getGADS: Using a relational
eatGADS data baseThis vignette illustrates how a relational eatGADS data
base can be accessed and used. Therefore, the vignette is targeted at
users who make use of an existing data base.
For illustrative purposes we use a small example data base based on
the campus files of the German PISA Plus assessment. The complete campus
files and the original data set can be accessed here
and here.
The data base is installed alongside eatGADS and the path
can be accessed via the system.file() function.
library(eatGADS)
db_path <- system.file("extdata", "pisa.db", package = "eatGADS")
db_path
#> [1] "C:/Users/beckerbz/AppData/Local/Temp/RtmpWwxvhW/Rinst44a833da3575/eatGADS/extdata/pisa.db"Relational data bases created by eatGADS provide an
alternative way of storing hierarchically structured data (e.g. from
educational large-scale assessments). Compared to conventional
approaches (one big or multiple .sav/.Rdata
files) this yields the following advantages:
.sav files)eatGADS we can choose which variables to load into
RRWe can inspect the data base structure with the
namesGADS() function. The function returns a named
list. Every list element represents a hierarchy level. The
corresponding character vector contains all variable names on this
hierarchy level.
nam <- namesGADS(db_path)
nam
#> $noImp
#> [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"
#>
#> $PVs
#> [1] "idstud" "dimension" "imp" "value"The example data base contains two hierarchy levels: A student level
(noImp) and a plausible value level (PVs). On
the student level, each row represents an individual student. On the
plausible value level, each row represents an imputation number of a
specific domain of an individual student.
We can access meta information of the variables in the data set using
the extractMeta() function.
# Meta data for one variable
extractMeta(db_path, "age")
#> varName varLabel format display_width labeled value valLabel missings data_table
#> 1 age Age of student at T1 F8.2 NA no NA <NA> <NA> noImpTo supply variables names we can also use the named list
nam extracted earlier. This way, we can extract all meta
information available for a hierarchy level.
extractMeta(db_path, nam$PVs)
#> varName varLabel format display_width labeled value
#> 236 idstud Student-ID F8.0 NA no NA
#> 451 idstud Student-ID F8.0 NA no NA
#> 452 dimension Achievement dimension (math, reading, science) <NA> NA yes 1
#> 453 dimension Achievement dimension (math, reading, science) <NA> NA yes 2
#> 454 dimension Achievement dimension (math, reading, science) <NA> NA yes 3
#> 455 imp Number of imputation of plausible values <NA> NA no NA
#> 456 value Plausible Value <NA> NA no NA
#> valLabel missings data_table
#> 236 <NA> <NA> noImp
#> 451 <NA> <NA> PVs
#> 452 ma valid PVs
#> 453 rea valid PVs
#> 454 sci valid PVs
#> 455 <NA> <NA> PVs
#> 456 <NA> <NA> PVsCommonly 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")).
# Meta data for manually chosen multiple variables
extractMeta(db_path, c("idstud", "schtype"))
#> varName varLabel format display_width labeled value
#> 236 idstud Student-ID F8.0 NA no NA
#> 360 schtype School track F8.0 NA yes 1
#> 361 schtype School track F8.0 NA yes 2
#> 362 schtype School track F8.0 NA yes 3
#> 451 idstud Student-ID F8.0 NA no NA
#> valLabel missings data_table
#> 236 <NA> <NA> noImp
#> 360 Gymnasium (academic track) valid noImp
#> 361 Realschule valid noImp
#> 362 schools with several courses of education valid noImp
#> 451 <NA> <NA> PVsTo extract a data set from the data base, we can use the function
getGADS(). If the data base is stored on a server drive,
getGADS_fast() provides identical functionality but
substantially increases the performance. With the vSelect
argument we specify our variable selection. It is important to note that
getGADS() returns a so called GADSdat object.
This object type contains complex meta information (that is for example
also available in a SPSS data set), and is therefore not
directly usable for data analysis. We can, however, use the
extractMeta() function on it to access the meta data.
gads1 <- getGADS(filePath = db_path, vSelect = c("idstud", "schtype", "gender"))
class(gads1)
#> [1] "GADSdat" "list"
extractMeta(gads1)
#> varName varLabel format display_width labeled value
#> 194 gender Gender F8.0 NA yes 1
#> 195 gender Gender F8.0 NA yes 2
#> 236 idstud Student-ID F8.0 NA no NA
#> 360 schtype School track F8.0 NA yes 1
#> 361 schtype School track F8.0 NA yes 2
#> 362 schtype School track F8.0 NA yes 3
#> valLabel missings
#> 194 Female valid
#> 195 Male valid
#> 236 <NA> <NA>
#> 360 Gymnasium (academic track) valid
#> 361 Realschule valid
#> 362 schools with several courses of education validGADSdatIf 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, and dropPartialLabels)?
