SlideShare ist ein Scribd-Unternehmen logo

The essence of data step programming

•Als PPT, PDF herunterladen•
•
Arthur8898
Arthur8898

The fundamental of SAS programming is DATA step programming. The essence of DATA step programming is to understand how SAS processes the data during the compilation and execution phases. In this paper, you will be exposed to what happens “behind the scenes” while creating a SAS dataset. You will learn how a new dataset is created, one observation at a time, from either a raw text file or an existing SAS dataset, to the program data vector (PDV) and from the PDV to the newly-created SAS dataset. Once you fully understand DATA step processing, learning the SUM and RETAIN statements will become easier to grasp. Relating to this topic, this paper will also cover BY-group processing.

BildungBusinessTechnologie
1 von 182
The Essence of DATA Step Programming Arthur Li City of Hope Comprehensive Cancer Center Department of Information Science
INTRODUCTION SAS programming DATA step programming Understanding how SAS processes the data during the compilation and execution phases Fundamental: Essence:
A COMMON BEFUDDLEMENT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
INTRODUCTION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
DATA STEP PROCESSING OVERVIEW Compilation phase: Each statement is scanned for syntax errors. Execution phase: The DATA step reads and processes the input data. If there is no syntax error A DATA step is processed in two-phase sequences :
DATA STEP PROCESSING OVERVIEW Program1: data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; Data Entry Error ,[object Object],[object Object],[object Object],[object Object],12-14 Weight 9-10 Height 1-7 Name Columns Variable names Barbara 61 12D John 62 175 Example1.txt 12345678901234567890
COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; ,[object Object],[object Object],Input buffer
COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV PDV is created Memory area where SAS builds its new data set, 1 observation at a time. Input buffer _N_ D _ERROR_ D
COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV PDV is created Automatic variables: _N_ = 1: 1 st observation is being processed _N_ = 2: 2 nd observation is being processed Input buffer _N_ D _ERROR_ D
COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV PDV is created Automatic variables: _ERROR_ = 1: signals the data error of the currently-processed observation Input buffer _N_ D _ERROR_ D
COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV A space is added to the PDV for each variable Input buffer _N_ D _ERROR_ D Height K Name K Weight K
COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV BMI is added to the PDV Input buffer _N_ D _ERROR_ D Height K Name K Weight K BMI K
COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV D = dropped K = kept Input buffer _N_ D _ERROR_ D Height K Name K Weight K BMI K
COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV ,[object Object],[object Object],[object Object],[object Object],[object Object],Input buffer _N_ D _ERROR_ D Height K Name K Weight K BMI K
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV ,[object Object],[object Object],[object Object],[object Object],Input buffer _N_ D _ERROR_ D Name K Height K Weight K BMI K
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV ,[object Object],[object Object],[object Object],[object Object],. . . Input buffer _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 0
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D ,[object Object],[object Object],[object Object],1 0 . . . Input buffer _N_ D _ERROR_ D Name K Height K Weight K BMI K Barbara 61 12D John 62 175 Example1.txt 12345678901234567890
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D 1 0 ,[object Object],. . . Input buffer _N_ D _ERROR_ D Name K Height K Weight K BMI K Barbara 61 12D John 62 175 Example1.txt 12345678901234567890
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D 1 0 ,[object Object],Barbara . . . Input buffer _N_ D _ERROR_ D Name K Height K Weight K BMI K Barbara 61 12D John 62 175 Example1.txt 12345678901234567890
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D 1 0 ,[object Object],Barbara . . . Input buffer _N_ D _ERROR_ D Name K Height K Weight K BMI K Barbara 61 12D John 62 175 Example1.txt 12345678901234567890
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D 1 0 . . ,[object Object],Barbara 61 Input buffer _N_ D _ERROR_ D Name K Height K Weight K BMI K Barbara 61 12D John 62 175 Example1.txt 12345678901234567890
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D 1 0 ,[object Object],Barbara 61 . . Input buffer _N_ D _ERROR_ D Name K Height K Weight K BMI K Barbara 61 12D John 62 175 Example1.txt 12345678901234567890
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D 1 0 ,[object Object],Barbara 61 . . Input buffer _N_ D _ERROR_ D Name K Height K Weight K BMI K Barbara 61 12D John 62 175 Example1.txt 12345678901234567890
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D ,[object Object],[object Object],Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 1 Barbara 61 . .
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D ,[object Object],Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 1 Barbara 61 . .
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D ,[object Object],[object Object],Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 1 Barbara 61 . .
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D ,[object Object],[object Object],Ex1: Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 1 . . 61 Barbara BMI Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 1 Barbara 61 . .
