trying to uses SAS Label like so, but not working. The output table still uses the inputs as variable names.
thank you
data names2;
input id m1 m2;
label id="id number" m1="first month" m2="second month";
datalines;
001 0 0 0 0
002 0 1 1 0
003 0 1 0 0
004 1 1 0 0
;
run;
Your code is correct, in the menu options you have to select view "Column Labels" instead of the default "Column Names"
Proc PRINT, by default, displays the column names (the variable name) in the column headers. Use the LABEL option to view the labels
Proc PRINT data=have label;
run;
ViewTable, the default, displays column names (the variable name) in the column headers. The menu action View / Column Labels will show the labels in the column headers.
Related
I am generating a report that does a look back over the past 45ish days. The requestor wants the dates as the column headers so I am trying to write a macro loop that just goes through the dates in reverse order to create the columns. I will then write logic to take that rows ID and that columns date to populate the values needed. However, I am having difficulty getting the date variable I have computed turned into the new column header.
The macro loop works and creates the correct date in the variable in each iteration, but how do I take that and make it a new column in the table?
Desired output is this:
ID
Name
08Nov2022
07Nov2022
06Nov2022
1
Cell 2
0
0
0
2
Cell 4
0
0
0
%LET iDayCount=45;
/* Create a new temp table by selecting the values from a permeanent table housing the category IDs, names and details
Call this temp.parent_table*/
%MACRO test;
DATA temp.parent_table;
SET temp.parenet_table;
%LET today=sysfunc(today));
%DO iCounter=0 %TO &iDayCount;
%LET colName=%sysfunc(intnx(day,-&iCounter),date9.);
/* THIS IS WHERE IT GOES OFF THE RAILS */
/* I want to use colName value as a new column in the temp.parent_table*/
&colName = 0;
%END
RUN;
%MEND;
%test;
The log has a note for each iteration:
NOTE: Line generated by the macro variable "COLNAME".
"08NOV2022
Each date in the note is underlined red with the error message:
Error 180-322: Statement is not valid or it is used out of proper order
As always your help is appreciated.
The easiest way to make a report that has date values as column headers is to use PROC REPORT. Store the date values in a variable and use it as an ACROSS variable in the report.
So if you have data like this:
Obs ID Name date value
1 1 Cell2 08NOV2022 1
2 1 Cell2 07NOV2022 2
3 1 Cell2 06NOV2022 3
4 2 Cell4 08NOV2022 4
5 2 Cell4 07NOV2022 5
6 2 Cell4 06NOV2022 6
You can make your report using code like this:
proc report ;
columns id name value,date ;
define id/group;
define name/group;
define value / sum ' ';
define date / across order=internal descending ' ';
run;
Result:
I have table in SAS Enterprise Guide like below:
ID - numeric
COL1 - numeric
ID
COL1
123
1
123
0
123
0
444
1
444
0
778
0
And I need to aggregate above table and create column 'TARGET':
TARGET = 1 for these IDs which at least once have '1' in the column 'COL1'
TARGET = 0 for these IDs which have never had a '1' in the column 'COL1'
Moreover I need to delete duplicates in ID, so as a result i need something like below :
ID
TARGET
123
1 -> had '1' at least once in COL1
444
1 -> had '1' at least once in COL1
778
0 -> never had '1' in COL1
How can I do that in SAS Enterprise Guide in normal SAS code or in PROC SQL ?
Your logic is equivalent to taking the maximum value of the column for each ID.
proc sql;
create table want as
select ID, max(col1) as Target
from have
group by ID;
quit;
You can use a DOW loop over the group, computing a repeated OR result.
Example:
data want;
do until (last.id);
set have;
by id;
if not target then
target = target or col1;
end;
run;
I created a report of the following form:
ID VAR1
VAR2
111 1
2
3
4
5
6
222 1
2
I need to follow a requirement that if a page break appears inside the ID block, then the ID value must be displayed on the next page. The following form is not acceptable:
ID VAR1
VAR2
111 1
2
3
4
-----PAGE BREAK----
5
6
222 1
2
The page break must not occur between VAR1 and VAR2, either:
VAR2
111 1
2
3
-------PAGE BREAK--------
4
5
6
222 1
2
The report should look like this:
ID VAR1
VAR2
111 1
2
3
4
-------PAGE BREAK-----
111 5
6
222 1
2
The question is - how to obtain the result? I don't want to present each ID on a separate page because unique ID blocks differ in length. So there is no simple solution like creating page break variable with different values for different IDs. I would like to avoid modifying any variables (except grouping/sorting variables) in the dataset I feed into proc report.
I would appreciate any input on this. Thanks.
You need to use the spanrows option, like is shown in this paper from PharmaSUG 2011 - Beyond the Basics: Advanced REPORT Procedure Tips and Tricks
Updated for SAS® 9.2. You don't share your code, but it goes on the PROC REPORT line. Here's the example from the paper:
data spanrows_example;
set sashelp.class
sashelp.class
sashelp.class;
run;
ods pdf file='c:\spanrows.pdf';
proc report nowd data= spanrows_example spanrows;
col sex age name height weight;
define sex / order;
run;
ods pdf close;
You can't necessarily get what you want as far as var1/var2, though, without forcing a page break if you're close to a page (which is quite challenging to calculate accurately).
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
Using PROC REPORT in SAS, if a certain ACROSS variable has 5 different value possibilities (for example, 1 2 3 4 5), but in my data set there are no observations where that variable is equal to, say, 5, how can I get the report to show the column for 5 and display 0 for the # of observations having that value?
Currently my PROC REPORT output is just not displaying those value columns that have no observations.
When push comes to shove, you can do some hacks like this. Notice that there are no missing on SEX variable of the SASHELP.CLASS:
proc format;
value $sex 'F' = 'female' 'M' = 'male' 'X' = 'other';
run;
options missing=0;
proc report data=sashelp.class nowd ;
column age sex;
define age/ group;
define sex/ across format=$sex. preloadfmt;
run;
options missing=.;
/*
Sex
Age female male other
11 1 1 0
12 2 3 0
13 2 1 0
14 2 2 0
15 2 2 0
16 0 1 0
*/