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import_spss: Importing data from ‘SPSS’

Benjamin Becker

2024-10-09

import_spss() allows importing data from SPSS (.sav and .zsav files) into R by using the R package haven.

This vignette illustrates a typical workflow of importing a SPSS file using import_spss() and extractData2(). For illustrative purposes we use a small example data set from the campus files of the German PISA Plus assessment. The complete campus files and the original data set can be accessed here and here.

Importing

library(eatGADS)

We can import an .sav data set via the import_spss() function. Checks on variable names (for data base compatibility) are performed automatically. Changes to the variable names are reported to the console. This behavior can be suppressed by setting checkVarNames = FALSE.

sav_path <- system.file("extdata", "pisa.zsav", package = "eatGADS")
gads_obj <- import_spss(sav_path)

GADSdat objects

The resulting object is of the class GADSdat. It is basically a named list containing the actual data (dat) and the meta data (labels).

class(gads_obj)
#> [1] "GADSdat" "list"
names(gads_obj)
#> [1] "dat"    "labels"

The names of the variables in a GADSdat object can be accessed via the namesGADS() function. The meta data of variables can be accessed via the extractMeta() function.

namesGADS(gads_obj)
#>   [1] "idstud"       "idschool"     "idclass"      "schtype"      "sameteach"    "g8g9"        
#>   [7] "ganztag"      "classsize"    "repeated"     "gender"       "age"          "language"    
#>  [13] "migration"    "hisced"       "hisei"        "homepos"      "books"        "pared"       
#>  [19] "computer_age" "internet_age" "int_use_a"    "int_use_b"    "truancy_a"    "truancy_b"   
#>  [25] "truancy_c"    "int_a"        "int_b"        "int_c"        "int_d"        "instmot_a"   
#>  [31] "instmot_b"    "instmot_c"    "instmot_d"    "norms_a"      "norms_b"      "norms_c"     
#>  [37] "norms_d"      "norms_e"      "norms_f"      "anxiety_a"    "anxiety_b"    "anxiety_c"   
#>  [43] "anxiety_d"    "anxiety_e"    "selfcon_a"    "selfcon_b"    "selfcon_c"    "selfcon_d"   
#>  [49] "selfcon_e"    "worketh_a"    "worketh_b"    "worketh_c"    "worketh_d"    "worketh_e"   
#>  [55] "worketh_f"    "worketh_g"    "worketh_h"    "worketh_i"    "intent_a"     "intent_b"    
#>  [61] "intent_c"     "intent_d"     "intent_e"     "behav_a"      "behav_b"      "behav_c"     
#>  [67] "behav_d"      "behav_e"      "behav_f"      "behav_g"      "behav_h"      "teach_a"     
#>  [73] "teach_b"      "teach_c"      "teach_d"      "teach_e"      "cognact_a"    "cognact_b"   
#>  [79] "cognact_c"    "cognact_d"    "cognact_e"    "cognact_f"    "cognact_g"    "cognact_h"   
#>  [85] "cognact_i"    "discpline_a"  "discpline_b"  "discpline_c"  "discpline_d"  "discpline_e" 
#>  [91] "relation_a"   "relation_b"   "relation_c"   "relation_d"   "relation_e"   "belong_a"    
#>  [97] "belong_b"     "belong_c"     "belong_d"     "belong_e"     "belong_f"     "belong_g"    
#> [103] "belong_h"     "belong_i"     "attitud_a"    "attitud_b"    "attitud_c"    "attitud_d"   
#> [109] "attitud_e"    "attitud_f"    "attitud_g"    "attitud_h"    "grade_de"     "grade_ma"    
#> [115] "grade_bio"    "grade_che"    "grade_phy"    "grade_sci"    "ma_pv1"       "ma_pv2"      
#> [121] "ma_pv3"       "ma_pv4"       "ma_pv5"       "rea_pv1"      "rea_pv2"      "rea_pv3"     
#> [127] "rea_pv4"      "rea_pv5"      "sci_pv1"      "sci_pv2"      "sci_pv3"      "sci_pv4"     
#> [133] "sci_pv5"
extractMeta(gads_obj, vars = c("schtype", "idschool"))
#>    varName     varLabel format display_width labeled value
#> 2 idschool    School-ID   F8.0            NA      no    NA
#> 4  schtype School track   F8.0            NA     yes     1
#> 5  schtype School track   F8.0            NA     yes     2
#> 6  schtype School track   F8.0            NA     yes     3
#>                                    valLabel missings
#> 2                                      <NA>     <NA>
#> 4                Gymnasium (academic track)    valid
#> 5                                Realschule    valid
#> 6 schools with several courses of education    valid