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1 st Iteration: B a r b a r a 6 1 1 2 D ,[object Object],Ex1: Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 1 . . 61 Barbara BMI Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 1 Barbara 61 . .
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1. The SAS system returns to the beginning of the DATA step Ex1: Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 1 . . 61 Barbara BMI Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 1 Barbara 61 . .
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 2. The values of the variables in the PDV are reset to missing _N_ ↑ 2 _ERROR_  0 Ex1: Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 1 . . 61 Barbara BMI Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 . . .
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 2 nd Iteration: J o h n 6 2 1 7 5 ,[object Object],[object Object],Ex1: Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 1 . . 61 Barbara BMI Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 . . .
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 2 nd Iteration: J o h n 6 2 1 7 5 ,[object Object],Ex1: Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 . 62 John 175 1 . . 61 Barbara BMI Weight Height Name
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 2 nd Iteration: J o h n 6 2 1 7 5 ,[object Object],Ex1: Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 31.8678 62 John 175 1 . . 61 Barbara BMI Weight Height Name
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 2 nd Iteration: J o h n 6 2 1 7 5 ,[object Object],Ex1: Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 31.8678 62 John 175 2 1 31.8678 175 62 John . . 61 Barbara BMI Weight Height Name
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 2 nd Iteration: J o h n 6 2 1 7 5 ,[object Object],Ex1: Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 31.8678 62 John 175 2 1 31.8678 175 62 John . . 61 Barbara BMI Weight Height Name
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV Ex1: 1. The SAS system returns to the beginning of the DATA step Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 31.8678 62 John 175 2 1 31.8678 175 62 John . . 61 Barbara BMI Weight Height Name
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV Ex1: 2. The values of the variables in the PDV are reset to missing _N_ ↑ 3 Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 2 1 31.8678 175 62 John . . 61 Barbara BMI Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 3 0 . . .
EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; proc print data =ex1; run ; ,[object Object],[object Object]
THE OUTPUT STATEMENT data ex1; set example1; BMI = 700 *weight/(height*height); run ; ,[object Object],[object Object],[object Object],output ;
THE OUTPUT STATEMENT data ex1; set example1; BMI = 700 *weight/(height*height); run ; ,[object Object],[object Object]
THE OUTPUT STATEMENT ,[object Object],[object Object]
THE DIFFERENCE BETWEEN READING A RAW DATASET AND READING A SAS DATASET data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; SAS dataset ,[object Object],Input buffer PDV _N_ D _ERROR_ D Name K Height K Weight K BMI K Barbara 61 12D John 62 175 Raw data 2 1 31.8678 175 62 John . . 61 Barbara BMI Weight Height Name
THE DIFFERENCE BETWEEN READING A RAW DATASET AND READING A SAS DATASET data ex1; set example1; BMI = 700 *weight/(height*height); output ; run ; SAS dataset ,[object Object],SAS dataset Input dataset: Example1 (after “set”) Output dataset: Ex1 (after “data”) PDV _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 1 175 62 John . 61 Barbara Weight Height Name 2 1 31.8678 175 62 John . . 61 Barbara BMI Weight Height Name
THE DIFFERENCE BETWEEN READING A RAW DATASET AND READING A SAS DATASET ,[object Object],[object Object],[object Object],[object Object],[object Object]
THE DIFFERENCE BETWEEN READING A RAW DATASET AND READING A SAS DATASET data ex1; set example1; BMI = 700 *weight/(height*height); output ; run ; PDV ,[object Object],[object Object],[object Object],Example1: 2 1 175 62 John 170 61 Barbara Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 0 . . .
THE DIFFERENCE BETWEEN READING A RAW DATASET AND READING A SAS DATASET data ex1; set example1; BMI = 700 *weight/(height*height); output ; run ; PDV ,[object Object],[object Object],[object Object],Example1: 2 1 175 62 John 170 61 Barbara Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 0 . Barbara 170 61
THE DIFFERENCE BETWEEN READING A RAW DATASET AND READING A SAS DATASET data ex1; set example1; BMI = 700 *weight/(height*height); output ; run ; PDV ,[object Object],[object Object],[object Object],Example1: 2 1 175 62 John 170 61 Barbara Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 0 Barbara 31.9807 170 61
THE DIFFERENCE BETWEEN READING A RAW DATASET AND READING A SAS DATASET data ex1; set example1; BMI = 700 *weight/(height*height); output ; run ; PDV ,[object Object],[object Object],[object Object],Example1: Ex1: 2 1 175 62 John 170 61 Barbara Weight Height Name 170 Weight 1 31.9807 61 Barbara BMI Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 0 Barbara 31.9807 170 61
THE DIFFERENCE BETWEEN READING A RAW DATASET AND READING A SAS DATASET data ex1; set example1; BMI = 700 *weight/(height*height); output ; run ; PDV 2 nd Iteration: ,[object Object],Example1: Ex1: Variables exist in the input dataset ,[object Object],[object Object],2 1 175 62 John 170 61 Barbara Weight Height Name 170 Weight 1 31.9807 61 Barbara BMI Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 Barbara . 170 61
THE DIFFERENCE BETWEEN READING A RAW DATASET AND READING A SAS DATASET data ex1; set example1; BMI = 700 *weight/(height*height); output ; run ; PDV 2 nd Iteration: ,[object Object],Example1: Ex1: Variables being created in the DATA step ,[object Object],2 1 175 62 John 170 61 Barbara Weight Height Name 170 Weight 1 31.9807 61 Barbara BMI Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 Barbara . 170 61
