Access and download German regional statistics from Datenguide
http://datengui.de. datenguideR provides a wrapper for their GraphQL
API and also includes metadata for all available statistics and regions.
Overview
First, install datenguideR from GitHub:
devtools::install_github("CorrelAid/datenguideR")Load package:
library(datenguideR)Get IDs of all available NUTS-1 regions:
datenguideR::dg_regions %>%
dplyr::filter(level == "nuts1") %>%
knitr::kable()| id | name | level | parent |
|---|---|---|---|
| 01 | Schleswig-Holstein | nuts1 | DG |
| 02 | Hamburg | nuts1 | DG |
| 03 | Niedersachsen | nuts1 | DG |
| 04 | Bremen | nuts1 | DG |
| 05 | Nordrhein-Westfalen | nuts1 | DG |
| 06 | Hessen | nuts1 | DG |
| 07 | Rheinland-Pfalz | nuts1 | DG |
| 08 | Baden-Württemberg | nuts1 | DG |
| 09 | Bayern | nuts1 | DG |
| 10 | Saarland | nuts1 | DG |
| 11 | Berlin | nuts1 | DG |
| 12 | Brandenburg | nuts1 | DG |
| 13 | Mecklenburg-Vorpommern | nuts1 | DG |
| 14 | Sachsen | nuts1 | DG |
| 15 | Sachsen-Anhalt | nuts1 | DG |
| 16 | Thüringen | nuts1 | DG |
Get all available meta data on statistics, substatistics, and parameters:
datenguideR::dg_descriptions
#> # A tibble: 3,419 x 11
#> stat_name stat_description stat_description_~ substat_name substat_descrip~
#> <chr> <chr> <chr> <chr> <chr>
#> 1 AENW01 Entsorgte/behande~ "**Entsorgte/beha~ NA NA
#> 2 AENW02 Abgelagerte Abfal~ "**Abgelagerte Ab~ NA NA
#> 3 AENW03 Entsorg.u.Behandl~ "**Entsorg.u.Beha~ NA NA
#> 4 AENW04 Entsorgte/behande~ "**Entsorgte/beha~ NA NA
#> 5 AENW05 Abgelagerte Abfal~ "**Abgelagerte Ab~ NA NA
#> 6 AENW06 Entsorg.u.Behandl~ "**Entsorg.u.Beha~ NA NA
#> 7 AEW001 Entsorgungs- und ~ "**Entsorgungs- u~ NA NA
#> 8 AEW001 Entsorgungs- und ~ "**Entsorgungs- u~ EBANL1 Entsorgungs- un~
#> 9 AEW001 Entsorgungs- und ~ "**Entsorgungs- u~ EBANL1 Entsorgungs- un~
#> 10 AEW001 Entsorgungs- und ~ "**Entsorgungs- u~ EBANL1 Entsorgungs- un~
#> # ... with 3,409 more rows, and 6 more variables: param_name <chr>,
#> # param_description <chr>, stat_description_en <chr>,
#> # stat_description_full_en <chr>, substat_description_en <chr>,
#> # param_description_en <chr>You can also use dg_search to look for a variable of interest. The
function will match your string with any strings in the
dg_descriptions data frame, returning only rows with those matches.
Looking for variables where the string “vote” appears somewhere in the documentation:
dg_search("vote")
#> # A tibble: 90 x 11
#> stat_name stat_description stat_description_~ substat_name substat_descrip~
#> <chr> <chr> <chr> <chr> <chr>
#> 1 AI0501 Zweitstimmenantei~ "**Zweitstimmenan~ NA NA
#> 2 AI0502 Zweitstimmenantei~ "**Zweitstimmenan~ NA NA
#> 3 AI0503 Zweitstimmenantei~ "**Zweitstimmenan~ NA NA
#> 4 AI0504 Zweitstimmenantei~ "**Zweitstimmenan~ NA NA
#> 5 AI0505 Zweitstimmenantei~ "**Zweitstimmenan~ NA NA
#> 6 AI0506 Wahlbeteiligung, ~ "**Wahlbeteiligun~ NA NA
#> 7 AI0601 Stimmenanteil CDU~ "**Stimmenanteil ~ NA NA
#> 8 AI0602 Stimmenanteil SPD~ "**Stimmenanteil ~ NA NA
#> 9 AI0603 Stimmenanteil FDP~ "**Stimmenanteil ~ NA NA
#> 10 AI0604 Stimmenanteil GRÜ~ "**Stimmenanteil ~ NA NA
#> # ... with 80 more rows, and 6 more variables: param_name <chr>,
#> # param_description <chr>, stat_description_en <chr>,
#> # stat_description_full_en <chr>, substat_description_en <chr>,
#> # param_description_en <chr>Note: Descriptions of variables are also available in English now!
