# `import_spss`: Importing data from ‘SPSS’

#### Benjamin Becker

#### 2022-06-24

`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 `extractData()`. 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"
    #>   [6] "g8g9"         "ganztag"      "classsize"    "repeated"     "gender"
    #>  [11] "age"          "language"     "migration"    "hisced"       "hisei"
    #>  [16] "homepos"      "books"        "pared"        "computer_age" "internet_age"
    #>  [21] "int_use_a"    "int_use_b"    "truancy_a"    "truancy_b"    "truancy_c"
    #>  [26] "int_a"        "int_b"        "int_c"        "int_d"        "instmot_a"
    #>  [31] "instmot_b"    "instmot_c"    "instmot_d"    "norms_a"      "norms_b"
    #>  [36] "norms_c"      "norms_d"      "norms_e"      "norms_f"      "anxiety_a"
    #>  [41] "anxiety_b"    "anxiety_c"    "anxiety_d"    "anxiety_e"    "selfcon_a"
    #>  [46] "selfcon_b"    "selfcon_c"    "selfcon_d"    "selfcon_e"    "worketh_a"
    #>  [51] "worketh_b"    "worketh_c"    "worketh_d"    "worketh_e"    "worketh_f"
    #>  [56] "worketh_g"    "worketh_h"    "worketh_i"    "intent_a"     "intent_b"
    #>  [61] "intent_c"     "intent_d"     "intent_e"     "behav_a"      "behav_b"
    #>  [66] "behav_c"      "behav_d"      "behav_e"      "behav_f"      "behav_g"
    #>  [71] "behav_h"      "teach_a"      "teach_b"      "teach_c"      "teach_d"
    #>  [76] "teach_e"      "cognact_a"    "cognact_b"    "cognact_c"    "cognact_d"
    #>  [81] "cognact_e"    "cognact_f"    "cognact_g"    "cognact_h"    "cognact_i"
    #>  [86] "discpline_a"  "discpline_b"  "discpline_c"  "discpline_d"  "discpline_e"
    #>  [91] "relation_a"   "relation_b"   "relation_c"   "relation_d"   "relation_e"
    #>  [96] "belong_a"     "belong_b"     "belong_c"     "belong_d"     "belong_e"
    #> [101] "belong_f"     "belong_g"     "belong_h"     "belong_i"     "attitud_a"
    #> [106] "attitud_b"    "attitud_c"    "attitud_d"    "attitud_e"    "attitud_f"
    #> [111] "attitud_g"    "attitud_h"    "grade_de"     "grade_ma"     "grade_bio"
    #> [116] "grade_che"    "grade_phy"    "grade_sci"    "ma_pv1"       "ma_pv2"
    #> [121] "ma_pv3"       "ma_pv4"       "ma_pv5"       "rea_pv1"      "rea_pv2"
    #> [126] "rea_pv3"      "rea_pv4"      "rea_pv5"      "sci_pv1"      "sci_pv2"
    #> [131] "sci_pv3"      "sci_pv4"      "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` (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 `extractData()`. 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 (`convertLabels`, `dropPartialLabels`, `convertVariables`)? See `?extractData` for more details.

    ## convert labeled variables to characters
    dat1 <- extractData(gads_obj, convertLabels = "character")
    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 <- extractData(gads_obj, convertLabels = "numeric")
    dat2[1:5, 1:10]
    #>   idstud idschool idclass schtype sameteach g8g9 ganztag classsize repeated
    #> 1      1      127     392       2         2   NA       1         9        1
    #> 2      2       65     201       3         1   NA       1        10        1
    #> 3      3       10      34       1         1    1       1        28        1
    #> 4      4      103     319       3         2   NA       1        12        1
    #> 5      5       57     179       2         2   NA       2        25        1
    #>   gender
    #> 1      1
    #> 2      1
    #> 3      2
    #> 4      2
    #> 5      1

    ## leave labeled variables as numeric but convert some variables to character
    dat3 <- extractData(gads_obj, convertLabels = "character",
                        convertVariables = c("gender", "language"))
    dat3[1:5, 1:10]
    #>   idstud idschool idclass schtype sameteach g8g9 ganztag classsize repeated
    #> 1      1      127     392       2         2   NA       1         9        1
    #> 2      2       65     201       3         1   NA       1        10        1
    #> 3      3       10      34       1         1    1       1        28        1
    #> 4      4      103     319       3         2   NA       1        12        1
    #> 5      5       57     179       2         2   NA       2        25        1
    #>   gender
    #> 1 Female
    #> 2 Female
    #> 3   Male
    #> 4   Male
    #> 5 Female

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