x <- system.file("sdmx/dedemo.zip", package = "STATcubeR") %>% sdmx_table()
+
diff --git a/search.json b/search.json
index 91aaedbe..f0b3cf96 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://statistikat.github.io/STATcubeR/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"The GNU General Public License, Version 2, June 1991 (GPLv2)","title":"The GNU General Public License, Version 2, June 1991 (GPLv2)","text":"Copyright (C) 1989, 1991 Free Software Foundation, Inc. 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://statistikat.github.io/STATcubeR/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"The GNU General Public License, Version 2, June 1991 (GPLv2)","text":"licenses software designed take away freedom share change . contrast, GNU General Public License intended guarantee freedom share change free software–make sure software free users. General Public License applies Free Software Foundation’s software program whose authors commit using . 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EXCEPT OTHERWISE STATED WRITING COPYRIGHT HOLDERS /PARTIES PROVIDE PROGRAM “” WITHOUT WARRANTY KIND, EITHER EXPRESSED IMPLIED, INCLUDING, LIMITED , IMPLIED WARRANTIES MERCHANTABILITY FITNESS PARTICULAR PURPOSE. ENTIRE RISK QUALITY PERFORMANCE PROGRAM . PROGRAM PROVE DEFECTIVE, ASSUME COST NECESSARY SERVICING, REPAIR CORRECTION. 12. EVENT UNLESS REQUIRED APPLICABLE LAW AGREED WRITING COPYRIGHT HOLDER, PARTY MAY MODIFY /REDISTRIBUTE PROGRAM PERMITTED , LIABLE DAMAGES, INCLUDING GENERAL, SPECIAL, INCIDENTAL CONSEQUENTIAL DAMAGES ARISING USE INABILITY USE PROGRAM (INCLUDING LIMITED LOSS DATA DATA RENDERED INACCURATE LOSSES SUSTAINED THIRD PARTIES FAILURE PROGRAM OPERATE PROGRAMS), EVEN HOLDER PARTY ADVISED POSSIBILITY DAMAGES.","code":""},{"path":"https://statistikat.github.io/STATcubeR/articles/od_list.html","id":"interactive-overview","dir":"Articles","previous_headings":"","what":"Interactive overview","title":"Available Datasets","text":"Since metadata contained OGD JSON files available German, following overview uses German labels. Click individual table cells get information.","code":""},{"path":"https://statistikat.github.io/STATcubeR/articles/od_list.html","id":"cli-usage","dir":"Articles","previous_headings":"","what":"CLI usage","title":"Available Datasets","text":"get simplified version summary, use od_list() function. uses webscraping techniques get dataset ids German labels based contents https://data.statistik.gv./web/catalog.jsp.","code":"all_datasets <- od_list() all_datasets # A tibble: 373 × 3 category id label 1 Neueste Daten OGD_vgr110_VGR_Personen_1 Hauptaggregate der VGR; Person… 2 Neueste Daten OGD_touextsai_Tour_HKL_1 Nächtigungsstatistik ab Novemb… 3 Neueste Daten OGD_touextsai_Tour_UA_1 Nächtigungsstatistik ab Novemb… 4 Neueste Daten OGD_konjunkturmonitor_KonMon_1 Konjunkturmonitor 5 Neueste Daten OGD_skesvg2010indikat_HVD_NFSK… Nicht-finanzielle Sektorkonten… 6 Neueste Daten OGD_pregps003_GHPI_20S_1 GHPI 2020; Sondergliederungen 7 Neueste Daten OGD_pregpi003_GHPI_20_1 Grosshandelspreisindex 2020 8 Neueste Daten OGD_vgr101_VGRJahresR_3 VGR-Jahresrechnungen, Hauptagg… 9 Neueste Daten OGD_vgr104_VGR_Konsum_2 Hauptaggregate der VGR; Konsum… 10 Neueste Daten OGD_skesvg2010_NFSK_ESVG10_1 Nicht-finanzielle Sektorkonten… # ℹ 363 more rows"},{"path":"https://statistikat.github.io/STATcubeR/articles/od_list.html","id":"overview-via-json","dir":"Articles","previous_headings":"","what":"Overview via json","title":"Available Datasets","text":"identify interesting dataset, consider downloading metadata json get details. json contains links metadata including link data.statistik.gv.. output generated OGD_touextsai_Tour_HKL_1.json shows summary available metadata. parts metadata can extracted $ using keys json specification.","code":"(id <- all_datasets$id[2]) #> [1] \"OGD_touextsai_Tour_HKL_1\" json <- od_json(id) json #> Nächtigungsstatistik ab November 1973 - Nächtigungen nach #> Herkunftsländern und Bundesländern #> #> Statistik Austria stellt monatlich aktualisierte Daten über Ankünfte #> und Übernachtungen auf Bundeslandebene getrennt nach #> Herkunftsländern ab November 1973 zur Verfügung #> #> Measures: Ankünfte, Übernachtungen #> Fields: Saison/Tourismusmonat, Bundesland, Herkunftsland #> Updated: 2024-10-08 12:36:22 #> Tags: Tourismus, Beherbergung, Nächtigungen, Wintersaison, Sommersaison, #> Herkunftsländer #> Categories: Wirtschaft und Tourismus json$extras$update_frequency #> [1] \"monatlich\""},{"path":"https://statistikat.github.io/STATcubeR/articles/od_list.html","id":"showcase","dir":"Articles","previous_headings":"","what":"Showcase","title":"Available Datasets","text":"Population Hospitalizations Earnings Household forecast Gross regional product population dataset measures Austrian population 2117 different regions. hospitalizations dataset timeseries 2009 2019 115 different medical procedures. structure earnings dataset showcases average earnings four different classifications. See tabulation article usage examples dataset. household forecast contains predictions number private households 4 household characteristics 2011 2080. GRP dataset contains GRP NUTS-3 regions 2000 2019.","code":"od_table(\"OGD_bevstandjbab2002_BevStand_2020\")$tabulate() # A STATcubeR tibble: 392,144 x 5 `Time section` Sex Commune (aggregation by p…¹ `Age in single years` Number * 1 2020-01-01 male Eisenstadt <10101> under 1 year old 77 2 2020-01-01 male Eisenstadt <10101> 1 year old 75 3 2020-01-01 male Eisenstadt <10101> 2 years old 70 4 2020-01-01 male Eisenstadt <10101> 3 years old 83 5 2020-01-01 male Eisenstadt <10101> 4 years old 67 6 2020-01-01 male Eisenstadt <10101> 5 years old 56 7 2020-01-01 male Eisenstadt <10101> 6 years old 75 8 2020-01-01 male Eisenstadt <10101> 7 years old 73 9 2020-01-01 male Eisenstadt <10101> 8 years old 74 10 2020-01-01 male Eisenstadt <10101> 9 years old 86 # ℹ 392,134 more rows # ℹ abbreviated name: ¹`Commune (aggregation by political district)` od_table(\"OGD_krankenbewegungen_ex_LEISTUNGEN_1\")$tabulate() # A STATcubeR tibble: 91,898 x 6 `Year of discharge` Sex `Age (four classes)` NUTS-2 region (place of resi…¹ * 1 2009-01-01 male Up to 14 years old Non-Austria 2 2009-01-01 male Up to 14 years old Non-Austria 3 2009-01-01 male Up to 14 years old Non-Austria 4 2009-01-01 male Up to 14 years old Non-Austria 5 2009-01-01 male Up to 14 years old Non-Austria 6 2009-01-01 male Up to 14 years old Non-Austria 7 2009-01-01 male Up to 14 years old Non-Austria 8 2009-01-01 male Up to 14 years old Non-Austria 9 2009-01-01 male Up to 14 years old Non-Austria 10 2009-01-01 male Up to 14 years old Non-Austria # ℹ 91,888 more rows # ℹ abbreviated name: ¹`NUTS-2 region (place of residence)` # ℹ 2 more variables: `Medical procedures - subchapters` , # `Medical procedures` od_table(\"OGD_veste309_Veste309_1\")$tabulate() # A STATcubeR tibble: 72 x 9 Sex Citizenship `Region (NUTS2)` `Form of employment` * 1 Sum total Total Total \"Total\" 2 Sum total Total Total \"Standard employment \" 3 Sum total Total Total \"Non-standard employment (total)\" 4 Sum total Total Total \"Non-standard employment: part-time… 5 Sum total Total Total \"Non-standard employment: fixed-ter… 6 Sum total Total Total \"Non-standard employment: marginal … 7 Sum total Total Total \"Non-standard employment: temporary… 8 Sum total Total AT11 Burgenland \"Total\" 9 Sum total Total AT12 Lower Austria \"Total\" 10 Sum total Total AT13 Vienna \"Total\" # ℹ 62 more rows # ℹ 5 more variables: `Arithmetic mean` , `1st quartile` , # `2nd quartile (median)` , `3rd quartile` , # `Number of employees` od_table(dat_name)$tabulate() # A STATcubeR tibble: 630 x 4 Time `Province (NUTS 2-Einheit) <9>` Private households at the end of…¹ * 1 2011-01-01 Burgenland 117588 2 2011-01-01 Carinthia 241461 3 2011-01-01 Lower Austria 682380 4 2011-01-01 Upper Austria 593029 5 2011-01-01 Salzburg 224629 6 2011-01-01 Styria 515258 7 2011-01-01 Tyrol 299024 8 2011-01-01 Vorarlberg 152948 9 2011-01-01 Vienna 843181 10 2012-01-01 Burgenland 118776 # ℹ 620 more rows # ℹ abbreviated name: ¹`Private households at the end of the year` # ℹ 1 more variable: `Annual average of private households` od_table(\"OGD_vgrrgr104_RGR104_1\")$tabulate() # A STATcubeR tibble: 1,114 x 6 Time `NUTS-3` Gross regional product; current …¹ * 1 2000-01-01 Mittelburgenland 597 2 2000-01-01 Nordburgenland 2641 3 2000-01-01 Südburgenland 1559 4 2000-01-01 Mostviertel-Eisenwurzen 4778 5 2000-01-01 Niederösterreich-Süd 4714 6 2000-01-01 Sankt Pölten 3647 7 2000-01-01 Waldviertel 3947 8 2000-01-01 Weinviertel 1722 9 2000-01-01 Wiener Umland-Nordteil 4841 10 2000-01-01 Wiener Umland-Südteil 9886 # ℹ 1,104 more rows # ℹ abbreviated name: ¹`Gross regional product; current prices in million Euro` # ℹ 3 more variables: `Gross regional product per inhabitant` , # `Gross regional product per person employed` , # `Change in % to previous year prices` "},{"path":"https://statistikat.github.io/STATcubeR/articles/od_resources.