# Handling meta data

#### Benjamin Becker

#### 2025-05-21

When trying to understand data, most often not only the actual data is required, but also so called meta data. Meta data usually includes:

- variable labels: Labels describing, what a variable in the data represents
- value labels: Labels describing, what a certain value on a certain variable represents

While the `data.frame` class in `R` supports value labels to a certain degree with the `factor` class, its functionality is limited. Other data formats like `.xlsx` or `.csv` support no meta data at all. Commercial software like `SPSS` provides such functionality but can not compete with the various tools for analyzing data that `R` provides.

`eatGADS` is an `R` package that was developed to bridge this gap. Its main purpose is providing a data format in `R` specifically designed for storing meta data together with data in one place. Therefore it provides an `S3` class called `GADSdat`. The following vignette concentrates on how to import data into the `GADSdat` format and work with it in the `R` environment. In collaboration with the `IQB Forschungsdatenzentrum (FDZ)` the package can also be used to distribute data.

Note that `eatGADS` also allows the handling of large hierarchical data structures via relational data bases. This functionality is explained in more detail in an additional vignette.

## Setup

The package can be installed from GitHub. Note that older R versions had issues with installations from online repositories like GitHub. `R` version `> 3.6.0` should work without any issues.

    devtools::install_github("beckerbenj/eatGADS")

    # loading the package
    library(eatGADS)

## Importing data into the `GADSdat` format

### Importing from SPSS

`R` offers a variety of tools to import data from all sorts of data formats. `SPSS` data (`.sav` files) can be imported directly into the `GADSdat` format, with `haven` used as a backend. Note that this is the easiest way to import data into the `GADSdat` format.

    # importing an SPSS file
    gads <- import_spss("path/example.sav")

### Importing from Excel etc.

All other file types should be imported into `R` first and then supplied as `data.frames` to `import_raw`. Below is a small selection of functions that import data as `data.frames`. For an extensive overview of importing functions using the package `readr` see also this book chapter, while the package `readxl` is explained in more detail on this [homepage] (https://readxl.tidyverse.org/). As these files are plain data files, meta data has to be supplied as separate data sheets.

Note that none of the `data.frames` can contain variables of the class `factor`, as this in itself constitutes meta data. If using base `R` to import data make sure to use the argument `stringsAsFactors = FALSE`. If necessary, convert `factors` to character via `as.character`.

    # importing text files
    input_txt <- read.table("path/example.txt", stringsAsFactors = FALSE)
    # importing German csv files (; separated)
    input_csv <- read.csv2("path/example.csv", stringsAsFactors = FALSE)
    # importing Excel files
    input_xlsx <- readxl::read_excel("path/example.xlsx")

`import_raw` takes three separate `data.frames` as input. The actual data set (`df`), the variable labels (`varLabels`) and the value labels (`valLabels`). These three objects have to be supplied in a very specific format.

The `varLabels` object has to contain two variables: `varName`, which should exactly correspond to the variable names in `df` and `varLabels` which should contain the desired variable labels as strings. Note that this `data.frame` should contain as many rows as there are variables in `df`.

The optional `valLabels` object has to contain four variables: `varName`, which should exactly correspond to the variable names in `df`; `values`, which should correspond to the respective values in `df` and has to be a numeric vector (labels for character vectors are currently not supported); `valLabels`, which should contain the value labels as strings; and `missings`, a column indicating whether the value indicates a missing value. Valid values for `missings` are `"valid"` = no missing code and `"miss"` = missing code. Note that this `data.frame` can not contain any `varNames` that are not variables in `df`. However, not all variables in `df` have to occur in `valLabels`.

