⚠️ This package is not owned, run, or endorsed by the ABS. Data contained in this package are compressed, re-projected, renamed and stored assfobjects to be useful for making maps in R. If conducting spatial analysis, or any analysis that requires precise area boundaries, please use the original shapefiles provided by the ABS and others.
absmapsdata is a user-generated package to make it easier for R users
to access ABS (and other) spatial structure names/codes and produce maps
using this data. The package contains compressed (lossy), tidied, and
lazily-loadable sf objects that hold geometric information about data
structures in Australia. It also contains a correspondences files
provided by the ABS.
✅ It is now recommended that you use
strayr::read_absmapto access data stored inabsmapsdata. To download and read these data files without installing the wholeabsmapsdatapackage, please usestrayr::read_absmap, for example:
# remotes::install_github("runapp-aus/strayr")
strayr::read_absmap("sa42021")
#> Simple feature collection with 108 features and 9 fields (with 19 geometries empty)
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 96.81696 ymin: -43.74047 xmax: 167.9969 ymax: -9.219923
#> Geodetic CRS: WGS 84
#> First 10 features:
#> sa4_code_2021 sa4_name_2021 gcc_code_2021 gcc_name_2021 state_code_2021 state_name_2021 areasqkm_2021 cent_lat cent_long geometry
#> 1 101 Capital Region 1RNSW Rest of NSW 1 New South Wales 51896.244 -35.55432 149.2427 MULTIPOLYGON (((150.3113 -3...
#> 2 102 Central Coast 1GSYD Greater Sydney 1 New South Wales 1681.009 -33.30788 151.2855 MULTIPOLYGON (((151.315 -33...
#> 3 103 Central West 1RNSW Rest of NSW 1 New South Wales 70297.060 -33.21932 148.3585 MULTIPOLYGON (((150.6107 -3...
#> 4 104 Coffs Harbour - Grafton 1RNSW Rest of NSW 1 New South Wales 13229.758 -29.81484 152.7740 MULTIPOLYGON (((153.2672 -3...
#> 5 105 Far West and Orana 1RNSW Rest of NSW 1 New South Wales 339355.646 -30.99476 145.0285 MULTIPOLYGON (((150.1106 -3...
#> 6 106 Hunter Valley exc Newcastle 1RNSW Rest of NSW 1 New South Wales 21491.292 -32.35426 150.9840 MULTIPOLYGON (((151.9978 -3...
#> 7 107 Illawarra 1RNSW Rest of NSW 1 New South Wales 1539.241 -34.43440 150.7712 MULTIPOLYGON (((150.8768 -3...
#> 8 108 Mid North Coast 1RNSW Rest of NSW 1 New South Wales 18851.499 -31.56224 152.3435 MULTIPOLYGON (((159.0685 -3...
#> 9 109 Murray 1RNSW Rest of NSW 1 New South Wales 97796.490 -34.42239 144.0206 MULTIPOLYGON (((147.6165 -3...
#> 10 110 New England and North West 1RNSW Rest of NSW 1 New South Wales 99139.900 -30.05970 150.7010 MULTIPOLYGON (((152.4876 -2...You probably don’t need to install the full absmapsdata package (see
above).
But if you want to, you can install absmapsdata from Github. The
package contains a lot of data, so installing using
remotes::install_github may fail if the download times out. If this
happens, set the timeout option to a large value and try again,
i.e. run:
options(timeout = 1000)
remotes::install_github("wfmackey/absmapsdata")The sf package is required to handle the sf objects:
library(sf)Available maps are listed below. These will be added to over time. If you would like to request a map to be added, let me know via an issue on this Github repo.
ASGS Main Structures
- Statistical Area 1 2011:
sa12011; 2016:sa12016; and 2021:sa12021. - Statistical Area 2 2011:
sa22011; 2016:sa22016; and 2021:sa22021. - Statistical Area 3 2011:
sa32011; 2016:sa32016; and 2021:sa32021. - Statistical Area 4 2011:
sa42011; 2016:sa42016; and 2021:sa42021. - Greater Capital Cities 2011:
gcc2011; 2016:gcc2016; and 2021:gcc2021. - Remoteness Areas 2011:
ra2011; and 2016:ra2016 - State 2011:
state2011; 2016:state2016; andstate2021.
