vignettes/cbsodata4.md
In statistiekcbs/cbsodata4: Statistics Netherlands (CBS) Open Data API Client v4
Statistics Netherlands (CBS) is the office that
produces all official statistics of the Netherlands.
For long CBS has put its data on the web in its online database
StatLine. Since 2014 this
data base has an open data web API based on the OData protocol. The
cbsodataR package allows
for retrieving data right into R using this API.
A new version of the web api has been developed which is based on the
OData4 protocol. This OData4 API contains major changes in how the
metadata and data is transported, hence this package cbsodata4. Since
the old web api will be phased out in due time, cbsodata4 is the
successor of cbsodataR.
This document describes how to use cbsodata4 to download data (and
meta data) from Statistics Netherlands. It offers very similar functions
as cbsodataR, so should be familiar to users of cbsodataR, but there
are differences so carefully check your code.
library(cbsodata4)
List of datasets (toc)
A list of datasets that are available can be loaded with with
cbs4_get_datasets() (cbs4_get_toc() is an alias)
datasets <- cbs4_get_datasets()
head(datasets[,c("Identifier", "Title", "Modified")])
| Identifier | Title | Modified |
|:-----------|:------------------------------------------------------------------------|:-----------|
| 00371edu | Basiseducatie; deelnemers naar leeftijd, 1993-2006 | 2013-06-05 |
| 00372 | Aardgasbalans; aanbod en verbruik | 2022-03-31 |
| 00377 | Elektriciteitsbalans; aanbod en verbruik 1919-2018 | 2019-08-13 |
| 03742 | Immi- en emigratie; leeftijd (31 dec.), burgerlijke staat, geboorteland | 2021-07-14 |
| 03747 | Overledenen; geslacht, leeftijd, burgerlijke staat, regio | 2021-09-06 |
| 03753 | Onderwijsinstellingen; grootte, soort, levensbeschouwelijke grondslag | 2021-06-30 |
Using cbs4_search a list of tables can be found that contain desired
search terms, e.g. “Diesel”:
datasets <- cbs4_search("Diesel")
head(datasets[,c("Identifier", "Title", "rel")])
| | Identifier | Title | rel |
|:-----|:-----------|:--------------------------------------------------------------------------|---------:|
| 1549 | 84991NED | Pompprijzen motorbrandstoffen; brandstofsoort, per kwartaal | 21.11548 |
| 312 | 80416ned | Pompprijzen motorbrandstoffen; brandstofsoort, per dag | 13.86831 |
| 1374 | 84596NED | Aardoliegrondstoffen- en aardolieproductenbalans; aanbod en verbruik | 13.52961 |
| 457 | 81567NED | Pompprijzen motorbrandstoffen; locatie tankstation, brandstofsoort | 13.31090 |
| 867 | 83406NED | Motorbrandstoffen; afzet in petajoule, gewicht en volume, 1946-april 2021 | 13.22246 |
| 156 | 71107ned | Verkeersprestaties personenauto’s; eigendom, brandstof, gewicht, leeftijd | 12.49478 |
Using an “Identifier” from cbs4_get_datasets information on the table
can be retrieved with cbs4_get_metadata
meta_petrol <- cbs4_get_metadata("80416ned")
meta_petrol
#> cbs odatav4: '80416ned':
#> "Pompprijzen motorbrandstoffen; brandstofsoort, per dag"
#> dimensions: Perioden
#> For more info use 'str(x)' or 'names(x)' to find out its properties.
The meta object contains all metadata properties of cbsodata in the form
of data.frames. Each data.frame describes properties of the CBS
table: “Dimensions”, “MeasureCodes” and one ore more “\Codes”
describing the meta data of the borders of a SN table.