    # Example data set
    df <- data.frame(ID = 1:4, sex = c(0, 0, 1, 1),
                     forename = c("Tim", "Bill", "Ann", "Chris"), stringsAsFactors = FALSE)
    # Example variable labels
    varLabels <- data.frame(varName = c("ID", "sex", "forename"),
                            varLabel = c("Person Identifier", "Sex as self reported",
                                         "first name as reported by teacher"),
                            stringsAsFactors = FALSE)
    # Example value labels
    valLabels <- data.frame(varName = rep("sex", 3),
                            value = c(0, 1, -99),
                            valLabel = c("male", "female", "missing - omission"),
                            missings = c("valid", "valid", "miss"), stringsAsFactors = FALSE)

    df
    #>   ID sex forename
    #> 1  1   0      Tim
    #> 2  2   0     Bill
    #> 3  3   1      Ann
    #> 4  4   1    Chris
    varLabels
    #>    varName                          varLabel
    #> 1       ID                 Person Identifier
    #> 2      sex              Sex as self reported
    #> 3 forename first name as reported by teacher
    valLabels
    #>   varName value           valLabel missings
    #> 1     sex     0               male    valid
    #> 2     sex     1             female    valid
    #> 3     sex   -99 missing - omission     miss

    # import
    gads <- import_raw(df = df, varLabels = varLabels, valLabels = valLabels)

## `GADSdat` class

The resulting object is of the class `GADSdat` and contains a data sheet and a meta data sheet.

    # Inpsect resulting object
    gads
    #> $dat
    #>   ID sex forename
    #> 1  1   0      Tim
    #> 2  2   0     Bill
    #> 3  3   1      Ann
    #> 4  4   1    Chris
    #>
    #> $labels
    #>    varName                          varLabel format display_width labeled value           valLabel
    #> 1       ID                 Person Identifier   <NA>            NA      no    NA               <NA>
    #> 2      sex              Sex as self reported   <NA>            NA     yes   -99 missing - omission
    #> 3      sex              Sex as self reported   <NA>            NA     yes     0               male
    #> 4      sex              Sex as self reported   <NA>            NA     yes     1             female
    #> 5 forename first name as reported by teacher   <NA>            NA      no    NA               <NA>
    #>   missings
    #> 1     <NA>
    #> 2     miss
    #> 3    valid
    #> 4    valid
    #> 5     <NA>
    #>
    #> attr(,"class")
    #> [1] "GADSdat" "list"

### Saving `GADSdat` objects

`GADSdat` objects, for example, can be saved as `RDS` files. This is also the preferred data format for distributing `GADSdat` objects to the `FDZ`.

    # Inpsect resulting object
    saveRDS(gads, "path/gads.RDS")

### Using `GADSdat` objects in R

`eatGADS` provides convenient functions for extracting data and meta data from `GADSdat` objects. `extractMeta` is used to access the meta data for specific variables (or all variables, if no specific variable name is provided).

    # Inpsect resulting object
    extractMeta(gads, vars = c("sex"))
    #>   varName             varLabel format display_width labeled value           valLabel missings
    #> 2     sex Sex as self reported   <NA>            NA     yes   -99 missing - omission     miss
    #> 3     sex Sex as self reported   <NA>            NA     yes     0               male    valid
    #> 4     sex Sex as self reported   <NA>            NA     yes     1             female    valid
    extractMeta(gads)
    #>    varName                          varLabel format display_width labeled value           valLabel
    #> 1       ID                 Person Identifier   <NA>            NA      no    NA               <NA>
    #> 2      sex              Sex as self reported   <NA>            NA     yes   -99 missing - omission
    #> 3      sex              Sex as self reported   <NA>            NA     yes     0               male
    #> 4      sex              Sex as self reported   <NA>            NA     yes     1             female
    #> 5 forename first name as reported by teacher   <NA>            NA      no    NA               <NA>
    #>   missings
    #> 1     <NA>
    #> 2     miss
    #> 3    valid
    #> 4    valid
    #> 5     <NA>

`extractData` is used to extract data. With its arguments the structure of the resulting data can be defined. If `convertMiss = TRUE`, which is the default, is used, values that are listed as missing codes are recoded to `NAs`. With the `convertLabels` argument it can be specified how value labels should be used. If set to `"character"` all labeled values are recoded to character, the same applies to “`factor`”. If set to `"numeric"`, the value labels are not applied.

    # Extract data without applying labels
    dat1 <- extractData(gads, convertMiss = TRUE, convertLabels = "numeric")
    dat1
    #>   ID sex forename
    #> 1  1   0      Tim
    #> 2  2   0     Bill
    #> 3  3   1      Ann
    #> 4  4   1    Chris

    dat2 <- extractData(gads, convertMiss = TRUE, convertLabels = "character")
    dat2
    #>   ID    sex forename
    #> 1  1   male      Tim
    #> 2  2   male     Bill
    #> 3  3 female      Ann
    #> 4  4 female    Chris

## Modifying `GADSdat` objects

`GADSdat` objects can also be modified even though only a certain amount of operations are supported. For smaller changes to the data and meta data a number of convenience functions exists. These functions allow modifying variable labels (`changeVarLabels`), modifying variable names (`changeVarNames`) and recoding values (`recodeGADS`).