ASGS Indigenous Structures
- Indigenous Locations 2021:
iloc2021 - Indigenous Areas 2021:
iare2021 - Indigenous Regions 2021:
ireg2021
Significant Urban Areas and Urban Centres and Localities
- Significant Urban Areas 2016:
sua2016; and 2021:sua2021 - Urban Centre and Locality 2016:
ucl2016; and 2021:ucl2021 - Section of State Range 2016:
sosr2016 - Section of State 2016:
sos2016
ASGS Non-ABS Structures
- Commonwealth Electoral Divisions 2018:
ced2018; and 2021:ced2021 - State Electoral Divisions 2018:
sed2018; 2021:sed2021; and 2022:sed2022 - Local Government Areas 2016:
lga2016; 2018:lga2018; 2021:lga2021; and 2022:lga2022 - Regions for the Internet Vacancy Index 2008:
regional_ivi2008 - Postcodes 2016:
postcode2016; and 2021:postcode2021 - Suburbs (SSC) 2016:
suburb2016; and (SAL) 2021:suburb2021 - Census of Population and Housing Destination Zones 2011:
dz2011; 2016:dz2016; and 2021:dz2021.
Non-ABS Australian Government Structures
- Employment Regions 2015-2020:
employment_regions2015 - BITRE Working Zones 2016:
bitre_work_zones2016 - NSW Local Health District 2023:
nsw_lhd2023 - Regional Development Australia areas 2015-16:
rda2016
Correspondences
This package also contains a number of 2016 population-weighted ABS correspondences (the most recent) that can be found on the data.gov.au website.
✅ Use
strayr::read_correspondence_tblto access correspondence this data, rather than loading the wholeabsmapsdatapackage, e.g.:
# remotes::install_github("runapp-aus/strayr")
strayr::read_correspondence_tbl(from_area = "sa2", from_year = 2011,
to_area = "sa2", to_year = 2016)
#> # A tibble: 2,426 × 6
#> SA2_MAINCODE_2011 SA2_NAME_2011 SA2_MAINCODE_2016 SA2_NAME_2016 ratio PERCENTAGE
#> <chr> <chr> <chr> <chr> <dbl> <chr>
#> 1 101011001 Goulburn 101051539 Goulburn 1 100
#> 2 101011002 Goulburn Region 101051540 Goulburn Region 1 100
#> 3 101011003 Yass 101061541 Yass 1 100
#> 4 101011004 Yass Region 101061542 Yass Region 1 100
#> 5 101011005 Young 101061543 Young 1 100
#> 6 101011006 Young Region 101061544 Young Region 1 100
#> 7 101021007 Braidwood 101021007 Braidwood 1 100
#> 8 101021008 Karabar 101021008 Karabar 1 100
#> 9 101021009 Queanbeyan 101021009 Queanbeyan 1 100
#> 10 101021010 Queanbeyan - East 101021010 Queanbeyan - East 1 100
#> # ℹ 2,416 more rowsWithin absmapsdata, you can retrieve correspondences with the
get_correspondence_absmaps function.
The absmapsdata package comes with pre-downloaded and pre-processed
data. To load a particular geospatial object: load the package, then
call the object (see list above for object names).
library(tidyverse)
library(sf)
library(absmapsdata)
mapdata1 <- sa32021
glimpse(mapdata1)
#> Rows: 359
#> Columns: 12
#> $ sa3_code_2021 <chr> "10102", "10103", "10104", "10105", "10106", "10201", "10202", "10301", "10302", "10303", "10304", "10401", "10402", "10501", "10502", "10503", "10601", "10602", "10603", "10604", "10701", "10702", "10703", "10704", "10801", "10802", "10803", "10804", "10805", "10901", "10902", "10903", "11001", "11002", "11003", "11004", "11101", "11102", "11103", "11201", "11202", "11203", "11301", "11302", "11303", "11401", "11402", "11501", "11502", "11503", "11504", "11601", "11602", "11603", "11701", "11702", "11703", "11801", "11802", "11901", "11902", "11903", "11904", "12001", "12002", "12003", "12101", "12102", "12103", "12104", "12201", "12202", "12203", "12301", "12302", "12303", "12401", "12402", "12403", "12404", "12405", "12501", "12502", "12503", "12504", "12601", "12602", "12701", "12702", "12703", "12801", "12802", "19797", "19999", "20101", "20102", "20103", "20201", "20202", "20203", "20301", "20302", "20303", "20401", "20402", "20403", "20501", "20502", "2…
