Ron cody sas book pdf free download

How does it know it's a new subject? It simply reads in a value for SUBJ and then uses the trailing O see Chapter 1, "Input and Infile," Example 10 to hold the line while it tests to see if it has a new subject or not. It uses the LAG function to make the test 0 and then treats the remainder of the input line accordingly.

The examples in this chapter that are based on the LAG function require that all the data for a single subject be grouped together in the input stream. Of course, the data would not then be input into the new data set being constructed, but rather would be set in from an existing SAS data set. Conclusion This chapter is a rather eclectic collection of programs and techniques which hangs together by each using the RETAIN statement in one way or another.

Now, for some practice. As an additional "learning experience," rewrite the code using a sum statement not a SUM function. The data are arranged so that a group code is followed by one or more scores for that group, and scores for any group can span more than one record of raw data unfortunately, this is not at all an uncommon pattern in which data can and do occur in the real world.

Hint: Here is one way to get started - there are others. Read every data item in the raw data file as a character value and test if it is an 'A', 'B' or 'C'. In its simplest form, it produces listings of the data set with each column headed by a variable name. By using a few options and additional statements, you can enhance the appearance of the output from PROC PRINT to generate fairly sophisticated reports with titles, descriptive column headings, totals and subtotals, and a count showing the number of observations.

O Actually, this is not true. Being explicit here doesn't cost much and is a good habit to build in the great "Battle of Debug. Also notice that the dates are presented as SAS date values i. We'll fix that shortly. This is fine when your variables all fit across one page. If this is not the case, your output will be difficult to read since the continued list of variables will not contain any identifying information. The alternate method is to include one or more ID variables.

An ID variable replaces the OBS column and prints on the left side of the page, even if the list of variables is continued on other pages. If you leave it in, it appears twice on the listing, once as the ID variable and once as one of the VAR variables. The output from this example is identical to the output from Example 1 except that the column labeled OBS is removed.

Formats The next step to improve this simple report is to include format and label information, along with titles. These do different things. The LABEL statement defines a set of variable labels which can be used instead of variable names as the column headings. Just creating the labels in the statement without turning them on with the option will not do the trick. The difference in the two placements is as follows: when a LABEL statement appears in a procedure, it makes available a set of labels for that procedure only.

When a LABEL statement is used in a DATA step when the data set is being created , the labels are available for any subsequent procedures which operate on the data set. Notice that the SSN Pretty slick! The final enhancement in this example to make the output more useful and readable is to use titles. In this example you create your own custom titles.

Titles in TITLE statements are enclosed in quotation marks, either single or double but they must match. It does, however, inform you that a re-sort is not necessary. It is not enough merely to sort the data set; you also have to tell the procedure that you want to process the data set by the BY group as well.

Your report appears in multiple sections, one for each BY group, with an identifying BY line above each subgroup. When a BY variable s is used, the N option also indicates the number of observations for each value of the BY variable s. Here is the final output. Note that only selected portions of the output are shown.

Your titles are left aligned instead of centered as they are for most of the sample listings in this book , and you omit the time, date, and page number. You may also want to start page numbering at a number of your choosing. Use these two options to control the number of lines printed per page and the number of characters per line. Using this option causes PROC PRINT to use the formatted widths of variables, if supplied, or default widths if you do not supply formats for character variables, these are the lengths of the variables as defined in the data set; for numeric variables, a width of 12 is used.

Since the width of each variable is constant for all observations, reports that span more than one page all have the same total column widths. Therefore, a multi-page report may use different column widths on different pages and the total column width may vary from page to page.

This can cause a quite obvious non-uniformity in the display from page to page. If a variable is formatted, this option uses the associated format to define the column width. If a variable is not formatted, this option uses the widest data value for a variable to display all values of that variable. Controlling the Orientation of the Column Headings The reports so far in this chapter all have the column headings printed horizontally. By using some of the options and statements in this chapter, you can customize your reports to a certain extent.

Write the code to produce the following report from the DONOR data set described in Problem , including the two-line title, column headings, date format, observation count, and sum of donations. Note that the title is flush left starts at the left margin and does not include the date or page number.

Using the same DONOR data set from Problems and , write the code to generate simple reports with the following characteristics: a variable names printed vertically and minimum width assigned to the columns b horizontal column headings and uniform printing if the report actually ran over several pages.

They can also be used to process the data and produce new data sets containing summary statistics. You don't have to be a statistician to use these procedures—they can be very useful for straightforward counting and adding. The examples in this chapter demonstrate several ways in which these procedures can be put to work. For those of you who are either not familiar or not comfortable with complex statistical terms and procedures, here is a very brief review of all you need to know about means and medians to get through this chapter.

In actuality, you've been dealing with arithmetic means since grade school grade point averages, batting averages, etc. Statisticians use the term "mean" instead of "average" so they can be more specific and sound more intellectual.

The other type of average used in this chapter is the "median. Half the numbers are below the median; half are above. You usually see medians computed on variables such as yearly salaries where extreme values can distort the true picture of "representativeness.

