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This program builds a SAS file called auto, which we will use to demonstrate the use of the where statement. (For information about creating SAS files from raw data, see the SAS Learning Module titled Inputting Raw Data into SAS.
DATA auto ;
LENGTH make $ 20 ;
INPUT make $ 1-17 price mpg rep78 hdroom trunk weight length turn
displ gratio foreign ;
CARDS;
AMC Concord 4099 22 3 2.5 11 2930 186 40 121 3.58 0
AMC Pacer 4749 17 3 3.0 11 3350 173 40 258 2.53 0
AMC Spirit 3799 22 . 3.0 12 2640 168 35 121 3.08 0
Audi 5000 9690 17 5 3.0 15 2830 189 37 131 3.20 1
Audi Fox 6295 23 3 2.5 11 2070 174 36 97 3.70 1
BMW 320i 9735 25 4 2.5 12 2650 177 34 121 3.64 1
Buick Century 4816 20 3 4.5 16 3250 196 40 196 2.93 0
Buick Electra 7827 15 4 4.0 20 4080 222 43 350 2.41 0
Buick LeSabre 5788 18 3 4.0 21 3670 218 43 231 2.73 0
Buick Opel 4453 26 . 3.0 10 2230 170 34 304 2.87 0
Buick Regal 5189 20 3 2.0 16 3280 200 42 196 2.93 0
Buick Riviera 10372 16 3 3.5 17 3880 207 43 231 2.93 0
Buick Skylark 4082 19 3 3.5 13 3400 200 42 231 3.08 0
Cad. Deville 11385 14 3 4.0 20 4330 221 44 425 2.28 0
Cad. Eldorado 14500 14 2 3.5 16 3900 204 43 350 2.19 0
Cad. Seville 15906 21 3 3.0 13 4290 204 45 350 2.24 0
Chev. Chevette 3299 29 3 2.5 9 2110 163 34 231 2.93 0
Chev. Impala 5705 16 4 4.0 20 3690 212 43 250 2.56 0
Chev. Malibu 4504 22 3 3.5 17 3180 193 31 200 2.73 0
Chev. Monte Carlo 5104 22 2 2.0 16 3220 200 41 200 2.73 0
Chev. Monza 3667 24 2 2.0 7 2750 179 40 151 2.73 0
Chev. Nova 3955 19 3 3.5 13 3430 197 43 250 2.56 0
Datsun 200 6229 23 4 1.5 6 2370 170 35 119 3.89 1
Datsun 210 4589 35 5 2.0 8 2020 165 32 85 3.70 1
Datsun 510 5079 24 4 2.5 8 2280 170 34 119 3.54 1
Datsun 810 8129 21 4 2.5 8 2750 184 38 146 3.55 1
Dodge Colt 3984 30 5 2.0 8 2120 163 35 98 3.54 0
Dodge Diplomat 4010 18 2 4.0 17 3600 206 46 318 2.47 0
Dodge Magnum 5886 16 2 4.0 17 3600 206 46 318 2.47 0
Dodge St. Regis 6342 17 2 4.5 21 3740 220 46 225 2.94 0
Fiat Strada 4296 21 3 2.5 16 2130 161 36 105 3.37 1
Ford Fiesta 4389 28 4 1.5 9 1800 147 33 98 3.15 0
Ford Mustang 4187 21 3 2.0 10 2650 179 43 140 3.08 0
Honda Accord 5799 25 5 3.0 10 2240 172 36 107 3.05 1
Honda Civic 4499 28 4 2.5 5 1760 149 34 91 3.30 1
Linc. Continental 11497 12 3 3.5 22 4840 233 51 400 2.47 0
Linc. Mark V 13594 12 3 2.5 18 4720 230 48 400 2.47 0
Linc. Versailles 13466 14 3 3.5 15 3830 201 41 302 2.47 0
Mazda GLC 3995 30 4 3.5 11 1980 154 33 86 3.73 1
Merc. Bobcat 3829 22 4 3.0 9 2580 169 39 140 2.73 0
Merc. Cougar 5379 14 4 3.5 16 4060 221 48 302 2.75 0
Merc. Marquis 6165 15 3 3.5 23 3720 212 44 302 2.26 0
Merc. Monarch 4516 18 3 3.0 15 3370 198 41 250 2.43 0
Merc. XR-7 6303 14 4 3.0 16 4130 217 45 302 2.75 0
Merc. Zephyr 3291 20 3 3.5 17 2830 195 43 140 3.08 0
Olds 98 8814 21 4 4.0 20 4060 220 43 350 2.41 0
Olds Cutl Supr 5172 19 3 2.0 16 3310 198 42 231 2.93 0
Olds Cutlass 4733 19 3 4.5 16 3300 198 42 231 2.93 0