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"File Management","text":"default, STATcubeR caches accessed resources data.statistik.gv.temporary directory current R session. Let’s examine example happens data structure earnings survey (SES) requested. First STATcubeR grab json metadata dataset https://data.statistik.gv./ogd/json?dataset=OGD_veste309_Veste309_1 check resources belong . resource, attributes name last_modified extracted json. also included od_table object $resources. last_modified tells us resource changed fileserver. resource exist cache last modified entry json newer cached file, downloaded server. Otherwise, cached version reused.","code":"od_cache_dir() #> [1] \"/tmp/RtmpALaRNA/STATcubeR/open_data/\" earnings <- od_table(\"OGD_veste309_Veste309_1\") earnings$resources # A data frame: 7 × 6 name last_modified cached size download parsed 1 meta.json 2022-03-24 11:29:48 2024-10-09 05:48:10 4062 400. NA 2 data.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 4931 124. 0.784 3 HEADER.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 516 123. 0.483 4 C-A11-0.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 159 124. 0.453 5 C-STAATS-0.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 697 124. 0.527 6 C-VEBDL-0.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 518 123. 0.457 7 C-BESCHV-0.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 641 123. 0.510"},{"path":"https://statistikat.github.io/STATcubeR/articles/od_resources.html","id":"access-and-updates","dir":"Articles","previous_headings":"","what":"Access and Updates","title":"File Management","text":"Cached files can accessed od_cache_file(). specified file exists cache, path file returned. Otherwise, file downloaded cache path returned. files use naming conventions open data fileserver. read files cache data.frames, use od_resource() parameters od_cache_file(). apply special parser dataset drops unneeded columns normalizes column names. parser behaves differently header files, data files fields. Json files can accessed od_json().","code":"od_cache_file(\"OGD_veste309_Veste309_1\") #> [1] \"/tmp/RtmpALaRNA/STATcubeR/open_data/OGD_veste309_Veste309_1.csv\" od_cache_file(\"OGD_veste309_Veste309_1\", \"C-A11-0\") #> [1] \"/tmp/RtmpALaRNA/STATcubeR/open_data/OGD_veste309_Veste309_1_C-A11-0.csv\" od_resource(\"OGD_veste309_Veste309_1\", \"C-A11-0\") # A data frame: 3 × 7 code label label_de label_en parent de_desc en_desc * 1 A11-1 NA insgesamt Sum total NA NA NA 2 A11-2 NA männlich Male NA NA NA 3 A11-3 NA weiblich Female NA NA NA json <- od_json(\"OGD_veste309_Veste309_1\") unlist(json$tags) #> [1] \"Staatsangehörigkeit\" \"Bundesland\" #> [3] \"Beschäftigungsverhältnis\""},{"path":"https://statistikat.github.io/STATcubeR/articles/od_resources.html","id":"clearing-and-changing","dir":"Articles","previous_headings":"","what":"Clearing and Changing","title":"File Management","text":"od_cache_clear(id) can used clear cache files belonging passed dataset id. saw earnings$resources contains 7 rows, therefore 7 files deleted cleanup. want use persistent directory like ~/.cache/STATcubeR/open_data/ caching, directory can changed od_cache_dir(new).","code":"od_cache_clear(\"OGD_veste309_Veste309_1\") #> deleted 7 files from '/tmp/RtmpALaRNA/STATcubeR/open_data/' od_cache_dir(\"~/.cache/STATcubeR/open_data/\")"},{"path":"https://statistikat.github.io/STATcubeR/articles/od_resources.html","id":"the-resources-field","dir":"Articles","previous_headings":"","what":"The resources field","title":"File Management","text":"Let’s go back $resources field earnings. already looked name last_modified. remaining columns can interpreted follows cached tells us last time cache file resource modified. size file size bytes download contains amount milliseconds used retrieve resource last updated. parsed reports amount milliseconds took od_resource() convert file contents data.frame() format. json file, parsing time always reported NA.","code":"earnings$resources # A data frame: 7 × 6 name last_modified cached size download parsed 1 meta.json 2022-03-24 11:29:48 2024-10-09 05:48:10 4062 400. NA 2 data.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 4931 124. 0.784 3 HEADER.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 516 123. 0.483 4 C-A11-0.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 159 124. 0.453 5 C-STAATS-0.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 697 124. 0.527 6 C-VEBDL-0.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 518 123. 