There is a wonderful little book by Dairy 1 Huff called How to Lie with Statistics, but that's a whole other story. Let's get back to reality with some examples. Each record in the data set represents the sale of a single item. This does not require a separate procedure to perform the sort. If you do not ask specifically for certain statistics to be produced, as in this example 0, the procedure automatically gives you the following: N, Mean, Standard Deviation, Minimum, and Maximum.

N is the number of non-missing data points and N Obs is the total SAS Programming by Example number of observations per group or sub-group.

In the present case, these numbers are equal because there are no missing data an easy goal to accomplish when you're making up the data, but not always that easy in real life. Although the above report is quite useful as is, PROC MEANS can also be used to produce an output data set containing the summary statistics instead of a printed report. The rest of the examples in this chapter do not produce a report directly but, instead, create only an output data set. How do you produce this output data set?

Following each of these keywords is a list of variables to be included in the newly created data set that will contain the values for the N, MEAN, SUM, etc. The creation of the output data set does not produce printed output. Let's use the actual data to explain these. The following figure should make this clear. It's not really that daunting once you get the hang of it. In other words, you want each subject to contribute equally to the overall mean.

You have to process the data twice: the first processing produces a mean for each subject, and the second processing uses the per-subject value to produce a mean over all subjects.

Suppose you have a data set which contains blood pressure readings for a number of subjects, but there are a variable number of observations per subject per year. You want a mean value for your readings per year, but you only want one reading per subject per year.

You then use this new data set to compute yearly means over all subjects. Doing this would include all readings over each year, including multiple readings per subject with different numbers of readings per subject and would, therefore, produce a weighted mean.

Subjects with more measurements per year would make a larger contribution to the overall mean. This is generally not what is wanted. Here is a set of programs which produce unweighted yearly means. This instructs the system not to print the resulting statistics. You really don't need to see output at this point because you are basically using the procedure to create another data set that will then be processed to produce the values that you really want, the unweighted yearly values.

This is fine in a simple application like this, but in general, new variables should have new unique names. It makes everything clearer. Where are the values for the overall population, and for each individual subject over years, as well as for each individual year over subjects? The answer is the NWAY option. You use this option to tell the system to produce output for only the highest level of class interactions. The result will be yearly unweighted means of the variables.

Had you not done this, you would have gotten the default set of statistics as you did in the earlier example. Each observation represents a letter mailed out to a resident asking for a contribution.

Sound difficult? In the DATA step, you calculate the rest of the variables you need. For example, if the values for heart rate for the group were 80, 70, and 60, the group mean would be 70 and the first subject's HR value of 80 would yield a percentage score of Variables that are accessed via a SET statement are not reinitialized with each new observation being built.

Values from the previous observation are carried forward and are replaced only with a new execution of the SET statement. Variable, Clever Comment Boxes In this last example you produce summary statistics and a report which includes a median. The example that follows was thought up around a.

The reader should be able to see the effects of too many clinical symptoms, too much antihistamine and decongestant medication, and too little sleep.

The pun in the first line of this paragraph was thought up by the other author RP after a full night's sleep and he accepts full responsibility. As you can see from the data, there are a variable number of visits for each patient.

Each record is to contain: 1. Let's examine the needed items one step at a time. Item 1 is easy and comes as a by-product of some other operations. We'll note it in passing. By doing this, you can calculate the mean of the numeric variable let's call it RATIO , which will be the proportion of visits that are routine i. The program and accompanying explanation will get it all done, but not necessarily in the order of the items listed here.

The sole purpose of this step is to create a numeric variable RATIO so that you can use the mean of this variable to indicate the proportion of visits that were routine. It's a simple concept. In any population, the sum of a binary variable values are 0 or 1 is equal to the total number of scores equal to 1, and the mean is equal to the proportion of the scores equal to 1.

Try it. While the alternative sets of code shown in the comment box are perhaps simpler to understand, the method used here gives you an opportunity to review two of the functions discussed in Chapter 5, "SAS Functions. Thus, each 'N' becomes a '0' and each 'Y' becomes a '1'. See Chapter 5, Examples 5 and 12 for more details on these two functions.

A note about the comment box itself is in order here. The rest of the box border is just some fancy comment fingerwork. Since you only want to keep the most recent i. You've seen these before, but it doesn't hurt to see them again.

Finally, here is the output. We hope it was worth the wait and the wade through that deep code. It also brings together techniques from several chapters of this book LAST. Conclusion In this chapter, you have seen how SAS procedures can produce data sets which contain summary information such as counts, means, medians, and sums.

These summary data sets can then be further manipulated in subsequent DATA steps to perform calculations on the summary data. You will find that, with a little practice, you will be able to use many of the techniques demonstrated in this chapter to save significant amounts of time and energy when you need to summarize data. Speaking of practice, here are the end-of-chapter problems: Problems The new data set should contain 4 observations.

No need to print the results of the mean-creating procedure. Create a report similar to the one produced in Chapter 10, Example 7, with the following two changes: 1. Include the mean heart rate HR. You can also use formats to create new variables as translated versions of other variables. The SAS System provides a plethora of ready-to-use formats and informats, but usually they are not enough. Learn how to use R to turn raw data into insight, knowledge, and understanding.

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