#> $ sa3_name_2021 <chr> "Queanbeyan", "Snowy Mountains", "South Coast", "Goulburn - Mulwaree", "Young - Yass", "Gosford", "Wyong", "Bathurst", "Lachlan Valley", "Lithgow - Mudgee", "Orange", "Clarence Valley", "Coffs Harbour", "Bourke - Cobar - Coonamble", "Broken Hill and Far West", "Dubbo", "Lower Hunter", "Maitland", "Port Stephens", "Upper Hunter", "Dapto - Port Kembla", "Illawarra Catchment Reserve", "Kiama - Shellharbour", "Wollongong", "Great Lakes", "Kempsey - Nambucca", "Lord Howe Island", "Port Macquarie", "Taree - Gloucester", "Albury", "Lower Murray", "Upper Murray exc. Albury", "Armidale", "Inverell - Tenterfield", "Moree - Narrabri", "Tamworth - Gunnedah", "Lake Macquarie - East", "Lake Macquarie - West", "Newcastle", "Richmond Valley - Coastal", "Richmond Valley - Hinterland", "Tweed Valley", "Griffith - Murrumbidgee (West)", "Tumut - Tumbarumba", "Wagga Wagga", "Shoalhaven", "Southern Highlands", "Baulkham Hills", "Dural - Wisemans Ferry", "Hawkesbury", "Rouse Hill - …
#> $ sa4_code_2021 <chr> "101", "101", "101", "101", "101", "102", "102", "103", "103", "103", "103", "104", "104", "105", "105", "105", "106", "106", "106", "106", "107", "107", "107", "107", "108", "108", "108", "108", "108", "109", "109", "109", "110", "110", "110", "110", "111", "111", "111", "112", "112", "112", "113", "113", "113", "114", "114", "115", "115", "115", "115", "116", "116", "116", "117", "117", "117", "118", "118", "119", "119", "119", "119", "120", "120", "120", "121", "121", "121", "121", "122", "122", "122", "123", "123", "123", "124", "124", "124", "124", "124", "125", "125", "125", "125", "126", "126", "127", "127", "127", "128", "128", "197", "199", "201", "201", "201", "202", "202", "202", "203", "203", "203", "204", "204", "204", "205", "205", "205", "205", "205", "206", "206", "206", "206", "206", "206", "206", "207", "207", "207", "208", "208", "208", "208", "209", "209", "209", "209", "210", "210", "210", "210", "210", "211", "211", "211", "211", "211", "…
#> $ sa4_name_2021 <chr> "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Central Coast", "Central Coast", "Central West", "Central West", "Central West", "Central West", "Coffs Harbour - Grafton", "Coffs Harbour - Grafton", "Far West and Orana", "Far West and Orana", "Far West and Orana", "Hunter Valley exc Newcastle", "Hunter Valley exc Newcastle", "Hunter Valley exc Newcastle", "Hunter Valley exc Newcastle", "Illawarra", "Illawarra", "Illawarra", "Illawarra", "Mid North Coast", "Mid North Coast", "Mid North Coast", "Mid North Coast", "Mid North Coast", "Murray", "Murray", "Murray", "New England and North West", "New England and North West", "New England and North West", "New England and North West", "Newcastle and Lake Macquarie", "Newcastle and Lake Macquarie", "Newcastle and Lake Macquarie", "Richmond - Tweed", "Richmond - Tweed", "Richmond - Tweed", "Riverina", "Riverina", "Riverina", "Southern Highlands and Shoalhaven", "Southern Highla…
#> $ gcc_code_2021 <chr> "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1GSYD", "1GSYD", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "19799", "19499", "2RVIC", "2RVIC", "2RVIC", "2RVIC", "2RVIC", "2RVIC", "2RVIC", "2RVIC", "2RVIC", "2RVIC", "2RVIC", "2RVIC", "2RVIC", "2RVIC", "2…
#> $ gcc_name_2021 <chr> "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Greater Sydney", "Greater Sydney", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sy…
#> $ state_code_2021 <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3",…
#> $ state_name_2021 <chr> "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New …
#> $ areasqkm_2021 <dbl> 6511.3971, 14284.5857, 9864.4876, 9099.9087, 12135.8653, 988.3863, 692.6225, 6984.1829, 41000.7206, 16044.6243, 6267.5322, 9263.1940, 3966.5637, 162762.4085, 146854.8470, 29738.3906, 8566.7812, 380.1169, 1071.9768, 11472.4168, 164.0176, 744.3423, 390.7982, 240.0824, 3133.0446, 5339.3246, 16.2918, 3687.7694, 6675.0682, 5597.0037, 63967.8828, 28231.6033, 16395.6494, 30566.4425, 32022.7204, 20155.0876, 138.0853, 507.6329, 224.8639, 1572.9178, 7390.6170, 1307.7853, 28859.6888, 9174.7261, 18950.5656, 4374.9473, 2330.0146, 72.0257, 569.2386, 2492.0101, 118.2237, 55.9289, 103.0657, 81.8889, 28.3677, 12.6700, 25.0658, 26.1862, 31.5480, 68.6675, 29.9648, 33.9469, 31.3480, 19.7477, 10.6577, 34.1437, 33.1733, 137.3989, 85.5463, 18.9809, 14.3455, 90.6494, 149.2123, 73.8692, 333.0123, 870.3606, 942.4079, 2456.9093, 356.8590, 178.8386, 33.1146, 33.0600, 25.6873, 56.9969, 47.0908, 22.1744, 47.1624, 291.5240, 123.7703, 124.9847, 55.6052, 240.2473, NA, NA, 448.3487, 3666.6730,…