Translated via the
googleLanguageR
package.
dg_search("vote") %>%
dplyr::select(stat_name, dplyr::contains("_en"))
#> # A tibble: 90 x 5
#> stat_name stat_descriptio~ stat_descriptio~ substat_descrip~ param_descripti~
#> <chr> <chr> <chr> <chr> <chr>
#> 1 AI0501 Second Vote Sha~ "** CDU / CSU s~ NA NA
#> 2 AI0502 SPD Second Vote~ "** SPD second ~ NA NA
#> 3 AI0503 FDP Second Vote~ "** Second vote~ NA NA
#> 4 AI0504 Second Vote Sha~ "** GREEN secon~ NA NA
#> 5 AI0505 Second Vote Sha~ "** Second vote~ NA NA
#> 6 AI0506 Voter Turnout, ~ "** Voter turno~ NA NA
#> 7 AI0601 CDU / CSU, Euro~ "** CDU / CSU v~ NA NA
#> 8 AI0602 SPD Vote Share,~ "** SPD vote sh~ NA NA
#> 9 AI0603 FDP Share of Vo~ "** FDP vote sh~ NA NA
#> 10 AI0604 Share of Votes ~ "** GREEN share~ NA NA
#> # ... with 80 more rowsThe main function of the package is dg_call. It gives access to all
API endpoints.
Simply pick a statistic and put it into dg_call() (infos can be
retrieved from dg_descriptions).
For example:
stat_name: AI0506 (Wahlbeteiligung, Bundestagswahl)region_id: 11 (stands for Berlin)
dg_call(region_id = "11",
year = 2017,
stat_name = "AI0506")
#> New names:
#> * name -> name...2
#> * name -> name...6
#> # A tibble: 1 x 9
#> id name...2 year value GENESIS_source name...6 stat_name stat_description
#> <chr> <chr> <int> <dbl> <chr> <chr> <chr> <chr>
#> 1 11 Berlin 2017 75.6 Regionalatlas ~ 99910 AI0506 Wahlbeteiligung~
#> # ... with 1 more variable: stat_description_en <chr>A slightly more complex call with substatistics:
stat_name: BETR08 (Landwirtschaftliche Betriebe mit Tierhaltung)substat_name: TIERA8 (Landwirtschaftliche Betriebe mit Viehhaltung)parameter:- TIERART2 (Rinder)
- TIERART3 (Schweine)
dg_call(region_id = "11",
year = c(2001, 2003, 2007),
stat_name = "BETR08",
substat_name = "TIERA8",
parameter = c("TIERART2", "TIERART3"))
#> New names:
#> * name -> name...2
#> * name -> name...7
#> # A tibble: 6 x 15
#> id name...2 year TIERA8 value GENESIS_source name...7 stat_name
#> <chr> <chr> <int> <chr> <int> <chr> <chr> <chr>
#> 1 11 Berlin 2001 TIERAR~ 8 Allgemeine Agrarstruktu~ 41120 BETR08
#> 2 11 Berlin 2001 TIERAR~ 7 Allgemeine Agrarstruktu~ 41120 BETR08
#> 3 11 Berlin 2003 TIERAR~ 9 Allgemeine Agrarstruktu~ 41120 BETR08
#> 4 11 Berlin 2003 TIERAR~ 7 Allgemeine Agrarstruktu~ 41120 BETR08
#> 5 11 Berlin 2007 TIERAR~ 11 Allgemeine Agrarstruktu~ 41120 BETR08
#> 6 11 Berlin 2007 TIERAR~ 5 Allgemeine Agrarstruktu~ 41120 BETR08
#> # ... with 7 more variables: stat_description <chr>, substat_name <chr>,
#> # substat_description <chr>, param_description <chr>,
#> # stat_description_en <chr>, substat_description_en <chr>,
#> # param_description_en <chr>If you give no parameters for a substat, it will default to return results for all of them.
