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.
Related
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.
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;
I'm new to SAS, and would greatly appreciate anyone who can help me formulate a code. Can someone please help me with formatting changing arrays based on the first column values?
So basically here's the original data:
Category Name1 Name2......... (Changes invariably)
#ofpeople 20 30
#ofproviders 10 5
#ofclaims 40 25
AmountBilled 50 100
AmountPaid 11 35
AmountDed 5 6
I would like to format the values under Name1 to infinite Name# and reformat them to dollar10.2 for any values under Category called 'AmountBilled','AmountPaid','AmountDed'.
Thank you so much for your help!
You can't conditionally format a column (like you might in excel). A variable/column has one format for the entire column. There are tricks to get around this, but they're invariably more complex than should be considered useful.
You can store the formatted value in a character variable, but it loses the ability to do math.
data have;
input category :$10. name1 name2;
datalines;
#ofpeople 20 30
#ofproviders 10 5
#ofclaims 40 25
AmountBilled 50 100
AmountPaid 11 35
AmountDed 5 6
;;;;
run;
data want;
set have;
array names name:; *colon is wildcard (starts with);
array newnames $10 newname1-newname10; *Arbitrarily 10, can be whatever;
if substr(category,1,6)='Amount' then do;
do _t = 1 to dim(names);
newnames[_t] = put(names[_t],dollar10.2);
end;
end;
run;
You could programmatically figure out the newname1000 endpoint using PROC CONTENTS or SQL's DICTIONARY.COLUMNS / SAS's SASHELP.VCOLUMN. Alternately, you could put out the original dataset as a three column dataset with many rows for each category (was it this way to begin with prior to a PROC TRANSPOSE?) and put the character variable there (not needing an array). To me that's the cleanest option.
data have_t;
set have;
array names name:;
format nameval $10.;
do namenum = 1 to dim(names);
if substr(category,1,6)='Amount' then nameval = put(names[namenum],dollar10.2 -l);
else nameval=put(names[namenum],10. -l); *left aligning here, change this if you want otherwise;
output; *now we have (namenum) rows per line. Test for missing(name) if you want only nonmissing rows output (if not every row has same number of names).
end;
run;
proc transpose data=have_t out=want_T(drop=_name_) prefix=name;
by category notsorted;
var nameval;
run;
Finally, depending on what you're actually doing with this, you may have superior options in terms of the output method. If you're doing PROC REPORT for example, you can use compute blocks to set the style (format) of the column conditionally in the report output.
I've tried googling and I haven't turned up any luck to my current problem. Perhaps someone can help?
I have a dataset with the following variables:
ID, AccidentDate
It's in long format, and each participant can have more than 1 accident, with participants having not necessarily an equal number of accidents. Here is a sample:
Code:
ID AccidentDate
1 1JAN2001
2 4MAY2001
2 16MAY2001
3 15JUN2002
3 19JUN2002
3 05DEC2002
4 04JAN2003
What I need to do is count the number of days between each individuals First and Last recorded accident date. I've been playing around with first.byvariable and last.byvariable commands, but I'm just not making any progress. Any tips? or Any links to a source?
Thank you,
Also. I posted this originally over at Talkstats.com (cross-posting etiquette)
Not sure what you mean by in long format
long format should be like this
id accident date
1 1 1JAN2001
1 2 1JAN2002
2 1 1JAN2001
2 2 1JAN2003
Then you can try proc sql like this
Proc Sql;
select id, max(date)-min(date) from table;
group by id;
run;
By long format I think you mean it is a "stacked" dataset with each person having multiple observations (instead of one row per person with multiple columns). In your situation, it is probably the correct way to have the data stored.
To do it with data steps, I think you are on the right track with first. and last.
I would do it like this:
proc sort data=accidents;
by id date;
run;
data accidents; set accidents;
by id accident; *this is important-it makes first. and last. available for use;
retain first last;
if first.date then first=date;
if last.date then last=date;
run;
Now you have a dataset with ID, Date, Date of First Accident, Date of Last Accident
You could calculate the time between with
data accidents; set accidents;
timebetween = last-first;
run;
You can't do this directly in the same data step since the "last" variable won't be accurate until it has parsed the last line and as such the data will be wrong for anything but the last accident observation.