names(meta_petrol)
#> [1] "Dimensions" "MeasureCodes" "PeriodenGroups" "PeriodenCodes"
#> [5] "Properties"
meta_petrol$MeasureCodes[, 1:3]
| Identifier | Index | Title |
|:-----------|------:|:---------------|
| A047220 | 1 | Benzine Euro95 |
| A047219 | 2 | Diesel |
| A047221 | 3 | Lpg |
meta_petrol$Dimensions
| @odata.type | Identifier | Title | Description | MapYear | ReleasePolicy | Kind | ContainsGroups | ContainsCodes |
|:------------------------------|:-----------|:---------|:------------|:--------|:--------------|:--------------|:---------------|:--------------|
| #Cbs.Ccb.Models.TimeDimension | Perioden | Perioden | | NA | FALSE | TimeDimension | TRUE | TRUE |
# just 1 dimension with the following categories:
head(meta_petrol$PeriodenCodes)
| Identifier | Index | Title | Description | DimensionGroupId | Status |
|:-----------|------:|:-------------------------|:------------|:-----------------|:-------|
| 20060101 | 1 | 2006 zondag 1 januari | | 0 | NA |
| 20060102 | 2 | 2006 maandag 2 januari | | 0 | NA |
| 20060103 | 3 | 2006 dinsdag 3 januari | | 0 | NA |
| 20060104 | 4 | 2006 woensdag 4 januari | | 0 | NA |
| 20060105 | 5 | 2006 donderdag 5 januari | | 0 | NA |
| 20060106 | 6 | 2006 vrijdag 6 januari | | 0 | NA |
Data retrieval
With cbs4_get_observations and cbs4_get_data data can be retrieved.
By default this will be downloaded in a temporary directory, but this
can be set explicitly with the argument download_dir.
cbs4_get_observations is the format in which the data is downloaded
from Statistic Netherlands (CBS). It is in so-called long format. It
contains one Measure column, describing the topics/variable, one
Value column describing the statistical value, one or more Dimension
columns and some extra columns with value specific metadata.
obs <- cbs4_get_observations("80416ned")
head(obs)
| Id | Measure | ValueAttribute | Value | Perioden |
|----:|:--------|:---------------|------:|---------:|
| 0 | A047220 | None | 1.325 | 20060101 |
| 1 | A047219 | None | 1.003 | 20060101 |
| 2 | A047221 | None | 0.543 | 20060101 |
| 3 | A047220 | None | 1.328 | 20060102 |
| 4 | A047219 | None | 1.007 | 20060102 |
| 5 | A047221 | None | 0.542 | 20060102 |
cbs4_get_data returns the data in so-called wide format in which each
Measure has its own column. For many uses this is a more natural
format. It is a pivoted version of cbs4_get_observations().
# same data, but pivoted
data <- cbs4_get_data("80416ned", name_measure_columns = FALSE)
head(data, 2)
| Perioden | A047219 | A047220 | A047221 |
|---------:|--------:|--------:|--------:|
| 20060101 | 1.003 | 1.325 | 0.543 |
| 20060102 | 1.007 | 1.328 | 0.542 |
By default the names of the columns are more readable with
cbs4_get_data
# same data, but pivoted
data <- cbs4_get_data("80416ned")
head(data, 2)
| Perioden | Diesel | Benzine Euro95 | Lpg |
|---------:|-------:|---------------:|------:|
| 20060101 | 1.003 | 1.325 | 0.543 |
| 20060102 | 1.007 | 1.328 | 0.542 |
Adding category label columns
The Dimension and Measure columns use codes/keys/identifiers to describe
categories. These can be found in the metadata, but can also be
automatically added using cbs4_add_label_columns.