Olds Delta 88 4890 18 4 4.0 20 3690 218 42 231 2.73 0
Olds Omega 4181 19 3 4.5 14 3370 200 43 231 3.08 0
Olds Starfire 4195 24 1 2.0 10 2730 180 40 151 2.73 0
Olds Toronado 10371 16 3 3.5 17 4030 206 43 350 2.41 0
Peugeot 604 12990 14 . 3.5 14 3420 192 38 163 3.58 1
Plym. Arrow 4647 28 3 2.0 11 3260 170 37 156 3.05 0
Plym. Champ 4425 34 5 2.5 11 1800 157 37 86 2.97 0
Plym. Horizon 4482 25 3 4.0 17 2200 165 36 105 3.37 0
Plym. Sapporo 6486 26 . 1.5 8 2520 182 38 119 3.54 0
Plym. Volare 4060 18 2 5.0 16 3330 201 44 225 3.23 0
Pont. Catalina 5798 18 4 4.0 20 3700 214 42 231 2.73 0
Pont. Firebird 4934 18 1 1.5 7 3470 198 42 231 3.08 0
Pont. Grand Prix 5222 19 3 2.0 16 3210 201 45 231 2.93 0
Pont. Le Mans 4723 19 3 3.5 17 3200 199 40 231 2.93 0
Pont. Phoenix 4424 19 . 3.5 13 3420 203 43 231 3.08 0
Pont. Sunbird 4172 24 2 2.0 7 2690 179 41 151 2.73 0
Renault Le Car 3895 26 3 3.0 10 1830 142 34 79 3.72 1
Subaru 3798 35 5 2.5 11 2050 164 36 97 3.81 1
Toyota Celica 5899 18 5 2.5 14 2410 174 36 134 3.06 1
Toyota Corolla 3748 31 5 3.0 9 2200 165 35 97 3.21 1
Toyota Corona 5719 18 5 2.0 11 2670 175 36 134 3.05 1
Volvo 260 11995 17 5 2.5 14 3170 193 37 163 2.98 1
VW Dasher 7140 23 4 2.5 12 2160 172 36 97 3.74 1
VW Diesel 5397 41 5 3.0 15 2040 155 35 90 3.78 1
VW Rabbit 4697 25 4 3.0 15 1930 155 35 89 3.78 1
VW Scirocco 6850 25 4 2.0 16 1990 156 36 97 3.78 1
;
RUN;
The where statement allows us to run procedures on a subset of records. For example, instead of printing all records in the file, the following program prints only cars where the value for rep78 is 3 or greater.
PROC PRINT DATA=auto; WHERE (rep78 >= 3); VAR make rep78; RUN;
Here is the output from the proc print. Note that we have directed SAS to print only two variables: make and rep78.
OBS MAKE rep78 1 AMC Concord 3 2 AMC Pacer 3 4 Audi 5000 5 5 Audi Fox 3 6 BMW 320i 4 7 Buick Century 3 8 Buick Electra 4 9 Buick LeSabre 3 11 Buick Regal 3 12 Buick Riviera 3 13 Buick Skylark 3 14 Cad. Deville 3 16 Cad. Seville 3 17 Chev. Chevette 3 18 Chev. Impala 4 19 Chev. Malibu 3 22 Chev. Nova 3 23 Datsun 200 4 24 Datsun 210 5 25 Datsun 510 4 26 Datsun 810 4 27 Dodge Colt 5 31 Fiat Strada 3 32 Ford Fiesta 4 33 Ford Mustang 3 34 Honda Accord 5 35 Honda Civic 4 36 Linc. Continental 3 37 Linc. Mark V 3 38 Linc. Versailles 3 39 Mazda GLC 4 40 Merc. Bobcat 4 41 Merc. Cougar 4 42 Merc. Marquis 3 43 Merc. Monarch 3 44 Merc. XR-7 4 45 Merc. Zephyr 3 46 Olds 98 4 47 Olds Cutl Supr 3 48 Olds Cutlass 3 49 Olds Delta 88 4 50 Olds Omega 3 52 Olds Toronado 3 54 Plym. Arrow 3 55 Plym. Champ 5 56 Plym. Horizon 3 59 Pont. Catalina 4 61 Pont. Grand Prix 3 62 Pont. Le Mans 3 65 Renault Le Car 3 66 Subaru 5 67 Toyota Celica 5 68 Toyota Corolla 5 69 Toyota Corona 5 70 Volvo 260 5 71 VW Dasher 4 72 VW Diesel 5 73 VW Rabbit 4 74 VW Scirocco 4
Consider the following program which compares repair records for foreign and domestic cars by creating a table of repairs (rep78) for each separately.
