cbsodataR, all data of Statistics Netherlands (CBS)

For long SN 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.

A list of tables can be retrieved using the cbs_get_datasets (cbs_get_toc) function.

library(dplyr) # not needed, but used in examples below
library(cbsodataR)

datasets <- cbs_get_datasets() 

datasets %>% 
  filter(Language == "en") %>% # only English tables
  select(Identifier, ShortTitle) 

## # A tibble: 828 x 2
##    Identifier ShortTitle                             
##    <chr>      <chr>                                  
##  1 80783eng   Agriculture; general farm type, region 
##  2 80784eng   Agriculture; labour force, region      
##  3 7100eng    Arable crops; production               
##  4 70671ENG   Fruit culture; area fruit orchards     
##  5 37738ENG   Vegetables; yield per kind of vegetable
##  6 71509ENG   Yield apples and pears, 1997 - 2017    
##  7 83981ENG   Livestock manure; key figures          
##  8 80274eng   Livestock cattle                       
##  9 7373eng    Livestock pigs                         
## 10 7425eng    Milk supply and dairy production       
## # … with 818 more rows

Search for tables

Tables can be searched for using the cbs_search function.

toc_apples <- cbs_search(c("elstar", "apple"), language = "en")
toc_apples[, c("Identifier", "ShortTitle", "score")]

## # A tibble: 1 x 3
##   Identifier ShortTitle                          score
##   <chr>      <chr>                               <dbl>
## 1 71509ENG   Yield apples and pears, 1997 - 2017  14.5

Other catalogs

Other catalogs with data are available:

catalogs <- cbs_get_catalogs()
catalogs$Identifier

##  [1] "CBS"      "MKB"      "IV3"      "MLZ"      "JM"       "RIVM"    
##  [7] "Politie"  "MVstat"   "AZW"      "InterReg"

Metadata

Using an “Identifier” from cbs_get_datasets or cbs_search information on the table can be retrieved with cbs_get_meta

apples <- cbs_get_meta('71509ENG')
apples

## 71509ENG: 'Yield apples and pears, 1997 - 2017', 2017
##   FruitFarmingRegions: 'Fruit farming regions'
##   Periods: 'Periods' 
## 
## Retrieve a default data selection with:
##  cbs_get_data(id = "71509ENG", FruitFarmingRegions = c("1", "2", 
## "4", "3", "5"), Periods = c("1997JJ00", "2012JJ00", "2013JJ00", 
## "2016JJ00"), select = c("FruitFarmingRegions", "Periods", "TotalAppleVarieties_1", 
## "CoxSOrangePippin_2", "DelbarestivaleDelcorf_3", "Elstar_4", 
## "GoldenDelicious_5", "Jonagold_6", "Jonagored_7", "RodeBoskoopRennetApple_10", 
## "OtherAppleVarieties_12", "TotalPearVarieties_13", "Conference_15", 
## "DoyenneDuComice_16", "CookingPears_17", "TriompheDeVienne_18", 
## "OtherPearVarieties_19", "TotalAppleVarieties_20", "CoxSOrangePippin_21", 
## "DelbarestivaleDelcorf_22", "Elstar_23", "GoldenDelicious_24", 
## "Jonagold_25", "Jonagored_26", "RodeBoskoopRennetApple_29", "OtherAppleVarieties_31", 
## "TotalPearVarieties_32", "Conference_34", "DoyenneDuComice_35", 
## "CookingPears_36", "TriompheDeVienne_37", "OtherPearVarieties_38"
## ))

The meta object contains all metadata properties of cbsodata (see the original documentation) in the form of data.frames. Each data.frame describes properties of the SN table.

names(apples)

## [1] "TableInfos"          "DataProperties"      "CategoryGroups"     
## [4] "FruitFarmingRegions" "Periods"

Data download

With cbs_get_data data can be retrieved. By default all data for this table will be downloaded in a temporary directory.

cbs_get_data('71509ENG') %>% 
  select(1:4) %>%  # demonstration purpose
  head()