obs <- cbs4_get_observations("80416ned")
obs <- cbs4_add_label_columns(obs)
head(obs)
| Id | Measure | MeasureLabel | ValueAttribute | Value | Perioden | PeriodenLabel |
|----:|:--------|:---------------|:---------------|------:|---------:|:-----------------------|
| 0 | A047220 | Benzine Euro95 | None | 1.325 | 20060101 | 2006 zondag 1 januari |
| 1 | A047219 | Diesel | None | 1.003 | 20060101 | 2006 zondag 1 januari |
| 2 | A047221 | Lpg | None | 0.543 | 20060101 | 2006 zondag 1 januari |
| 3 | A047220 | Benzine Euro95 | None | 1.328 | 20060102 | 2006 maandag 2 januari |
| 4 | A047219 | Diesel | None | 1.007 | 20060102 | 2006 maandag 2 januari |
| 5 | A047221 | Lpg | None | 0.542 | 20060102 | 2006 maandag 2 januari |
or
data <- cbs4_get_data("80416ned")
data <- cbs4_add_label_columns(data)
head(data, 2)
| Perioden | PeriodenLabel | Diesel | Benzine Euro95 | Lpg |
|---------:|:-----------------------|-------:|---------------:|------:|
| 20060101 | 2006 zondag 1 januari | 1.003 | 1.325 | 0.543 |
| 20060102 | 2006 maandag 2 januari | 1.007 | 1.328 | 0.542 |
Adding Date column
The period/time columns of Statistics Netherlands (CBS) contain coded
time periods: e.g. 2018JJ00 (i.e. 2018), 2018KW03 (i.e. 2018 Q3),
2016MM04 (i.e. 2016 April). With cbs4_add_date_column the time periods
will be converted and added to the data:
obs <- cbs4_get_observations("80416ned")
obs <- cbs4_add_date_column(obs)
head(obs)
| Id | Measure | ValueAttribute | Value | Perioden | Perioden_Date | Perioden_freq |
|----:|:--------|:---------------|------:|---------:|:--------------|:--------------|
| 0 | A047220 | None | 1.325 | 20060101 | 2006-01-01 | D |
| 1 | A047219 | None | 1.003 | 20060101 | 2006-01-01 | D |
| 2 | A047221 | None | 0.543 | 20060101 | 2006-01-01 | D |
| 3 | A047220 | None | 1.328 | 20060102 | 2006-01-02 | D |
| 4 | A047219 | None | 1.007 | 20060102 | 2006-01-02 | D |
| 5 | A047221 | None | 0.542 | 20060102 | 2006-01-02 | D |
data <- cbs4_get_data("80416ned")
data <- cbs4_add_date_column(data)
head(data)
| Perioden | Perioden_Date | Perioden_freq | Diesel | Benzine Euro95 | Lpg |
|---------:|:--------------|:--------------|-------:|---------------:|------:|
| 20060101 | 2006-01-01 | D | 1.003 | 1.325 | 0.543 |
| 20060102 | 2006-01-02 | D | 1.007 | 1.328 | 0.542 |
| 20060103 | 2006-01-03 | D | 1.007 | 1.332 | 0.540 |
| 20060104 | 2006-01-04 | D | 1.020 | 1.348 | 0.550 |
| 20060105 | 2006-01-05 | D | 1.021 | 1.347 | 0.550 |
| 20060106 | 2006-01-06 | D | 1.023 | 1.353 | 0.549 |
Adding a Unit column
Each Measure has a measure unit, which can be added to observations
with cbs4_add_unit_column()
obs <- cbs4_get_observations("80416ned")
obs <- cbs4_add_unit_column(obs)
head(obs)
| Id | Measure | ValueAttribute | Value | Unit |
|----:|:--------|:---------------|------:|:-----------|
| 0 | A047220 | None | 1.325 | euro/liter |
| 1 | A047219 | None | 1.003 | euro/liter |
| 2 | A047221 | None | 0.543 | euro/liter |
| 3 | A047220 | None | 1.328 | euro/liter |
| 4 | A047219 | None | 1.007 | euro/liter |
| 5 | A047221 | None | 0.542 | euro/liter |
Filter data before download
It is possible to restrict the download using filter statements. This
may shorten the download time considerably.
Filter
Filter statements for the columns can be used to restrict the download.
Note the following:
- To filter you will need to use the values found in the
Identifier
column in the cbs4_get_metadata objects. e.g. for year 2020, the
code is “2020JJ00”.