0.457 7 C-BESCHV-0.csv 2022-03-24 11:29:48 2024-10-09 05:48:11 641 123. 0.510"},{"path":"https://statistikat.github.io/STATcubeR/articles/od_resources.html","id":"whats-in-the-cache","dir":"Articles","previous_headings":"","what":"What’s in the cache?","title":"File Management","text":"od_cache_summary() give overview files available cache directory. returned table contains one row every dataset. column updated contains last modified date datasets json file. json, data header give file sizes bytes corresponding files. fields total size fields n_fields number classification files available. can get clear picture much disk space used dataset. Note od_cache_summary() gathers information local file system based filenames, file.mtime() file.size().","code":"od_cache_summary() #> NULL"},{"path":"https://statistikat.github.io/STATcubeR/articles/od_resources.html","id":"download-history","dir":"Articles","previous_headings":"","what":"Download history","title":"File Management","text":"get history files downloaded server, use od_downloads(). file, timestamp download recorded well download time milliseconds.","code":"od_downloads() # A data frame: 7 × 3 time file downloaded * 1 2024-10-09 05:48:11 OGD_veste309_Veste309_1_C-BESCHV-0.csv 123. 2 2024-10-09 05:48:11 OGD_veste309_Veste309_1_C-VEBDL-0.csv 123. 3 2024-10-09 05:48:11 OGD_veste309_Veste309_1_C-STAATS-0.csv 124. 4 2024-10-09 05:48:11 OGD_veste309_Veste309_1_C-A11-0.csv 124. 5 2024-10-09 05:48:11 OGD_veste309_Veste309_1_HEADER.csv 123. 6 2024-10-09 05:48:11 OGD_veste309_Veste309_1.csv 124. 7 2024-10-09 05:48:10 OGD_veste309_Veste309_1.json 400."},{"path":"https://statistikat.github.io/STATcubeR/articles/od_table.html","id":"import-and-overview","dir":"Articles","previous_headings":"","what":"Import and overview","title":"Open Government Data","text":"import dataset, provide dataset id argument. returns object class od_table, bundles data OGD portal corresponds dataset. Printing object show summary contents. dataset contains number cancer patients several classification fields tumor type differentiates <95> types cancers reporting period spans <37> years (1983 2019). regional variable contains <9> NUTS-2 regions Austria. demographic variable “Sex” reported <2> levels","code":"table <- od_table(\"OGD_krebs_ext_KREBS_1\") table #> Cancer statistics by reporting year, province of residence and #> localisation of cancer #> #> Dataset: OGD_krebs_ext_KREBS_1 (data.statistik.gv.at) #> Measures: Number of records F-KRE #> Fields: Tumore ICD/10 3-Steller <98>, Reporting year <40>, Province of #> residence <9>, Sex <2> #> #> Request: [2024-10-09 05:48:16.500158] #> STATcubeR: 0.5.2"},{"path":"https://statistikat.github.io/STATcubeR/articles/od_table.html","id":"convert-to-a-data-frame","dir":"Articles","previous_headings":"","what":"Convert to a data frame","title":"Open Government Data","text":"method $tabulate() can used turn object data.frame long format, contains labeled data. dataset contains 49190 rows. every combination tumor type, year, region sex contain separate row number rows following. 95×37×9×2=63270 95\\times37\\times9\\times2 = 63270 means table fairly dense. might case OGD datasets.","code":"table$tabulate() # A STATcubeR tibble: 49,190 x 5 `Tumore ICD/10 3-Steller` `Reporting year` Province of residenc…¹ Sex * 1 Bösartige Neubildung de… 1983-01-01 \"Burgenland \" male 2 Bösartige Neubildung de… 1983-01-01 \"Carinthia\" male 3 Bösartige Neubildung de… 1983-01-01 \"Carinthia\" female 4 Bösartige Neubildung de… 1983-01-01 \"Lower Austria\" male 5 Bösartige Neubildung de… 1983-01-01 \"Lower Austria\" female 6 Bösartige Neubildung de… 1983-01-01 \"Upper Austria\" male 7 Bösartige Neubildung de… 1983-01-01 \"Upper Austria\" female 8 Bösartige Neubildung de… 1983-01-01 \"Salzburg\" male 9 Bösartige Neubildung de… 1983-01-01 \"Styria\" male 10 Bösartige Neubildung de… 1983-01-01 \"Styria\" female # ℹ 49,180 more rows # ℹ abbreviated name: ¹`Province of residence` # ℹ 1 more variable: `Number of records F-KRE` "},{"path":"https://statistikat.github.io/STATcubeR/articles/od_table.html","id":"metadata","dir":"Articles","previous_headings":"","what":"Metadata","title":"Open Government Data","text":"section show different metadata components contained table object relate resources OGD server.","code":"table$resources$name #> [1] \"OGD_krebs_ext_KREBS_1.json\" #> [2] \"OGD_krebs_ext_KREBS_1.csv\" #> [3] \"OGD_krebs_ext_KREBS_1_HEADER.csv\" #> [4] \"OGD_krebs_ext_KREBS_1_C-TUM_ICD10_3ST-0.csv\" #> [5] \"OGD_krebs_ext_KREBS_1_C-BERJ-0.csv\" #> [6] \"OGD_krebs_ext_KREBS_1_C-BUNDESLAND-0.csv\" #> [7] \"OGD_krebs_ext_KREBS_1_C-KRE_GESCHLECHT-0.csv\" #> attr(,\"class\") #> [1] \"ogd_file\" \"character\""},{"path":"https://statistikat.github.io/STATcubeR/articles/od_table.html","id":"header","dir":"Articles","previous_headings":"Metadata","what":"Header","title":"Open Government Data","text":"labels columns data.frame representation generated OGD_krebs_ext_KREBS_1_HEADER.csv can extracted table object via $header. Additional metadata columns can obtained via $meta. See STATcubeR data article details.","code":"table$header # STATcubeR metadata: 5 x 6 code label label_de label_en 1 F-KRE NA Anzahl der Datensätze F-KRE Number of records F-KRE 2 C-TUM_ICD10_3ST-0 NA Tumore ICD/10 3-Steller NA 3 C-BERJ-0 NA Berichtsjahr Reporting year 4 C-BUNDESLAND-0 NA Bundesland Province of residence 5 C-KRE_GESCHLECHT-0 NA Geschlecht Sex # … with 2 more columns: 'de_desc', 'en_desc'"},{"path":"https://statistikat.github.io/STATcubeR/articles/od_table.html","id":"field-infos","dir":"Articles","previous_headings":"Metadata","what":"Field infos","title":"Open Government Data","text":"method table$field() can used get information specific classification fields. contain data {dataset_id}_{field_code}.csv. Unlike metadata sc_table, od_table class always contains German English labels. can used label dataset. Tumor type Year Province Sex following call gives access German English labels 95 different tumor types \"cancer type\" classification. Click \"Year\" see information years. OGD_krebs_ext_KREBS_1_C-TUM_ICD10_3ST-0.csv reporting period spans 37 years (1983 2019). classification elements parsed format representation. OGD_krebs_ext_KREBS_1_C-BERJ-0.csv regional classification contains 9 elements correspond NUTS2 regions (“Bundesländer”) Austria. OGD_krebs_ext_KREBS_1_C-BUNDESLAND-0.csv Sex coded dichotomous variable classification elements \"male\" \"female\". OGD_krebs_ext_KREBS_1_C-KRE_GESCHLECHT-0.csv","code":"table$field(\"C-TUM_ICD10_3ST-0\") # STATcubeR metadata: 98 x 10 code label 1 TUM_ICD10_3ST-C00 Bösartige Neubildung der Lippe 2 TUM_ICD10_3ST-C01 Bösartige Neubildung des Zungengrundes 3 TUM_ICD10_3ST-C02 Bösartige Neubildung sonstiger und nicht näher bezeic… 4 TUM_ICD10_3ST-C03 Bösartige Neubildung des Zahnfleisches 5 TUM_ICD10_3ST-C04 Bösartige Neubildung des Mundbodens # ℹ 93 more rows # ℹ 1 more variable: parsed # … with 7 more columns: 'label_de', 'label_en', 'parent', 'de_desc', 'en_desc', 'visible', 'order' table$field(\"C-BERJ-0\") # STATcubeR metadata: 40 x 10 code label parsed 1 BERJ-1983 1983 1983-01-01 2 BERJ-1984 1984 1984-01-01 3 BERJ-1985 1985 1985-01-01 4 BERJ-1986 1986 1986-01-01 5 BERJ-1987 1987 1987-01-01 # ℹ 35 more rows # … with 7 more columns: 'label_de', 'label_en', 'parent', 'de_desc', 'en_desc', 'visible', 'order' table$field(\"C-BUNDESLAND-0\") # STATcubeR metadata: 9 x 10 code label parsed 1 BUNDESLAND-1 \"Burgenland \" \"Burgenland \" 2 BUNDESLAND-2 \"Carinthia\" \"Carinthia\" 3 BUNDESLAND-3 \"Lower Austria\" \"Lower Austria\" 4 BUNDESLAND-4 \"Upper Austria\" \"Upper Austria\" 5 BUNDESLAND-5 \"Salzburg\" \"Salzburg\" # ℹ 4 more rows # … with 7 more columns: 'label_de', 'label_en', 'parent', 'de_desc', 'en_desc', 'visible', 'order' table$field(\"C-KRE_GESCHLECHT-0\") # STATcubeR metadata: 2 x 10 code label parsed 1 GESCHLECHT-1 male male 2 GESCHLECHT-2 female female # … with 7 more columns: 'label_de', 'label_en', 'parent', 'de_desc', 'en_desc', 'visible', 'order'"},{"path":"https://statistikat.github.io/STATcubeR/articles/od_table.html","id":"json-metadata","dir":"Articles","previous_headings":"Metadata","what":"json Metadata","title":"Open Government Data","text":"json metadata file OGD_krebs_ext_KREBS_1.json available via $json binding. Cancer Earnings Economic Trend Monitor","code":"table$json #> Krebsstatistik #> #> Krebsstatistik nach Krebslokalisation (ICD10), Geschlecht und #> Wohnbundesland #> #> Measures: Anzahl der Datensätze F-KRE #> Fields: Tumore ICD/10 3-Steller, Berichtsjahr, Bundesland, Geschlecht #> Updated: 2024-01-25 16:03:34 #> Tags: Krebsstatistik, Krebslokalisation-ICD10, Geschlecht, Wohnbundesland #> Categories: Gesundheit od_json(\"OGD_veste309_Veste309_1\") #> Verdienststrukturerhebung 2018 Bruttostundenverdienste in EUR nach #> Staatsangehörigkeit, Bundesland und Beschäftigungsverhältnis #> #> Verdienststruktur nach Geschlecht, Staatsangehörigkeit, Bundesland #> und Beschäftigungsverhältnis #> #> Measures: Arithmetisches Mittel, 1. Quartil, 2. Quartil (Median), 3. #> Quartil, Zahl d unselbst Beschäftigten #> Fields: Geschlecht, Staatsangehörigkeit, Bundesland (NUTS 2), Form des #> Beschäftigungsverhältnisses #> Updated: 2022-03-24 11:29:48 #> Tags: Staatsangehörigkeit, Bundesland, Beschäftigungsverhältnis #> Categories: Arbeit, Bevölkerung od_json(\"OGD_konjunkturmonitor_KonMon_1\") #> Konjunkturmonitor #> #> Measures: Produktionsindex Industrie (at; 2021=100), Technische #> Gesamtproduktion Industrie in Tsd. € (KJE), Umsatzindex Industrie #> (2021=100), Umsatz Industrie inTsd.