`getGADS`: Using a relational eatGADS data base"
In eatGADS: Data Management of Large Hierarchical Data

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This 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

Why?

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:

the data set is often smaller on disk (especially compared to .sav files)
meta data can be accessed without loading the actual data
no need for reshaping, as the different hierarchy levels can be accessed independently
saving working memory: often R struggles with large data sets; with eatGADS we can choose which variables to load into R
flexible application of value labels and missing codes for analyses in R

Inspecting the data base

We 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

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")

To 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)

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")).

# Meta data for manually chosen multiple variables
extractMeta(db_path, c("idstud", "schtype"))

Extract data from data base

To 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)
extractMeta(gads1)

Extract 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, 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)

## convert selected labeled variable(s) to character, convert missings to NA
dat2 <- extractData2(gads1, labels2character = c("schtype"))
head(dat2)

## convert all labeled variables to character, convert missings to NA
dat3 <- extractData2(gads1, labels2character = namesGADS(gads1))
head(dat3)

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

Selecting different hierarchy levels

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)
head(dat1)

If 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)
head(dat2)

These 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.

Trend data bases

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)
dat_trend <- extractData2(gads_trend)
head(dat_trend)