abscorr

The abscorr package provides tidy versions of common structures used by the Australian Bureau of Statistics (ABS).

This package is currently in development and subject to change. The lifecycle badge will be changed to stable when structures are stable (should be relatively soon).

Contribute to this package: people are actively encouraged to contribute to this package.

Installation

You can install the current version of abscorr with:

remotes::install_github("runapp-aus/abscorr")

Structures

Current structures stored in abscorr are:

• anzsco: occupation levels of the Australian and New Zealand Standard Classification of Occupations (ANZSCO), First Edition, Revision 1, 2009. Cat. 1220.0.
• anzsic: industry levels of the Australian and New Zealand Standard Industrial Classification (ANZSIC), 2006 (Revision 1.0). Cat. 1292.0.
• asced_foe: field of education levels of the Australian Standard Classification of Education (ASCED), 2001. Cat. 1272.0.
• asced_qual: qualification levels of the Australian Standard Classification of Education (ASCED), 2001. Cat. 1272.0.

The abscorr package also loads absmapsdata, which contains the following structures and their geometry as sf objects:

ASGS Main Structures

• sa12011: Statistical Area 1 2011
• sa12016: Statistical Area 1 2016
• sa22011: Statistical Area 2 2011
• sa22016: Statistical Area 2 2016
• sa32011: Statistical Area 3 2011
• sa32016: Statistical Area 3 2016
• sa42011: Statistical Area 4 2011
• sa42016: Statistical Area 4 2016
• gcc2011: Greater Capital Cities 2011
• gcc2016: Greater Capital Cities 2016
• ra2011: Remoteness Areas 2011
• ra2016: Remoteness Areas 2016
• state2011: State 2011
• state2016: State 2016

ASGS Non-ABS Structures

• ced2018: Commonwealth Electoral Divisions 2018
• sed2018: State Electoral Divisions 2018
• lga2016: Local Government Areas 2016
• lga2018: Local Government Areas 2018
• regional_ivi2008: Regions for the Internet Vacancy Index 2008
• postcodes2016: Postcodes 2016
• dz2011: Census of Population and Housing Destination Zones 2011
• dz2016: Census of Population and Housing Destination Zones 2016

Using abscorr

Loading the package will lazily load the structures listed above. Call them with their name:

library(abscorr)
#> Loading required package: absmapsdata
library(dplyr)

library(abscorr)
#> Loading required package: absmapsdata
library(dplyr, warn.conflicts = FALSE)

glimpse(anzsco)
#> Rows: 1,180
#> Columns: 11
#> $ anzsco1_code <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1…
#> $ anzsco1      <chr> "Managers", "Managers", "Managers", "Managers", "Managers…
#> $ anzsco2_code <chr> "10", "11", "11", "11", "11", "11", "11", "11", "11", "12…
#> $ anzsco2      <chr> "Managers, nfd", "Chief Executives, General Managers and …
#> $ anzsco3_code <chr> "100", "110", "111", "111", "111", "111", "111", "111", "…
#> $ anzsco3      <chr> "Managers, nfd", "Chief Executives, General Managers and …
#> $ anzsco4_code <chr> "1000", "1100", "1110", "1111", "1112", "1112", "1113", "…
#> $ anzsco4      <chr> "Managers, nfd", "Chief Executives, General Managers and …
#> $ anzsco6_code <chr> "100000", "110000", "111000", "111111", "111211", "111212…
#> $ anzsco6      <chr> "Managers, nfd", "Chief Executives, General Managers and …
#> $ skill_level  <chr> NA, NA, NA, "1", "1", "1", "1", "1", "1", NA, NA, "1", "1…
glimpse(anzsic)
#> Rows: 506
#> Columns: 8
#> $ anzsic_division_code    <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "…
#> $ anzsic_division         <chr> "Agriculture, Forestry and Fishing", "Agricult…
#> $ anzsic_subdivision_code <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "…
#> $ anzsic_subdivision      <chr> "Agriculture", "Agriculture", "Agriculture", "…
#> $ anzsic_group_code       <chr> "11", "11", "11", "11", "11", "12", "12", "12"…
#> $ anzsic_group            <chr> "Nursery and Floriculture Production", "Nurser…
#> $ anzsic_class_code       <chr> "111", "112", "113", "114", "115", "121", "122…
#> $ anzsic_class            <chr> "Nursery Production (Under Cover)", "Nursery P…
glimpse(asced_foe)
#> Rows: 439
#> Columns: 6
#> $ foe2_code <chr> "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", …
#> $ foe2      <chr> "Natural and Physical Sciences", "Natural and Physical Scien…
#> $ foe4_code <chr> "0100", "0101", "0101", "0101", "0101", "0103", "0103", "010…
#> $ foe4      <chr> "Natural and Physical Sciences, nfd", "Mathematical Sciences…
#> $ foe6_code <chr> "010000", "010100", "010101", "010103", "010199", "010300", …
#> $ foe6      <chr> "Natural and Physical Sciences, nfd", "Mathematical Sciences…
glimpse(asced_qual)
#> Rows: 64
#> Columns: 6
#> $ qual1_code <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "2",…
#> $ qual1      <chr> "Postgraduate Degree Level", "Postgraduate Degree Level", "…
#> $ qual2_code <chr> "11", "11", "11", "11", "11", "11", "12", "12", "12", "12",…
#> $ qual2      <chr> "Doctoral Degree Level", "Doctoral Degree Level", "Doctoral…
#> $ qual3_code <chr> "111", "112", "113", "114", "115", "116", "121", "122", "12…
#> $ qual3      <chr> "Higher Doctorate", "Doctorate by Research", "Doctorate by …
glimpse(sa42016)
#> Rows: 107
#> Columns: 10
#> $ sa4_code_2016   <chr> "101", "102", "103", "104", "105", "106", "107", "108"…
#> $ sa4_name_2016   <chr> "Capital Region", "Central Coast", "Central West", "Co…
#> $ gcc_code_2016   <chr> "1RNSW", "1GSYD", "1RNSW", "1RNSW", "1RNSW", "1RNSW", …
#> $ gcc_name_2016   <chr> "Rest of NSW", "Greater Sydney", "Rest of NSW", "Rest …
#> $ state_code_2016 <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",…
#> $ state_name_2016 <chr> "New South Wales", "New South Wales", "New South Wales…
#> $ areasqkm_2016   <dbl> 51895.5631, 1681.0724, 70297.0604, 13229.7578, 339363.…
#> $ cent_long       <dbl> 149.2450, 151.2855, 148.3558, 152.7739, 145.0269, 150.…
#> $ cent_lat        <dbl> -35.56480, -33.30797, -33.21697, -29.81603, -30.98611,…
#> $ geometry        <list> [150.31133, 150.31258, 150.30964, 150.31133, -35.6658…
glimpse(ced2018)
#> Rows: 169
#> Columns: 6
#> $ ced_code_2018 <chr> "101", "102", "103", "104", "105", "106", "107", "108", …
#> $ ced_name_2018 <chr> "Banks", "Barton", "Bennelong", "Berowra", "Blaxland", "…
#> $ areasqkm_2018 <dbl> 49.4460, 39.6466, 58.6052, 749.6359, 61.1166, 98.3974, 3…
#> $ cent_long     <dbl> 151.0465, 151.1274, 151.0985, 151.0385, 151.0090, 151.15…
#> $ cent_lat      <dbl> -33.96482, -33.94082, -33.79360, -33.56993, -33.89634, -…
#> $ geometry      <list> [151.01558, 151.01209, 151.00812, 151.00593, 151.00422,…