THE DIFFERENCE BETWEEN READING A RAW DATASET AND READING A SAS DATASET data ex1; set example1; BMI = 700 *weight/(height*height); output ; run ; PDV ,[object Object],[object Object],[object Object],Example1: Ex1: 2 1 175 62 John 170 61 Barbara Weight Height Name 170 Weight 1 31.9807 61 Barbara BMI Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 John . 175 62
THE RETAIN STATEMENT Consider the following dataset: ,[object Object],4 A03 3 . A02 2 3 A01 1 SCORE ID 7 3 3 TOTAL
THE RETAIN STATEMENT Consider the following dataset: ,[object Object],[object Object],[object Object],Problem : TOTAL is a new variable that you want to create  TOTAL will be set to missing in the PDV at the beginning of every iteration of the execution. 4 A03 3 . A02 2 3 A01 1 SCORE ID 7 3 3 TOTAL
THE RETAIN STATEMENT ,[object Object],RETAIN VARIABLE <VALUE>; ,[object Object]
THE RETAIN STATEMENT ,[object Object],RETAIN VARIABLE <VALUE>; Name of the variable that we will want to retain ,[object Object],[object Object],[object Object]
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV ,[object Object],_N_ D _ERROR_ D ID K Total K 4 A03 3 . A02 2 3 A01 1 SCORE ID Score K
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV ,[object Object],[object Object],[object Object],1 st Iteration: 4 A03 3 . A02 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score K 1 0 . 0
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV ,[object Object],1 st Iteration: 4 A03 3 . A02 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score K 1 0 3 0 A01
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV ,[object Object],[object Object],1 st Iteration: 4 A03 3 . A02 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score K 1 0 3 0 A01
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV ,[object Object],1 st Iteration: 4 A03 3 . A02 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score K 1 0 3 3 A01
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV ,[object Object],1 st Iteration: Ex2_2: 4 A03 3 . A02 2 3 A01 1 SCORE ID 3 SCORE 3 TOTAL A01 1 ID _N_ D _ERROR_ D ID K Total K Score K 1 0 3 3 A01
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV ,[object Object],[object Object],[object Object],2 nd Iteration: Ex2_2: 4 A03 3 . A02 2 3 A01 1 SCORE ID 3 SCORE 3 TOTAL A01 1 ID _N_ D _ERROR_ D ID K Total K Score K 2 0 3 3 A01
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV ,[object Object],2 nd Iteration: Ex2_2: 4 A03 3 . A02 2 3 A01 1 SCORE ID 3 SCORE 3 TOTAL A01 1 ID _N_ D _ERROR_ D ID K Total K Score K 2 0 . 3 A02
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV ,[object Object],2 nd Iteration: Ex2_2: 4 A03 3 . A02 2 3 A01 1 SCORE ID 3 SCORE 3 TOTAL A01 1 ID _N_ D _ERROR_ D ID K Total K Score K 2 0 . 3 A02
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV 2 nd Iteration: Ex2_2: ,[object Object],[object Object],4 A03 3 . A02 2 3 A01 1 SCORE ID . 3 SCORE 3 3 TOTAL A02 2 A01 1 ID _N_ D _ERROR_ D ID K Total K Score K 2 0 . 3 A02
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV 3 rd Iteration: Ex2_2: ,[object Object],[object Object],[object Object],4 A03 3 . A02 2 3 A01 1 SCORE ID . 3 SCORE 3 3 TOTAL A02 2 A01 1 ID _N_ D _ERROR_ D ID K Total K Score K 3 0 . 3 A02
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV 3 rd Iteration: Ex2_2: ,[object Object],4 A03 3 . A02 2 3 A01 1 SCORE ID . 3 SCORE 3 3 TOTAL A02 2 A01 1 ID _N_ D _ERROR_ D ID K Total K Score K 3 0 4 3 A03
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV 3 rd Iteration: Ex2_2: ,[object Object],4 A03 3 . A02 2 3 A01 1 SCORE ID . 3 SCORE 3 3 TOTAL A02 2 A01 1 ID _N_ D _ERROR_ D ID K Total K Score K 3 0 4 7 A03
THE RETAIN STATEMENT data ex2_2; set ex2; retain total 0 ; total = sum(total, score); run ; PDV 3 rd Iteration: Ex2_2: ,[object Object],[object Object],4 A03 3 . A02 2 3 A01 1 SCORE ID 4 . 3 SCORE 7 3 3 TOTAL A03 3 A02 2 A01 1 ID _N_ D _ERROR_ D ID K Total K Score K 3 0 4 7 A03
THE SUM STATEMENT ,[object Object],VARIABLE + EXPRESSION; ,[object Object],[object Object],[object Object],[object Object],[object Object]
THE SUM STATEMENT data ex2_2; set ex2; run ; retain total 0 ; total = sum(total, score); The previous program can be re-written as…
THE SUM STATEMENT data ex2_2; set ex2; run ; The previous program can be re-written as… total + score;
THE SUBSETTING IF STATEMENT ,[object Object],IF EXPRESSION ; ,[object Object],[object Object],[object Object]
THE SUBSETTING IF STATEMENT ,[object Object],IF EXPRESSION ; ,[object Object],[object Object],[object Object],[object Object]
THE BY-GROUP PROCESSING IN THE DATA STEP One observation per subject Multiple observations per subject -- Longitudinal data ,[object Object],[object Object],4 A03 3 . A02 2 3 A01 1 SCORE ID 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID
THE BY-GROUP PROCESSING IN THE DATA STEP ,[object Object],[object Object],[object Object],[object Object],SAS reads the 1 st observation for ID = A01 SAS reads the last observation for ID = A01 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 0 1 0 0 1 FIRST.ID 1 0 1 0 0 LAST.ID
THE BY-GROUP PROCESSING IN THE DATA STEP ,[object Object],proc sort data =ex3; by id; run ; data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 6 A02 2 9 A01 1 TOTAL ID
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV 1 st iteration: ,[object Object],[object Object],[object Object],[object Object],2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 1 0 1 1 . 0
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],[object Object],1 st iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 1 0 1 0 A01 3 0