#> $ cent_lat <dbl> -35.44896, -36.43821, -36.49582, -34.51746, -34.57987, -33.36538, -33.22577, -33.62949, -33.32460, -32.73172, -33.27015, -29.61853, -30.27334, -30.72069, -31.08376, -31.85714, -32.62339, -32.70975, -32.69747, -32.10863, -34.48802, -34.36940, -34.64775, -34.25191, -32.33511, -30.86219, -31.55253, -31.41010, -31.84254, -35.76686, -33.86749, -35.39610, -30.72879, -29.44918, -29.64855, -31.08234, -33.02049, -33.04451, -32.87335, -28.86381, -28.78515, -28.35996, -34.06690, -35.73247, -34.91271, -35.07490, -34.49305, -33.73057, -33.54744, -33.30486, -33.62124, -33.76872, -33.69875, -33.77430, -33.94581, -33.91009, -33.88907, -33.88240, -33.94346, -33.92755, -33.92423, -33.96919, -33.95968, -33.85335, -33.87047, -33.88577, -33.80717, -33.62190, -33.73470, -33.83129, -33.80026, -33.63983, -33.71020, -34.05521, -34.07844, -34.14538, -33.63700, -33.99326, -33.80979, -33.62393, -33.78409, -33.85547, -33.79294, -33.84752, -33.79734, -33.75421, -33.80293, -33.94584, -33.86…
#> $ cent_long <dbl> 149.6018, 148.9415, 149.8079, 149.6046, 148.6786, 151.2181, 151.3816, 149.6380, 147.4696, 149.8411, 148.9309, 152.7807, 152.7582, 146.4193, 142.7151, 148.8425, 151.2252, 151.5487, 151.9036, 150.7010, 150.7995, 150.7156, 150.7665, 150.9321, 152.1573, 152.6451, 159.0768, 152.5312, 152.0660, 147.1760, 143.2897, 145.0855, 151.7462, 151.3484, 149.6374, 150.5642, 151.6616, 151.4939, 151.7103, 153.4263, 152.9349, 153.3564, 145.8446, 148.2424, 147.4461, 150.3802, 150.3394, 150.9953, 151.0147, 150.7785, 150.8930, 150.9083, 150.8611, 150.8382, 151.2003, 151.1613, 151.1997, 151.2556, 151.2422, 151.0148, 151.0893, 151.0778, 151.1331, 151.1150, 151.1680, 151.1057, 151.1869, 151.1497, 151.1510, 151.2269, 151.2700, 151.2793, 151.2297, 150.7197, 150.8535, 150.6125, 150.4258, 150.2882, 150.6784, 150.7466, 150.7859, 151.0453, 151.0465, 150.9726, 150.9921, 151.0737, 151.1136, 150.7463, 150.8694, 150.9300, 151.1441, 151.0444, NA, NA, 143.8156, 144.1049, 143.5081, 144.2889, 144.35…
#> $ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((149.979 -35..., MULTIPOLYGON (((149.0973 -3..., MULTIPOLYGON (((150.3345 -3..., MULTIPOLYGON (((149.0114 -3..., MULTIPOLYGON (((147.7137 -3..., MULTIPOLYGON (((151.315 -33..., MULTIPOLYGON (((151.485 -33..., MULTIPOLYGON (((149.323 -33..., MULTIPOLYGON (((149.1264 -3..., MULTIPOLYGON (((150.5587 -3..., MULTIPOLYGON (((148.3297 -3..., MULTIPOLYGON (((153.2785 -2..., MULTIPOLYGON (((153.2672 -3..., MULTIPOLYGON (((144.3929 -2..., MULTIPOLYGON (((140.9993 -2..., MULTIPOLYGON (((148.8107 -3..., MULTIPOLYGON (((151.406 -32..., MULTIPOLYGON (((151.6954 -3..., MULTIPOLYGON (((151.98 -32...., MULTIPOLYGON (((151.406 -32..., MULTIPOLYGON (((150.9291 -3..., MULTIPOLYGON (((150.8979 -3..., MULTIPOLYGON (((150.8768 -3..., MULTIPOLYGON (((150.8595 -3..., MULTIPOLYGON (((152.5444 -3..., MULTIPOLYGON (((153.0169 -3..., MULTIPOLYGON (((159.0685 -3..., MULTIPOLYGON (((152.9666 -3..., MULTIPOLYGON (((152.8044 -3..., MULTIPOLYGON (((147.756 -35..., M…Or
mapdata2 <- sa22016
glimpse(mapdata2)
#> Rows: 2,310
#> Columns: 15