## # A tibble: 6 x 4
##   FruitFarmingRegions Periods  TotalAppleVarieties_1 CoxSOrangePippin_2
##   <chr>               <chr>                    <int>              <int>
## 1 1                   1997JJ00                   420                 43
## 2 1                   1998JJ00                   518                 40
## 3 1                   1999JJ00                   568                 39
## 4 1                   2000JJ00                   461                 27
## 5 1                   2001JJ00                   408                 30
## 6 1                   2002JJ00                   354                 17

Data download from a link

The opendata portal of CBS (https://opendata.cbs.nl/dataportaal/#/CBS/en) allows for finding a table and making a selection within this table manually. Such a selection can be stored in a hyperlink (click the “share” button). This link can also be used with cbsodataR using the cbs_get_data_from_link function.

cbs_get_data_from_link("https://opendata.cbs.nl/dataportaal/#/CBS/en/dataset/71509ENG/table?dl=193CB") %>% 
  select(1:4) %>% 
  head()

## Executing:
## cbs_get_data(id = "71509ENG", FruitFarmingRegions = c("1", "2", "3", "4", "5"), Periods = c("1997JJ00", "2012JJ00", "2013JJ00", "2016JJ00"), select = c("FruitFarmingRegions", "Periods", "TotalAppleVarieties_1", "CoxSOrangePippin_2", "DelbarestivaleDelcorf_3", "Elstar_4", "GoldenDelicious_5", "Jonagold_6", "Jonagored_7", "Junami_8", "Kanzi_9", "RodeBoskoopRennetApple_10", "Rubens_11", "OtherAppleVarieties_12", "TotalAppleVarieties_20", "CoxSOrangePippin_21", "DelbarestivaleDelcorf_22", "Elstar_23", "GoldenDelicious_24", "Jonagold_25", "Jonagored_26", "Junami_27", "Kanzi_28", "RodeBoskoopRennetApple_29", "Rubens_30", "OtherAppleVarieties_31"), base_url = "http://opendata.cbs.nl")

## # A tibble: 6 x 4
##   FruitFarmingRegions Periods  TotalAppleVarieties_1 CoxSOrangePippin_2
##   <chr>               <chr>                    <int>              <int>
## 1 1                   1997JJ00                   420                 43
## 2 1                   2012JJ00                   282                  3
## 3 1                   2013JJ00                   314                  4
## 4 1                   2016JJ00                   317                  2
## 5 2                   1997JJ00                    86                 10
## 6 2                   2012JJ00                    34                  0

Adding category labels

The first columns are categorical columns: they contain codes. The labels for these columns can be added with the function cbs_add_label_columns.

cbs_get_data('71509ENG') %>%
  cbs_add_label_columns() %>% 
  select(1:4) %>% 
  head()

## # A tibble: 6 x 4
##   FruitFarmingRegions FruitFarmingRegions_label Periods  Periods_label
##   <chr>               <fct>                     <chr>    <fct>        
## 1 1                   Total Netherlands         1997JJ00 1997         
## 2 1                   Total Netherlands         1998JJ00 1998         
## 3 1                   Total Netherlands         1999JJ00 1999         
## 4 1                   Total Netherlands         2000JJ00 2000         
## 5 1                   Total Netherlands         2001JJ00 2001         
## 6 1                   Total Netherlands         2002JJ00 2002

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 cbs_add_date_column the time periods will be converted and added to the data:

cbs_get_data('71509ENG') %>%
  cbs_add_date_column() %>% 
  select(2:4) %>% 
  head()

## # A tibble: 6 x 3
##   Periods  Periods_Date Periods_freq
##   <chr>    <date>       <fct>       
## 1 1997JJ00 1997-01-01   Y           
## 2 1998JJ00 1998-01-01   Y           
## 3 1999JJ00 1999-01-01   Y           
## 4 2000JJ00 2000-01-01   Y           
## 5 2001JJ00 2001-01-01   Y           
## 6 2002JJ00 2002-01-01   Y

This can be useful for further time series analysis, but also for displaying. It is also possible to convert the dates to numbers:

cbs_get_data('71509ENG') %>%
  cbs_add_date_column(date_type = "numeric") %>% 
  select(2:4) %>% 
  head()