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],1 st iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 1 0 1 0 A01 3 0
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],1 st iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 1 0 1 0 A01 3 3
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],1 st iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 1 0 1 0 A01 3 3
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],2 nd iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 2 0 1 0 A01 3 3
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],[object Object],2 nd iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 2 0 0 0 A01 4 3
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],2 nd iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 2 0 0 0 A01 4 3
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],2 nd iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 2 0 0 0 A01 4 7
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],2 nd iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 2 0 0 0 A01 4 7
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],3 rd iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 3 0 0 0 A01 4 7
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],[object Object],3 rd iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 3 0 0 1 A01 2 7
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],3 rd iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 3 0 0 1 A01 2 7
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],3 rd iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 3 0 0 1 A01 2 9
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],3 rd iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 3 0 0 1 A01 2 9
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],[object Object],Ex3_1: 3 rd iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 3 0 0 1 A01 2 9
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],Ex3_1: 4 th iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 4 0 0 1 A01 2 9
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],[object Object],Ex3_1: 4 th iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 4 0 1 0 A02 4 9
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],Ex3_1: 4 th iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 4 0 1 0 A02 4 0
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],Ex3_1: 4 th iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 4 0 1 0 A02 4 4
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV Ex3_1: ,[object Object],[object Object],4 th iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 4 0 1 0 A02 4 4
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],Ex3_1: 5 th iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 5 0 1 0 A02 4 4
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],[object Object],[object Object],Ex3_1: 5 th iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 5 0 0 1 A02 2 4
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],Ex3_1: 5 th iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 5 0 0 1 A02 2 4
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],Ex3_1: 5 th iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 5 0 0 1 A02 2 6
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV ,[object Object],Ex3_1: 5 th iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 5 0 0 1 A02 2 6
THE BY-GROUP PROCESSING IN THE DATA STEP data ex3_1 (drop=score); set ex3; by id; if first.id = 1 then total = 0 ; total + score; if last.id = 1 ; run ; PDV Ex3_1: ,[object Object],[object Object],5 th iteration: 2 A02 5 4 A02 4 2 A01 3 4 A01 2 3 A01 1 SCORE ID 6 A02 2 9 A01 1 TOTAL ID _N_ D _ERROR_ D ID K Total K Score D FIRST.ID D LAST.ID D 5 0 0 1 A02 2 6
RESTRUCTURING DATASETS ,[object Object],data with one observation per subject (the wide format) data with multiple observations per subject (the long format) 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
RESTRUCTURING DATASETS ,[object Object],data with one observation per subject (the wide format) data with multiple observations per subject (the long format) S1 – S3 SCORE Distinguish different measurements for each subject 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
RESTRUCTURING DATASETS ,[object Object],[object Object],[object Object],[object Object]
FROM WIDE FORMAT TO LONG FORMAT Wide: Long: ,[object Object],[object Object],[object Object],[object Object],data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],[object Object],4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID K ID . D S1 . D S2 . D S3 . K TIME . K SCORE 1 K _N_
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 . K TIME . K SCORE 1 K _N_
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 1 K TIME . K SCORE 1 K _N_
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 1 K TIME 3 K SCORE 1 K _N_
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 1 K TIME 3 K SCORE 1 K _N_ 1 TIME 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 2 K TIME 3 K SCORE 1 K _N_ 1 TIME 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 2 K TIME 4 K SCORE 1 K _N_ 1 TIME 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 2 K TIME 4 K SCORE 1 K _N_ 2 1 TIME 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 3 K TIME 4 K SCORE 1 K _N_ 2 1 TIME 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 3 K TIME 5 K SCORE 1 K _N_ 2 1 TIME 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 3 K TIME 5 K SCORE 1 K _N_ 3 2 1 TIME 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 1 st iteration: ,[object Object],[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 3 K TIME 5 K SCORE 1 K _N_ 3 2 1 TIME 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],[object Object],[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 3 D