#> $ sa2_code_2016 <chr> "101021007", "101021008", "101021009", "101021010", "101021011", "101021012", "101031013", "101031014", "101031015", "101031016", "101041017", "101041018", "101041019", "101041020", "101041021", "101041022", "101041023", "101041024", "101041025", "101041026", "101041027", "101051539", "101051540", "101061541", "101061542", "101061543", "101061544", "102011028", "102011029", "102011030", "102011031", "102011032", "102011033", "102011034", "102011035", "102011036", "102011037", "102011038", "102011039", "102011040", "102011041", "102011042", "102011043", "102021044", "102021045", "102021046", "102021047", "102021048", "102021049", "102021050", "102021051", "102021052", "102021053", "102021054", "102021055", "102021056", "102021057", "103011058", "103011059", "103011060", "103011061", "103021062", "103021063", "103021064", "103021065", "103021066", "103021067", "103021068", "103021069", "103031070", "103031071", "103031072", "103031073", "103031074", "103031075",…
#> $ sa2_5dig_2016 <chr> "11007", "11008", "11009", "11010", "11011", "11012", "11013", "11014", "11015", "11016", "11017", "11018", "11019", "11020", "11021", "11022", "11023", "11024", "11025", "11026", "11027", "11539", "11540", "11541", "11542", "11543", "11544", "11028", "11029", "11030", "11031", "11032", "11033", "11034", "11035", "11036", "11037", "11038", "11039", "11040", "11041", "11042", "11043", "11044", "11045", "11046", "11047", "11048", "11049", "11050", "11051", "11052", "11053", "11054", "11055", "11056", "11057", "11058", "11059", "11060", "11061", "11062", "11063", "11064", "11065", "11066", "11067", "11068", "11069", "11070", "11071", "11072", "11073", "11074", "11075", "11076", "11077", "11078", "11079", "11080", "11081", "11082", "11083", "11084", "11085", "11086", "11087", "11088", "11089", "11090", "11091", "11092", "11093", "11094", "11095", "11096", "11097", "11098", "11099", "11100", "11101", "11102", "11103", "11104", "11105", "11106", "11107", "11108", "1…
#> $ sa2_name_2016 <chr> "Braidwood", "Karabar", "Queanbeyan", "Queanbeyan - East", "Queanbeyan Region", "Queanbeyan West - Jerrabomberra", "Bombala", "Cooma", "Cooma Region", "Jindabyne - Berridale", "Batemans Bay", "Batemans Bay - South", "Bega - Tathra", "Bega-Eden Hinterland", "Broulee - Tomakin", "Deua - Wadbilliga", "Eden", "Eurobodalla Hinterland", "Merimbula - Tura Beach", "Moruya - Tuross Head", "Narooma - Bermagui", "Goulburn", "Goulburn Region", "Yass", "Yass Region", "Young", "Young Region", "Avoca Beach - Copacabana", "Box Head - MacMasters Beach", "Calga - Kulnura", "Erina - Green Point", "Gosford - Springfield", "Kariong", "Kincumber - Picketts Valley", "Narara", "Niagara Park - Lisarow", "Point Clare - Koolewong", "Saratoga - Davistown", "Terrigal - North Avoca", "Umina - Booker Bay - Patonga", "Wamberal - Forresters Beach", "Woy Woy - Blackwall", "Wyoming", "Bateau Bay - Killarney Vale", "Blue Haven - San Remo", "Budgewoi - Buff Point - Halekulani", "Chittaway Bay - T…
#> $ sa3_code_2016 <chr> "10102", "10102", "10102", "10102", "10102", "10102", "10103", "10103", "10103", "10103", "10104", "10104", "10104", "10104", "10104", "10104", "10104", "10104", "10104", "10104", "10104", "10105", "10105", "10106", "10106", "10106", "10106", "10201", "10201", "10201", "10201", "10201", "10201", "10201", "10201", "10201", "10201", "10201", "10201", "10201", "10201", "10201", "10201", "10202", "10202", "10202", "10202", "10202", "10202", "10202", "10202", "10202", "10202", "10202", "10202", "10202", "10202", "10301", "10301", "10301", "10301", "10302", "10302", "10302", "10302", "10302", "10302", "10302", "10302", "10303", "10303", "10303", "10303", "10303", "10303", "10304", "10304", "10304", "10304", "10401", "10401", "10401", "10402", "10402", "10402", "10402", "10402", "10402", "10402", "10402", "10402", "10501", "10501", "10501", "10501", "10501", "10502", "10502", "10503", "10503", "10503", "10503", "10503", "10503", "10503", "10503", "10601", "10601", "1…