dg_call(region_id = "11",
year = c(2001, 2003, 2007),
stat_name = "BETR08",
substat_name = "TIERA8")
#> New names:
#> * name -> name...2
#> * name -> name...7
#> # A tibble: 23 x 15
#> id name...2 year TIERA8 value GENESIS_source name...7 stat_name
#> <chr> <chr> <int> <chr> <int> <chr> <chr> <chr>
#> 1 11 Berlin 2001 TIERART~ 3 Allgemeine Agrarstruk~ 41120 BETR08
#> 2 11 Berlin 2001 TIERART2 8 Allgemeine Agrarstruk~ 41120 BETR08
#> 3 11 Berlin 2001 TIERART~ 6 Allgemeine Agrarstruk~ 41120 BETR08
#> 4 11 Berlin 2001 TIERART~ 0 Allgemeine Agrarstruk~ 41120 BETR08
#> 5 11 Berlin 2001 TIERART3 7 Allgemeine Agrarstruk~ 41120 BETR08
#> 6 11 Berlin 2001 TIERART4 8 Allgemeine Agrarstruk~ 41120 BETR08
#> 7 11 Berlin 2001 TIERART~ 15 Allgemeine Agrarstruk~ 41120 BETR08
#> 8 11 Berlin 2001 GESAMT 37 Allgemeine Agrarstruk~ 41120 BETR08
#> 9 11 Berlin 2003 TIERART~ 0 Allgemeine Agrarstruk~ 41120 BETR08
#> 10 11 Berlin 2003 TIERART2 9 Allgemeine Agrarstruk~ 41120 BETR08
#> # ... with 13 more rows, and 7 more variables: stat_description <chr>,
#> # substat_name <chr>, substat_description <chr>, param_description <chr>,
#> # stat_description_en <chr>, substat_description_en <chr>,
#> # param_description_en <chr>Instead of specifying a region_id for individual Bundesland (state)
data you can also use nuts_nr to receive data for NUTS-1, NUTS-2
and NUTS-3.
Just leave region_id blank and provide either a nuts_nr (or
lau_nr) to get data for multiple regions at once.
stat_name: AI0506 (Wahlbeteiligung, Bundestagswahl)
dg_call(nuts_nr = 1,
year = 2017,
stat_name = "AI0506")
#> # A tibble: 16 x 9
#> id year value name GENESIS_source GENESIS_source_~ stat_name
#> <chr> <int> <dbl> <chr> <chr> <chr> <chr>
#> 1 10 2017 76.6 Saarland Regionalatlas Deu~ 99910 AI0506
#> 2 11 2017 75.6 Berlin Regionalatlas Deu~ 99910 AI0506
#> 3 12 2017 73.7 Brandenburg Regionalatlas Deu~ 99910 AI0506
#> 4 13 2017 70.9 Mecklenburg-~ Regionalatlas Deu~ 99910 AI0506
#> 5 14 2017 75.4 Sachsen Regionalatlas Deu~ 99910 AI0506
#> 6 15 2017 68.1 Sachsen-Anha~ Regionalatlas Deu~ 99910 AI0506
#> 7 16 2017 74.3 Thüringen Regionalatlas Deu~ 99910 AI0506
#> 8 01 2017 76.3 Schleswig-Ho~ Regionalatlas Deu~ 99910 AI0506
#> 9 02 2017 76 Hamburg Regionalatlas Deu~ 99910 AI0506
#> 10 03 2017 76.4 Niedersachsen Regionalatlas Deu~ 99910 AI0506
#> 11 04 2017 70.8 Bremen Regionalatlas Deu~ 99910 AI0506
#> 12 05 2017 75.4 Nordrhein-We~ Regionalatlas Deu~ 99910 AI0506
#> 13 06 2017 77 Hessen Regionalatlas Deu~ 99910 AI0506
#> 14 07 2017 77.7 Rheinland-Pf~ Regionalatlas Deu~ 99910 AI0506
#> 15 08 2017 78.3 Baden-Württe~ Regionalatlas Deu~ 99910 AI0506
#> 16 09 2017 78.1 Bayern Regionalatlas Deu~ 99910 AI0506
#> # ... with 2 more variables: stat_description <chr>, stat_description_en <chr>stat_name: BETR08 (Landwirtschaftliche Betriebe mit Tierhaltung)substat_name: TIERA8 (Landwirtschaftliche Betriebe mit Viehhaltung)
dg_call(nuts_nr = 1,