PROC FREQ DATA=auto;
TABLES rep78*foreign ;
RUN;
TABLE OF rep78 BY FOREIGN
rep78 FOREIGN
Frequency=
Percent =
Row Pct =
Col Pct = 0= 1= Total
============================
1 = 2 = 0 = 2
= 2.90 = 0.00 = 2.90
= 100.00 = 0.00 =
= 4.17 = 0.00 =
============================
2 = 8 = 0 = 8
= 11.59 = 0.00 = 11.59
= 100.00 = 0.00 =
= 16.67 = 0.00 =
============================
3 = 27 = 3 = 30
= 39.13 = 4.35 = 43.48
= 90.00 = 10.00 =
= 56.25 = 14.29 =
============================
4 = 9 = 9 = 18
= 13.04 = 13.04 = 26.09
= 50.00 = 50.00 =
= 18.75 = 42.86 =
============================
5 = 2 = 9 = 11
= 2.90 = 13.04 = 15.94
= 18.18 = 81.82 =
= 4.17 = 42.86 =
============================
Total 48 21 69
69.57 30.43 100.00
Using the where statement, we restrict the analysis to only cars with a repair rating of 3 or more (rep78 >= 3):
PROC FREQ DATA=auto;
WHERE (rep78 >= 3);
TABLES rep78*foreign ;
RUN;
TABLE OF rep78 BY FOREIGN
rep78 FOREIGN
Frequency=
Percent =
Row Pct =
Col Pct = 0= 1= Total
============================
3 = 27 = 3 = 30
= 45.76 = 5.08 = 50.85
= 90.00 = 10.00 =
= 71.05 = 14.29 =
============================
4 = 9 = 9 = 18
= 15.25 = 15.25 = 30.51
= 50.00 = 50.00 =
= 23.68 = 42.86 =
============================
5 = 2 = 9 = 11
= 3.39 = 15.25 = 18.64
= 18.18 = 81.82 =
= 5.26 = 42.86 =
============================
Total 38 21 59
64.41 35.59 100.00
The where statement works with most SAS procedures. The following program prints only records for which the car has a repair rating of 2 or less:
PROC PRINT DATA=auto; WHERE (rep78 <= 2); VAR make price rep78 ; RUN; OBS MAKE price rep78 3 AMC Spirit 3799 . 10 Buick Opel 4453 . 15 Cad. Eldorado 14500 2 20 Chev. Monte Carlo 5104 2 21 Chev. Monza 3667 2 28 Dodge Diplomat 4010 2 29 Dodge Magnum 5886 2 30 Dodge St. Regis 6342 2 51 Olds Starfire 4195 1 53 Peugeot 604 12990 . 57 Plym. Sapporo 6486 . 58 Plym. Volare 4060 2 60 Pont. Firebird 4934 1 63 Pont. Phoenix 4424 . 64 Pont. Sunbird 4172 2
In the example above, note that some of the records print a '.' instead of a value for rep78. These are records where rep78 is missing. SAS stores missing values for numeric variables as '.' and treats them as negative infinity, or the lowest number possible. To exclude missing values, modify the where statement as follows (the rep78 ^= . indicates rep78 is not equal to missing).
PROC PRINT DATA=auto; WHERE (rep78 <= 2) and (rep78 ^= .) ; VAR make price rep78 ; RUN;
Note that there are no missing values in the listing.
OBS MAKE price rep78 15 Cad. Eldorado 14500 2 20 Chev. Monte Carlo 5104 2 21 Chev. Monza 3667 2 28 Dodge Diplomat 4010 2 29 Dodge Magnum 5886 2 30 Dodge St. Regis 6342 2 51 Olds Starfire 4195 1 58 Plym. Volare 4060 2 60 Pont. Firebird 4934 1 64 Pont. Sunbird 4172 2
Similarly, this where statement yields the same result:
PROC PRINT DATA=auto; WHERE (. < rep78 <= 2); VAR make price rep78 ; RUN;
This program generates summary statistics for price, but only for cars with repair histories of 1 or 2:
PROC MEANS DATA=auto; WHERE (rep78 = 1) OR (rep78 = 2) ; VAR price ; RUN;
Here is the output from the proc means. By default, proc means will generate the following statistics: mean, minimum and maximum values, standard deviation, and the number of non-missing values for the analysis variable (in this case price).
Analysis Variable : price N Mean Std Dev Minimum Maximum ---------------------------------------------------------- 10 5687.00 3216.38 3667.00 14500.00 ----------------------------------------------------------
To see summary statistics for price for cars with repair histories of 3, 4 or 5, modify the where statement accordingly:
PROC MEANS DATA=auto; WHERE (rep78 = 3) or (rep78 = 4) or (rep78 = 5) ; VAR price ; RUN;
Or:
PROC MEANS DATA=auto; WHERE (3 <= rep78 <= 5) ; VAR price ; RUN; Analysis Variable : price N Mean Std Dev Minimum Maximum ---------------------------------------------------------- 59 6223.85 2880.45 3291.00 15906.00 ----------------------------------------------------------
The where statement also works with the in operator as follows:
PROC MEANS DATA=auto; WHERE rep78 in (3,4,5); VAR price ; RUN;
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