In this chapter, you learn to

Descriptive statistics such as the mean, minimum, and maximum provide useful information about numeric data. The MEANS procedure provides these and other data summarization tools, as well as helpful options for controlling your output.

Procedure Syntax

The MEANS procedure can include many statements and options for specifying needed statistics. For simplicity, let's consider the procedure in its basic form.

General form, basic MEANS procedure:

  PROC MEANS <DATA=SAS-data-set>

        <statistic-keyword(s)><option(s)>;

  RUN;

where

• SAS-data-set is the name of the data set to be used

• statistic-keyword(s) specify the statistics to compute

• option(s) control the content, analysis, and appearance of output.

In its simplest form, PROC MEANS prints the n-count (number of nonmissing values), the mean, the standard deviation, and the minimum and maximum values of every numeric variable in a data set.

proc means data=perm.survey;
run;

The default statistics that the MEANS procedure produces (n-count, mean, standard deviation, minimum, and maximum) are not always the ones that you need. You might prefer to limit output to the mean of the values. Or you might need to compute a different statistic, such as the median or range of the values.

To specify statistics, include statistic keywords as options in the PROC MEANS statement. When you specify a statistic in the PROC MEANS statement, default statistics are not produced. For example, to see the median and range of Perm.Survey numeric values, add the MEDIAN and RANGE keywords as options.

proc means data=perm.survey median range;
run;

The following keywords can be used with PROC MEANS to compute statistics:

By default, PROC MEANS output automatically uses the BEST w. format to display numeric values in the report.

The BESTw. format is the default format that SAS uses for writing numeric values. When there is no format specification, SAS chooses the format that provides the most information about the value according to the available field width. At times, this can result in unnecessary decimal places, making your output hard to read.

proc means data=clinic.diabetes min max;
run;

To limit decimal places, use the MAXDEC= option in the PROC MEANS statement, and set it equal to the length that you prefer.

proc means data=clinic.diabetes min max maxdec=0;
run;

By default, the MEANS procedure generates statistics for every numeric variable in a data set. But you'll typically want to focus on just a few variables, particularly if the data set is large. It also makes sense to exclude certain types of variables. The values of ID, for example, are unlikely to yield useful statistics.

To specify the variables that PROC MEANS analyzes, add a VAR statement and list the variable names.

proc means data=clinic.diabetes min max maxdec=0;
   var age height weight;
run;

In addition to listing variables separately, you can use a numbered range of variables.

proc means data=perm.survey mean stderr maxdec=2;
   var item1-item5;
run;

You will often want statistics for grouped observations, instead of for observations as a whole. For example, census numbers are more useful when grouped by region than when viewed as a national total. To produce separate analyses of grouped observations, add a CLASS statement to the MEANS procedure.

PROC MEANS does not generate statistics for CLASS variables, because their values are used only to categorize data. CLASS variables can be either character or numeric, but they should contain a limited number of discrete values that represent meaningful groupings.

The output of the program shown below is categorized by values of the variables Survive and Sex. The order of the variables in the CLASS statement determines their order in the output table.

proc means data=clinic.heart maxdec=1;
   var arterial heart cardiac urinary;
   class survive sex;
run;

Like the CLASS statement, the BY statement specifies variables to use for categorizing observations.

But BY and CLASS differ in two key ways:

proc sort data=clinic.heart out=work.heartsort;
   by survive sex;
run;
proc means data=work.heartsort maxdec=1;
   var arterial heart cardiac urinary;
   by survive sex;
run;

You might want to create an output SAS data set that contains just the summarized variable. You can do this by using the OUTPUT statement in PROC MEANS.

When you use the OUTPUT statement, the summary statistics N, MEAN, STD, MIN, and MAX are produced for all of the numeric variables or for all of the variables that are listed in a VAR statement by default. To specify which statistics to produce, use the STATISTIC= option.

Specifying the STATISTIC= option

You can specify which statistics to produce in the output data set. To do so, you must specify the statistic and then list all of the variables. The variables must be listed in the same order as in the VAR statement. You can specify more than one statistic in the OUTPUT statement.

The following program creates a typical PROC MEANS report and also creates a summarized output data set.

proc means data=clinic.diabetes;
   var age height weight;
   class sex;
   output out=work.sum_gender
      mean=AvgAge AvgHeight AvgWeight
      min=MinAge MinHeight MinWeight;
run;

To see the contents of the output data set, submit the following PROC PRINT step.

proc print data=work.sum_gender;
run;

proc means data=clinic.diabetes noprint;
   var age height weight;
   class sex;
   output out=work.sum_gender
      mean=AvgAge AvgHeight AvgWeight;
run;

You can also create a summarized output data set by using PROC SUMMARY. When you use PROC SUMMARY, you use the same code to produce the output data set that you would use with PROC MEANS.

The difference between the two procedures is that PROC MEANS produces a report by default (remember that you can use the NOPRINT option to suppress the default report). By contrast, to produce a report in PROC SUMMARY, you must include a PRINT option in the PROC SUMMARY statement.