meta <- cbs4_get_metadata("60006")
tail(meta$PeriodenCodes)
| | Identifier | Index | Title | Description | DimensionGroupId | Status |
|:----|:-----------|------:|:-----------------|:-------------------|:-----------------|:-----------|
| 155 | 2020JJ00 | 155 | 2020 | | 1 | Definitief |
| 156 | 2021KW01 | 156 | 2021 1e kwartaal | Voorlopige cijfers | 0 | Voorlopig |
| 157 | 2021KW02 | 157 | 2021 2e kwartaal | Voorlopige cijfers | 0 | Voorlopig |
| 158 | 2021KW03 | 158 | 2021 3e kwartaal | Voorlopige cijfers | 0 | Voorlopig |
| 159 | 2021KW04 | 159 | 2021 4e kwartaal | Voorlopige cijfers | 0 | Voorlopig |
| 160 | 2021JJ00 | 160 | 2021 | Voorlopige cijfers | 1 | Voorlopig |
meta$MeasureCodes[,c("Identifier","Title")]
| Identifier | Title |
|:-----------|:-----------------------------|
| M003026 | Theoretisch beschikbare uren |
| M002994_2 | Totaal niet-productieve uren |
| M003031 | Vorst- en neerslagverlet |
| M003013 | Overig |
| M003019 | Productieve uren |
- To filter for values in a column add
<column_name> = values to
cbs4_get_observations (or cbs4_get_data)
e.g. Perioden = c("2019KW04", "2020KW01")
obs <- cbs4_get_observations("60006"
, Measure = c("M003026","M003019") # selection on Measures
, Perioden = c("2019KW04", "2020KW01") # selection on Perioden
)
cbs4_add_label_columns(obs)
| Id | Measure | MeasureLabel | ValueAttribute | Value | Perioden | PeriodenLabel |
|----:|:--------|:-----------------------------|:---------------|------:|:---------|:-----------------|
| 740 | M003026 | Theoretisch beschikbare uren | None | 530 | 2019KW04 | 2019 4e kwartaal |
| 744 | M003019 | Productieve uren | None | 370 | 2019KW04 | 2019 4e kwartaal |
| 750 | M003026 | Theoretisch beschikbare uren | None | 520 | 2020KW01 | 2020 1e kwartaal |
| 754 | M003019 | Productieve uren | None | 400 | 2020KW01 | 2020 1e kwartaal |
- To filter for values in a column that have a substring e.g. “JJ” you
can use
<column_name> = contains(<substring>) to cbs4_get_data
e.g. Perioden = contains("JJ")
data <- cbs4_get_data("60006"
, Measure = c("M003026","M003019") # selection on Measures
, Perioden = contains("2019") # retrieve all periods with 2019
)
data
| Perioden | Productieve uren | Theoretisch beschikbare uren |
|:---------|-----------------:|-----------------------------:|
| 2019JJ00 | 1475 | 2090 |
| 2019KW01 | 375 | 510 |
| 2019KW02 | 415 | 520 |
| 2019KW03 | 320 | 530 |
| 2019KW04 | 370 | 530 |
- To combine values and substring use the “\|” operator:
Periods = contains("2019") | "2020KW01"
data <- cbs4_get_data("60006"
, Measure = c("M003026","M003019") # selection on Measures
, Perioden = contains("2019") | "2020KW01" # retrieve all periods with 2019
)
data
| Perioden | Productieve uren | Theoretisch beschikbare uren |
|:---------|-----------------:|-----------------------------:|
| 2019JJ00 | 1475 | 2090 |
| 2019KW01 | 375 | 510 |
| 2019KW02 | 415 | 520 |
| 2019KW03 | 320 | 530 |
| 2019KW04 | 370 | 530 |
| 2020KW01 | 400 | 520 |
query with odata v4 syntax
For the adventurous, it is possible to specify a odata v4
query themselves.
# supply your own odata 4 query
cbs4_get_data("84287NED", query = "$filter=Perioden eq '2019MM12'")
| BedrijfstakkenBranchesSBI2008 | Perioden | Vacature-indicator |
|:------------------------------|:---------|-------------------:|
| 300007 | 2019MM12 | 0.23 |
| 307500 | 2019MM12 | 0.07 |
| 350000 | 2019MM12 | 0.15 |
| T001081 | 2019MM12 | 0.21 |
Download data
Data and metadata of a table can also be downloaded explicitly by using
cbs4_download. This can be an option if you don’t want to load the
data into memory (which both cbs4_get_data and cbs4_get_observations
do), but only store it on disk.
cbs4_download("60006", download_dir = "./60006") # will download data and metadata in csv format.
Other catalogs
CBS / Statistics Netherlands also offers collections of datasets that
are not part of the main collections: so-called catalogs. These can be
retrieved with cbs4_get_catalogs().
catalogs <- cbs4_get_catalogs()
catalogs[,1:2]
| Identifier | Index |
|:-----------|------:|
| CBS | 1 |
| CBS-asd | 2 |
Another options is to set the catalog argument in cbs4_get_datasets
to NULL
ds <- cbs4_get_datasets()
nrow(ds)
#> [1] 1620
ds_all <- cbs4_get_datasets(catalog = NULL)
nrow(ds_all)
#> [1] 1623
ds_asd <- cbs4_get_datasets(catalog = "CBS-asd")
nrow(ds_asd)
#> [1] 3
statistiekcbs/cbsodata4 documentation built on April 1, 2022, 8:53 p.m.
