I am working in SAS with a dataset with a lot of numeric values which I have standardised as follows:
proc standard data=df mean=0 std=1
out=df;
run;
Is there any easy way to deal with outliers (+/- 3standard deviation) for all numeric values? Ideally I would want to change all of those to + or - 3x standard deviation, or in worst case remove them.
You have to run through the data twice. There are may ways you can adjust your output. Here's a simple way using a datastep:
Assuming your dataset has a standardized variable called 'test':
Data adjusted;
set df;
if test > 3 then test=3;
if test < -3 then test =-3;
run;
just remember your new dataset will no longer have a mean of 0 and a standard deviation of 1
No sample data provided so I generated 5 random variables with N(0,2) distribution for the purpose of demonstration for removing outliers from N(0,1).
If you have multiple columns to remove outliers from, you could create a macro or just loop through an array.
DATA have;
INPUT var1 var2 var3 var4 var5;
DATALINES;
-0.8458048655231136 -2.1737985573160485 -2.122482432573275 1.8746296707673902 -2.799009287469253
-1.9927731684115295 1.8230096873238637 0.5964656531490122 -1.6465532407305106 3.9430012045284184
0.0294083016125659 1.3877418982525658 -1.3398372120124733 -0.8195179339297752 4.742490300459201
-0.5215716306745832 -3.35412129416837 1.1558155344985737 -1.0073681302151822 2.425914724408619
-2.817574234024364 3.5161858163738424 -2.1822454739704744 0.060674570200235534 0.25898913069677443
-3.941905381717187 4.969013776451821 2.021891632999466 -1.1526212617289868 1.2864391876960568
;
run;
* variable of all columns to remove outliers from ;
%LET column_names=var1 var2 var3 var4 var5;
DATA want;
SET have;
ARRAY columns {*} &column_names.;
DO i=1 to dim(columns);
if columns[i]>3 then columns[i]=3;
if columns[i]<-3 then columns[i]=-3;
END;
DROP i;
RUN;
Related
I just performed the fisher test in R and in Excel on a 2x2 table with the contents 1 6 and 7 2. I can't manage to do this in sas.
data my_table;
input var1 var2 ##;
datalines;
1 6 7 2
;
proc freq data=my_table;
tables var1*var2 / fisher;
run;
The test somehow ignores that the table consists of the 4 variables but when I print the table it looks fine. In the test the contents of the table are 0, 1, 1, 0. I guess I need to change something when creating the data but what?
You do NOT have two variables that each have two categories.
Read the data in this way instead.
data have ;
do var1=1,2 ; do var2=1,2;
input count ##;
output;
end; end;
datalines;
1 6 7 2
;
Now VAR1 and VAR2 both have two possible values and COUNT has the number of cases for the particular combination. Use the WEIGHT statement to tell PROC FREQ to use COUNT as the number of cases.
proc freq data=have ;
weight count ;
tables var1*var2 / fisher ;
run;
I have a data set in SAS that has multiple columns that have missing data. This post replaces all the missing values in the entire data set with zeros. But since it goes through the entire data set you can't just replace the zero with the mean or median for that column. How do I replace missing data with the mean of that column?
There are only 5 or so columns so the script doesn't need to go through the entire data set.
PROC STDIZE has an option to do just this. The REPONLY option tells it you want it to only replace missing values, and METHOD=MEAN tells it how you want to replace those values. (PROC EXPAND also could be used, if you are using time series data, but if you're just using mean, STDIZE is the simpler one.)
For example:
data missing_class;
set sashelp.class;
if _N_=5 then call missing(age);
if _N_=7 then call missing(height);
if _N_=9 then call missing(weight);
run;
proc stdize data=missing_class out=imputed_class
method=mean reponly;
var age height weight;
run;
Ideally, you would want to use PROC MI to do multiple imputation and get a more accurate representation of missing values; however, if you wish to use the average, and alternate way of doing so can be done with PROC MEANS and a data step.
/* Set up data */
data have(index=(sex) );
set sashelp.class;
if(_N_ IN(3,7,9,12) ) then call missing(height);
run;
/* Calculate mean of all non-missing values */
proc means data=have noprint;
by sex;
output out=means mean(height) = imp_height;
run;
/* Merge avg. values with original data */
data want;
merge have
means;
by sex;
if(missing(height) ) then height = imp_height;
drop imp_height;
run;
You can use the mean function in proc sql to replace only the missing observations in each column:
data temp;
input var1 var2 var3 var4 var5;
datalines;
. 2 3 4 .
6 7 8 9 10
. 12 . . 15
16 17 18 19 .