#> $ sa3_name_2016 <chr> "Queanbeyan", "Queanbeyan", "Queanbeyan", "Queanbeyan", "Queanbeyan", "Queanbeyan", "Snowy Mountains", "Snowy Mountains", "Snowy Mountains", "Snowy Mountains", "South Coast", "South Coast", "South Coast", "South Coast", "South Coast", "South Coast", "South Coast", "South Coast", "South Coast", "South Coast", "South Coast", "Goulburn - Mulwaree", "Goulburn - Mulwaree", "Young - Yass", "Young - Yass", "Young - Yass", "Young - Yass", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Gosford", "Wyong", "Wyong", "Wyong", "Wyong", "Wyong", "Wyong", "Wyong", "Wyong", "Wyong", "Wyong", "Wyong", "Wyong", "Wyong", "Wyong", "Bathurst", "Bathurst", "Bathurst", "Bathurst", "Lachlan Valley", "Lachlan Valley", "Lachlan Valley", "Lachlan Valley", "Lachlan Valley", "Lachlan Valley", "Lachlan Valley", "Lachlan Valley", "Lithgow - Mudgee", "Lithgow - Mudgee", "Lith…
#> $ sa4_code_2016 <chr> "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "101", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "102", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "103", "104", "104", "104", "104", "104", "104", "104", "104", "104", "104", "104", "104", "105", "105", "105", "105", "105", "105", "105", "105", "105", "105", "105", "105", "105", "105", "105", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "106", "107", "107", "107", "107", "107", "107", "107", "107", "107", "107", "…
#> $ sa4_name_2016 <chr> "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Capital Region", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coast", "Central Coa…
#> $ gcc_code_2016 <chr> "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1GSYD", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1…
#> $ gcc_name_2016 <chr> "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Rest of NSW", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Greater Sydney", "Rest of NSW", "Rest of NSW",…
#> $ state_code_2016 <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",…
#> $ state_name_2016 <chr> "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New South Wales", "New …
#> $ areasqkm_2016 <dbl> 3418.3525, 6.9825, 4.7634, 13.0034, 3054.4099, 13.6789, 3989.3618, 103.6371, 6250.8748, 3939.5484, 63.7074, 30.8638, 183.8985, 4710.2068, 24.2329, 2781.9988, 94.8138, 1460.0117, 97.3395, 165.4118, 252.6520, 64.7865, 9035.1221, 99.7342, 5680.8782, 505.7528, 5849.8086, 6.4376, 32.0860, 767.9512, 33.7934, 16.9124, 8.3063, 12.0887, 7.7021, 16.7316, 6.7874, 4.7683, 10.1073, 25.2284, 13.7221, 17.4225, 8.3810, 10.8595, 20.9797, 9.3531, 24.4986, 11.2490, 346.7662, 35.4946, 114.1172, 9.1793, 13.8576, 10.5216, 27.8463, 42.8900, 15.0334, 119.5048, 94.0341, 3798.2628, 2972.3813, 15858.8501, 242.2010, 3037.1557, 4598.0438, 3294.0766, 234.7556, 5418.1649, 8317.4729, 119.9844, 2514.0700, 65.8948, 2852.0843, 8043.8635, 2448.7276, 1642.6748, 45.2499, 99.5118, 4480.0957, 106.2537, 8445.8909, 711.0493, 1001.0481, 25.6688, 33.6996, 363.9090, 1987.6714, 57.6881, 118.2251, 108.4124, 270.2414, 56843.6928, 45551.3981, 12142.3534, 21216.0023, 27011.6794, 170.1153, 146690.0605, 10474.1…
#> $ cent_long <dbl> 149.7932, 149.2328, 149.2255, 149.2524, 149.3911, 149.2028, 149.0455, 149.1194, 149.0822, 148.6089, 150.1819, 150.2104, 149.9021, 149.7191, 150.1616, 149.7464, 149.8909, 150.0722, 149.8821, 150.1072, 150.0737, 149.7196, 149.6046, 148.9159, 148.8932, 148.3531, 148.4956, 151.4302, 151.3847, 151.1753, 151.4026, 151.3479, 151.2946, 151.4031, 151.3341, 151.3728, 151.3163, 151.3577, 151.4280, 151.2982, 151.4499, 151.3057, 151.3641, 151.4674, 151.5242, 151.5609, 151.4283, 151.4948, 151.3241, 151.5759, 151.3351, 151.5769, 151.5120, 151.5546, 151.4121, 151.4581, 151.4353, 149.5589, 149.6292, 149.5330, 149.7757, 147.0338, 148.6611, 148.6829, 147.9249, 148.0259, 148.1899, 148.0015, 146.9843, 150.1248, 150.0514, 149.6012, 150.0281, 149.5433, 150.3854, 149.1439, 149.1126, 149.1259, 148.8476, 152.9354, 152.7379, 153.2662, 152.7199, 153.1042, 153.0957, 153.0329, 152.6205, 153.1406, 153.0371, 152.9991, 153.1669, 145.7465, 145.4847, 148.2240, 147.0807, 148.0662, 141.4793, 142.…
