# Comprehensive Data Cleaning Guide

#### Benjamin Becker, Marlen Holtmann, Johanna Busse

#### 2025-05-21

`eatGADS` allows importing data from `SPSS` files basically without any loss of data or meta data. `eatGADS` stores these data and meta data in so-called `GADSdat` objects. These objects are lists of length two, containing both the data (`"dat"`) as well as the meta data (`"labels"`).

    class(pisa)
    #> [1] "GADSdat" "list"
    names(pisa)
    #> [1] "dat"    "labels"

As `GADSdat` objects have this specific structure, conventional data modification tools are not suitable for `GADSdat` objects. Instead, `eatGADS` provides designated data cleaning and data wrangling functions for most common data cleaning and data wrangling tasks. This is especially relevant for data cleaning steps that require simultaneous modification of both the data and the meta data (e.g., recoding of values and value labels).

In this vignette

- the structure of `GADSdat`objects is illustrated and
- common data cleaning and data wrangling are explained.

For illustrative purposes, a small example data set from the campus files of the German PISA Plus assessment (called `pisa`) is used. The complete campus files and the original data set can be accessed here and here.

    library(eatGADS)
    gads <- pisa

## Data structure

The raw data in a `GADSdat` object are represented as raw, unlabeled values. They can be accessed via `GADSdat$dat`. For the extraction of data suitable for data analyses, see the `extractData2()` function.

    pisa$dat[1:5, 1:5]
    #>   idstud idschool idclass schtype sameteach
    #> 1      1      127     392       2         2
    #> 2      2       65     201       3         1
    #> 3      3       10      34       1         1
    #> 4      4      103     319       3         2
    #> 5      5       57     179       2         2

## Meta data structure

Meta data are stored in a `GADSdat` object with the following structure:

    #>    varName varLabel format display_width labeled value valLabel missings
    #> 17  gender   Gender   F8.0            NA     yes     1   Female    valid
    #> 18  gender   Gender   F8.0            NA     yes     2     Male    valid

- `varLabel`: a variable label; describes a variable in the data
- `format`: the `SPSS` format of a variable; `F` represents numeric, `A` represents character variables
- `display_width`: the display width of a variable in the `SPSS` format
- `labeled`: does the variable contain value labels or missing tags?

On value level, additional meta data can be stored, namely:

- `valLabel`: value labels; labels describing, what a certain value on a certain variable represents
- `missings`: missing tags (sometimes also referred to as missing codes) indicating whether a certain value represents a valid (`"valid"`) or a missing (`"miss"`) value

Via the function `extractMeta()` the existing meta data of one, several or all variables in a `GADSdat` object can be inspected. This function is used frequently throughout the vignette to check whether changes to meta data have been performed successfully.

    extractMeta(gads, vars = c("hisei", "schtype"))
    #>    varName                             varLabel format display_width labeled value
    #> 5  schtype                         School track   F8.0            NA     yes     1
    #> 6  schtype                         School track   F8.0            NA     yes     2
    #> 7  schtype                         School track   F8.0            NA     yes     3
    #> 39   hisei Highest parental occupational status   F8.2            NA      no    NA
    #>                                     valLabel missings
    #> 5                 Gymnasium (academic track)    valid
    #> 6                                 Realschule    valid
    #> 7  schools with several courses of education    valid
    #> 39                                      <NA>     <NA>

## Modifying meta data

This section discusses changes on meta data level, such as changes to variable names or labels.

#### Changing variable names

Changes to meta data on variable level are straightforward. Variable names can be changed with the `changeVarNames()` function. The old variable names are overwritten. Multiple variable names can be adjusted at once.

    # inspect original meta data
    extractMeta(gads, vars = "hisei")
    #>    varName                             varLabel format display_width labeled value valLabel
    #> 39   hisei Highest parental occupational status   F8.2            NA      no    NA     <NA>
    #>    missings
    #> 39     <NA>

    # Change variable name
    gads_labeled <- changeVarNames(GADSdat = gads, oldNames = "hisei", newNames = "hisei_new")

    # inspect modified meta data
    extractMeta(gads_labeled, vars = "hisei_new")
    #>      varName                             varLabel format display_width labeled value valLabel
    #> 39 hisei_new Highest parental occupational status   F8.2            NA      no    NA     <NA>
    #>    missings
    #> 39     <NA>

#### Changing variable labels

Variable labels can be adjusted analogously via the `changeVarLabels()` function. Again, multiple variable labels can be adjusted at once.

