### SAS Learning Module Collapsing across observations, advanced

#### 1. Introduction

This module illustrates how to collapse across variables using retained variables. First, let's read in a sample dataset named kids which includes the variables famid (family id) and wt (kids weight in pounds).

DATA kids;
LENGTH kidname $4 sex$ 1;
INPUT famid kidname birth age wt sex ;
CARDS;
1 Beth 1  9  60  f
1 Bob  2  6  40  m
1 Barb 3  3  20  f
2 Andy 1  8  80  m
2 Al   2  6  50  m
2 Ann  3  2  20  f
3 Pete 1  6  60  m
3 Pam  2  4  40  f
3 Phil 3  2  20  m
;
RUN;

PROC PRINT DATA=kids;
RUN;

The output is shown below.

OBS    KIDNAME    SEX    FAMID    BIRTH    AGE    WT
1      Beth       f       1        1       9     60
2      Bob        m       1        2       6     40
3      Barb       f       1        3       3     20
4      Andy       m       2        1       8     80
5      Al         m       2        2       6     50
6      Ann        f       2        3       2     20
7      Pete       m       3        1       6     60
8      Pam        f       3        2       4     40
9      Phil       m       3        3       2     20

#### 2. Computing a running total with implicitly retained variables

There are times when a running total for a particular variable is desired. For example, suppose that a variable representing the running total of the weights for each person in the dataset needs to be computed. This can be done by using implicitly retained variables in a data step. In the example below, the implicitly retained variable is sumwt, where the weight of the current observation (wt) is added to the last value of sumwt. This results in a new total for each observation. This is why it is called a running total, because the value of sumwt at each observation is the sum of all the previous observations plus the current observation, NOT the sum of ALL observations in the dataset. The value of sumwt at the last observation, however, IS the sum for ALL observations in the dataset, because it is adding the sum of all the previous observations, plus its own value, and hence is the sum across ALL observations in the dataset.

DATA sum ;
SET kids ;

sumwt + wt ;

RUN;

PROC PRINT DATA=sum;
VAR famid wt sumwt ;
RUN;

The output is shown below.

OBS    FAMID    WT    SUMWT
 1       1      60      60
2       1      40     100
3       1      20     120
4       2      80     200
5       2      50     250
6       2      20     270
7       3      60     330
8       3      40     370
9       3      20     390

#### 3. Computing a running count and average with implicitly retained variables

Implicitly retained variables can also be used to keep a running count. Hence, if one has the running total, and the running count, the running mean then is simply the quotient of the two. Below is an example that computes the running total as sumwt, the running count as the variable cnt, and the variable meanwt, which is equal to the sumwt divided by cnt. Note that meanwt is not retained because it has an equals sign in its formula AND it is not declared as a retained variable on a RETAIN statement. The variables sumwt and cnt are retained (implicitly) because there is no equals sign, and the terms 'sumwt + wt' and 'cnt + 1' implicitly declare the variables sumwt and cnt as retained variables, which will be used as counters at each observation.

DATA sum2 ;
SET kids ;

sumwt + wt ;
cnt + 1 ;
meanwt = sumwt / cnt ;

RUN;

PROC PRINT DATA=sum2 ;
VAR famid wt sumwt cnt meanwt ;
RUN;

The output is shown below.

OBS    FAMID    WT    SUMWT    CNT     MEANWT

1       1      60      60      1     60.0000
2       1      40     100      2     50.0000
3       1      20     120      3     40.0000
4       2      80     200      4     50.0000
5       2      50     250      5     50.0000
6       2      20     270      6     45.0000
7       3      60     330      7     47.1429
8       3      40     370      8     46.2500
9       3      20     390      9     43.3333

#### 4. Computing a running total using first. variables

This section achieves the same goal as the above section, but uses a different approach. Here the implicitly retained variables sumwt and cnt are initialized to zero for the first observation within each family. This is what the first.famid variable is used for. If the current observation is the first observation within a family, then sumwt and cnt are set to zero, and the observations that follow within each family have sumwt and cnt defined by the terms 'sumwt + wt' and 'cnt + 1', each being a function of the previous observations value for sumwt and cnt. Note that the variable first.famid exists only because famid was declared with the BY statement.