## # A tibble: 6 x 3
##   Periods  Periods_numeric Periods_freq
##   <chr>              <int> <fct>       
## 1 1997JJ00            1997 Y           
## 2 1998JJ00            1998 Y           
## 3 1999JJ00            1999 Y           
## 4 2000JJ00            2000 Y           
## 5 2001JJ00            2001 Y           
## 6 2002JJ00            2002 Y

Select and filter`

It is possible 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 $Key column in the cbs_get_meta objects. e.g. for year 2020, the code is “2020JJ00”.

apples <- cbs_get_meta('71509ENG')
names(apples)

## [1] "TableInfos"          "DataProperties"      "CategoryGroups"     
## [4] "FruitFarmingRegions" "Periods"

# meta data for column Periods
head(apples$Periods[,1:2])

##        Key Title
## 1 1997JJ00  1997
## 2 1998JJ00  1998
## 3 1999JJ00  1999
## 4 2000JJ00  2000
## 5 2001JJ00  2001
## 6 2002JJ00  2002

#meta data for column FruitFarmingRegions
head(apples$FruitFarmingRegions[,1:2 ])

##   Key             Title
## 1   1 Total Netherlands
## 2   2      Region North
## 3   4       Region West
## 4   3    Region Central
## 5   5      Region South

To filter for values in a column add <column_name> = values to cbs_get_data e.g. Periods = c("2019JJ00", "2020JJ0")

  cbs_get_data( '71509ENG'
              , Periods=c('2000JJ00','2001JJ00') # selection on Periods column
              , FruitFarmingRegions = "1" # selection on FruitFarmingRegions
              #
              # restrict the columns to the following as found in
              # apples$DataProperties with "select"
              , select = c("FruitFarmingRegions", "Periods", "TotalAppleVarieties_1")  
              ) %>% 
  cbs_add_label_columns()

## # A tibble: 2 x 5
##   FruitFarmingRegi… FruitFarmingRegion… Periods  Periods_label TotalAppleVariet…
##   <chr>             <fct>               <chr>    <fct>                     <int>
## 1 1                 Total Netherlands   2000JJ00 2000                        461
## 2 1                 Total Netherlands   2001JJ00 2001                        408

To filter for values in a column that have a substring e.g. “JJ” you can use <column_name> = has_substring(<substring>) to cbs_get_data e.g. Periods = has_substring("KW")

  cbs_get_data( '71509ENG'
              , Periods = has_substring('2000') # selection on Periods column
              , FruitFarmingRegions = "1" # selection on FruitFarmingRegions
              #
              # restrict the columns to the following as found in
              # cbs_get_meta("71509ENG")$DataProperties with "select"
              , select = c("FruitFarmingRegions", "Periods", "TotalAppleVarieties_1")  
              ) %>% 
    cbs_add_label_columns()

## # A tibble: 1 x 5
##   FruitFarmingRegi… FruitFarmingRegion… Periods  Periods_label TotalAppleVariet…
##   <chr>             <fct>               <chr>    <fct>                     <int>
## 1 1                 Total Netherlands   2000JJ00 2000                        461

To combine values and substring use the “|” operator: Periods = eq("2020JJ00") | has_substring("KW")

  cbs_get_data( '71509ENG'
              , Periods = eq("2010JJ00") | has_substring('2000') # selection on Periods column
              , FruitFarmingRegions = "1" # selection on FruitFarmingRegions
              #
              # restrict the columns to the following as found in
              # cbs_get_meta("71509ENG")$DataProperties with "select"
              , select = c("FruitFarmingRegions", "Periods", "TotalAppleVarieties_1")  
              ) %>% 
    cbs_add_label_columns()

## # A tibble: 2 x 5
##   FruitFarmingRegi… FruitFarmingRegion… Periods  Periods_label TotalAppleVariet…
##   <chr>             <fct>               <chr>    <fct>                     <int>
## 1 1                 Total Netherlands   2000JJ00 2000                        461
## 2 1                 Total Netherlands   2010JJ00 2010                        334