;
1 10
2 .
3 20
4 30
5 .
6 .
7 40
;
run;
proc expand data=have out=Expanded;
convert amount=amount_expanded / method=join;
id id; /*second is column name */
run;
For more on the proc expand see documentation: https://support.sas.com/documentation/onlinedoc/ets/132/expand.pdf
This works:
data have;
input id amount;
cards;
1 10
2 .
3 20
4 30
5 .
6 .
7 40
;
run;
proc sort data=have out=reversed;
by descending id;
run;
data retain_non_missing;
set reversed;
retain next_non_missing;
if amount ne . then next_non_missing = amount;
run;
proc sort data=retain_non_missing out=ordered;
by id;
run;
data final;
set ordered;
retain last_non_missing;
if amount ne . then last_non_missing = amount;
if amount = . then amount = (last_non_missing + next_non_missing) / 2;
run;
but as ever, will need extra error checking etc for production use.
The key idea is to sort the data into reverse order, allowing it to use RETAIN to carry the next_non_missing value back up the data set. When sorted back into the correct order, you then have enough information to interpolate the missing values.
There may well be a PROC to do this in a more controlled way (I don't know anything about PROC STANDARDIZE, mentioned in Reeza's comment) but this works as a data step solution.
Here's an alternative requiring no sorting. It does require IDs to be sequential, though that can be worked around if they're not.
What it does is uses two set statements, one that gets the main (and previous) amounts, and one that sets until the next amount is found. Here I use the sequence of id variables to guarantee it will be the right record, but you could write this differently if needed (keeping track of what loop you're on) if the id variables aren't sequential or in an order of any sort.
I use the first.amount check to make sure we don't try to execute the second set statement more than we should (which would terminate early).
You need to do two things differently if you want first/last rows treated differently. Here I assume prev_amount is 0 if it's the first row, and I assume last_amount is missing, meaning the last row just gets the last prev_amount repeated, while the first row is averaged between 0 and the next_amount. You can treat either one differently if you choose, I don't know your data.
data have;
input Id Amount;
datalines;
1 10
2 .
3 20
4 30
5 .
6 .
7 40
;;;;
run;
data want;
set have;
by amount notsorted; *so we can tell if we have consecutive missings;
retain prev_amount; *next_amount is auto-retained;
if not missing(amount ) then prev_amount=amount;
else if _n_=1 then prev_amount=0; *or whatever you want to treat the first row as;
else if first.amount then do;
do until ((next_id > id and not missing(next_amount)) or (eof));
set have(rename=(id=next_id amount=next_amount)) end=eof;
end;
amount = mean(prev_amount,next_amount);
end;
else amount = mean(prev_amount,next_amount);
run;
Say that my data set has quite a lot of missing/invalid values and I would like to remove (or drop) the entire variable (or column) if it contains too many invalid values.
Take the following example, the variable 'gender' has quite a lot of "#N/A"s. I would like to remove that variable if a certain percentage of the data points in there are "#N/A"s, say more than 50%, more than 30%.
In addition, I would like to make the percentage a configurable value, i.e., I am willing to remove the entire variable if more than x% of the observations under that variable are "#N/A". And I also want to be able to define what an invalid value is, could be "#N/A", could be "Invalid Value", could be " ", could be anything else that I pre-define.
data dat;
input id score gender $;
cards;
1 10 1
1 10 1
1 9 #N/A
1 9 #N/A
1 9 #N/A
1 8 #N/A
2 9 #N/A
2 8 #N/A
2 9 #N/A
2 9 2
2 10 2
;
run;
Please make the solution as generalized as possible. For example, if the real data set contains thousands of variables, I need to be able to loop through all those variables instead of referencing their variable names one by one. Furthermore, the data set could contain more than just "#N/A" as bad values, other things like ".", "Invalid Obs", "N.A." could also exist at the same time.
PS: Actually I thought of a way to make this problem easier. We could probably read in all the data points as numerical values, so that all the "#N/A", "N.A.", " " stuff get turned into ".", which makes the drop criterion easier. Hope that helps you solve this problem for me ...
Update: below is the code I am working on. Got stuck at the last block.
data dat;
input id $ score $ gender $;
cards;
1 10 1
1 10 1
1 9 #N/A
1 9 #N/A
1 9 #N/A
1 8 #N/A
2 9 #N/A
2 8 #N/A
2 9 #N/A
2 9 2
2 10 2
;
run;
proc contents data=dat out=test0(keep=name type) noprint;
/*A DATA step is used to subset the test0 data set to keep only the character */
/*variables and exclude the one ID character variable. A new list of numeric*/
/*variable names is created from the character variable name with a "_n" */
/*appended to the end of each name. */
data test0;
set test0;
if type=2;
newname=trim(left(name))||"_n";
/*The macro system option SYMBOLGEN is set to be able to see what the macro*/
/*variables resolved to in the SAS log. */
options symbolgen;
/*PROC SQL is used to create three macro variables with the INTO clause. One */
/*macro variable named c_list will contain a list of each character variable */
/*separated by a blank space. The next macro variable named n_list will */
/*contain a list of each new numeric variable separated by a blank space. The */
/*last macro variable named renam_list will contain a list of each new numeric */
/*variable and each character variable separated by an equal sign to be used on*/
/*the RENAME statement. */
proc sql noprint;
select trim(left(name)), trim(left(newname)),
trim(left(newname))||'='||trim(left(name))
into :c_list separated by ' ', :n_list separated by ' ',
:renam_list separated by ' '
from test0;
quit;
/*The DATA step is used to convert the numeric values to character. An ARRAY */
/*statement is used for the list of character variables and another ARRAY for */
/*the list of numeric variables. A DO loop is used to process each variable */
/*to convert the value from character to numeric with the INPUT function. The */
/*DROP statement is used to prevent the character variables from being written */
/*to the output data set, and the RENAME statement is used to rename the new */
/*numeric variable names back to the original character variable names. */
data test2;
set dat;
array ch(*) $ &c_list;
array nu(*) &n_list;
do i = 1 to dim(ch);
nu(i)=input(ch(i),8.);
end;
drop i &c_list;
rename &renam_list;
run;
data test3;
set test2;
array myVars(*) &c_list;
countTotal=1;
do i = 1 to dim(myVars);
myCounter = count(.,myVars(i));
/* if sum(countMissing)/sum(countTotal) lt 0.5 then drop VNAME(myVars(i)); */
end;
run;
The problem is, and where I got stuck on, is that I am not able to drop the variables that I want to drop. And the reason is because I do not want to use the variable names in the drop function. Instead, I want it done in a loop where I can reference the variable names with the looper "i". I tried to use the array "myVars(i)" but it doesnt seem to work with the drop function.