DATA sum3 ;
SET kids ;
BY famid ;

* this resets the running total to 0 at the start of a family ;

IF first.famid THEN
DO;
sumwt = 0;
cnt   = 0;
END;

sumwt + wt ;
cnt + 1 ;
meanwt = sumwt / cnt ;
RUN;

PROC PRINT DATA=sum3 ;
VAR famid wt sumwt cnt meanwt ;
RUN;

The output is shown below.

OBS    FAMID    WT    SUMWT    CNT    MEANWT

1       1      60      60      1       60
2       1      40     100      2       50
3       1      20     120      3       40
4       2      80      80      1       80
5       2      50     130      2       65
6       2      20     150      3       50
7       3      60      60      1       60
8       3      40     100      2       50
9       3      20     120      3       40

#### 5. Outputting observations using last. variables

This next section is almost identical to the above section, except that here ONLY the last observation within each family is outputted to the dataset sum4. This is what the variable last.famid is used for. Note (again) that the variables first.famid and last.famid only exist because famid was declared with the by statement. Lastly, only the variables famid, sumwt, cnt and meanwt are kept in the dataset sum4. This is achieved using the keep statement followed by the list of variables one wants to keep.

DATA sum4 ;
SET kids ;

BY famid ;

IF first.famid THEN
DO;
sumwt = 0;
cnt   = 0;
END;

sumwt + wt ;
cnt + 1 ;
meanwt = sumwt / cnt ;

IF last.famid THEN
DO;
OUTPUT;
END;

KEEP famid sumwt cnt meanwt ;

RUN;

PROC PRINT DATA=sum4 ;
RUN;

The output is shown below.

OBS    FAMID    SUMWT    CNT    MEANWT
1       1       120      3       40
2       2       150      3       50
3       3       120      3       40

#### 6. Computing a running total with explicitly retained variables

In the above sections, all retained variables were implicitly declared with the terms 'sumwt + wt' and 'cnt + 1'. retained variables can also be explicitly declared using the retain statement. In the example below notice that the variables sumwt and cnt are listed in the retain statement. Moreover, notice that the terms 'sumwt + wt' and 'cnt + 1' have been replaced with the equations 'sumwt = sumwt + wt' and 'cnt = cnt + 1'. When variables are declared as retained variables, explicitly, the counter equations must by given. However, when variables are declared as retained variables implicitly, ONLY the terms on the right side of the counter equations are required.

DATA sum5 ;
SET kids ;
BY famid ;

RETAIN sumwt cnt ;

IF first.famid THEN
DO;
sumwt = 0;
cnt   = 0;
END;

sumwt = sumwt + wt ;
cnt = cnt + 1 ;
meanwt = sumwt / cnt ;

IF last.famid THEN OUTPUT;

KEEP famid sumwt cnt meanwt ;

RUN;

PROC PRINT DATA=sum5 ;
RUN;

The output is shown below.

OBS    FAMID    SUMWT    CNT    MEANWT
1       1       120      3       40
2       2       150      3       50
3       3       120      3       40

#### 7. Sorting data before collapsing across observations

All of the previous sections have worked on the assumption that the data are sorted by famid, which is true of the sample dataset kids defined in section 1. However, if this is not the case, and the data are not sorted by famid, then the results of a counter may be incorrect. Additionally, in some instances, you may need to temporarily sort a dataset, but you may not want to sort the main data file. The example below sorts the dataset kids with proc sort and names the sorted output dataset sortkids. The dataset sum6 then uses the dataset sortkids instead of the kids dataset.

PROC SORT DATA=kids OUT=sortkids ;
BY famid ;
RUN ;

DATA sum6 ;
SET sortkids ;
RETAIN sumwt cnt ;

BY famid ;

IF first.famid THEN
DO;
sumwt = 0;
cnt   = 0;
END;

sumwt = sumwt + wt ;
cnt = cnt + 1 ;
meanwt = sumwt / cnt ;

IF last.famid THEN OUTPUT;

KEEP famid sumwt cnt meanwt ;

RUN;

PROC PRINT DATA=sum6 ;
RUN;

The output is shown below.

OBS    FAMID    SUMWT    CNT    MEANWT
1       1       120      3       40
2       2       150      3       50
3       3       120      3       40

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