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Statistics Netherlands (CBS) is the office that produces all official statistics of the Netherlands.
For long CBS has put its data on the web in its online database StatLine. Since 2014 this data base has an open data web API based on the OData protocol. The cbsodataR package allows for retrieving data right into R using this API.
A new version of the web api has been developed which is based on the
OData4 protocol. This OData4 API contains major changes in how the
metadata and data is transported, hence this package cbsodata4. Since
the old web api will be phased out in due time, cbsodata4 is the
successor of cbsodataR.
This document describes how to use cbsodata4 to download data (and
meta data) from Statistics Netherlands. It offers very similar functions
as cbsodataR, so should be familiar to users of cbsodataR, but there
are differences so carefully check your code.
library(cbsodata4)
List of datasets (toc)
A list of datasets that are available can be loaded with with
cbs4_get_datasets() (cbs4_get_toc() is an alias)
datasets <- cbs4_get_datasets()
head(datasets[,c("Identifier", "Title", "Modified")])
| Identifier | Title | Modified | |:-----------|:------------------------------------------------------------------------|:-----------| | 00371edu | Basiseducatie; deelnemers naar leeftijd, 1993-2006 | 2013-06-05 | | 00372 | Aardgasbalans; aanbod en verbruik | 2022-03-31 | | 00377 | Elektriciteitsbalans; aanbod en verbruik 1919-2018 | 2019-08-13 | | 03742 | Immi- en emigratie; leeftijd (31 dec.), burgerlijke staat, geboorteland | 2021-07-14 | | 03747 | Overledenen; geslacht, leeftijd, burgerlijke staat, regio | 2021-09-06 | | 03753 | Onderwijsinstellingen; grootte, soort, levensbeschouwelijke grondslag | 2021-06-30 |
Using cbs4_search a list of tables can be found that contain desired
search terms, e.g. “Diesel”:
datasets <- cbs4_search("Diesel")
head(datasets[,c("Identifier", "Title", "rel")])
| | Identifier | Title | rel | |:-----|:-----------|:--------------------------------------------------------------------------|---------:| | 1549 | 84991NED | Pompprijzen motorbrandstoffen; brandstofsoort, per kwartaal | 21.11548 | | 312 | 80416ned | Pompprijzen motorbrandstoffen; brandstofsoort, per dag | 13.86831 | | 1374 | 84596NED | Aardoliegrondstoffen- en aardolieproductenbalans; aanbod en verbruik | 13.52961 | | 457 | 81567NED | Pompprijzen motorbrandstoffen; locatie tankstation, brandstofsoort | 13.31090 | | 867 | 83406NED | Motorbrandstoffen; afzet in petajoule, gewicht en volume, 1946-april 2021 | 13.22246 | | 156 | 71107ned | Verkeersprestaties personenauto’s; eigendom, brandstof, gewicht, leeftijd | 12.49478 |
Using an “Identifier” from cbs4_get_datasets information on the table
can be retrieved with cbs4_get_metadata
meta_petrol <- cbs4_get_metadata("80416ned")
meta_petrol
#> cbs odatav4: '80416ned':
#> "Pompprijzen motorbrandstoffen; brandstofsoort, per dag"
#> dimensions: Perioden
#> For more info use 'str(x)' or 'names(x)' to find out its properties.
The meta object contains all metadata properties of cbsodata in the form
of data.frames. Each data.frame describes properties of the CBS
table: “Dimensions”, “MeasureCodes” and one ore more “\Codes”
describing the meta data of the borders of a SN table.