S1 4 D S2 5 D S3 . K TIME . K SCORE 2 K _N_ 3 2 1 TIME 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 4 D S1 . D S2 2 D S3 . K TIME . K SCORE 2 K _N_ 3 2 1 TIME 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 4 D S1 . D S2 2 D S3 1 K TIME . K SCORE 2 K _N_ 3 2 1 TIME 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 4 D S1 . D S2 2 D S3 1 K TIME 4 K SCORE 2 K _N_ 3 2 1 TIME 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 4 D S1 . D S2 2 D S3 1 K TIME 4 K SCORE 2 K _N_ 1 3 2 1 TIME 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 4 D S1 . D S2 2 D S3 2 K TIME 4 K SCORE 2 K _N_ 1 3 2 1 TIME 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 4 D S1 . D S2 2 D S3 2 K TIME . K SCORE 2 K _N_ 1 3 2 1 TIME 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 4 D S1 . D S2 2 D S3 2 K TIME . K SCORE 2 K _N_ 1 3 2 1 TIME 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 4 D S1 . D S2 2 D S3 3 K TIME . K SCORE 2 K _N_ 1 3 2 1 TIME 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 4 D S1 . D S2 2 D S3 3 K TIME 2 K SCORE 2 K _N_ 1 3 2 1 TIME 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 4 D S1 . D S2 2 D S3 3 K TIME 2 K SCORE 2 K _N_ 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM WIDE FORMAT TO LONG FORMAT Wide: data long (drop=s1-s3); set wide; time = 1 ; score = s1; if not missing(score) then output ; time = 2 ; score = s2; if not missing(score) then output ; time = 3 ; score = s3; if not missing(score) then output ; run ; 2 nd iteration: ,[object Object],[object Object],Long: 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID A01 K ID 4 D S1 . D S2 2 D S3 3 K TIME 2 K SCORE 2 K _N_ 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM LONG FORMAT TO WIDE FORMAT 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM LONG FORMAT TO WIDE FORMAT ,[object Object],[object Object],[object Object],4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM LONG FORMAT TO WIDE FORMAT if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; ,[object Object],[object Object],[object Object],RETAIN 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID S3 S1 S3 S2 S1
FROM LONG FORMAT TO WIDE FORMAT proc sort data =long; by id; run ; data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . . . . 1 1 1 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . . 3 1 A01 1 1 1 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . . 3 1 A01 0 1 1 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 3 3 1 A01 0 1 1 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 3 3 1 A01 0 1 1 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 3 3 1 A01 0 1 2 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 3 3 1 A01 0 1 2 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 3 4 2 A01 0 1 2 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 3 4 2 A01 0 0 2 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . 4 3 4 2 A01 0 0 2 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . 4 3 4 2 A01 0 0 2 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . 4 3 4 2 A01 0 0 3 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . 4 3 5 3 A01 0 0 3 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . 4 3 5 3 A01 1 0 3 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 3 5 3 A01 1 0 3 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 3 5 3 A01 1 0 3 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 3 5 3 A01 1 0 3 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 3 5 3 A01 1 0 4 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 3 4 1 A02 1 0 4 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 3 4 1 A02 0 1 4 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 4 4 1 A02 0 1 4 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 4 4 1 A02 0 1 4 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 4 4 1 A02 0 1 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 4 2 3 A02 0 1 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 4 2 3 A02 1 0 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 2 4 4 2 3 A02 1 0 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 2 4 4 2 3 A02 1 0 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],How to fix this? 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 2 4 4 2 3 A02 1 0 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 2 4 4 A02 2 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ;
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 3 5 3 A01 1 0 4 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 3 4 1 A02 1 0 4 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 5 4 3 4 1 A02 0 1 4 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . . 4 1 A02 0 1 4 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 4 4 1 A02 0 1 4 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 4 4 1 A02 0 1 4 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 4 4 1 A02 0 1 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 4 2 3 A02 0 1 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 4 2 3 A02 1 0 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID . . 4 2 3 A02 1 0 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 2 . 4 2 3 A02 1 0 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 2 . 4 2 3 A02 1 0 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 3 S1 4 S2 5 A01 1 S3 ID
FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; ,[object Object],[object Object],[object Object],3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID 2 . 4 2 3 A02 1 0 5 K S3 K S2 K S1 D SCORE D TIME K ID D LAST.ID D FIRST.ID D _N_ 2 . 4 A02 2 3 S1 4 S2 5 A01 1 S3 ID
CONCLUSION ,[object Object],[object Object]
REFERENCES ,[object Object]
ACKNOWLEDGEMENT ,[object Object]
CONTACT INFORMATION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Empfohlen