    ### wrapper functions
    # Modify variable labels
    gads2 <- changeVarLabels(gads, varName = c("ID"), varLabel = c("Test taker ID"))
    extractMeta(gads2, vars = "ID")
    #>   varName      varLabel format display_width labeled value valLabel missings
    #> 1      ID Test taker ID   <NA>            NA      no    NA     <NA>     <NA>

    # Modify variable name
    gads3 <- changeVarNames(gads, oldNames = c("ID"), newNames = c("idstud"))
    extractMeta(gads3, vars = "idstud")
    #>   varName          varLabel format display_width labeled value valLabel missings
    #> 1  idstud Person Identifier   <NA>            NA      no    NA     <NA>     <NA>
    extractData(gads3)
    #>   idstud    sex forename
    #> 1      1   male      Tim
    #> 2      2   male     Bill
    #> 3      3 female      Ann
    #> 4      4 female    Chris

    # recode GADS
    gads4 <- recodeGADS(gads, varName = "sex", oldValues = c(0, 1, -99), newValues = c(1, 2, 99))
    extractMeta(gads4, vars = "sex")
    #>   varName             varLabel format display_width labeled value           valLabel missings
    #> 2     sex Sex as self reported   <NA>            NA     yes     1               male    valid
    #> 3     sex Sex as self reported   <NA>            NA     yes     2             female    valid
    #> 4     sex Sex as self reported   <NA>            NA     yes    99 missing - omission     miss
    extractData(gads4, convertLabels = "numeric")
    #>   ID sex forename
    #> 1  1   1      Tim
    #> 2  2   1     Bill
    #> 3  3   2      Ann
    #> 4  4   2    Chris

For simultaneous changes to multiple variables a set of functions is implemented that extract a table for changes and applies the changes as written into this change table. To enable an easier work flow the change table could also be saved as an Excel file, modified via Excel and again imported into `R`. See the help pages of the respective functions for more details.

    # extract changeTable
    varChanges <- getChangeMeta(gads, level = "variable")
    # modify changeTable
    varChanges[varChanges$varName == "ID", "varLabel_new"] <- "Test taker ID"
    # Apply changes
    gads5 <- applyChangeMeta(varChanges, gads)
    extractMeta(gads5, vars = "ID")
    #>   varName      varLabel format display_width labeled value valLabel missings
    #> 1      ID Test taker ID   <NA>            NA      no    NA     <NA>     <NA>

## Writing SPSS files

Objects of the class `GADSdat` can also be exported into the SPSS format, utilizing `haven`. Note that this function is slightly experimental and problems with specific character strings might occur.

    write_spss(gads, "path/example_out.sav")

If the `haven` format is preferred for working in `R`, a `GADSdat` object can also be transformed to its equivalent `tibble` format, as if the data was imported from SPSS via `haven`.

    haven_dat <- export_tibble(gads)
    haven_dat
    #> # A tibble: 4 × 3
    #>      ID sex        forename
    #>   <dbl> <dbl+lbl>  <chr>
    #> 1     1 0 [male]   Tim
    #> 2     2 0 [male]   Bill
    #> 3     3 1 [female] Ann
    #> 4     4 1 [female] Chris
    lapply(haven_dat, attributes)
    #> $ID
    #> $ID$label
    #> [1] "Person Identifier"
    #>
    #>
    #> $sex
    #> $sex$label
    #> [1] "Sex as self reported"
    #>
    #> $sex$na_values
    #> [1] -99
    #>
    #> $sex$class
    #> [1] "haven_labelled_spss" "haven_labelled"
    #>
    #> $sex$labels
    #> missing - omission               male             female
    #>                -99                  0                  1
    #>
    #>
    #> $forename
    #> $forename$label
    #> [1] "first name as reported by teacher"
    #>
    #> $forename$na_values
    #> character(0)