names(meta_petrol)
#> [1] "Dimensions" "MeasureCodes" "PeriodenGroups" "PeriodenCodes"
#> [5] "Properties"
meta_petrol$MeasureCodes[, 1:3]
| Identifier | Index | Title | |:-----------|------:|:---------------| | A047220 | 1 | Benzine Euro95 | | A047219 | 2 | Diesel | | A047221 | 3 | Lpg |
meta_petrol$Dimensions
| @odata.type | Identifier | Title | Description | MapYear | ReleasePolicy | Kind | ContainsGroups | ContainsCodes | |:------------------------------|:-----------|:---------|:------------|:--------|:--------------|:--------------|:---------------|:--------------| | #Cbs.Ccb.Models.TimeDimension | Perioden | Perioden | | NA | FALSE | TimeDimension | TRUE | TRUE |
# just 1 dimension with the following categories:
head(meta_petrol$PeriodenCodes)
| Identifier | Index | Title | Description | DimensionGroupId | Status | |:-----------|------:|:-------------------------|:------------|:-----------------|:-------| | 20060101 | 1 | 2006 zondag 1 januari | | 0 | NA | | 20060102 | 2 | 2006 maandag 2 januari | | 0 | NA | | 20060103 | 3 | 2006 dinsdag 3 januari | | 0 | NA | | 20060104 | 4 | 2006 woensdag 4 januari | | 0 | NA | | 20060105 | 5 | 2006 donderdag 5 januari | | 0 | NA | | 20060106 | 6 | 2006 vrijdag 6 januari | | 0 | NA |
Data retrieval
With cbs4_get_observations and cbs4_get_data data can be retrieved.
By default this will be downloaded in a temporary directory, but this
can be set explicitly with the argument download_dir.
cbs4_get_observations is the format in which the data is downloaded
from Statistic Netherlands (CBS). It is in so-called long format. It
contains one Measure column, describing the topics/variable, one
Value column describing the statistical value, one or more Dimension
columns and some extra columns with value specific metadata.
obs <- cbs4_get_observations("80416ned")
head(obs)
| Id | Measure | ValueAttribute | Value | Perioden | |----:|:--------|:---------------|------:|---------:| | 0 | A047220 | None | 1.325 | 20060101 | | 1 | A047219 | None | 1.003 | 20060101 | | 2 | A047221 | None | 0.543 | 20060101 | | 3 | A047220 | None | 1.328 | 20060102 | | 4 | A047219 | None | 1.007 | 20060102 | | 5 | A047221 | None | 0.542 | 20060102 |
cbs4_get_data returns the data in so-called wide format in which each
Measure has its own column. For many uses this is a more natural
format. It is a pivoted version of cbs4_get_observations().
# same data, but pivoted
data <- cbs4_get_data("80416ned", name_measure_columns = FALSE)
head(data, 2)
| Perioden | A047219 | A047220 | A047221 | |---------:|--------:|--------:|--------:| | 20060101 | 1.003 | 1.325 | 0.543 | | 20060102 | 1.007 | 1.328 | 0.542 |
By default the names of the columns are more readable with
cbs4_get_data
# same data, but pivoted
data <- cbs4_get_data("80416ned")
head(data, 2)
| Perioden | Diesel | Benzine Euro95 | Lpg | |---------:|-------:|---------------:|------:| | 20060101 | 1.003 | 1.325 | 0.543 | | 20060102 | 1.007 | 1.328 | 0.542 |
Adding category label columns
The Dimension and Measure columns use codes/keys/identifiers to describe
categories. These can be found in the metadata, but can also be
automatically added using cbs4_add_label_columns.
