### SPSS Learning Module Subsetting data

#### 1. Introduction

This module demonstrates how to subset data based on variables (using the keep and drop subcommands on the save command) and how to subset observations using the select if command.  The SPSS file structure is similar to a spreadsheet.  An SPSS data file contains variables, which are like columns on a spreadsheet, and observations (or cases or subjects) which are like the rows on a spreadsheet.  Sometimes data files contain information that is superfluous to a particular analysis and you might want to make a data file that has just the variables and/or observations you need for that analysis.

The following program reads the instream raw data file and creates an SPSS data file called auto.sav.  (For information about creating SPSS files from raw data, see the SPSS Learning Module on Inputting Data into SPSS.)

DATA LIST FIXED/
make  (A17) price 19-23 mpg 25-26 rep78 28 hdroom 30-32 (F,1)
trunk 34-35 weight 37-40 length 42-44 turn 46-47
displ 49-51 gratio 53-56 (F,2) foreign 58.
BEGIN DATA.
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
END DATA.
SAVE OUTFILE = 'auto.sav'.

We will now use the display names command to see the names of the variables in the current data file.

DISPLAY NAMES.

As we expect, this shows us the names of all of the variables that we read on the data list command.

Currently Defined Variables

MAKE      MPG       HDROOM    WEIGHT    TURN      DISPL     GRATIO    FOREIGN
PRICE     REP78     TRUNK     LENGTH

#### 2. Subsetting variables

If we wanted to examine the relationship between mpg and price for various makes, but had no interest in the automobile's dimensions, we could create a smaller file called auto2.sav that will have just these three variables:

SAVE OUTFILE= 'auto2.sav'
/KEEP make mpg price.

This does not influence the current data file.  If we use the display names command now, we will see that all of the variables are still present.

DISPLAY NAMES.

As we see below, all of the variables are still present.

Currently Defined Variables

MAKE      MPG       HDROOM    WEIGHT    TURN      DISPL     GRATIO    FOREIGN
PRICE     REP78     TRUNK     LENGTH

If we read the file auto2.sav, we will see that the file auto2.sav has been subsetted and has just the variables make mpg and price.

GET FILE = 'auto2.sav'.
DISPLAY NAMES.

As we see below, just make mpg and price are present.

Currently Defined Variables

MAKE      MPG       PRICE

Let's get the auto.sav file, and then illustrate subsetting using the drop subcommand.  We will save the file calling it auto3.sav and we will use the drop subcommand to drop all of the variables except for make mpg and price.  We then get the file auto3.sav and then issue the display names command.

GET FILE='auto.sav'.
SAVE OUTFILE= 'auto3.sav'
/DROP rep78 hdroom trunk weight length turn displ gratio foreign.
GET FILE='auto3.sav'.
DISPLAY NAMES.

As we expect, the file auto3.sav contains just the variables make price and mpg.  It would be more common to use the dropsubcommand when you want to drop just a small number of variables, and it would be more common to use the keep subcommand when you want to keep just a small number of variables.

Currently Defined Variables

MAKE      PRICE     MPG

#### 3. Subsetting observations

The above illustrates the use of keep and drop subcommands for subsetting variables.  The following will illustrate the use of select if to subset observations (sometimes called cases).

The auto file contains a variable rep78 with data values from 1 to 5, and missing, which we ascertain from running the following program:

GET FILE='auto.sav'.
FREQUENCIES VARIABLES=rep78.

The table below shows that there are 5 observations where rep78 is missing, and 69 observations where rep78 is not missing.

REP78
Valid     Cum
Value Label                 Value  Frequency  Percent  Percent  Percent
1         2      2.7      2.9      2.9
2         8     10.8     11.6     14.5
3        30     40.5     43.5     58.0
4        18     24.3     26.1     84.1
5        11     14.9     15.9    100.0
.         5      6.8   Missing
-------  -------  -------
Total        74    100.0    100.0

Valid cases      69      Missing cases      5

If we are only interested in cars with data where rep78 is not missing, we may eliminate records with missing data from the file by using select if as shown below.

SELECT IF NOT MISSING(rep78).
FREQUENCIES VARIABLES=rep78.

As we see below, the select if eliminated the observations with missing values.  At this point we could save the file and we would have just the 69 observations where rep78 was not missing.

REP78
Valid     Cum
Value Label                 Value  Frequency  Percent  Percent  Percent
1         2      2.9      2.9      2.9
2         8     11.6     11.6     14.5
3        30     43.5     43.5     58.0
4        18     26.1     26.1     84.1
5        11     15.9     15.9    100.0
-------  -------  -------
Total        69    100.0    100.0
Valid cases      69      Missing cases      0

The following program gets the auto.sav file and then uses select if to select just the observations where rep78 is 3 or smaller.

GET FILE='auto.sav'.
SELECT IF (REP78 <= 3).
FREQUENCIES VARIABLES=rep78.

The frequencies results are shown below, confirming that the select if worked correctly.  If we wanted, we could save the file at this time to create a subset that has just the observations where rep78 is 3 or smaller.

REP78
Valid     Cum
Value Label                 Value  Frequency  Percent  Percent  Percent
1         2      5.0      5.0      5.0
2         8     20.0     20.0     25.0
3        30     75.0     75.0    100.0
-------  -------  -------
Total        40    100.0    100.0
Valid cases      40      Missing cases      0

The following program is similar to the one above, except that it selects the observations where rep78 is 4 or higher.

GET FILE='auto.sav'.
SELECT IF (REP78 >= 4).
FREQUENCIES VARIABLES=rep78.

The results from frequencies is shown below, confirming that just the observations where rep78 is 4 or greater have been selected.

REP78
Valid     Cum
Value Label                 Value  Frequency  Percent  Percent  Percent
4        18     62.1     62.1     62.1
5        11     37.9     37.9    100.0
-------  -------  -------
Total        29    100.0    100.0
Valid cases      29      Missing cases      0

#### 4. Problems to look out for

• When you create a subset of your original data, sometimes you may drop variables or cases that you did not intend to drop.  If you find variables or cases are gone that should not be gone, double check your subsetting commands.