Example

The following example creates an output data set but does not create a report:

proc summary data=clinic.diabetes;
   var age height weight;
   class sex;
   output out=work.sum_gender
      mean=AvgAge AvgHeight AvgWeight;
run;

If you placed a PRINT option in the PROC SUMMARY statement above, this program would produce the same report as if you replaced the word SUMMARY with MEANS.

proc summary data=clinic.diabetes print;
   var age height weight;
   class sex;
   output out=work.sum_gender
      mean=AvgAge AvgHeight AvgWeight;
run;

The FREQ procedure is a descriptive procedure as well as a statistical procedure. It produces one-way and n-way frequency tables, and it concisely describes your data by reporting the distribution of variable values. You can use the FREQ procedure to create crosstabulation tables that summarize data for two or more categorical variables by showing the number of observations for each combination of variable values.

Procedure Syntax

The FREQ procedure can include many statements and options for controlling frequency output. For simplicity, let's consider the procedure in its basic form.

General form, basic FREQ procedure:

   PROC FREQ <DATA=SAS-data-set>;

   RUN;

where SAS-data-set is the name of the data set to be used.

By default, PROC FREQ creates a one-way table with the frequency, percent, cumulative frequency, and cumulative percent of every value of all variables in a data set.

For example, the following FREQ procedure creates a frequency table for each variable in the data set Parts.Widgets. All the unique values are shown for ItemName, LotSize, and Region.

proc freq data=parts.widgets;
run;

By default, the FREQ procedure creates frequency tables for every variable in your data set. But this isn't always what you want. A variable that has continuous numeric values—such as DateTime—can result in a lengthy and meaningless table. Likewise, a variable that has a unique value for each observation—such as FullName—is unsuitable for PROC FREQ processing. Frequency distributions work best with variables whose values can be described as categorical, and whose values are best summarized by counts rather than by averages.

To specify the variables to be processed by the FREQ procedure, include a TABLES statement.

Example

The order in which the variables appear in the TABLES statement determines the order in which they are listed in the PROC FREQ report.

Consider the SAS data set Finance.Loans. The variables Rate and Months are best described as categorical values, so they are the best choices for frequency tables.

proc freq data=finance.loans;
   tables rate months;
run;

In addition to listing variables separately, you can use a numbered range of variables.

proc freq data=perm.survey;
   tables item1-item3;
run;

Adding the NOCUM option to your TABLES statement suppresses the display of cumulative frequencies and cumulative percentages in one-way frequency tables and in list output. The syntax for the NOCUM option shown below.

TABLES variable(s) /  NOCUM;

So far, you have used the FREQ procedure to create one-way frequency tables. The table results show total frequency counts for the values within the data set. However, it is often helpful to crosstabulate frequencies with the values of other variables. For example, census data is typically crosstabulated with a variable that represents geographical regions.

The simplest crosstabulation is a two-way table. To create a two-way table, join two variables with an asterisk (*) in the TABLES statement of a PROC FREQ step.

When crosstabulations are specified, PROC FREQ produces tables with cells that contain

For example, the following program creates the two-way table shown below.

Note that the first variable, Weight, forms the table rows, and the second variable, Height, forms the columns; reversing the order of the variables in the TABLES statement would reverse their positions in the table. Note also that the statistics are listed in the legend box.

For a frequency analysis of more than two variables, use PROC FREQ to create n-way crosstabulations. A series of two-way tables is produced, with a table for each level of the other variables.

For example, suppose you want to add the variable Sex to your crosstabulation of Weight and Height in the data set Clinic.Diabetes. Add Sex to the TABLES statement, joined to the other variables with an asterisk (*).

tables sex*weight*height;

Determining the Table Layout

The order of the variables is important. In n-way tables, the last two variables of the TABLES statement become the two-way rows and columns. Variables that precede the last two variables in the TABLES statement stratify the crosstabulation tables.

       levels 
                       ↓

tables  sex*weight*height; 
                        ↑        ↑ 

           rows + columns = two-way tables

Notice the structure of the output that is produced by the program shown below.

Changing the Table Format

Beginning in SAS 9, adding the CROSSLIST option to your TABLES statement displays crosstabulation tables in ODS column format. This option creates a table that has a table definition that you can customize by using the TEMPLATE procedure.

Notice the structure of the output that is produced by the program shown below.

When three or more variables are specified, the multiple levels of n-way tables can produce considerable output. Such bulky, often complex crosstabulations are often easier to read as a continuous list. Although this eliminates row and column frequencies and percents, the results are compact and clear.

To generate list output for crosstabulations, add a slash (/) and the LIST option to the TABLES statement in your PROC FREQ step.

TABLES variable-1 *variable-2 <* ... variable-n> / LIST;

Example

Adding the LIST option to our Clinic.Diabetes program puts its frequencies in a simple, short table.

Example

Suppose you want to use only the percentages of Sex and Weight combinations in the data set Clinic.Diabetes. To block frequency counts and row and column percentages, add the NOFREQ, NOROW, and NOCOL options to your program's TABLES statement.

Notice that Percent is the only statistic that remains in the table's legend box.

Select the best answer for each question. After completing the quiz, check your answers using the answer key in the appendix.