Using Change Streams to Keep Up with Your Data
Using Change Streams to Keep Up with Your DataUsing Change Streams to Keep Up with Your Data
Using Change Streams to Keep Up with Your DataMongoDB
 
Time Series Analysis on Egg depositions (in millions) of age-3 Lake Huron Blo...
Time Series Analysis on Egg depositions (in millions) of age-3 Lake Huron Blo...Time Series Analysis on Egg depositions (in millions) of age-3 Lake Huron Blo...
Time Series Analysis on Egg depositions (in millions) of age-3 Lake Huron Blo...ShuaiGao3
 
When best to use the %let statement, the symput routine, or the into clause t...
When best to use the %let statement, the symput routine, or the into clause t...When best to use the %let statement, the symput routine, or the into clause t...
When best to use the %let statement, the symput routine, or the into clause t...Arthur8898
 
Super TypeScript II Turbo - FP Remix (NG Conf 2017)
Super TypeScript II Turbo - FP Remix (NG Conf 2017)Super TypeScript II Turbo - FP Remix (NG Conf 2017)
Super TypeScript II Turbo - FP Remix (NG Conf 2017)Sean May
 
List Processing in ABAP
List Processing in ABAPList Processing in ABAP
List Processing in ABAPsapdocs. info
 
1582627
15826271582627
1582627tabish
 
List Processing in ABAP
List Processing in ABAPList Processing in ABAP
List Processing in ABAPsapdocs. info
 
Write a program to convert a given INFIX into POSTFIX. Make sure .pdf
Write a program to convert a given INFIX into POSTFIX. Make sure .pdfWrite a program to convert a given INFIX into POSTFIX. Make sure .pdf
Write a program to convert a given INFIX into POSTFIX. Make sure .pdfFOREVERPRODUCTCHD
 

Empfohlen

Using Change Streams to Keep Up with Your Data
Using Change Streams to Keep Up with Your DataUsing Change Streams to Keep Up with Your Data
Using Change Streams to Keep Up with Your DataMongoDB
 
Time Series Analysis on Egg depositions (in millions) of age-3 Lake Huron Blo...
Time Series Analysis on Egg depositions (in millions) of age-3 Lake Huron Blo...Time Series Analysis on Egg depositions (in millions) of age-3 Lake Huron Blo...
Time Series Analysis on Egg depositions (in millions) of age-3 Lake Huron Blo...ShuaiGao3
 
When best to use the %let statement, the symput routine, or the into clause t...
When best to use the %let statement, the symput routine, or the into clause t...When best to use the %let statement, the symput routine, or the into clause t...
When best to use the %let statement, the symput routine, or the into clause t...Arthur8898
 
Super TypeScript II Turbo - FP Remix (NG Conf 2017)
Super TypeScript II Turbo - FP Remix (NG Conf 2017)Super TypeScript II Turbo - FP Remix (NG Conf 2017)
Super TypeScript II Turbo - FP Remix (NG Conf 2017)Sean May
 
List Processing in ABAP
List Processing in ABAPList Processing in ABAP
List Processing in ABAPsapdocs. info
 
1582627
15826271582627
1582627tabish
 
List Processing in ABAP
List Processing in ABAPList Processing in ABAP
List Processing in ABAPsapdocs. info
 
Write a program to convert a given INFIX into POSTFIX. Make sure .pdf
Write a program to convert a given INFIX into POSTFIX. Make sure .pdfWrite a program to convert a given INFIX into POSTFIX. Make sure .pdf
Write a program to convert a given INFIX into POSTFIX. Make sure .pdfFOREVERPRODUCTCHD
 
Labsheet 7 FP 201
Labsheet 7 FP 201Labsheet 7 FP 201
Labsheet 7 FP 201rohassanie
 
ECMAScript 6
ECMAScript 6ECMAScript 6
ECMAScript 6Piotr Lewandowski
 
Plsql programs(encrypted)
Plsql programs(encrypted)Plsql programs(encrypted)
Plsql programs(encrypted)Karunakar Singh Thakur
 
Stack - Data Structure - Notes
Stack - Data Structure - NotesStack - Data Structure - Notes
Stack - Data Structure - NotesOmprakash Chauhan
 
Stacks.ppt
Stacks.pptStacks.ppt
Stacks.pptOliverKane3
 
Stacks.ppt
Stacks.pptStacks.ppt
Stacks.pptOliverKane3
 
Pytest: escreva menos, teste mais
Pytest: escreva menos, teste maisPytest: escreva menos, teste mais
Pytest: escreva menos, teste maisErick Wilder
 
Oracle PL/SQL - Creative Conditional Compilation
Oracle PL/SQL - Creative Conditional CompilationOracle PL/SQL - Creative Conditional Compilation
Oracle PL/SQL - Creative Conditional CompilationScott Wesley
 