21 . 23 24 25
;
run;
proc sql;
create table temp2 as select
case when missing(var1) then mean(var1) else var1 end as var1,
case when missing(var2) then mean(var2) else var2 end as var2,
case when missing(var3) then mean(var3) else var3 end as var3,
case when missing(var4) then mean(var4) else var4 end as var4,
case when missing(var5) then mean(var5) else var5 end as var5
from temp;
quit;
And, as Joe mentioned, you can use coalesce instead if you prefer that syntax:
coalesce(var1, mean(var1)) as var1
I have a large dataset in SAS which I know is almost sorted; I know the first and second levels are sorted, but the third level is not. Furthermore, the first and second levels contain a large number of distinct values and so it is even less desirable to sort the first two columns again when I know it is already in the correct order. An example of the data is shown below:
ID Label Frequency
1 Jon 20
1 John 5
2 Mathieu 2
2 Mathhew 7
2 Matt 5
3 Nat 1
3 Natalie 4
Using the "presorted" option on a proc sort seems to only check if the data is sorted on every key, otherwise it does a full sort of the data. Is there any way to tell SAS that the first two columns are already sorted?
If you've previously sorted the dataset by the first 2 variables, then regardless of the sortedby information on the dataset, SAS will take less CPU time to sort it *. This is a natural property of most decent sorting algorithms - it's much less work to sort something that's already nearly sorted.
* As long as you don't use the force option in the proc sort statement, which forces it to do redundant sorting.
Here's a little test I ran:
option fullstimer;
/*Make sure we have plenty of rows with the same 1 + 2 values, so that sorting by 1 + 2 doesn't imply that the dataset is already sorted by 1 + 2 + 3*/
data test;
do _n_ = 1 to 10000000;
var1 = round(rand('uniform'),0.0001);
var2 = round(rand('uniform'),0.0001);
var3 = round(rand('uniform'),0.0001);
output;
end;
run;
/*Sort by all 3 vars at once*/
proc sort data = test out = sort_all;
by var1 var2 var3;
run;
/*Create a baseline dataset already sorted by 2/3 vars*/
/*N.B. proc sort adds sortedby information to the output dataset*/
proc sort data = test out = baseline;
by var1 var2;
run;
/*Sort baseline by all 3 vars*/
proc sort data = baseline out = sort_3a;
by var1 var2 var3;
run;
/*Remove sort information from baseline dataset (leaving the order of observations unchanged)*/
proc datasets lib = work nolist nodetails;
modify baseline (sortedby = _NULL_);
run;
quit;
/*Sort baseline dataset again*/
proc sort data = baseline out = sort_3b;
by var1 var2 var3;
run;
The relevant results I got were as follows:
SAS took 8 seconds to sort the original completely unsorted dataset by all 3 variables.
SAS took 4 seconds to sort by 3/3 starting from the baseline dataset already sorted by 2/3 variables.
SAS took 4 seconds to sort by 3/3 starting from the same baseline dataset after removing the sort information from it.
The relevant metric from the log output is the amount of user CPU time.
Of course, if the almost-sorted dataset is very large and contains lots of other variables, you may wish to avoid the sort due to the write overhead when replacing it. Another approach you could take would be to create a composite index - this would allow you to do things involving by group processing, for example.
/*Alternative option - index the 2/3 sorted dataset on all 3 vars rather than sorting it*/
proc datasets lib = work nolist nodetails;
/*Replace the sort information*/
modify baseline(sortedby = var1 var2);
run;
/*Create composite index*/
modify baseline;
index create index1 = (var1 var2 var3);
run;
quit;
Creating an index requires a read of the whole dataset, as does the sort, but only a fraction of the work involved in writing it out again, and might be faster than a 2/3 to 3/3 sort in some situations.
Hi another quick question
in proc sql we have on which is used for conditional join is there something similar for sas data step
for example
proc sql;
....
data1 left join data2
on first<value<last
quit;
can we replicate this in sas datastep
like
data work.combined
set data1(in=a) data2(in=b)
if a then output;
run;
You can also can reproduce sql join in one DATA-step using hash objects. It can be really fast but depends on the size of RAM of your machine since this method loads one table into memory. So the more RAM - the larger dataset you can wrap into hash. This method is particularly effective for look-ups in relatively small reference table.
data have1;
input first last;
datalines;
1 3
4 7
6 9
;
run;
data have2;
input value;
datalines;
2
5
6
7
;
run;
data want;
if _N_=1 then do;
if 0 then set have2;
declare hash h(dataset:'have2');
h.defineKey('value');
h.defineData('value');
h.defineDone();
declare hiter hi('h');
end;
set have1;
rc=hi.first();
do while(rc=0);
if first<value<last then output;
rc=hi.next();
end;
drop rc;
run;
The result:
value first last
2 1 3
5 4 7
6 4 7
7 6 9
Yes there is a simple (but subtle) way in just 7 lines of code.
What you intend to achieve is intrinsically a conditional Cartesian join which can be done by a do-looped set statement. The following code use the test dataset from Dmitry and a modified version of the code in the appendix of SUGI Paper 249-30
data data1;
input first last;
datalines;
1 3
4 7
6 9
;
run;
data data2;
input value;
datalines;
2
5
6
7
;
run;
/***** by data step looped SET *****/
DATA CART_data;
SET data1;
DO i=1 TO NN; /*NN can be referenced before set*/
SET data2 point=i nobs=NN; /*point=i - random access*/
if first<value<last then OUTPUT; /*conditional output*/
END;
RUN;
/***** by SQL *****/
proc sql;
create table cart_SQL as
select * from data1
left join data2
on first<value<last;
quit;
One can easily see that the results coincide.
