import_spss: Importing data from ‘SPSS’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.
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)As haven currently does not always import value labels and missing codes for long string variables correctly, specific warnings for these variables are issued. I recommend checking the correct import of these variables.
GADSdat objectsThe resulting object is of the class GADSdat. It is basically a named list containing the actual data (dat) and the meta data (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 validCommonly 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")).
GADSdatIf 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 FemaleIn 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.