Per default, all missing tags are applied, meaning all values tagged
as missing are recoded to NA
(convertMiss == TRUE). Furthermore, per default, all value
labels are dropped (labels2character = NULL,
labels2factor = NULL, labels2ordered = NULL).
If for specific variables, value labels should be applied and the
resulting variable should be a character variable, this can specified
via, for example, setting
labels2character = c("var1", "var2").
## leave all labeled variables as numeric, convert missings to NA
dat1 <- extractData2(gads1)
head(dat1)
#> idstud schtype gender
#> 1 1 2 1
#> 2 2 3 1
#> 3 3 1 2
#> 4 4 3 2
#> 5 5 2 1
#> 6 6 3 1
## convert selected labeled variable(s) to character, convert missings to NA
dat2 <- extractData2(gads1, labels2character = c("schtype"))
head(dat2)
#> idstud schtype gender
#> 1 1 Realschule 1
#> 2 2 schools with several courses of education 1
#> 3 3 Gymnasium (academic track) 2
#> 4 4 schools with several courses of education 2
#> 5 5 Realschule 1
#> 6 6 schools with several courses of education 1
## convert all labeled variables to character, convert missings to NA
dat3 <- extractData2(gads1, labels2character = namesGADS(gads1))
head(dat3)
#> idstud schtype gender
#> 1 1 Realschule Female
#> 2 2 schools with several courses of education Female
#> 3 3 Gymnasium (academic track) Male
#> 4 4 schools with several courses of education Male
#> 5 5 Realschule Female
#> 6 6 schools with several courses of education FemaleIn general, we recommend leaving labeled variables as numeric and
converting values with missing codes to NA. If required,
values labels can always be accessed via using
extractMeta() on the GADSdat object or the
data base.
An important feature of eatGADS relational data bases
are that data sets are automatically returned on the correct hierarchy
level. For an overview of different data structures, see “Tidy Data” or this
article explaining long and wide format using repeated measures. In
educational large-scale assessments, data usually contain multiple
imputations or plausible values. Packages that enable us analyzing these
types of data (like eatRep) often
require these data in the long format.
The function getGADS() extracts data automatically in
the appropriate structure, depending on our variable selection. If we
select only variables from the student level, the data returned is on
the student level. Each student is represented in a single row.
gads1 <- getGADS(db_path, vSelect = c("schtype", "g8g9"))
dat1 <- extractData2(gads1)
dim(dat1)
#> [1] 500 3
head(dat1)
#> idstud schtype g8g9
#> 1 1 2 NA
#> 2 2 3 NA
#> 3 3 1 1
#> 4 4 3 NA
#> 5 5 2 NA
#> 6 6 3 NAIf additionally variables from the plausible Value data table are
extracted, the returned data structure changes. In the PVs
data table, data is stored on the “student x dimension x plausible value
number” level. The returned data has exactly this structure.
gads2 <- getGADS(db_path, vSelect = c("schtype", "value"))
dat2 <- extractData2(gads2)
dim(dat2)
#> [1] 7500 5
head(dat2)
#> idstud schtype dimension imp value
#> 1 1 2 1 1 0.15372011
#> 2 1 2 2 1 0.43914365
#> 3 1 2 3 1 0.13177617
#> 4 1 2 1 2 -0.04119330
#> 5 1 2 2 2 0.01991714
#> 6 1 2 3 2 0.67830064These two examples highlight another feature of
getGADS(): Only variables of substantial interest have to
be selected for extraction. The correct ID variables are added
automatically.
In educational large-scale assessments, a common challenge is
reporting longitudinal developments (trends).
getTrendGADS allows extracting data from multiple data
bases with identical variables in it.
trend_path1 <- system.file("extdata", "trend_gads_2020.db", package = "eatGADS")
trend_path2 <- system.file("extdata", "trend_gads_2015.db", package = "eatGADS")
trend_path3 <- system.file("extdata", "trend_gads_2010.db", package = "eatGADS")eatGADS comes with three small trend data bases which
can be used for illustrative purposes.
gads_trend <- getTrendGADS(filePaths = c(trend_path1, trend_path2, trend_path3),
vSelect = c("idstud", "dimension", "score"),
years = c(2020, 2015, 2010), fast = FALSE)
#> ----- Loading GADS 2020 -----
#> ----- Loading GADS 2015 -----
#> ----- Loading GADS 2010 -----
dat_trend <- extractData2(gads_trend)
head(dat_trend)
#> idstud dimension imp score year
#> 1 61 1 2 434.1060 2020
#> 2 61 2 2 434.1060 2020
#> 3 62 1 1 426.8199 2020
#> 4 62 2 1 426.8199 2020
#> 5 63 1 2 427.7327 2020
#> 6 63 2 2 427.7327 2020