You need the latest development version of declared.
remotes::install_github('dusadrian/declared')The survey class will be derived from the dataset
class.
This documentation is not updated yet to the development version of the [dataset] package.
obs_id <- c("Saschia Iemand", "Jane Doe",
"Jack Doe", "Pim Iemand", "Matti Virtanen" )
sex <- declared ( c(1,1,0,-1,1),
labels = c(Male = 0, Female = 1, DK = -1),
na_values = -1)
geo <- c("NL-ZH", "IE-05", "GB-NIR", "NL-ZH", "FI1C")
difficulty_bills <- declared (
c(0,1,2,-1,0),
labels = c(Never = 0, Time_to_time = 1, Always = 2, DK = -1)
)
age_exact <- declared (
c( 34,45,21,55,-1),
labels = c( A = 34,A = 45,A = 21, A= 55, DK = -1)
)
listen_spotify <- declared (
c(0,1,9,0,1),
labels = c( No = 0, Yes = 1,Inap = 9),
na_values = 9
)
raw_survey <- data.frame (
obs_id = obs_id,
geo = geo,
listen_spotify = listen_spotify,
sex = sex,
age_exact = age_exact,
difficulty_bills = difficulty_bills
)
survey_dataset <- dataset( x= raw_survey,
title = "Tiny Survey",
author = person("Jane", "Doe")
)
dataset_bibentry(survey_dataset)
#> Doe J (2024). "Tiny Survey."
dublincore(survey_dataset)It is a good practice to define valid, but not present labels in
declared, because in the retrospective harmonization
workflow they may be concatenated (binded) together with further
observations that do have the currently not used label.
In this example, the DK or declined label is not in
use.
# This is not valied in declared
listen_spotify <- declared(
c(0,1,9,0,1),
labels = c( No = 0, Yes = 1,Inap = 9, DK =-1),
na_values = c(9, -1)
)
print(listen_spotify)
#> <declared<numeric>[5]>
#> [1] 0 1 NA(9) 0 1
#> Missing values: 9, -1
#>
#> Labels:
#> value label
#> 0 No
#> 1 Yes
#> 9 Inap
#> -1 DK
c(listen_spotify, declared(
c(-1,-1,-1),
labels = c( No = 0, Yes = 1,Inap = 9, DK =-1)
))
#> <declared<numeric>[8]>
#> [1] 0 1 NA(9) 0 1 NA(-1) NA(-1) NA(-1)
#> Missing values: -1, 9
#>
#> Labels:
#> value label
#> -1 DK
#> 0 No
#> 1 Yes
#> 9 Inap
summary(listen_spotify)
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.0 0.0 0.5 0.5 1.0 1.0 1
dc_tiny_survey <- dublincore(
title = "Tiny Survey",
creator = person("Daniel", "Antal"),
identifier = 'example-1',
publisher = "Example Publishing",
subject = "Surveys",
language = "en")The survey class inherits elements of the
dataset class, but it will be more strictly defined. I am
considering to make declared every single column except for
the obs_id. Even numeric types with
Inap and DK would map nicely to
CL_OBS_STATUS SDMX codes that make missing observation
explicit, and try to categorize them.
print(dc_tiny_survey, "Bibtex")@Misc{, title = {Tiny Survey}, author = {Daniel Antal}, identifier = {example-1}, publisher = {Example Publishing}, year = {:tba}, language = {en}, relation = {:unas}, format = {:unas}, rights = {:tba}, type = {DCMITYPE:Dataset}, datasource = {:unas}, coverage = {:unas}, }
Is the summary method implemented for
declared? Both dataset and survey
will need new print and summary methods.
summary(survey_dataset)
#> Doe J (2024). "Tiny Survey."
#> Further metadata: describe(survey_dataset)
#> obs_id geo listen_spotify sex
#> Length:5 Length:5 Min. :0.0 Min. :0.00
#> Class :character Class :character 1st Qu.:0.0 1st Qu.:0.75
#> Mode :character Mode :character Median :0.5 Median :1.00
#> Mean :0.5 Mean :0.75
#> 3rd Qu.:1.0 3rd Qu.:1.00
#> Max. :1.0 Max. :1.00
#> NA's :1 NA's :1
#> age_exact difficulty_bills
#> Min. :-1.0 Min. :-1.0
#> 1st Qu.:21.0 1st Qu.: 0.0
#> Median :34.0 Median : 0.0
#> Mean :30.8 Mean : 0.4
#> 3rd Qu.:45.0 3rd Qu.: 1.0
#> Max. :55.0 Max. : 2.0
#> The survey (should) contain the entire processing
history from creation, and optionally the DataCite schema
for publication created with datacite_add(). A similar
dublincore_add function uses the Dublin Core metadata
definitions.
Eventually, a connection to the packages zen4R will make sure that the correctly described dataset can get a Zenodo record, receive a DOI, the DOI recorded in the object, and upload to Zenodo.