Also note that from SAS 9.2 documentation: "At compilation time, SAS reads the descriptor portion of each data set and assigns the value of the NOBS= variable automatically. Thus, you CAN refer to the NOBS= variable BEFORE the SET statement. The variable is available in the DATA step but is not added to any output data set."
There isn't a direct way to do this with a MERGE. This is one example where the SQL method is clearly superior to any SAS data step methods, as anything you do will take much more code and possibly more time.
However, depending on the data, it's possible a few approaches may make sense. In particular, the format merge.
If data1 is fairly small (even, say, millions of records), you can make a format out of it. Like so:
data fmt_set;
set data1;
format label $8.;
start=first; *set up the names correctly;
end=last;
label='MATCH';
fmtname='DATA1F';
output;
if _n_=1 then do; *put out a hlo='o' line which is for unmatched lines;
start=.; *both unnecessary but nice for clarity;
end=.;
label='NOMATCH';
hlo='o';
output;
end;
run;
proc format cntlin=fmt_set; *import the dataset;
quit;
data want;
set data2;
if put(value,DATA1F.)="MATCH";
run;
This is very fast to run, unless data1 is extremely large (hundreds of millions of rows, on my system) - faster than a data step merge, if you include sort time, since this doesn't require a sort. One major limitation is that this will only give you one row per data2 row; if that is what is desired, then this will work. If you want repeats of data2 then you can't do it this way.
If data1 may have overlapping rows (ie, two rows where start/end overlap each other), you also will need to address this, since start/end aren't allowed to overlap normally. You can set hlo="m" for every row, and "om" for the non-match row, or you can resolve the overlaps.
I'd still do the sql join, however, since it's much shorter to code and much easier to read, unless you have performance issues, or it doesn't work the way you want it to.
Here's another solution, using a temporary array to hold the lookup dataset. Performance is probably similar to Dmitry's hash-based solution, but this should also work for people still using versions of SAS prior to 9.1 (i.e. when hash objects were first introduced).
I've reused Dmitry's sample datasets:
data have1;
input first last;
datalines;
1 3
4 7
6 9
;
run;
data have2;
input value;
datalines;
2
5
6
7
;
run;
/*We need a macro var with the number of obs in the lookup dataset*/
/*This is so we can specify the dimension for the array to hold it*/
data _null_;
if 0 then set have2 nobs = nobs;
call symput('have2_nobs',put(nobs,8.));
stop;
run;
data want_temparray;
array v{&have2_nobs} _temporary_;
do _n_ = 1 to &have2_nobs;
set have2 (rename=(value=value_array));
v{_n_}=value_array;
end;
do _n_ = 1 by 1 until (eof_have1);
set have1 end = eof_have1;
value=.;
do i=1 to &have2_nobs;
if first < v{i} < last then do;
value=v{i};
output;
end;
end;
if missing(value) then output;
end;
drop i value_array;
run;
Output:
value first last
2 1 3
5 4 7
6 4 7
7 6 9
This matches the output from the equivalent SQL:
proc sql;
create table want_sql as
select * from
have1 left join have2
on first<value<last
;
quit;
run;
How can you create a SAS data set from another dataset using only the last n observations from original dataset. This is easy when you know the value of n. If I don't know 'n' how can this be done?
This assumes you have a macro variable that says how many observations you want. NOBS tells you the number of observations in the dataset currently without reading the whole thing.
%let obswant=5;
data want;
set sashelp.class nobs=obscount;
if _n_ gt (obscount-&obswant.);
run;
Using Joe's example of a macro variable to specify the number of observations you want, here is another answer:
%let obswant = 10;
data want;
do _i_=nobs-(&obswant-1) to nobs;
set have point=_i_ nobs=nobs;
output;
end;
stop; /* Needed to stop data step */
run;
This should perform better since it only reads the specific observations you want.
If the dataset is large, you might not want to read the whole dataset. Instead you could try a construction that reads the total number of Observations in the dataset first. So if you want to have the last of observations:
data t;
input x;
datalines;
1
2
3
4
;
%let dsid=%sysfunc(open(t));
%let num=%sysfunc(attrn(&dsid,nlobs));
%let rc=%sysfunc(close(&dsid));
%let number = 2;
data tt;
set t (firstobs = %eval(&num.-&number.+1));
run;
For the sake of variety, here's another approach (not necessarily a better one)
%let obswant=5;
proc sql noprint;
select nlobs-&obswant.+1 into :obscalc
from dictionary.tables
where libname='SASHELP' and upcase(memname)='CLASS';
quit;
data want;
set sashelp.class (firstobs=&obscalc.);
run;
You can achive this using the
_nobs_ and _n_ variables. First, create a temporary variable to store the total no of obs. Then compare the automatic variable N to nobs.
data a;
set sashelp.class nobs=_nobs_;
if _N_ gt _nobs_ -5;
run;