obs <- cbs4_get_observations("80416ned")
obs <- cbs4_add_label_columns(obs)
head(obs)
| Id | Measure | MeasureLabel | ValueAttribute | Value | Perioden | PeriodenLabel | |----:|:--------|:---------------|:---------------|------:|---------:|:-----------------------| | 0 | A047220 | Benzine Euro95 | None | 1.325 | 20060101 | 2006 zondag 1 januari | | 1 | A047219 | Diesel | None | 1.003 | 20060101 | 2006 zondag 1 januari | | 2 | A047221 | Lpg | None | 0.543 | 20060101 | 2006 zondag 1 januari | | 3 | A047220 | Benzine Euro95 | None | 1.328 | 20060102 | 2006 maandag 2 januari | | 4 | A047219 | Diesel | None | 1.007 | 20060102 | 2006 maandag 2 januari | | 5 | A047221 | Lpg | None | 0.542 | 20060102 | 2006 maandag 2 januari |
or
data <- cbs4_get_data("80416ned")
data <- cbs4_add_label_columns(data)
head(data, 2)
| Perioden | PeriodenLabel | Diesel | Benzine Euro95 | Lpg | |---------:|:-----------------------|-------:|---------------:|------:| | 20060101 | 2006 zondag 1 januari | 1.003 | 1.325 | 0.543 | | 20060102 | 2006 maandag 2 januari | 1.007 | 1.328 | 0.542 |
Adding Date column
The period/time columns of Statistics Netherlands (CBS) contain coded
time periods: e.g. 2018JJ00 (i.e. 2018), 2018KW03 (i.e. 2018 Q3),
2016MM04 (i.e. 2016 April). With cbs4_add_date_column the time periods
will be converted and added to the data:
obs <- cbs4_get_observations("80416ned")
obs <- cbs4_add_date_column(obs)
head(obs)
| Id | Measure | ValueAttribute | Value | Perioden | Perioden_Date | Perioden_freq | |----:|:--------|:---------------|------:|---------:|:--------------|:--------------| | 0 | A047220 | None | 1.325 | 20060101 | 2006-01-01 | D | | 1 | A047219 | None | 1.003 | 20060101 | 2006-01-01 | D | | 2 | A047221 | None | 0.543 | 20060101 | 2006-01-01 | D | | 3 | A047220 | None | 1.328 | 20060102 | 2006-01-02 | D | | 4 | A047219 | None | 1.007 | 20060102 | 2006-01-02 | D | | 5 | A047221 | None | 0.542 | 20060102 | 2006-01-02 | D |
data <- cbs4_get_data("80416ned")
data <- cbs4_add_date_column(data)
head(data)
| Perioden | Perioden_Date | Perioden_freq | Diesel | Benzine Euro95 | Lpg | |---------:|:--------------|:--------------|-------:|---------------:|------:| | 20060101 | 2006-01-01 | D | 1.003 | 1.325 | 0.543 | | 20060102 | 2006-01-02 | D | 1.007 | 1.328 | 0.542 | | 20060103 | 2006-01-03 | D | 1.007 | 1.332 | 0.540 | | 20060104 | 2006-01-04 | D | 1.020 | 1.348 | 0.550 | | 20060105 | 2006-01-05 | D | 1.021 | 1.347 | 0.550 | | 20060106 | 2006-01-06 | D | 1.023 | 1.353 | 0.549 |
Adding a Unit column
Each Measure has a measure unit, which can be added to observations
with cbs4_add_unit_column()
obs <- cbs4_get_observations("80416ned")
obs <- cbs4_add_unit_column(obs)
head(obs)
| Id | Measure | ValueAttribute | Value | Unit | |----:|:--------|:---------------|------:|:-----------| | 0 | A047220 | None | 1.325 | euro/liter | | 1 | A047219 | None | 1.003 | euro/liter | | 2 | A047221 | None | 0.543 | euro/liter | | 3 | A047220 | None | 1.328 | euro/liter | | 4 | A047219 | None | 1.007 | euro/liter | | 5 | A047221 | None | 0.542 | euro/liter |
Filter data before download
It is possible to restrict the download using filter statements. This may shorten the download time considerably.
Filter
Filter statements for the columns can be used to restrict the download. Note the following:
- To filter you will need to use the values found in the
Identifiercolumn in thecbs4_get_metadataobjects. e.g. for year 2020, the code is “2020JJ00”.