    extractMeta(gads_labeled, vars = "hisei_new")
    #>      varName                             varLabel format display_width labeled value valLabel
    #> 39 hisei_new Highest parental occupational status   F8.2            NA      no    NA     <NA>
    #>    missings
    #> 39     <NA>

    # Change variable label
    gads_labeled <- changeVarLabels(GADSdat = gads_labeled, varName = "hisei_new",
                                    varLabel = "Parental occupational status (highest)")

    extractMeta(gads_labeled, vars = "hisei_new")
    #>      varName                               varLabel format display_width labeled value valLabel
    #> 39 hisei_new Parental occupational status (highest)   F8.2            NA      no    NA     <NA>
    #>    missings
    #> 39     <NA>

#### Changing SPSS format

The same applies for the `SPSS` format of a variable using the `changeSPSSformat()` function.

    extractMeta(gads_labeled, "hisei_new")
    #>      varName                               varLabel format display_width labeled value valLabel
    #> 39 hisei_new Parental occupational status (highest)   F8.2            NA      no    NA     <NA>
    #>    missings
    #> 39     <NA>

    # Change SPSS format
    gads_labeled <- changeSPSSformat(GADSdat = gads_labeled, varName = "hisei_new",
                                     format = "F10.2")

    extractMeta(gads_labeled, "hisei_new")
    #>      varName                               varLabel format display_width labeled value valLabel
    #> 39 hisei_new Parental occupational status (highest)  F10.2            NA      no    NA     <NA>
    #>    missings
    #> 39     <NA>

#### Changing value labels

Changes to meta data on value level follow the same principle. With the `changeValLabels()` function, value labels can be added or modified. Note that value labels and missing codes should be given to numeric values, even if a variable is a character variable.

    # Adding value labels
    extractMeta(gads_labeled, "hisei_new")
    #>      varName                               varLabel format display_width labeled value valLabel
    #> 39 hisei_new Parental occupational status (highest)  F10.2            NA      no    NA     <NA>
    #>    missings
    #> 39     <NA>
    gads_labeled <- changeValLabels(GADSdat = gads_labeled, varName = "hisei_new",
                                    value = c(-94, -99), valLabel = c("miss1", "miss2"))
    extractMeta(gads_labeled, "hisei_new")
    #>      varName                               varLabel format display_width labeled value valLabel
    #> 38 hisei_new Parental occupational status (highest)  F10.2            NA     yes   -99    miss2
    #> 39 hisei_new Parental occupational status (highest)  F10.2            NA     yes   -94    miss1
    #>    missings
    #> 38    valid
    #> 39    valid

    # Changing value labels
    gads_labeled <- changeValLabels(GADSdat = gads_labeled, varName = "hisei_new",
                                    value = c(-94, -99),
                                    valLabel = c("missing: Question omitted",
                                                 "missing: Not administered"))
    extractMeta(gads_labeled, "hisei_new")
    #>      varName                               varLabel format display_width labeled value
    #> 38 hisei_new Parental occupational status (highest)  F10.2            NA     yes   -99
    #> 39 hisei_new Parental occupational status (highest)  F10.2            NA     yes   -94
    #>                     valLabel missings
    #> 38 missing: Not administered    valid
    #> 39 missing: Question omitted    valid

#### Removing value labels

Value labels can be deleted using the `removeValLabels()` function.

    # Removing value labels
    extractMeta(gads_labeled, "schtype")
    #>   varName     varLabel format display_width labeled value                                  valLabel
    #> 4 schtype School track   F8.0            NA     yes     1                Gymnasium (academic track)
    #> 5 schtype School track   F8.0            NA     yes     2                                Realschule
    #> 6 schtype School track   F8.0            NA     yes     3 schools with several courses of education
    #>   missings
    #> 4    valid
    #> 5    valid
    #> 6    valid
    gads_labeled <- removeValLabels(GADSdat = gads_labeled, varName = "schtype",
                                    value = 1:3)
    extractMeta(gads_labeled, "schtype")
    #>   varName     varLabel format display_width labeled value valLabel missings
    #> 4 schtype School track   F8.0            NA      no    NA     <NA>     <NA>

#### Changing missing tags

Missing tags (sometimes also referred to as missing codes) can be modified using the `changeMissings()` function. Valid entries for `missings` are `"miss"` and `"valid"`.

