WHERE and IF Statements in SAS

Master the difference between WHERE and IF Statements in SAS for efficient data filtering. Learn when to use WHERE statements for speed on existing variables and IF statements for new variables. Our guide includes syntax examples, performance tips, and helps you avoid common subsetting errors to clean your datasets faster.

Differentiate between WHERE and IF Statements in SAS

There is a fundamental difference between WHERE and IF Statements in SAS. The breakdown of differences between WHERE and IF statements in SAS is as follows:

Key Differences Where and If Statements in SAS

Where Statement in SAS

The WHERE statement in SAS acts as a filter at the source. It tells SAS only to read observations that meet a specific condition from the input dataset(s). This happens very early in the process, often before the Program Data Vector (PDV) is fully constructed.

When to use WHERE Statement in SAS: Almost always for simple filtering, especially when working with large datasets, as it significantly improves performance by reducing I/O.

Example 1: Basic Filtering in a DATA Step

data high_earners;
    set sashelp.class;
    where age > 13; /* Only reads observations where Age is >13 */
run;

In this case, observations where age <= 13 are never even loaded into the PDV for processing.

Example 2: Using a WHERE Dataset Option

This is very powerful in procedures or when merging.

proc print data=sashelp.class(where=(sex='F')); /* Prints only females */
run;

data combined;
    merge ds1(where=(valid=1)) /* Merge only valid records from ds1 */
          ds2;
    by id;
run;

IF Statement in SAS

The IF statement in SAS is a processing-time filter. The entire observation is read into the PDV, all variables are calculated, and then the IF condition is evaluated. If the condition is false, the OUTPUT statement is bypassed (for that observation), and SAS returns to the beginning of the Data Step to process the next observation.

When to use IF Statement in SAS: When your filtering condition involves variables created within the Data Step or requires complex logic that must be executed row-by-row.

Example 1: Filtering on a New Variable

data tall_people;
set sashelp.class;
height_inches = height * 2.54; /* Create a new variable */
if height_inches > 64; /* Subsetting IF statement;*/
/* Equivalent to: if height_inches <= 64 then delete; */
run;

This works perfectly because the new variable height_inches exists in the PDV by the time the IF statement is executed.

Example 2: Complex Row-by-Row Logic

data flagged_records;
set mydata;
if some_var = . then do; /* Check for missing value */
error_flag = 'M'; /* Set a flag */
error_count + 1; /* Increment a counter */
end;
if error_flag = 'M'; /* Output only the records with errors */
run;

This kind of multi-step logic is not possible with a WHERE statement.

What is the Special Case of Subsetting IF vs DELETE Statements?

A common use of an IF statement is the subsetting IF (if condition;). This outputs an observation only if the condition is true. Its logical opposite is if condition then delete; which deletes an observation if the condition is true.

data adults;
    set people;
    if age >= 18; * Output if true;
run;

/* Is logically equivalent to: */

data adults;
    set people;
    if age < 18 then delete; * Delete if true;
run;

WHERE or IF Statement: Which One to Use?

  • Use WHERE when:
    • You are filtering based on variables that exist in the input dataset.
    • You want the most efficient processing, especially for large data.
    • You are working in a PROC step (like PROC PRINT, PROC SORT).
    • You want to use special operators like CONTAINS or LIKE.
  • Use IF when:
    • You need to filter based on a variable created within the same Data Step.
    • Your filtering logic is complex and requires other SAS statements (like DO loops or ARRAY processing).
    • You are already reading every observation into the PDV for other necessary calculations, and the efficiency gain of WHERE is negligible.

The WHERE statement can be used …. IF statement cannot be used

  • WHERE statement can be used in procedures to subset data, while the IF statement cannot be used in procedures.
  • WHERE can be used as a data set option, while IF cannot be used as a data set option.
  • WHERE statement is more efficient than the IF statement. It tells SAS not to read all observations from the data set
  • WHERE statement can be used to search for all similar character values that sound alike, while the IF statement cannot be used.
  • WHERE statement can not be used when reading data using the INPUT statement, whereas the IF statement can be used.
  • Multiple IF statements can be used to execute multiple conditional statements
  • When it is required to use newly created variables, use an IF statement, as it doesn’t require variables to exist in the READIN data set.  

What is the one statement to set the criteria of data that can be coded in any step?

A WHERE statement can set the criteria for any data set in a data step or a proc step.

General Knowledge Quizzes

Online Quiz Sampling Distribution 16

Master the fundamentals of Online Quiz Sampling Distribution with this 20-question MCQ quiz. Test your knowledge on bias, standard deviation of proportions, sampling methods (like quota, judgment, and stratified), and key statistical measures—essential exam prep for statistics students, data analysts, and data scientists. Let us start with the Online Quiz Sampoing Distribution now.

