SAS Overview

                         SAS at the first glance may seem like an alien language, especially for someone with a non coding background (like me), but sooner than later you will start to realize that it is quite close to 'English' and suprisingly enough fun to learn, taking into consideration its user - friendly interface and English like syntax. Today I would like to use this opportunity to take you through 10 different problems in SAS from the book "Learning SAS by Example: A Programmers Guide" by Ron Cody using 'SAS University Edition'. The topics we will be covering are as follows-

1) Conditional Processing
2) Iterative Processing : Looping
3) Working with Dates
4) Subsetting and Combining 
5) Working with Numeric Functions


The format I will be following here is as follows -
1) The problem statement
2) Then, the code that I have used in SAS Studio
3) The output displayed
4) Finally, what have we got to learn from it.

Conditional Processing

Question 1 (Chapter 7 Prob 1)

Using IF and ELSE IF statements, we will compute two new variables as follows:
Grade (numeric), with a value of 6 if Age is 12 and a value of 8 if Age is 13. The quiz grades have numerical equivalents as follows: A = 95, B = 85, C = 75,  D = 70, and F = 65. Using this information, we compute a course grade (Course) as a weighted average of the Quiz (20%), Midterm (30%) and Final (50%). 

Code -

Result -

Learning - We have learnt here to use If Else function along with the method to find Weighted Average.

Question 2 (Chapter 7 Prob 7)

We are provided with a data set by the name 'Bicycles'. The variables of the data set are as follows-
Country
Model
Manufacturer
Units
Unit Cost
Total Sales

Using the Bicycles data set, we will list all the observations for Road Bikes that cost more than $2,500 or Hybrids that cost more than $660. The variable Model contains the type of bike and UnitCost contains the cost.

Code -

Result -

Learning - In this program we have learnt how to use where and or function in the proc print data step to filter out data.

Iterative Processing : Looping

Question 3 (Chapter 8 Prob 3)

Let us modify the program here so that each observation contains a subject number (Subj), starting with 1: 


Code -

Result -

Learning - We have learned to add a new Column into the output without using datalines or DO loop.



Question 4 (Chapter 8 Prob 5)

We will now create and print a data set with variables N and LogN, where LogN is the natural log of N (the function is LOG). Then, we will use a DO loop to create a table showing values of N and LogN for values of N going from 1 to 20. 

Code -












Result -


























Learning - We have learnt here how to use DO loop with reference to Logarithm function.

Working with Dates

Question 5 (Chapter 9 Prob 1)

We have several lines of data, consisting of a subject number and two dates (date of       birth and visit date). 
The subject starts in column 1 (and is 3 bytes long), 
The date of birth starts in column 4 and is in the form mm/dd/yyyy, 
The visit date starts in column 14 and is in the form nnmmmyyyy
The lines of data to create a temporary SAS data set called Dates.Let us format both dates using the DATE9. format and include the subject’s age at the time of the visit in this data set. 
0011021195011Nov2006    
0020102195525May2005    
0031225200525Dec2006 

Code -

Result -

Learning - Here we have learned to play around with dates. Firstly, we learned to format dates into 2 different formats. Second, we have learned to find the difference between two dates and consequently compute the Age. 



Question 6 (Chapter 9 Prob 4)

We are given a dataset named Hosp. Using the Hosp data set, we will compute the subject’s ages two ways: 
As of January 1, 2006 (AgeJan1)
As of today’s date (AgeToday) 
The variable DOB represents the date of birth. We will take the integer portion of both ages and list the first 10 observations.

Code - 












Result -




























Learning - Similar to the previous question, we have used yrdif function, the difference is that this time we have used it twice. Once with today() function to find the age as of today and once age as of 1st Jan 2006 

Sub-setting and Combining

Question 7 (Chapter 10 Prob 1)

We are given a dataset 'Blood'. Using the Blood dataset,we will create two temporary SAS data sets called Subset_A and Subset_B. In both of these data sets we will include a variable called Combined equal to .001 times WBC plus RBC. Subset_A will consist of observations from Blood where Gender is equal to Female and BloodType is equal to AB. Subset_B will consist of all observations from Blood where Gender is equal to Female, BloodType is equal to AB, and Combined is greater than or equal to 14.

Code -



Result - 

Learning - In this program, we have learned how to use subsetting to split a data set into 2 or more sets using a single datastep and at the same time keep the subsetting conditional. 



Question 8 (Chapter 10 Prob 7)

We are given a dataset Using the Gym data set, we create a new, temporary SAS data set (Percent) that contains all the variables found in Gym plus a new variable (CostPercent) that represents the Cost as a percentage of the average cost for all subjects. Using PROC MEANS to create a SAS data set containing the mean cost, we will round this value to the nearest percent.

Code -

Result - 

Learning - We have now learnt how to first calculate average, then using that find the percentage of the observation based on the average.

Working with Numeric Functions

Question 9 (Chapter 11 Prob 1)

We are given a dataset by the name of 'Health'. Using this data set Health, we will compute the body mass index (BMI) defined as the weight in kilograms divided by the height (in meters) squared. We then create four other variables based on BMI: 
1) BMIRound is the BMI rounded to the nearest integer 
2) BMITenth is the BMI rounded to the nearest tenth 
3) BMIGroup is the BMI rounded to the nearest 5 
4) BMITrunc is the BMI with a fractional amount truncated. 
Conversion factors that we are using are: 1 Kg equals 2.2 Lbs and 1 inch = .0254 meters. 

Code -

Result -

Learning - In this program, we have learnt how to round off observations as well as truncate the observations.



Question 10 (Chapter 11 Prob 3)

We again use the Blood dataset to create a new, temporary SAS data set (Miss_Blood). We will set Gender, RBC, and Chol to a missing value if WBC is missing.

Code-

Results -

Learning - In this program we learn how to deal with missing values using 'missing' function as well 'call missing function.

In conclusion, we have learnt a few basic SAS concepts. I hope I have contributed towards some value addition. Thank you for your patience. 

This is the link to the Datasets and SAS codes used in the blogpost -
https://drive.google.com/file/d/0B2sVurZ_f97PTmRPdGN4eU55S3M/view