    # Defining missings
    extractMeta(gads_labeled, "hisei_new")
    #>      varName                               varLabel format display_width labeled value
    #> 38 hisei_new Parental occupational status (highest)  F10.2            NA     yes   -99
    #> 39 hisei_new Parental occupational status (highest)  F10.2            NA     yes   -94
    #>                     valLabel missings
    #> 38 missing: Not administered    valid
    #> 39 missing: Question omitted    valid
    gads_labeled <- changeMissings(GADSdat = gads_labeled, varName = "hisei_new",
                                   value = c(-94, -99), missings = c("miss", "miss"))
    extractMeta(gads_labeled, "hisei_new")
    #>      varName                               varLabel format display_width labeled value
    #> 36 hisei_new Parental occupational status (highest)  F10.2            NA     yes   -99
    #> 37 hisei_new Parental occupational status (highest)  F10.2            NA     yes   -94
    #>                     valLabel missings
    #> 36 missing: Not administered     miss
    #> 37 missing: Question omitted     miss

#### Checking and adjusting missing tags and value labels

Usually an alignment of value labels and missing codes is desirable. For example, in the variable `hisei_new` the value `-94` has received a missing tag and the value label `"missing: Questions omitted"`. To make these alignments easier, the functions `checkMissings()` and `checkMissingsByValues()` exist. `checkMissings()` allows searching for regular expressions in the value labels and comparing missing tags and vice versa. Per default, missing codes are automatically adjusted (`addMissingCode = TRUE`) and value label mismatches just reported (`addMissingLabel = FALSE`). `checkMissingsByValues()` allows searching for labeled values in a specific value range (e.g., `-50:-99`).

    # Creating a new value label for a missing value but leaving the missing code as valid
    gads_labeled <- changeValLabels(GADSdat = gads_labeled, varName = "gender",
                                    value = -94, valLabel = "missing: Question omitted")
    # Creating a new missing code but leaving the value label empty
    gads_labeled <- changeMissings(GADSdat = gads_labeled, varName = "gender",
                                    value = -99, missings = "miss")

    # Checking value label and missing code alignment
    gads_labeled2 <- checkMissings(gads_labeled, missingLabel = "missing")
    #> The following variables have value labels including the term 'missing' which are not coded as missing:
    #> gender
    #> 'miss' is inserted into column missings for 1 rows.
    #> The following variables have values coded as missings but value labels do not include the term 'missing':
    #> gender

    # Checking missing tags for a certain value range
    gads_labeled <- checkMissingsByValues(gads_labeled, missingValues = -50:-99)
    #> The following variables have values in the 'missingValues' range which are not coded as missing:
    #> gender
    #> 'miss' is inserted into column missings for 1 rows.

#### Reusing meta data

Sometimes one variable already contains the meta data which should be added to another variable. `reuseMeta()` can copy meta data from one variable (`other_varName`) to another variable (`varName`), even across different data sets. The function allows us to transfer the complete meta data, only value labels or a specific selection of value labels (only valid values or missing codes). In the example below we transfer only the missing codes from variable `hisei_new` to variable `age`.

    extractMeta(gads_labeled, "age")
    #>    varName             varLabel format display_width labeled value valLabel missings
    #> 18     age Age of student at T1   F8.2            NA      no    NA     <NA>     <NA>
    gads_labeled <- reuseMeta(GADSdat = gads_labeled, varName = "age",
                              other_GADSdat = gads_labeled, other_varName = "hisei_new",
                              missingLabels = "only", addValueLabels = TRUE)
    extractMeta(gads_labeled, "age")
    #>    varName             varLabel format display_width labeled value                  valLabel
    #> 18     age Age of student at T1   F8.2            NA     yes   -99 missing: Not administered
    #> 19     age Age of student at T1   F8.2            NA     yes   -94 missing: Question omitted
    #>    missings
    #> 18     miss
    #> 19     miss

## Adding and removing variables

In `GADSdat` objects, meta data is stored alongside with the actual data set. Therefore, changes to the actual data often imply changes to the meta data. If a variable is removed from the data set, its meta data is no longer needed. If a new variable is created, new meta data needs to be created. If a variable is recoded, the meta data entries need to be recoded accordingly.

#### Selecting or removing variables

If a certain subset of variables in the `GADSdat` is needed, individual variables can either be extracted via `extractVars()` or removed via `removeVars()`.

