Our datasets are defined in a way that their dimensions can be easily
and unambiguously reduced to triples for RDF applications; they can be
easily serialized to, or synchronized with semantic web applications
with the rdflib
package.
Because our datasets conform the tidy data concept, they can be
reduced into long-form triples.
Dimension reductions of the dataset
Our datasets are tidy.
example_dataset <- readxl::read_excel(
system.file("extdata", "rdf_example.xlsx", package = "dataset"),
sheet = "dataset-wide")
example_dataset
#> # A tibble: 8 × 8
#> rowid time geo sex value unit freq status
#> <dbl> <dbl> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 1 2021 NL F 9 NR A A
#> 2 2 2021 BE F 8 NR A E
#> 3 3 2021 NL M 10 NR A A
#> 4 4 2021 BE M 7 NR A A
#> 5 5 2022 NL F 10 NR A A
#> 6 6 2022 BE F 11 NR A A
#> 7 7 2022 NL M NA NR A O
#> 8 8 2022 BE M 10 NR A A
You can start to reduce the dimensions, for example, with uniting the
dimensions. In this case, the row identifier becomes more and more a
unique resource identifier, i.e. a URI.
Eventually you can reduce the entire dataset into a triple. The
uri uniquely defines the observations, the
component maintains the W3C/SDMX datacube models main
structural element, and the value field the value of the
dimension, measurement or attribute.
library(tidyr)
library(dplyr)
example_long <- example_dataset %>%
unite("uri",
all_of(c("rowid", "time", "geo", "sex", "value", "unit", "freq", "status")),
remove = FALSE) %>%
mutate_all ( as.character) %>%
pivot_longer(
cols = -any_of("uri"),
names_to = "component",
values_to = "value"
)
The benefit of standard codelists
In this example, except for the measurement of the observation, we
used only SDMX-attribute conform variable names and codes. The advantage
of this approach is that it is very easy to increase the dimensions of
the dataset, and add human-readable labels, potentially in many natural
languages.
set.seed(2022)
library(statcodelists)
example_long %>%
filter (.data$component == "sex") %>%
left_join(statcodelists::CL_SEX %>%
rename ( value = .data$id ),
by = "value") %>%
bind_rows (
example_long %>%
filter (.data$component == "freq") %>%
left_join(statcodelists::CL_FREQ %>%
dplyr::rename ( value = .data$id ),
by = "value")
) %>%
bind_rows (
example_long %>%
filter (.data$component == "status") %>%
left_join(statcodelists::CL_OBS_STATUS %>%
dplyr::rename ( value = .data$id ),
by = "value")
) %>%
group_by (.data$component) %>%
sample_frac( size = 0.3 ) %>%
kableExtra::kbl() %>%
kableExtra::kable_paper()
|
uri
|
component
|
value
|
name
|
description
|
name_locale
|
description_locale
|
|
4_2021_BE_M_7_NR_A_A
|
freq
|
A
|
Annual
|
To be used for data collected or disseminated every year
|
en
|
en
|
|
3_2021_NL_M_10_NR_A_A
|
freq
|
A
|
Annual
|
To be used for data collected or disseminated every year
|
en
|
en
|
|
6_2022_BE_F_11_NR_A_A
|
sex
|
F
|
Female
|
NA
|
en
|
NA
|
|
7_2022_NL_M_NA_NR_A_O
|
sex
|
M
|
Male
|
NA
|
en
|
NA
|
|
3_2021_NL_M_10_NR_A_A
|
status
|
A
|
Normal value
|
To be used as default value if no value is provided or when no special
coded qualification is assumed. Usually, it can be assumed that the
source agency assigns sufficient confidence to the provided observation
and/or the value is not expected to be dramatically revised.
|
en
|
en
|
|
4_2021_BE_M_7_NR_A_A
|
status
|
A
|
Normal value
|
To be used as default value if no value is provided or when no special
coded qualification is assumed. Usually, it can be assumed that the
source agency assigns sufficient confidence to the provided observation
and/or the value is not expected to be dramatically revised.
|
en
|
en
|