Online Quiz Sampling Distribution with Answers

Online Quiz Sampling Distribution with Answers

  • Bias, which occurs when randomly drawn samples from a population fail to represent the whole population, is classified as
  • If the proportion of the population is 10.5, then the proportional mean of the sampling distribution is
  • The value of the estimator is subtracted from the mean and then divided by the standard deviation to calculate
  • In statistical analysis, a sample size is considered small if
  • Sample statistics are denoted by the
  • A procedure in which the number of elements in the stratum is not proportional to the number of elements in the population is classified as
  • Method of sampling in which random sampling will not be possible because the population is widely spread is classified as
  • In the sampling distribution, the standard deviation must be equal to
  • Elements in the sample with specific characteristics are divided into the sample size to calculate
  • The method of random sampling, which is also called the  area sampling method, is classified as
  • In sampling, measures such as variance, mean, and standard deviation are considered as
  • If the value of $p$ is 0.70 and the sample size is 28, then the value of the standard deviation of the sample proportion is
  • If the value of $p$ is 0.70 and the sample size is 28, then the value of the standard deviation of the sample proportion is
  • Quota sampling, judgment sampling, and convenience sampling are classified as types of
  • Bias occurred in the collection of the sample because of confusing questions in the questionnaire, which is classified as
  • Bias in which a few respondents respond to the offered questionnaire is classified as
  • The difference between the corresponding population and the unbiased estimate in terms of absolute value is classified as
  • Bias, which occurs when a randomly drawn sample from a population fails to represent the whole population, is classified as
  • A border patrol checkpoint that stops every passenger van is using
  • Under equal allocation in stratified sampling, the sample from each stratum is

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Test your understanding of fundamental statistics with this 20-question about Sampling and Sampling Distribution MCQs Test. This Sampling Distribution MCQs Test Quiz covers key concepts like the Central Limit Theorem, standard error, types of sampling methods (random, stratified), and how to calculate standard deviation for sample proportions. Perfect for students studying statistics to assess their knowledge and prepare for exams. Let us start with the Online Sampling Distribution MCQs Test now.

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Online MCQS about Sampling and Sampling Distribution with answers

1. The difference between the corresponding population and the unbiased estimate in terms of absolute value is classified as

 
 
 
 

2. When statistical inference is made on the basis of sample results about characteristics of a population, then this is classified as

 
 
 
 

3. If the population standard deviation is not known, then the formula used to calculate the standard error is as follows:

 
 
 
 

4. Uncertainty of elements can be reduced with the estimation of

 
 
 
 

5. Measures in sampling that are results of sample analyses are called

 
 
 
 

6. If the value of $\overline{x}$ is 70 and $\mu$ of the sampling distribution is 15 with a standard deviation of 20, then the standard normal variable is

 
 
 
 

7. Type of sampling in which desired and useful information is gathered from the best position holder is classified as

 
 
 
 

8. The distribution of the difference of proportions is approximately a normal standard distribution only if

 
 
 
 

9. Important principles to determine valid statistical inference must include

 
 
 
 

10. If $p$ is equal to 0.65, the value of $N$ is 25000, whereas the sample size is 50, then the value of the standard deviation of the sample proportion is

 
 
 
 

11. Type of sampling in which each element of the population has an equally likely chance of occurrence in a random sample is classified as

 
 
 
 

12. In the sampling distribution, the formula for calculating the standard deviation of the sample proportion is as

 
 
 
 

13. Conditions such as a large sample size to represent the population and samples must be drawn randomly are included in

 
 
 
 

14. The standard deviation of a sampling distribution is also classified as

 
 
 
 

15. In the sample distribution, degree of freedom is calculated as

 
 
 
 

16. All values in the sample distribution that can freely vary in the selected random sample from the population are indicated as

 
 
 
 

17. The procedure of selecting the desired portion from the population that describes the characteristics of the whole population is

 
 
 
 

18. Theorem, which states that as the sample size increases, the sampling distribution must approach the normal distribution, is classified as

 
 
 
 

19. A distribution, which consists of all values of the sample statistic of sampling, is classified as

 
 
 
 

20. In stratified sampling, a sample drawn randomly from strata is classified as

 
 
 
 


Online Sampling Distribution MCQs Test with Answers

  • Theorem, which states that as the sample size increases, the sampling distribution must approach the normal distribution, is classified as
  • Conditions such as a large sample size to represent the population and samples must be drawn randomly are included in
  • Measures in sampling that are results of sample analyses are called
  • All values in the sample distribution that can freely vary in the selected random sample from the population are indicated as
  • If the population standard deviation is not known, then the formula used to calculate the standard error is as follows:
  • If the value of $\overline{x}$ is 70 and $\mu$ of the sampling distribution is 15 with a standard deviation OF 20, then the standard normal variable is
  • Uncertainty of elements can be reduced with the estimation of
  • The procedure of selecting the desired portion from the population that describes the characteristics of the whole population is
  • The standard deviation of a sampling distribution is also classified as
  • In the sampling distribution, the formula for calculating the standard deviation of the sample proportion is as
  • In stratified sampling, a sample drawn randomly from strata is classified as
  • The difference between the corresponding population and the unbiased estimate in terms of absolute value is classified as
  • Important principles to determine valid statistical inference must include
  • A distribution, which consists of all values of the sample statistic of sampling, is classified as
  • The distribution of the difference of proportions is approximately a normal standard distribution only if
  • When statistical inference is made on the basis of sample results about characteristics of a population, then this is classified as
  • Type of sampling in which desired and useful information is gathered from the best position holder is classified as
  • Type of sampling in which each element of the population has an equally likely chance of occurrence in a random sample is classified as
  • If $p$ is equal to 0.65, the value of $N$ is 25000, whereas the sample size is 50, then the value of the standard deviation of the sample proportion is
  • In the sample distribution, degree of freedom is calculated as

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