    # Selecting variables
    gads_motint <- extractVars(gads_labeled,
                               vars = c("int_a", "int_b", "int_c", "int_d", "instmot_a"))
    #> Removing the following rows from meta data: idstud, idschool, idclass, schtype, sameteach, g8g9, ganztag, classsize, repeated, gender, age, language, migration, hisced, hisei_new, homepos, books, pared, computer_age, internet_age, int_use_a, int_use_b, truancy_a, truancy_b, truancy_c, instmot_b, instmot_c, instmot_d, norms_a, norms_b, norms_c, norms_d, norms_e, norms_f, anxiety_a, anxiety_b, anxiety_c, anxiety_d, anxiety_e, selfcon_a, selfcon_b, selfcon_c, selfcon_d, selfcon_e, worketh_a, worketh_b, worketh_c, worketh_d, worketh_e, worketh_f, worketh_g, worketh_h, worketh_i, intent_a, intent_b, intent_c, intent_d, intent_e, behav_a, behav_b, behav_c, behav_d, behav_e, behav_f, behav_g, behav_h, teach_a, teach_b, teach_c, teach_d, teach_e, cognact_a, cognact_b, cognact_c, cognact_d, cognact_e, cognact_f, cognact_g, cognact_h, cognact_i, discpline_a, discpline_b, discpline_c, discpline_d, discpline_e, relation_a, relation_b, relation_c, relation_d, relation_e, belong_a, belong_b, belong_c, belong_d, belong_e, belong_f, belong_g, belong_h, belong_i, attitud_a, attitud_b, attitud_c, attitud_d, attitud_e, attitud_f, attitud_g, attitud_h, grade_de, grade_ma, grade_bio, grade_che, grade_phy, grade_sci, ma_pv1, ma_pv2, ma_pv3, ma_pv4, ma_pv5, rea_pv1, rea_pv2, rea_pv3, rea_pv4, rea_pv5, sci_pv1, sci_pv2, sci_pv3, sci_pv4, sci_pv5
    #> No rows added to meta data.
    namesGADS(gads_motint)
    #> [1] "int_a"     "int_b"     "int_c"     "int_d"     "instmot_a"

    gads_int <- removeVars(gads_motint, vars = "instmot_a")
    #> Removing the following rows from meta data: instmot_a
    #> No rows added to meta data.
    namesGADS(gads_int)
    #> [1] "int_a" "int_b" "int_c" "int_d"

#### Cloning a variable

A variable can be cloned using the `cloneVariable()` function. Both the data and meta data are cloned. This function can be helpful if a modified copy of a variable should be created. For this purpose, the variable can be cloned and later modified (e.g., via `recodeGADS`).

    # Clone the variable "sameteach"
    gads_labeled <- cloneVariable(gads_labeled, varName = "sameteach", new_varName = "sameteach2")

#### Adding variables

Adding variables to a `GADSdat` object is unfortunately not straight forward and requires utilizing the underlying object structure. For adding variables, the `dat` object needs to be abstracted, so that new variables can be added to it. Afterwards, the meta data needs to be added using the `updateMeta()` function.

    # Extract the data
    newDat <- gads_labeled$dat
    # Adding a variable
    newDat$classsize_kat <- ifelse(newDat$classsize > 15,
                                             yes = "big", no = "small")
    # Updating meta data
    gads_labeled2 <- updateMeta(gads_labeled, newDat = newDat)
    #> No rows removed from meta data.
    #> Adding meta data for the following variables: classsize_kat
    extractMeta(gads_labeled2, "classsize_kat")
    #>                     varName varLabel format display_width labeled value valLabel missings
    #> classsize_kat classsize_kat     <NA>   <NA>            NA      no    NA     <NA>     <NA>

## Recoding

`eatGADS` provides functionality for the manual and semi-automatic recoding of variables.

#### Removing all values from a variable

For instance for the purpose of ensuring the anonymity of person in a data set, it is sometimes desirable to empty sensitive variables. This can be performed using the `emptyTheseVariables()` function.

    # Empty a variable completely
    gads_labeled <- emptyTheseVariables(gads_labeled, vars = "idschool")
    # Resulting frequency table
    table(gads_labeled$dat$idschool, useNA = "ifany")
    #>
    #> <NA>
    #>  500

#### Manual recoding

The function `recodeGADS()` allows the manual recoding of a variable.