#> $ cent_lat <dbl> -35.45508, -35.37590, -35.35103, -35.35520, -35.44408, -35.37760, -36.87794, -36.25023, -36.12715, -36.49170, -35.70108, -35.78418, -36.72504, -36.92872, -35.84601, -36.17742, -37.03135, -35.77465, -36.90019, -35.94713, -36.31306, -34.75057, -34.51647, -34.82284, -34.87668, -34.32114, -34.30978, -33.47548, -33.50845, -33.33970, -33.42324, -33.42524, -33.43976, -33.46983, -33.39606, -33.38428, -33.44820, -33.47745, -33.44511, -33.52917, -33.40970, -33.48517, -33.40474, -33.37960, -33.20405, -33.22673, -33.35442, -33.25791, -33.17296, -33.18047, -33.31651, -33.14104, -33.32910, -33.27305, -33.30483, -33.24205, -33.27656, -33.39912, -33.41528, -33.44042, -33.88803, -32.90815, -33.79104, -33.74021, -33.46160, -33.87923, -33.17294, -32.95368, -33.90016, -33.47862, -33.30741, -32.60394, -32.74829, -32.40578, -33.16048, -33.60788, -33.30131, -33.24941, -33.14654, -29.67722, -29.63487, -29.42054, -30.44796, -30.28213, -30.31514, -30.20839, -30.21362, -30.20613, -30.37…
#> $ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((149.7606 -3..., MULTIPOLYGON (((149.2192 -3..., MULTIPOLYGON (((149.2315 -3..., MULTIPOLYGON (((149.2315 -3..., MULTIPOLYGON (((149.2563 -3..., MULTIPOLYGON (((149.2064 -3..., MULTIPOLYGON (((148.4221 -3..., MULTIPOLYGON (((149.0751 -3..., MULTIPOLYGON (((148.5512 -3..., MULTIPOLYGON (((148.5512 -3..., MULTIPOLYGON (((150.1978 -3..., MULTIPOLYGON (((150.2336 -3..., MULTIPOLYGON (((149.9931 -3..., MULTIPOLYGON (((150.0238 -3..., MULTIPOLYGON (((150.1987 -3..., MULTIPOLYGON (((150.046 -36..., MULTIPOLYGON (((149.9129 -3..., MULTIPOLYGON (((149.979 -35..., MULTIPOLYGON (((149.9368 -3..., MULTIPOLYGON (((150.1196 -3..., MULTIPOLYGON (((150.2307 -3..., MULTIPOLYGON (((149.6879 -3..., MULTIPOLYGON (((149.08 -34...., MULTIPOLYGON (((148.8828 -3..., MULTIPOLYGON (((149.08 -34...., MULTIPOLYGON (((148.2258 -3..., MULTIPOLYGON (((147.8245 -3..., MULTIPOLYGON (((151.414 -33..., MULTIPOLYGON (((151.3799 -3..., MULTIPOLYGON (((151.2046 -3..., M…The resulting sf object contains one observation per area (in the
following examples, one observation per sa3). It stores the geometry
information in the geometry variable, which is a nested list
describing the area’s polygon. The object can be joined to a standard
data.frame or tibble and can be used with dplyr functions.
We do all this so we can create gorgeous maps. And with the sf object
in hand, plotting a map via ggplot and geom_sf is simple.
map <-
sa32016 %>%
filter(gcc_name_2016 == "Greater Melbourne") %>% # let's just look Melbourne
ggplot()
geom_sf(aes(geometry = geometry)) # use the geometry variable
map
The data also include centroids of each area, and we can add these
points to the map with the cent_lat and cent_long variables using
geom_point.
map <- sa32016 %>%
filter(gcc_name_2016 == "Greater Melbourne") %>% # let's just look Melbourne
ggplot()
geom_sf(aes(geometry = geometry)) # use the geometry variable
geom_point(aes(cent_long, cent_lat)) # use the centroid long (x) and lats (y)
map
Cool. But this all looks a bit ugly. We can pretty it up using ggplot
tweaks. See the comments on each line for its objective. Also note that
we’re filling the areas by their areasqkm size, another variable
included in the sf object (we’ll replace this with more interesting
data in the next section).
map <- sa32016 %>%
filter(gcc_name_2016 == "Greater Melbourne") %>% # let's just look Melbourne
ggplot()
geom_sf(aes(geometry = geometry, # use the geometry variable
fill = areasqkm_2016), # fill by area size
lwd = 0, # remove borders
show.legend = FALSE) # remove legend
geom_point(aes(cent_long,
cent_lat), # use the centroid long (x) and lats (y)
colour = "white") # make the points white
theme_void() # clears other plot elements
coord_sf()
map
At some point, we’ll want to join our spatial data with data-of-interest. The variables in our mapping data—stating the numeric code and name of each area and parent area—will make this relatively easy.
For example: suppose we had a simple dataset of median income by SA3 over time.