€ (KJE), Auftragseingangsindex #> Industrie (2021=100), Beschäftigtenindex Industrie (2021=100), #> Beschäftigte Industrie gesamt (KJE), Produktivitätsindex Industrie je #> unselbständig Beschäftigtem (2021=100), Produktivitätsindex Industrie je #> geleisteter Arbeitsstunde (2021=100), Erzeugerpreisindex für den #> Produzierenden Bereich (2021=100; NACE B-E), … (78 more) #> Fields: Berichtszeitraum, Wertangabe #> Updated: 2024-10-08 11:00:04 #> Tags: Konjunkturdaten #> Categories: Wirtschaft und Tourismus, Arbeit, Bevölkerung, Finanzen und #> Rechnungswesen"},{"path":"https://statistikat.github.io/STATcubeR/articles/od_table.html","id":"section","dir":"Articles","previous_headings":"","what":"Open Government Data","title":"Open Government Data","text":"print method shows part metadata. information can extracted using keys json object.","code":"table$json$extras$publisher #> [1] \"Statistik Austria, Guglgasse 13, 1110 Wien, Austria\" table$json$extras$update_frequency #> [1] \"jährlich\" table$json$resources[[1]]$url #> [1] \"https://data.statistik.gv.at/data/OGD_krebs_ext_KREBS_1.csv\""},{"path":"https://statistikat.github.io/STATcubeR/articles/od_table.html","id":"table-contents","dir":"Articles","previous_headings":"","what":"Table Contents","title":"Open Government Data","text":"get raw microdata OGD_krebs_ext_KREBS_1.csv , use table$data. output similar returned read.csv2(\"OGD_krebs_ext_KREBS_1.csv\"). od_table() makes sure levels factor columns order metadata. mentioned , labeled version data can obtained via table$tabulate(). labeling done taking raw dataset joining labels $header $field(). Time variables converted format satisfy certain STATcube standards. can read $tabulate() tabulation article.","code":"table$data # A STATcubeR tibble: 49,190 x 5 `C-TUM_ICD10_3ST-0` `C-BERJ-0` `C-BUNDESLAND-0` `C-KRE_GESCHLECHT-0` `F-KRE` * 1 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-1 GESCHLECHT-1 2 2 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-2 GESCHLECHT-1 8 3 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-2 GESCHLECHT-2 2 4 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-3 GESCHLECHT-1 6 5 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-3 GESCHLECHT-2 2 # ℹ 49,185 more rows levels(table$data$`C-BUNDESLAND-0`) == table$field(\"C-BUNDESLAND-0\")$code #> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE table$tabulate() # A STATcubeR tibble: 49,190 x 5 `Tumore ICD/10 3-Steller` `Reporting year` Province of residenc…¹ Sex * 1 Bösartige Neubildung der… 1983-01-01 \"Burgenland \" male 2 Bösartige Neubildung der… 1983-01-01 \"Carinthia\" male 3 Bösartige Neubildung der… 1983-01-01 \"Carinthia\" female 4 Bösartige Neubildung der… 1983-01-01 \"Lower Austria\" male 5 Bösartige Neubildung der… 1983-01-01 \"Lower Austria\" female # ℹ 49,185 more rows # ℹ abbreviated name: ¹`Province of residence` # ℹ 1 more variable: `Number of records F-KRE` "},{"path":"https://statistikat.github.io/STATcubeR/articles/od_table.html","id":"sauerkraut","dir":"Articles","previous_headings":"","what":"A Trip to Germany","title":"Open Government Data","text":"possible switch language used labeling dataset using $language field. field can used get set language. Allowed options \"en\" English \"de\" German. option affects print() method well output $tabulate(). English labels available, German labels used fallback mechanism.","code":"table$language #> [1] \"en\" table$language <- \"de\" table$language #> [1] \"de\" table #> Krebsstatistik #> #> Dataset: OGD_krebs_ext_KREBS_1 (data.statistik.gv.at) #> Measures: Anzahl der Datensätze F-KRE #> Fields: Tumore ICD/10 3-Steller <98>, Berichtsjahr <40>, Bundesland <9>, #> Geschlecht <2> #> #> Request: [2024-10-09 05:48:16.500158] #> STATcubeR: 0.5.2 table$tabulate() # A STATcubeR tibble: 49,190 x 5 `Tumore ICD/10 3-Steller` Berichtsjahr Bundesland Geschlecht * 1 Bösartige Neubildung der