    # Original data and meta data
    gads_labeled$dat$gender[1:10]
    #>  [1] 1 1 2 2 1 1 2 2 1 1
    extractMeta(gads_labeled, "gender")
    #>    varName varLabel format display_width labeled value                  valLabel missings
    #> 14  gender   Gender   F8.0            NA     yes   -99                      <NA>     miss
    #> 15  gender   Gender   F8.0            NA     yes   -94 missing: Question omitted     miss
    #> 16  gender   Gender   F8.0            NA     yes     1                    Female    valid
    #> 17  gender   Gender   F8.0            NA     yes     2                      Male    valid
    # Recoding
    gads_labeled <- recodeGADS(GADSdat = gads_labeled, varName = "gender",
                               oldValues = c(1, 2), newValues = c(10, 20))
    # New data and meta data
    gads_labeled$dat$gender[1:10]
    #>  [1] 10 10 20 20 10 10 20 20 10 10
    extractMeta(gads_labeled, "gender")
    #>    varName varLabel format display_width labeled value                  valLabel missings
    #> 14  gender   Gender   F8.0            NA     yes   -99                      <NA>     miss
    #> 15  gender   Gender   F8.0            NA     yes   -94 missing: Question omitted     miss
    #> 16  gender   Gender   F8.0            NA     yes    10                    Female    valid
    #> 17  gender   Gender   F8.0            NA     yes    20                      Male    valid

Moreover, `recodeGADS()` allows recoding values without value labels or even `NA` values.

    # Recoding of NA values
    gads_labeled$dat$int_a[1:10]
    #>  [1]  2  2  3  2  1  2 NA NA NA NA
    gads_labeled <- recodeGADS(GADSdat = gads_labeled, varName = "int_a",
                               oldValues = NA, newValues = -94)
    gads_labeled$dat$int_a[1:10]
    #>  [1]   2   2   3   2   1   2 -94 -94 -94 -94

#### Setting values to NA

For recoding specific values into `NA` values, the function `recode2NA()` exists. It allows recoding a specific value across multiple variables (while `recodeGADS()` allows recoding multiple values for a single variable). Existing value labels for the specified values are deleted. For each variable it is reported how many cases have been recoded.

    # Recoding of values as Missing/NA
    gads_labeled$dat$schtype[1:10]
    #>  [1] 2 3 1 3 2 3 1 3 2 1
    gads_labeled <- recode2NA(gads_labeled, recodeVars = c("hisei_new", "schtype"),
                              value = "3")
    #> Recodes in variable hisei_new: 0
    #> Recodes in variable schtype: 111
    gads_labeled$dat$schtype[1:10]
    #>  [1]  2 NA  1 NA  2 NA  1 NA  2  1

#### Automatically recoding a character variable to a labeled numeric variable

A character variable can be automatically recoded into a labeled numeric variable via `multiChar2fac()`.

    # Example data set
    test_df <- data.frame(id = 1:5, varChar = c("german", "English",
                                                "english", "POLISH", "polish"),
                            stringsAsFactors = FALSE)
    test_gads <- import_DF(test_df)

    # Recoding a character variable to numeric
    test_gads2 <- multiChar2fac(test_gads, vars = "varChar", var_suffix = "_new")
    extractMeta(test_gads2, "varChar_new")
    #>       varName  varLabel format display_width labeled value valLabel missings
    #> 3 varChar_new (recoded)  F10.0            NA     yes     1  English    valid
    #> 4 varChar_new (recoded)  F10.0            NA     yes     2   POLISH    valid
    #> 5 varChar_new (recoded)  F10.0            NA     yes     3  english    valid
    #> 6 varChar_new (recoded)  F10.0            NA     yes     4   german    valid
    #> 7 varChar_new (recoded)  F10.0            NA     yes     5   polish    valid

Via the argument `convertCase` upper and lower case can be automatically adjusted.

    # Recoding a character variable to numeric while simplying case
    test_gads2 <- multiChar2fac(test_gads, vars = "varChar", var_suffix = "_new",
                                convertCase = "upperFirst")
    extractMeta(test_gads2, "varChar_new")
    #>       varName  varLabel format display_width labeled value valLabel missings
    #> 3 varChar_new (recoded)  F10.0            NA     yes     1  English    valid
    #> 4 varChar_new (recoded)  F10.0            NA     yes     2   German    valid
    #> 5 varChar_new (recoded)  F10.0            NA     yes     3   Polish    valid

#### Automatically recoding a variable with a template

A variable can be automatically recoded into a labeled numeric variable via `autoRecode()`. This can be desirable, for instance, for the recoding of identifier variables.

    id_df <- data.frame(id = c(1101, 1102, 1103, 1104, 1105),
                        varChar = c("german", "English", "english", "POLISH", "polish"),
                            stringsAsFactors = FALSE)
    id_gads <- import_DF(id_df)

    # Recoding a character variable to numeric
    id_gads2 <- autoRecode(id_gads, var = "id", var_suffix = "_new")
    id_gads2$dat[, c("id", "id_new")]
    #>     id id_new
    #> 1 1101      1
    #> 2 1102      2
    #> 3 1103      3
    #> 4 1104      4
    #> 5 1105      5

## Variable sorting

The sorting of variables in a `GADSdat` can be adjusted for individual variables and for the complete set of variables.