# Read data in some data
income <- read_csv("https://raw.githubusercontent.com/wfmackey/absmapsdata/master/img/data/median_income_sa3.csv")
#> Rows: 2148 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (2): sa3_name_2016, year
#> dbl (1): median_income
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(income)
#> # A tibble: 6 × 3
#> sa3_name_2016 year median_income
#> <chr> <chr> <dbl>
#> 1 Queanbeyan 2010-11 51858
#> 2 Snowy Mountains 2010-11 35884
#> 3 South Coast 2010-11 30908
#> 4 Goulburn - Mulwaree 2010-11 38269
#> 5 Young - Yass 2010-11 39489
#> 6 Gosford 2010-11 38189This income data contains a variable sa3_name_2016, and we can use
dplyr::left_join() to combine with our mapping data.
combined_data <- left_join(income,
sa32016,
by = "sa3_name_2016")Now that we have a tidy dataset with 1) the income data we want to plot, and 2) the geometry of the areas, we can plot income by area:
map <- combined_data %>%
filter(gcc_name_2016 == "Greater Melbourne") %>% # let's just look Melbourne
ggplot()
geom_sf(aes(geometry = geometry, # use the geometry variable
fill = median_income), # fill by unemployment rate
lwd = 0) # remove borders
theme_void() # clears other plot elements
labs(fill = "Median income")
map
✅ Use
strayr::read_correspondence_tblto access correspondence this data, rather than loading the wholeabsmapsdatapackage, e.g.:
# remotes::install_github("runapp-aus/strayr")
strayr::read_correspondence_tbl(from_area = "sa2", from_year = 2011,
to_area = "sa2", to_year = 2016)
#> Reading file found in /var/folders/98/c8srjgc55kbfzlrnl3c9yy2w0000gn/T//RtmpBsnhzT
#> # A tibble: 2,426 × 6
#> SA2_MAINCODE_2011 SA2_NAME_2011 SA2_MAINCODE_2016 SA2_NAME_2016 ratio PERCENTAGE
#> <chr> <chr> <chr> <chr> <dbl> <chr>
#> 1 101011001 Goulburn 101051539 Goulburn 1 100
#> 2 101011002 Goulburn Region 101051540 Goulburn Region 1 100
#> 3 101011003 Yass 101061541 Yass 1 100
#> 4 101011004 Yass Region 101061542 Yass Region 1 100
#> 5 101011005 Young 101061543 Young 1 100
#> 6 101011006 Young Region 101061544 Young Region 1 100
#> 7 101021007 Braidwood 101021007 Braidwood 1 100
#> 8 101021008 Karabar 101021008 Karabar 1 100
#> 9 101021009 Queanbeyan 101021009 Queanbeyan 1 100
#> 10 101021010 Queanbeyan - East 101021010 Queanbeyan - East 1 100
#> # ℹ 2,416 more rowsYou can use the absmapsdata::get_correspondence_absmaps function to
get population-weighted correspondence tables provided by the
ABS.
Note that while there are lots of correspondence tables, not every
combination is available.
For example:
get_correspondence_absmaps("cd", 2006,
"sa1", 2016)
#> # A tibble: 92,336 × 5
#> CD_CODE_2006 SA1_MAINCODE_2016 SA1_7DIGITCODE_2016 ratio PERCENTAGE
#> <chr> <chr> <chr> <dbl> <chr>
#> 1 1010101 10902117908 1117908 0.477 47.705709950000002
#> 2 1010101 10902117909 1117909 0.486 48.579130499999998
#> 3 1010101 10902117910 1117910 0.0372 3.7151597000000001
#> 4 1010102 10902117907 1117907 0.210 21.012930999999998
#> 5 1010102 10902117908 1117908 0.281 28.062155199999999
#> 6 1010102 10902117910 1117910 0.509 50.924913799999999
#> 7 1010103 10902117907 1117907 1 100
#> 8 1010104 10902117901 1117901 0.510 51.007496400000001
#> 9 1010104 10902117907 1117907 0.490 48.992503599999999
#> 10 1010105 10902117907 1117907 1 100
#> # ℹ 92,326 more rowsThe motivation for this package is that maps are cool and fun and are,
sometimes, the best way to communicate data. And making maps is R with
ggplot is relatively easy when you have the right object.
Getting the right object is not technically difficult, but requires
research into the best-thing-to-do at each of the following steps:
- Find the ASGS ABS spatial-data page and determine the right file to download.
- Read the shapefile into
Rusing one-of-many import tools. - Convert the object into something usable.
- Clean up any inconsistencies and apply consistent variable naming/values across areas and years.
- Find an appropriate compression function and level to optimise output.
For me at least, finding the correct information and developing the best
set of steps was a little bit interesting but mostly tedious and
annoying. The absmapsdata package holds this data for you, so you can
spend more time making maps, and less time on Stack Overflow, the ABS
website, and lovely-people’s wonderful
blogs.
The best avenue is via a Github issue at wfmackey/absmapsdata/issues. This is also the best place to request data that isn’t yet available in the package.