Lippe 1983-01-01 Burgenland männlich 2 Bösartige Neubildung der Lippe 1983-01-01 Kärnten männlich 3 Bösartige Neubildung der Lippe 1983-01-01 Kärnten weiblich 4 Bösartige Neubildung der Lippe 1983-01-01 Niederösterreich männlich 5 Bösartige Neubildung der Lippe 1983-01-01 Niederösterreich weiblich # ℹ 49,185 more rows # ℹ 1 more variable: `Anzahl der Datensätze F-KRE` "},{"path":"https://statistikat.github.io/STATcubeR/articles/od_table.html","id":"further-reading","dir":"Articles","previous_headings":"","what":"Further reading","title":"Open Government Data","text":"See available datasets article list datasets compatible od_table(). Open data datasets often contain large amount rows. Check tabulation article see can summarized compact form. STATcubeR caches files requested server hood. caching article explains caches stored.","code":""},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_cache.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Caching API Responses","text":"Caching disabled default. order activate deactivate caching, use functions sc_cache_enable() sc_cache_disable() caching directory can displayed changed using sc_cache_dir()","code":"sc_cache_enable() #> Caching will be available for this session. Add #> #> STATCUBE_CACHE = TRUE #> STATCUBE_CACHE_DIR = \"~/.STATcubeR_cache\" #> #> to your .Renviron to enable caching persistently. sc_cache_dir() #> [1] \"~/.STATcubeR_cache\" sc_cache_dir(\"~/.cache/STATcubeR/api\") sc_cache_dir() #> [1] \"~/.cache/STATcubeR/api\""},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_cache.html","id":"using-the-cache","dir":"Articles","previous_headings":"","what":"Using the cache","title":"Caching API Responses","text":"Caching affect calls sc_table() sc_schema() well “derived” functions: sc_table_saved(), sc_table_custom(), sc_schema_db(), sc_schema_catalogue(). resource requested several times, last valid API response reused. Invalid responses (404 responses) added cache. Cache files always contain unparsed API responses returned httr::GET() httr::POST(). Responses stored rds format. caching enabled, corresponding cache files object class sc_schema sc_table can retrieved using sc_cache_files(). Note first call sc_cache_files() returned two paths. Since table requested two languages, two API responses necessary construct table object. content cache files can parsed using readRDS() httr::content(). gives direct access API response list() format. example, following syntax can used extract database info response.","code":"sc_example(\"accomodation\") %>% sc_table(language = \"both\") %>% sc_cache_files() #> [1] \"~/.cache/STATcubeR/api/3Ks85P6Vxkk-GGbnYJd6d3+st8A=.rds\" #> [2] \"~/.cache/STATcubeR/api/vM1s-as1gKR9YGp25eRQQeIEosY=.rds\" sc_schema_catalogue() %>% sc_cache_files() #> [1] \"~/.cache/STATcubeR/api/a1nJzuFIzQxUJT5mZcPhhjiGs9I=.rds\" sc_example(\"accomodation\") %>% sc_table() %>% sc_cache_files() %>% readRDS() %>% httr::content() %>% .[[\"database\"]] %>% str() #> List of 4 #> $ id : chr \"detouextregsai\" #> $ uri : chr \"str:database:detouextregsai\" #> $ label : chr \"Accomodation statistics as of 1974 according to seasons\" #> $ annotationKeys:List of 1 #> ..$ : chr \"Q\""},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_cache.html","id":"cleaning-the-cache","dir":"Articles","previous_headings":"","what":"Cleaning the cache","title":"Caching API Responses","text":"Cache files can deleted individually using paths returned sc_cache_files(). Alternatively, use sc_cache_clear() delete files cache.","code":"sc_cache_clear() #> deleted 12 entries from the cache"},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_cache.html","id":"should-i-use-caching","dir":"Articles","previous_headings":"","what":"Should I use caching?","title":"Caching API Responses","text":"using STATcubeR interactively, answer probably . However, building applications rely STATcube data caching can useful way decrease traffic STATcube server. Another use case caching writing rmarkdown documents rely STATcube data. Caching makes documents reproducible quicker render. Please note currently reliable way invalidate cache. Therefore, API responses reused even resources get updated server.","code":""},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_data.html","id":"constructing-sc_data-objects","dir":"Articles","previous_headings":"","what":"Constructing sc_data objects","title":"The STATcubeR Data Class","text":"sc_data class exported STATcubeR. Therefore, objects class created one following functions od_table() obtains data OGD portal. See OGD article sc_table_saved() sc_table_custom() also use /table endpoint. However, request specified via ids rather json file. illustrate, use one OGD datasets showcase functionality class. Notice however, objects created sc_table() can used interchangeably.","code":"x <- od_table(\"OGD_krebs_ext_KREBS_1\")"},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_data.