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfHoldier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfagholdier
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingTechSoup
 
Class 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdfClass 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdfAyushMahapatra5
 
General AI for Medical Educators April 2024
General AI for Medical Educators April 2024General AI for Medical Educators April 2024
General AI for Medical Educators April 2024Janet Corral
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Sapana Sha
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfciinovamais
 
fourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingfourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingTeacherCyreneCayanan
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Krashi Coaching
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationnomboosow
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeThiyagu K
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityGeoBlogs
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdfSoniaTolstoy
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDThiyagu K
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104misteraugie
 

Weitere ähnliche Inhalte

Ähnlich wie The essence of data step programming

Labsheet 7 FP 201
Labsheet 7 FP 201Labsheet 7 FP 201
Labsheet 7 FP 201rohassanie
 
Stack - Data Structure - Notes
Stack - Data Structure - NotesStack - Data Structure - Notes
Stack - Data Structure - NotesOmprakash Chauhan
 
Pytest: escreva menos, teste mais
Pytest: escreva menos, teste maisPytest: escreva menos, teste mais
Pytest: escreva menos, teste maisErick Wilder
 
Oracle PL/SQL - Creative Conditional Compilation
Oracle PL/SQL - Creative Conditional CompilationOracle PL/SQL - Creative Conditional Compilation
Oracle PL/SQL - Creative Conditional CompilationScott Wesley
 

Ähnlich wie The essence of data step programming (8)

Labsheet 7 FP 201
Labsheet 7 FP 201Labsheet 7 FP 201
Labsheet 7 FP 201
 
ECMAScript 6
ECMAScript 6ECMAScript 6
ECMAScript 6
 
Plsql programs(encrypted)
Plsql programs(encrypted)Plsql programs(encrypted)
Plsql programs(encrypted)
 
Stack - Data Structure - Notes
Stack - Data Structure - NotesStack - Data Structure - Notes
Stack - Data Structure - Notes
 
Stacks.ppt
Stacks.pptStacks.ppt
Stacks.ppt
 
Stacks.ppt
Stacks.pptStacks.ppt
Stacks.ppt
 
Pytest: escreva menos, teste mais
Pytest: escreva menos, teste maisPytest: escreva menos, teste mais
Pytest: escreva menos, teste mais
 
Oracle PL/SQL - Creative Conditional Compilation
Oracle PL/SQL - Creative Conditional CompilationOracle PL/SQL - Creative Conditional Compilation
Oracle PL/SQL - Creative Conditional Compilation
 

Kürzlich hochgeladen

Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfHoldier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfagholdier
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingTechSoup
 
Class 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdfClass 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdfAyushMahapatra5
 
General AI for Medical Educators April 2024
General AI for Medical Educators April 2024General AI for Medical Educators April 2024
General AI for Medical Educators April 2024Janet Corral
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Sapana Sha
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfciinovamais
 
fourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingfourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingTeacherCyreneCayanan
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Krashi Coaching
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationnomboosow
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeThiyagu K
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityGeoBlogs
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdfSoniaTolstoy
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDThiyagu K
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104misteraugie
 
9548086042 for call girls in Indira Nagar with room service
9548086042 for call girls in Indira Nagar with room service9548086042 for call girls in Indira Nagar with room service
9548086042 for call girls in Indira Nagar with room servicediscovermytutordmt
 
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...fonyou31
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...christianmathematics
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13Steve Thomason
 
Web & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfWeb & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfJayanti Pande
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdfQucHHunhnh
 

Kürzlich hochgeladen (20)

Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfHoldier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdf
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
Class 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdfClass 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdf
 
General AI for Medical Educators April 2024
General AI for Medical Educators April 2024General AI for Medical Educators April 2024
General AI for Medical Educators April 2024
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 
fourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingfourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writing
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communication
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activity
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
9548086042 for call girls in Indira Nagar with room service
9548086042 for call girls in Indira Nagar with room service9548086042 for call girls in Indira Nagar with room service
9548086042 for call girls in Indira Nagar with room service
 
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13
 
Web & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfWeb & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdf
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 