year = c(2001, 2003, 2007),
stat_name = "BETR08",
substat_name = "TIERA8")
#> # A tibble: 383 x 15
#> id year TIERA8 value name GENESIS_source GENESIS_source_~ stat_name
#> <chr> <int> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 10 2001 TIERAR~ 374 Saarl~ Allgemeine Agrar~ 41120 BETR08
#> 2 10 2001 TIERAR~ 964 Saarl~ Allgemeine Agrar~ 41120 BETR08
#> 3 10 2001 TIERAR~ 199 Saarl~ Allgemeine Agrar~ 41120 BETR08
#> 4 10 2001 TIERAR~ 67 Saarl~ Allgemeine Agrar~ 41120 BETR08
#> 5 10 2001 TIERAR~ 275 Saarl~ Allgemeine Agrar~ 41120 BETR08
#> 6 10 2001 TIERAR~ 237 Saarl~ Allgemeine Agrar~ 41120 BETR08
#> 7 10 2001 TIERAR~ 383 Saarl~ Allgemeine Agrar~ 41120 BETR08
#> 8 10 2001 GESAMT 1494 Saarl~ Allgemeine Agrar~ 41120 BETR08
#> 9 10 2003 TIERAR~ 337 Saarl~ Allgemeine Agrar~ 41120 BETR08
#> 10 10 2003 TIERAR~ 930 Saarl~ Allgemeine Agrar~ 41120 BETR08
#> # ... with 373 more rows, and 7 more variables: stat_description <chr>,
#> # substat_description <chr>, param_description <chr>,
#> # stat_description_en <chr>, substat_description_en <chr>,
#> # param_description_en <chr>, year_id <chr>stat_name: BAU018 Total non-residential buildingssubstat_name: BAUAHZ Main Type of Heating
dg_call(nuts_nr = 1,
stat_name = "BAU018",
substat_name = "BAUAHZ",
year = 2016)
#> # A tibble: 217 x 15
#> id year BAUAHZ value name GENESIS_source GENESIS_source_~ stat_name
#> <chr> <int> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 10 2016 INSGES~ 369 Saarl~ Statistik der Ba~ 31111 BAU018
#> 2 10 2016 BAUAHZ2 1 Saarl~ Statistik der Ba~ 31111 BAU018
#> 3 10 2016 BAUAHZ4 2 Saarl~ Statistik der Ba~ 31111 BAU018
#> 4 10 2016 BAUAHZ5 29 Saarl~ Statistik der Ba~ 31111 BAU018
#> 5 10 2016 BAUAHZ1 13 Saarl~ Statistik der Ba~ 31111 BAU018
#> 6 10 2016 BAUAHZ3 93 Saarl~ Statistik der Ba~ 31111 BAU018
#> 7 10 2016 BAUAHZ6 231 Saarl~ Statistik der Ba~ 31111 BAU018
#> 8 10 2016 BAUAHZ1 21 Saarl~ Statistik der Ba~ 31111 BAU018
#> 9 10 2016 BAUAHZ3 94 Saarl~ Statistik der Ba~ 31111 BAU018
#> 10 10 2016 INSGES~ 321 Saarl~ Statistik der Ba~ 31111 BAU018
#> # ... with 207 more rows, and 7 more variables: stat_description <chr>,
#> # substat_description <chr>, param_description <chr>,
#> # stat_description_en <chr>, substat_description_en <chr>,
#> # param_description_en <chr>, year_id <chr>You can also use dg_map to plot retrieved data on a Germany map. This
currently only supports NUTS-1 and NUTS-2. Arguments are (mostly)
identical to dg_call.
dg_map(nuts_nr = 1,
year = 2017,
stat_name = "AI0506")
The output is a ggplot object and can be manipulated further.
dg_map(nuts_nr = 1,
year = 2017,
stat_name = "AI0506") +
ggthemes::theme_map() +
ggplot2::scale_fill_viridis_c("Voter Turnout") +
ggplot2::ggtitle("Voter Turnout in German Parliamentary Election (2017)") +
ggplot2::theme(legend.position = "right")
You
can also return the data and use your own plotting functions with
return_data = TRUE.