#### Relocating a specific variable

The function `relocateVariable()` allows the relocation of a single variable within a `GADSdat` object.

    namesGADS(gads_labeled)[1:5]
    #> [1] "idstud"    "idschool"  "idclass"   "schtype"   "sameteach"

    # Relocate a single variable within a the data set
    gads_labeled <- relocateVariable(GADSdat = gads_labeled, var = "idschool",
                               after = "schtype")
    namesGADS(gads_labeled)[1:5]
    #> [1] "idstud"    "idclass"   "schtype"   "idschool"  "sameteach"

    # Relocate a single variable to the beginning of the data set
    gads_labeled <- relocateVariable(GADSdat = gads_labeled, var = "idschool",
                               after = NULL)
    namesGADS(gads_labeled)[1:5]
    #> [1] "idschool"  "idstud"    "idclass"   "schtype"   "sameteach"

#### Ordering all variables

The function `orderLike()` allows reordering all variables within a `GADSdat` object.

## Changing meta data (and data) via an Excel sheet

So far, the introduced functions work well for modifying the meta data of small data sets or for individual variables. However, we are frequently interested in modifying the meta data of a larger number of variables simultaneously. For this purpose `eatGADS` provides a workflow that works well with Excel spreadsheets. Thereby changes to meta data are divided into two levels: the variable and the value level.

#### Variable level

We start by extracting this change table via the `getChangeMeta()` function.

    # variable level
    meta_var <- getChangeMeta(GADSdat = pisa, level = "variable")

While in principle one could modify the change table directly in R, it is more convenient to do this in Excel. The change table can be written to `.xlsx` via the `eatAnalysis::write_xlsx()` function. To perform changes, entries are made into the corresponding “_new”-columns.

    # write to Excel
    eatAnalysis::write_xlsx(meta_var, row.names = FALSE, "variable_changes.xlsx")

The Excel file can be read back into `R` via `readxl::read_xlsx()`.

    # write to Excel
    meta_var_changed <- readxl::read_excel("variable_changes.xlsx", col_types = rep("text", 8))

The `applyChangeMeta()` function applies the meta data changes to the `GADSdat` object.

    gads2 <- applyChangeMeta(meta_var_changed, GADSdat = pisa)
    extractMeta(gads2, vars = c("idstud", "idschool", "schoolType"))
    #>      varName                    varLabel format display_width labeled value
    #> 2     idstud Student Identifier Variable   F8.0            NA      no    NA
    #> 3   idschool                   School-ID  F10.0            NA      no    NA
    #> 5 schoolType                School track   F8.0            NA     yes     1
    #> 6 schoolType                School track   F8.0            NA     yes     2
    #> 7 schoolType                School track   F8.0            NA     yes     3
    #>                                    valLabel missings
    #> 2                                      <NA>     <NA>
    #> 3                                      <NA>     <NA>
    #> 5                Gymnasium (academic track)    valid
    #> 6                                Realschule    valid
    #> 7 schools with several courses of education    valid

#### Value level

At value level, information on value, value labels or missings can be changed. The general workflow is identical.

    # value level
    meta_val <- getChangeMeta(GADSdat = pisa, level = "value")

    # write to Excel
    eatAnalysis::write_xlsx(meta_val, row.names = FALSE, "value_changes.xlsx")

    # write to Excel
    meta_val_changed <- readxl::read_excel("value_changes.xlsx",
                                           col_types = c("text", rep(c("numeric", "text", "text"), 2)))

    gads3 <- applyChangeMeta(meta_val_changed, GADSdat = pisa)
    extractMeta(gads3, vars = c("schtype", "sameteach"))
    #>     varName                               varLabel format display_width labeled value
    #> 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
    #> 7 sameteach Same math teacher in both school years   F8.0            NA     yes    10
    #> 8 sameteach Same math teacher in both school years   F8.0            NA     yes    20
    #>                                    valLabel missings
    #> 4                            Acamedic Track    valid
    #> 5                                Realschule    valid
    #> 6 schools with several courses of education    valid
    #> 7                                        No    valid
    #> 8                                       Yes    valid