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When trying to understand data, most often not only the actual data is required, but also so called meta data. Meta data usually includes:
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.
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.
GADSdat
formatR
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.
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
classThe 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"
GADSdat
objectsGADSdat
objects, for example, can be saved as
RDS
files. This is also the preferred data format for
distributing GADSdat
objects to the FDZ
.
GADSdat
objects in ReatGADS
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
GADSdat
objectsGADSdat
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>
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.
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)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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