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"The STATcubeR Data Class","text":"data table can extracted using active binding $data. Notice OGD_krebs_ext_KREBS_1 includes codes possibly totals. data always provided long format one column field one column measure. explained labeled data can obtained Tabulation section.","code":"x$data # A STATcubeR tibble: 49,190 x 5 `C-TUM_ICD10_3ST-0` `C-BERJ-0` `C-BUNDESLAND-0` `C-KRE_GESCHLECHT-0` `F-KRE` * 1 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-1 GESCHLECHT-1 2 2 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-2 GESCHLECHT-1 8 3 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-2 GESCHLECHT-2 2 4 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-3 GESCHLECHT-1 6 5 TUM_ICD10_3ST-C00 BERJ-1983 BUNDESLAND-3 GESCHLECHT-2 2 # ℹ 49,185 more rows"},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_data.html","id":"metadata","dir":"Articles","previous_headings":"","what":"Metadata","title":"The STATcubeR Data Class","text":"Metadata sc_data object includes labels information relevant correctly parse raw data. active binding $meta contains least entries $source, $measures $fields.","code":""},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_data.html","id":"source","dir":"Articles","previous_headings":"Metadata","what":"Source","title":"The STATcubeR Data Class","text":"source field contains information data source. important entries code label. bottom, see additional information source available, namely label_en, label_de, etc. additional metadata entries might available sc_table objects.","code":"x$meta$source # STATcubeR metadata: 1 x 7 code label lang 1 OGD_krebs_ext_KREBS_1 Cancer statistics by reporting year, province of … en # … with 4 more columns: 'label_de', 'label_en', 'requested', 'scr_version'"},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_data.html","id":"measures","dir":"Articles","previous_headings":"Metadata","what":"Measures","title":"The STATcubeR Data Class","text":"part metadata data.frame one row measure. contains codes labels well number NAs found $data particular column.","code":"x$meta$measures # STATcubeR metadata: 1 x 7 code label NAs 1 F-KRE Number of records F-KRE 0 # … with 4 more columns: 'label_de', 'label_en', 'de_desc', 'en_desc'"},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_data.html","id":"fields","dir":"Articles","previous_headings":"Metadata","what":"Fields","title":"The STATcubeR Data Class","text":"fields entry summarizes classification fields .e. categorical variables. includes codes labels well total code registered particular field.","code":"x$meta$fields # STATcubeR metadata: 4 x 9 code label total_code nitems type 1 C-TUM_ICD10_3ST-0 Tumore ICD/10 3-Steller NA 98 Category 2 C-BERJ-0 Reporting year NA 40 Time (year) 3 C-BUNDESLAND-0 Province of residence NA 9 Category 4 C-KRE_GESCHLECHT-0 Sex NA 2 Category # … with 4 more columns: 'label_de', 'label_en', 'de_desc', 'en_desc'"},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_data.html","id":"field-information","dir":"Articles","previous_headings":"","what":"Field information","title":"The STATcubeR Data Class","text":"get info specific fields, use $field() method. return classification elements data.frame.","code":""},{"path":"https://statistikat.github.io/STATcubeR/articles/sc_data.html","id":"section","dir":"Articles","previous_headings":"","what":"The STATcubeR Data Class","title":"The STATcubeR Data Class","text":"Tumor types Year Province Sex","code":"x$field(\"Tumore\") # STATcubeR metadata: 98 x 10 code label 1 TUM_ICD10_3ST-C00 Bösartige Neubildung der Lippe 2 TUM_ICD10_3ST-C01 Bösartige Neubildung des Zungengrundes 3 TUM_ICD10_3ST-C02 Bösartige Neubildung sonstiger und nicht näher bezeic… 4 TUM_ICD10_3ST-C03 Bösartige Neubildung des Zahnfleisches 5 TUM_ICD10_3ST-C04 Bösartige Neubildung des Mundbodens # ℹ 93 more rows # ℹ 1 more variable: parsed # … with 7 more columns: 'label_de', 'label_en', 'parent', 'de_desc', 'en_desc', 'visible', 'order' x$field(\"Reporting year\") # STATcubeR metadata: 40 x 10 code label parsed