The essence of data step programming

  • 1. The Essence of DATA Step Programming Arthur Li City of Hope Comprehensive Cancer Center Department of Information Science
  • 2. INTRODUCTION SAS programming DATA step programming Understanding how SAS processes the data during the compilation and execution phases Fundamental: Essence:
  • 3.
  • 4.
  • 5. DATA STEP PROCESSING OVERVIEW Compilation phase: Each statement is scanned for syntax errors. Execution phase: The DATA step reads and processes the input data. If there is no syntax error A DATA step is processed in two-phase sequences :
  • 6.
  • 7.
  • 8. COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV PDV is created Memory area where SAS builds its new data set, 1 observation at a time. Input buffer _N_ D _ERROR_ D
  • 9. COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV PDV is created Automatic variables: _N_ = 1: 1 st observation is being processed _N_ = 2: 2 nd observation is being processed Input buffer _N_ D _ERROR_ D
  • 10. COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV PDV is created Automatic variables: _ERROR_ = 1: signals the data error of the currently-processed observation Input buffer _N_ D _ERROR_ D
  • 11. COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV A space is added to the PDV for each variable Input buffer _N_ D _ERROR_ D Height K Name K Weight K
  • 12. COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV BMI is added to the PDV Input buffer _N_ D _ERROR_ D Height K Name K Weight K BMI K
  • 13. COMPILATION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV D = dropped K = kept Input buffer _N_ D _ERROR_ D Height K Name K Weight K BMI K
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29. EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 1. The SAS system returns to the beginning of the DATA step Ex1: Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 1 . . 61 Barbara BMI Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 1 1 Barbara 61 . .
  • 30. EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV 2. The values of the variables in the PDV are reset to missing _N_ ↑ 2 _ERROR_  0 Ex1: Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 1 . . 61 Barbara BMI Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 . . .
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36. EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV Ex1: 1. The SAS system returns to the beginning of the DATA step Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 _N_ D _ERROR_ D Name K Height K Weight K BMI K 2 0 31.8678 62 John 175 2 1 31.8678 175 62 John . . 61 Barbara BMI Weight Height Name
  • 37. EXECUTION PHASE data ex1; infile 'C:rthurxample1.txt' ; input name $ 1 - 7 height 9 - 10 weight 12 - 14 ; BMI = 700 *weight/(height*height); output ; run ; PDV Ex1: 2. The values of the variables in the PDV are reset to missing _N_ ↑ 3 Input buffer Barbara 61 12D John 62 175 Example1.txt 12345678901234567890 2 1 31.8678 175 62 John . . 61 Barbara BMI Weight Height Name _N_ D _ERROR_ D Name K Height K Weight K BMI K 3 0 . . .
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
  • 51.
  • 52.
  • 53.
  • 54.
  • 55.
  • 56.
  • 57.
  • 58.
  • 59.
  • 60.
  • 61.
  • 62.
  • 63.
  • 64.
  • 65.
  • 66.
  • 67.
  • 68.
  • 69.
  • 70.
  • 71. THE SUM STATEMENT data ex2_2; set ex2; run ; retain total 0 ; total = sum(total, score); The previous program can be re-written as…
  • 72. THE SUM STATEMENT data ex2_2; set ex2; run ; The previous program can be re-written as… total + score;
  • 73.
  • 74.
  • 75.
  • 76.
  • 77.
  • 78.
  • 79.
  • 80.
  • 81.
  • 82.
  • 83.
  • 84.
  • 85.
  • 86.
  • 87.
  • 88.
  • 89.
  • 90.
  • 91.
  • 92.
  • 93.
  • 94.
  • 95.
  • 96.
  • 97.
  • 98.
  • 99.
  • 100.
  • 101.
  • 102.
  • 103.
  • 104.
  • 105.
  • 106.
  • 107.
  • 108.
  • 109.
  • 110.
  • 111.
  • 112.
  • 113.
  • 114.
  • 115.
  • 116.
  • 117.
  • 118.
  • 119.
  • 120.
  • 121.
  • 122.
  • 123.
  • 124.
  • 125.
  • 126.
  • 127.
  • 128.
  • 129.
  • 130.
  • 131.
  • 132.
  • 133. FROM LONG FORMAT TO WIDE FORMAT 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
  • 134.
  • 135.
  • 136. FROM LONG FORMAT TO WIDE FORMAT proc sort data =long; by id; run ; data wide (drop=time score); set long; by id; retain s1 - s3; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ; 4 3 S1 . 4 S2 2 A02 2 5 A01 1 S3 ID 3 1 3 2 1 TIME 2 A02 5 4 A02 4 5 A01 3 4 A01 2 3 A01 1 SCORE ID
  • 137.
  • 138.
  • 139.
  • 140.
  • 141.
  • 142.
  • 143.
  • 144.
  • 145.
  • 146.
  • 147.
  • 148.
  • 149.
  • 150.
  • 151.
  • 152.
  • 153.
  • 154.
  • 155.
  • 156.
  • 157.
  • 158.
  • 159.
  • 160.
  • 161.
  • 162.
  • 163.
  • 164.
  • 165. FROM LONG FORMAT TO WIDE FORMAT data wide (drop=time score); set long; by id; retain s1 - s3; if first.id then do ; s1 = . ; s2 = . ; s3 = . ; end ; if time = 1 then s1 = score; else if time = 2 then s2 = score; else s3 = score; if last.id; run ;
  • 166.
  • 167.
  • 168.
  • 169.
  • 170.
  • 171.
  • 172.
  • 173.
  • 174.
  • 175.
  • 176.
  • 177.
  • 178.
  • 179.
  • 180.
  • 181.
  • 182.