The absmapsdata
package exists to make it easier to produce maps from
ABS data in R. The package contains compressed, tidied, and
lazily-loadable sf
objects containing geometric information about ABS
data structures.
Before we get into the ‘what problem is this package solving’ details, let’s look at some examples so that you can copy-paste into your own script and replicate out-of-the-box (and impress your friends).
You can install absmapsdata
from github with:
# install.packages("devtools")
devtools::install_github("wfmackey/absmapsdata")
The sf
package is required to handle the sf
objects:
# install.packages("sf")
library(sf)
And we will use the tidyverse
packages in this example:
# install.packages("tidyverse")
library(tidyverse)
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. (Or send me an email: [email protected])
ASGS Main Structures
- Statistical Area 1 2011:
sa12011
- Statistical Area 1 2016:
sa12016
- Statistical Area 2 2011:
sa22011
- Statistical Area 2 2016:
sa22016
- Statistical Area 3 2011:
sa32011
- Statistical Area 3 2016:
sa32016
- Statistical Area 4 2011:
sa42011
- Statistical Area 4 2016:
sa42016
- Greater Capital Cities 2011:
gcc2011
- Greater Capital Cities 2016:
gcc2016
- Remoteness Areas 2011:
ra2011
- Remoteness Areas 2016:
ra2016
- State 2011:
state2011
- State 2016:
state2016
ASGS Non-ABS Structures
- Commonwealth Electoral Divisions 2018:
ced2018
- State Electoral Divisions 2018:
sed2018
- Local Government Areas 2016:
lga2016
- Local Government Areas 2018:
lga2018
The absmapsdata
package comes with pre-downloaded and pre-processed
data. To load a particular geospatial object, simply load the package
and call the object:
library(absmapsdata)
mapdata1 <- sa32011
glimpse(mapdata1)
#> Observations: 351
#> Variables: 12
#> $ sa3_code_2011 <chr> "10101", "10102", "10103", "10104", "10201", "10…
#> $ sa3_name_2011 <chr> "Goulburn - Yass", "Queanbeyan", "Snowy Mountain…
#> $ sa4_code_2011 <chr> "101", "101", "101", "101", "102", "102", "103",…
#> $ sa4_name_2011 <chr> "Capital Region", "Capital Region", "Capital Reg…
#> $ gcc_code_2011 <chr> "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1GSYD", "1G…
#> $ gcc_name_2011 <chr> "Rest of NSW", "Rest of NSW", "Rest of NSW", "Re…
#> $ state_code_2011 <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1"…
#> $ state_name_2011 <chr> "New South Wales", "New South Wales", "New South…
#> $ albers_sqkm <dbl> 21236.6140, 6511.1214, 14281.8301, 9864.9397, 98…
#> $ cent_lat <dbl> 149.0763, 149.6013, 148.9416, 149.8063, 151.2182…
#> $ cent_long <dbl> -34.55399, -35.44940, -36.43958, -36.49934, -33.…
#> $ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((149.1198 -3..., MUL…
Or
mapdata2 <- sa22016
glimpse(mapdata2)
#> Observations: 2,310
#> Variables: 15
#> $ sa2_main_2016 <chr> "101021007", "101021008", "101021009", "10102101…
#> $ sa2_5dig_2016 <chr> "11007", "11008", "11009", "11010", "11011", "11…
#> $ sa2_name_2016 <chr> "Braidwood", "Karabar", "Queanbeyan", "Queanbeya…
#> $ sa3_code_2016 <chr> "10102", "10102", "10102", "10102", "10102", "10…
#> $ sa3_name_2016 <chr> "Queanbeyan", "Queanbeyan", "Queanbeyan", "Quean…
#> $ sa4_code_2016 <chr> "101", "101", "101", "101", "101", "101", "101",…
#> $ sa4_name_2016 <chr> "Capital Region", "Capital Region", "Capital Reg…
#> $ gcc_code_2016 <chr> "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1RNSW", "1R…
#> $ gcc_name_2016 <chr> "Rest of NSW", "Rest of NSW", "Rest of NSW", "Re…
#> $ state_code_2016 <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1"…
#> $ state_name_2016 <chr> "New South Wales", "New South Wales", "New South…
#> $ areasqkm_2016 <dbl> 3418.3525, 6.9825, 4.7634, 13.0034, 3054.4099, 1…
#> $ cent_lat <dbl> 149.7932, 149.2328, 149.2255, 149.2524, 149.3911…
#> $ cent_long <dbl> -35.45508, -35.37590, -35.35103, -35.35520, -35.…
#> $ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((149.7606 -3..., MUL…
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 centorids 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_lat, cent_long)) # use the centroid lat and longs
map
Cool. But, sidenote, 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_lat,
cent_long), # use the centroid lat and longs
colour = "white") + # make the points white
theme_void() + # clears other plot elements
coord_sf(datum = NA) # fixes a gridline bug in theme_void()
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("data/median_income_sa3.csv")
#> Parsed with column specification:
#> cols(
#> sa3_name_2016 = col_character(),
#> year = col_character(),
#> median_income = col_double()
#> )
This 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
coord_sf(datum = NA) + # fixes a gridline bug in theme_void()
labs(fill = "Median income")
map
The 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
R
using 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 absmapsdata
package simple holds compressed and easy to use data
(sf
objects) for you use.
It is a data-only-based sibling of absmaps
, which holds functionality
to download more shapefile data from the ABS and compress it to a level
you desire. However, this comes at a cost: the mapping software that
absmaps
is built on can be a bit fiddly to install.
If you would like to do these things, please feel free to install
absmapsdata
.
Fair enough! The best avenue is via a Github issue at (wfmackey/absmapsdata). This is also the best place to request data that isn’t yet available in the package.