turnout_dat <- dg_map(nuts_nr = 1,
year = 2017,
stat_name = "AI0506",
return_data = T)
turnout_dat
#> Simple feature collection with 16 features and 18 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 5.866251 ymin: 47.27012 xmax: 15.04181 ymax: 55.05653
#> Geodetic CRS: WGS 84
#> First 10 features:
#> GID_0 NAME_0 GID_1 NAME_1 VARNAME_1
#> 1 DEU Germany DEU.1_1 Baden-Württemberg <NA>
#> 2 DEU Germany DEU.2_1 Bayern Bavaria
#> 3 DEU Germany DEU.3_1 Berlin <NA>
#> 4 DEU Germany DEU.4_1 Brandenburg <NA>
#> 5 DEU Germany DEU.5_1 Bremen <NA>
#> 6 DEU Germany DEU.6_1 Hamburg <NA>
#> 7 DEU Germany DEU.7_1 Hessen Hesse
#> 8 DEU Germany DEU.8_1 Mecklenburg-Vorpommern Mecklenburg-West Pomerania
#> 9 DEU Germany DEU.9_1 Niedersachsen Lower Saxony
#> 10 DEU Germany DEU.10_1 Nordrhein-Westfalen North Rhine-Westphalia
#> NL_NAME_1 TYPE_1 ENGTYPE_1 CC_1 HASC_1 id year value
#> 1 <NA> Land State 08 DE.BW 08 2017 78.3
#> 2 <NA> Freistaat <NA> 09 DE.BY 09 2017 78.1
#> 3 <NA> Land State 11 DE.BE 11 2017 75.6
#> 4 <NA> Land State 12 DE.BR 12 2017 73.7
#> 5 <NA> Freie Hansestadt State 04 DE.HB 04 2017 70.8
#> 6 <NA> Freie und Hansestadt State 02 DE.HH 02 2017 76.0
#> 7 <NA> Land State 06 DE.HE 06 2017 77.0
#> 8 <NA> Land State 13 DE.MV 13 2017 70.9
#> 9 <NA> Land State 03 DE.NI 03 2017 76.4
#> 10 <NA> Land State 05 DE.NW 05 2017 75.4
#> GENESIS_source GENESIS_source_nr stat_name
#> 1 Regionalatlas Deutschland 99910 AI0506
#> 2 Regionalatlas Deutschland 99910 AI0506
#> 3 Regionalatlas Deutschland 99910 AI0506
#> 4 Regionalatlas Deutschland 99910 AI0506
#> 5 Regionalatlas Deutschland 99910 AI0506
#> 6 Regionalatlas Deutschland 99910 AI0506
#> 7 Regionalatlas Deutschland 99910 AI0506
#> 8 Regionalatlas Deutschland 99910 AI0506
#> 9 Regionalatlas Deutschland 99910 AI0506
#> 10 Regionalatlas Deutschland 99910 AI0506
#> stat_description stat_description_en
#> 1 Wahlbeteiligung, Bundestagswahl Voter Turnout, Federal Election
#> 2 Wahlbeteiligung, Bundestagswahl Voter Turnout, Federal Election
#> 3 Wahlbeteiligung, Bundestagswahl Voter Turnout, Federal Election
#> 4 Wahlbeteiligung, Bundestagswahl Voter Turnout, Federal Election
#> 5 Wahlbeteiligung, Bundestagswahl Voter Turnout, Federal Election
#> 6 Wahlbeteiligung, Bundestagswahl Voter Turnout, Federal Election
#> 7 Wahlbeteiligung, Bundestagswahl Voter Turnout, Federal Election
#> 8 Wahlbeteiligung, Bundestagswahl Voter Turnout, Federal Election
#> 9 Wahlbeteiligung, Bundestagswahl Voter Turnout, Federal Election
#> 10 Wahlbeteiligung, Bundestagswahl Voter Turnout, Federal Election
#> geometry
#> 1 MULTIPOLYGON (((8.708021 47...
#> 2 MULTIPOLYGON (((9.740664 47...
#> 3 MULTIPOLYGON (((13.17136 52...
#> 4 MULTIPOLYGON (((12.26716 52...
#> 5 MULTIPOLYGON (((8.711424 53...
#> 6 MULTIPOLYGON (((10.2106 53....
#> 7 MULTIPOLYGON (((8.680588 49...
#> 8 MULTIPOLYGON (((11.49583 54...
#> 9 MULTIPOLYGON (((6.761945 53...
#> 10 MULTIPOLYGON (((8.059656 50...Check out some use cases. For example here:
datenguideR builds on the amazing work of Datenguide and their GraphQL API. We especially thank Simon Jockers, Simon Wörpel, and Christian Rijke for their constructive feedback, helpful comments, and overall support while developing the package.
The data is retrieved via the Datenguide API from the German Federal Statistical Office and the statistical offices of the German states. Data being used via this package has to be credited according to the Datenlizenz Deutschland – Namensnennung – Version 2.0.
This package was created with devtools, usethis, and roxygen2. Continuous integration was done with Travis CI.
datenguideR is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.