Commonly, the most informative columns are varLabel (containing variable labels), value (referencing labeled values), valLabel (containing value labels) and missings (missing tag: is a labeled value a missing value ("miss") or not ("valid")).

Extracting data from GADSdat

If we want to use the data for analyses in R we have to extract it from the GADSdat object via the function extractData2(). In doing so, we have to make two important decisions: (a) how should values marked as missing values be treated (convertMiss)? And (b) how should labeled values in general be treated (labels2character, labels2factor, labels2ordered, dropPartialLabels)?

If a variable name is not provided under any of labels2character, labels2factor, labels2ordered, all value labels of the corresponding variable are simply dropped. If a variable name is provided under labels2character, the value labels of the corresponding variable are applied and the resulting variable is a character variable. labels2factor converts variables to factor and labels2ordered converts variables to ordered factors.

See ?extractData2 for more details.

## convert all labeled variables to character
dat1 <- extractData2(gads_obj, labels2character = namesGADS(gads_obj))
dat1[1:5, 1:10]
#>   idstud idschool idclass                                   schtype sameteach
#> 1      1      127     392                                Realschule       Yes
#> 2      2       65     201 schools with several courses of education        No
#> 3      3       10      34                Gymnasium (academic track)        No
#> 4      4      103     319 schools with several courses of education       Yes
#> 5      5       57     179                                Realschule       Yes
#>                     g8g9 ganztag classsize               repeated gender
#> 1                   <NA>      No         9 Did not repeat a grade Female
#> 2                   <NA>      No        10 Did not repeat a grade Female
#> 3 G8 - 8 years to abitur      No        28 Did not repeat a grade   Male
#> 4                   <NA>      No        12 Did not repeat a grade   Male
#> 5                   <NA>     Yes        25 Did not repeat a grade Female

## leave labeled variables as numeric
dat2 <- extractData2(gads_obj)
dat2[1:5, 1:10]
#>   idstud idschool idclass schtype sameteach g8g9 ganztag classsize repeated gender
#> 1      1      127     392       2         2   NA       1         9        1      1
#> 2      2       65     201       3         1   NA       1        10        1      1
#> 3      3       10      34       1         1    1       1        28        1      2
#> 4      4      103     319       3         2   NA       1        12        1      2
#> 5      5       57     179       2         2   NA       2        25        1      1

## leave labeled variables as numeric but convert some variables to character and some to factor
dat3 <- extractData2(gads_obj, labels2character = c("gender", "language"),
                     labels2factor = c("schtype", "sameteach"))
dat3[1:5, 1:10]
#>   idstud idschool idclass                                   schtype sameteach g8g9 ganztag
#> 1      1      127     392                                Realschule       Yes   NA       1
#> 2      2       65     201 schools with several courses of education        No   NA       1
#> 3      3       10      34                Gymnasium (academic track)        No    1       1
#> 4      4      103     319 schools with several courses of education       Yes   NA       1
#> 5      5       57     179                                Realschule       Yes   NA       2
#>   classsize repeated gender
#> 1         9        1 Female
#> 2        10        1 Female
#> 3        28        1   Male
#> 4        12        1   Male
#> 5        25        1 Female

In general, we recommend leaving labeled variables as numeric and converting values with missing codes to NA. Both are the default behavior for extractData2(). If required, value labels can always be accessed via using extractMeta() on the GADSdat object or the data base.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.