meta <- cbs4_get_metadata("60006")
tail(meta$PeriodenCodes)
| | Identifier | Index | Title | Description | DimensionGroupId | Status | |:----|:-----------|------:|:-----------------|:-------------------|:-----------------|:-----------| | 155 | 2020JJ00 | 155 | 2020 | | 1 | Definitief | | 156 | 2021KW01 | 156 | 2021 1e kwartaal | Voorlopige cijfers | 0 | Voorlopig | | 157 | 2021KW02 | 157 | 2021 2e kwartaal | Voorlopige cijfers | 0 | Voorlopig | | 158 | 2021KW03 | 158 | 2021 3e kwartaal | Voorlopige cijfers | 0 | Voorlopig | | 159 | 2021KW04 | 159 | 2021 4e kwartaal | Voorlopige cijfers | 0 | Voorlopig | | 160 | 2021JJ00 | 160 | 2021 | Voorlopige cijfers | 1 | Voorlopig |
meta$MeasureCodes[,c("Identifier","Title")]
| Identifier | Title | |:-----------|:-----------------------------| | M003026 | Theoretisch beschikbare uren | | M002994_2 | Totaal niet-productieve uren | | M003031 | Vorst- en neerslagverlet | | M003013 | Overig | | M003019 | Productieve uren |
- To filter for values in a column add
<column_name> = valuestocbs4_get_observations(orcbs4_get_data) e.g.Perioden = c("2019KW04", "2020KW01")
obs <- cbs4_get_observations("60006"
, Measure = c("M003026","M003019") # selection on Measures
, Perioden = c("2019KW04", "2020KW01") # selection on Perioden
)
cbs4_add_label_columns(obs)
| Id | Measure | MeasureLabel | ValueAttribute | Value | Perioden | PeriodenLabel | |----:|:--------|:-----------------------------|:---------------|------:|:---------|:-----------------| | 740 | M003026 | Theoretisch beschikbare uren | None | 530 | 2019KW04 | 2019 4e kwartaal | | 744 | M003019 | Productieve uren | None | 370 | 2019KW04 | 2019 4e kwartaal | | 750 | M003026 | Theoretisch beschikbare uren | None | 520 | 2020KW01 | 2020 1e kwartaal | | 754 | M003019 | Productieve uren | None | 400 | 2020KW01 | 2020 1e kwartaal |
- To filter for values in a column that have a substring e.g. “JJ” you
can use
<column_name> = contains(<substring>)tocbs4_get_datae.g.Perioden = contains("JJ")
data <- cbs4_get_data("60006"
, Measure = c("M003026","M003019") # selection on Measures
, Perioden = contains("2019") # retrieve all periods with 2019
)
data
| Perioden | Productieve uren | Theoretisch beschikbare uren | |:---------|-----------------:|-----------------------------:| | 2019JJ00 | 1475 | 2090 | | 2019KW01 | 375 | 510 | | 2019KW02 | 415 | 520 | | 2019KW03 | 320 | 530 | | 2019KW04 | 370 | 530 |
- To combine values and substring use the “\|” operator:
Periods = contains("2019") | "2020KW01"
data <- cbs4_get_data("60006"
, Measure = c("M003026","M003019") # selection on Measures
, Perioden = contains("2019") | "2020KW01" # retrieve all periods with 2019
)
data
| Perioden | Productieve uren | Theoretisch beschikbare uren | |:---------|-----------------:|-----------------------------:| | 2019JJ00 | 1475 | 2090 | | 2019KW01 | 375 | 510 | | 2019KW02 | 415 | 520 | | 2019KW03 | 320 | 530 | | 2019KW04 | 370 | 530 | | 2020KW01 | 400 | 520 |
query with odata v4 syntax
For the adventurous, it is possible to specify a odata v4 query themselves.
# supply your own odata 4 query
cbs4_get_data("84287NED", query = "$filter=Perioden eq '2019MM12'")
| BedrijfstakkenBranchesSBI2008 | Perioden | Vacature-indicator | |:------------------------------|:---------|-------------------:| | 300007 | 2019MM12 | 0.23 | | 307500 | 2019MM12 | 0.07 | | 350000 | 2019MM12 | 0.15 | | T001081 | 2019MM12 | 0.21 |
Download data
Data and metadata of a table can also be downloaded explicitly by using
cbs4_download. This can be an option if you don’t want to load the
data into memory (which both cbs4_get_data and cbs4_get_observations
do), but only store it on disk.
cbs4_download("60006", download_dir = "./60006") # will download data and metadata in csv format.
Other catalogs
CBS / Statistics Netherlands also offers collections of datasets that
are not part of the main collections: so-called catalogs. These can be
retrieved with cbs4_get_catalogs().
catalogs <- cbs4_get_catalogs()
catalogs[,1:2]
| Identifier | Index | |:-----------|------:| | CBS | 1 | | CBS-asd | 2 |
Another options is to set the catalog argument in cbs4_get_datasets
to NULL
ds <- cbs4_get_datasets()
nrow(ds)
#> [1] 1620
ds_all <- cbs4_get_datasets(catalog = NULL)
nrow(ds_all)
#> [1] 1623
ds_asd <- cbs4_get_datasets(catalog = "CBS-asd")
nrow(ds_asd)
#> [1] 3
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