My understanding is that SAS processes drop statements during data step compilation, i.e. before it looks at any of the data from any input datasets. Therefore, you cannot use the vname function like that to select variables to drop, as it doesn't evaluate the variable names until the data step has finished compiling and has moved on to execution.
You will need to output a temporary dataset or view containing all your variables, including the ones you don't want, build up a list of variables that you want to drop, in a macro variable, then drop them in a subsequent data step.
Refer to this paper and page 3 in particular for more details of which things run during compilation rather than execution:
http://www.lexjansen.com/nesug/nesug11/ds/ds04.pdf
In general, you'll find this sort of thing simplified using built in procs - this is SAS's bread and butter. You just need to restate the question.
What you want is to drop variables with a % of missing/bad data higher than 50%, so you need a frequency table of variables, right?
So - use PROC FREQ. This is the simplified version (only looks for "#N/A"), but it should be easy to modify the last step to make it look for other values (and to sum up the percents for them). Or, like you'll see in the linked question (from my comment on the question), you can use a special format that puts all invalid values to one formatted value, and all valid values to another formatted value. (You'll have to construct this format.)
Concept: use PROC FREQ to get frequency table, then look at that dataset to find the rows with > 50% of the rows and an invalid value in the F_ column.
This won't work with actual missing (" " or .); you'll need to add the /MISSING option to PROC FREQ if you have those also.
data dat;
input id $ score $ gender $;
cards;
1 10 1
1 10 1
1 9 #N/A
1 9 #N/A
1 9 #N/A
1 8 #N/A
2 9 #N/A
2 8 #N/A
2 9 #N/A
2 9 2
2 10 2
;
run;
*shut off ODS for the moment, and only use ODS OUTPUT, so we do not get a mess in our results window;
ods exclude all;
ods output onewayfreqs=freq_tables;
proc freq data=dat;
tables id score gender;
run;
ods output close;
ods exclude none;
*now we check for variables that match our criteria;
data has_missing;
set freq_tables;
if coalescec(of f_:) ='#N/A' and percent>50;
varname = substr(table,7);
run;
*now we put those into a macro variable to drop;
proc sql;
select varname
into :droplist separated by ' '
from has_missing;
quit;
*and we drop them;
data dat_fixed;
set dat;
drop &droplist.;
run;
I have three data sets of inpatient, outpatient, and professional claims. I want to find the number of unique people who have a claim related to tobacco use (1=yes tobacco, 0=tobacco) in ANY of these three data sets.
Therefore, the data sets pretty much are all:
data inpatient;
input Patient_ID Tobacco;
datalines;
1 0
2 1
3 1
4 1
5 0
;
run;
I am trying to merge the inpatient, outpatient, and professional so that I am left with those patient ids that have a tobacco claim in any of the three data sets using:
data tobaccoall;
merge inpatient outpatient professional;
by rid;
run;
However, it is overwriting some of the 1's with 0's in the new data set. How do I better merge the data sets to find if the patient has a claim in ANY of the datasets?
When you merge data sets in SAS that share variable names, the values from the data set listed on the right in the merge statement overwrite the values from data set to its left. In order to keep each value, you'd want to rename the variables before merging. You can do this in the merge statement by adding a rename= option after each data set.
If you want a single variable that represents whether a tobacco claim exists in any of the three variables, you could create a new variable using the max function to combine the three different values.
data tobaccoall;
merge inpatient (rename=(tobacco=tobacco_in))
outpatient (rename=(tobacco=tobacco_out))
professional (rename=(tobacco=tobacco_pro));
by rid;
tobacco_any = max(tobacco_in,tobacco_out,tobacco_pro,0);
run;
If your data were 1=has .=doesn't have (missing), then you could use the UPDATE statement, which mostly works like Merge except it wouldn't overwrite nonmissing data with missing.
For example:
data inpatient;
input Patient_ID Tobacco;
datalines;
1 .
2 1
3 1
4 1
5 .
;
run;
data outpatient;
input Patient_ID Tobacco;
datalines;
1 1
2 1
3 .
4 .
5 .
;
run;
data want;
update inpatient outpatient;
by patient_id;
run;