Assuming the data looks like:
ID AccidentDate
1 1JAN2001
2 4MAY2001
2 16MAY2001
3 15JUN2002
3 19JUN2002
3 05DEC2002
4 04JAN2003
You have the right idea. Retain the first accident date in order to have access to both the first and last dates. Then calculate the difference.
proc sort data=accidents;
by id accidentdate
run;
data accidents;
set accidents;
by id;
retain first_accidentdate;
if first.id then first_accidentdate = accidentdate;
if last.id then do;
daysbetween = date - first_accidentdate
output;
end;
run;
To my disappointment, the following code, which sums up 'value' by week from 'master' for weeks which appear in 'transaction' does not work -
data master;
input week value;
datalines;
1 10
1 20
1 30
2 40
2 40
2 50
3 15
3 25
3 35
;
run;
data transaction;
input change_week ;
datalines;
1
3
;
run;
data _null_;
set transaction;
do until(done);
set master end=done;
where week=change_week;
sum = sum(value, sum);
end;
file print;
put week= sum=;
run;
SAS complains, rightly, because it doesn't see 'change_week' in master and does not know how to operate on it.
Surely there must be a way of doing some operation on a subset of a master set (of course, suitably indexed), given a transaction dataset... Does any one know?
I believe this is the closest answer to what the asker has requested.
This method uses an index on week on the large dataset, allowing for the possibility of invalid week values in the transaction dataset, and without requiring either dataset to be sorted in any particular order. Performance will probably be better if the master dataset is in week order.
For small transaction datasets, this should perform quite a lot better than the other solutions as it only retrieves the required observations from the master dataset. If you're dealing with > ~30% of the records in the master dataset in a single transaction dataset, Quentin's method may sometimes perform better due to the overhead of using the index.
data master(index = (week));
input week value;
datalines;
1 10
1 20
1 30
2 40
2 40
2 50
3 15
3 25
3 35
;
run;
data transaction;
input week ;
datalines;
1
3
4
;
run;
data _null_;
set transaction;
file print;
do until(done);
set master key = week end=done;
/*Prevent implicit retain from previous row if the key isn't found,
or we've read past the last record for the current key*/
if _IORC_ ne 0 then do;
_ERROR_ = 0;
call missing(value);
end;
else sum = sum(value, sum);
end;
put week= sum=;
run;
N.B. for this to work, the indexed variable in the master dataset must have exactly the same name and type as the variable in the transaction dataset. Also, the index must be of the non-unique variety in order to accommodate multiple rows with the same key value.
Also, it is possible to replace the set master... statement with an equivalent modify master... statement if you want to apply transactional changes directly, i.e. without SAS making a massive temp file and replacing the original.
You are correct, there are many ways to do this in SAS. Your example is inefficient because (once we got it working) it would still require a full read of "master" for ever line of "transaction".
(The reason you got the error was because you used where instead of if. In SAS, the sub-setting where in a data step is only aware of columns already existing within the data set it's sub-setting. They keep two options because there where is faster when it's usable.)
An alternative solution would be use proc sql. Hopefully this example is self-explanatory:
proc sql;
select
a.change_week,
sum(b.value) as value
from
transaction as a,
master as b
where a.change_week = b.week
group by change_week;
quit;
I don't suggest below solution (would like #Jeff's SQL solution or even a hash better). But just for playing with data step logic, I think below approach would work, if you trust that every key in transaction will exist in master. It relies on the fact that both datasets are sorted, so only makes one pass of each dataset.
On first iteration of the DATA step, it reads the first record from the transaction dataset, then keeps reading through the master dataset until it finds all the matching records for that key, then the DATA step loop iterates and it does it again for the next transaction record.
1003 data _null_;
1004 set transaction;
1005 by change_week;
1006
1007 do until(last.week and _found);
1008 set master;
1009 by week;
1010
1011 if week=change_week then do;
1012 sum = sum(value, sum);
1013 _found=1;
1014 end;
1015 end;
1016
1017 *file print;
1018 put week= sum= ;
1019 run;
week=1 sum=60
week=3 sum=75