read.abares - Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources #667
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Checks for read.abares (v0.1.0)git hash: cd3d61e5
Important: All failing checks above must be addressed prior to proceeding (Checks marked with 👀 may be optionally addressed.) Package License: MIT + file LICENSE 1. Package DependenciesDetails of Package Dependency Usage (click to open)
The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.
Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table. basefile.path (32), tempdir (13), c (12), dirname (7), url (4), lapply (2), list (2), list.files (2), append (1), message (1), nchar (1) data.tablefread (9), fifelse (7), as.data.table (3), rbindlist (1), setcolorder (1) read.abaresclear_cache (1), get_aagis_regions (1), get_abares_trade (1), get_abares_trade_regions (1), get_agfd (1), get_historical_forecast_database (1), get_historical_national_estimates (1), get_soil_thickness (1), inspect_cache (1), print.read.abares.agfd.nc.files (1) purrrmap (4), modify_depth (1), quietly (1) httr2req_options (2), request (2), req_retry (1) terrarast (4) methodsnew (3) openxlsx2read_xlsx (3) sfread_sf (2), st_read (1) starsread_ncdf (2), read_stars (1) statsvar (2), dt (1) tidynctidync (2), hyper_tibble (1) stringrstr_locate (1), str_sub (1) toolsR_user_dir (2) utilsunzip (2) readtextreadtext (1) NOTE: Some imported packages appear to have no associated function calls; please ensure with author that these 'Imports' are listed appropriately. 2. Statistical PropertiesThis package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing. Details of statistical properties (click to open)
The package has:
Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages
All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the The final measure (
2a. Network visualisationClick to see the interactive network visualisation of calls between objects in package 3.
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| id | name | conclusion | sha | run_number | date |
|---|---|---|---|---|---|
| 11649832899 | pages build and deployment | success | 980cc6 | 19 | 2024-11-03 |
| 11649818126 | pkgdown.yaml | success | cd3d61 | 19 | 2024-11-03 |
| 11649818121 | R-CMD-check.yaml | failure | cd3d61 | 19 | 2024-11-03 |
| 11649818125 | test-coverage.yaml | failure | cd3d61 | 19 | 2024-11-03 |
3b. goodpractice results
R CMD check with rcmdcheck
R CMD check generated the following error:
- checking tests ...
Running ‘spelling.R’
Comparing ‘spelling.Rout’ to ‘spelling.Rout.save’ ... OK
Running ‘testthat.R’
ERROR
Running the tests in ‘tests/testthat.R’ failed.
Last 13 lines of output:
Ran 1/1 deferred expressions
There do not appear to be any files cached for {read.abares}.
There do not appear to be any files cached for {read.abares}.
-- Locally Available {read.abares} Cached Files --------------------------------
- test.R
-- Locally Available {read.abares} Cached Files --------------------------------
- test.R
Ran 1/1 deferred expressions
Will return stars object with 612226 cells.
No projection information found in nc file.
Coordinate variable units found to be degrees,
assuming WGS84 Lat/Lon.
Killed
R CMD check generated the following test_fail:
-
This file is part of the standard setup for testthat.
It is recommended that you do not modify it.
Where should you do additional test configuration?
Learn more about the roles of various files in:
* https://r-pkgs.org/testing-design.html#sec-tests-files-overview
* https://testthat.r-lib.org/articles/special-files.html
library(testthat)
library(read.abares)
Attaching package: 'read.abares'
The following object is masked from 'package:graphics':
plot
The following object is masked from 'package:base':
plot
test_check("read.abares")
Ran 1/1 deferred expressions
Ran 2/2 deferred expressions
Ran 1/1 deferred expressions
Ran 1/1 deferred expressions
-- Locally Available ABARES AGFD NetCDF Files ----------------------------------
- f2022.c1991.p2022.t2022.nc
- f2022.c1992.p2022.t2022.nc
- f2022.c1993.p2022.t2022.nc
- f2022.c1994.p2022.t2022.nc
- f2022.c1995.p2022.t2022.nc
- f2022.c1996.p2022.t2022.nc
- f2022.c1997.p2022.t2022.nc
- f2022.c1998.p2022.t2022.nc
- f2022.c1999.p2022.t2022.nc
- f2022.c2000.p2022.t2022.nc
- f2022.c2001.p2022.t2022.nc
- f2022.c2002.p2022.t2022.nc
- f2022.c2003.p2022.t2022.nc
- f2022.c2004.p2022.t2022.nc
- f2022.c2005.p2022.t2022.nc
- f2022.c2006.p2022.t2022.nc
- f2022.c2007.p2022.t2022.nc
- f2022.c2008.p2022.t2022.nc
- f2022.c2009.p2022.t2022.nc
- f2022.c2010.p2022.t2022.nc
- f2022.c2011.p2022.t2022.nc
- f2022.c2012.p2022.t2022.nc
- f2022.c2013.p2022.t2022.nc
- f2022.c2014.p2022.t2022.nc
- f2022.c2015.p2022.t2022.nc
- f2022.c2016.p2022.t2022.nc
- f2022.c2017.p2022.t2022.nc
- f2022.c2018.p2022.t2022.nc
- f2022.c2019.p2022.t2022.nc
- f2022.c2020.p2022.t2022.nc
- f2022.c2021.p2022.t2022.nc
- f2022.c2022.p2022.t2022.nc
- f2022.c2023.p2022.t2022.nc
-- Locally Available ABARES AGFD NetCDF Files ----------------------------------
- f2022.c1991.p2022.t2022.nc
- f2022.c1992.p2022.t2022.nc
- f2022.c1993.p2022.t2022.nc
- f2022.c1994.p2022.t2022.nc
- f2022.c1995.p2022.t2022.nc
- f2022.c1996.p2022.t2022.nc
- f2022.c1997.p2022.t2022.nc
- f2022.c1998.p2022.t2022.nc
- f2022.c1999.p2022.t2022.nc
- f2022.c2000.p2022.t2022.nc
- f2022.c2001.p2022.t2022.nc
- f2022.c2002.p2022.t2022.nc
- f2022.c2003.p2022.t2022.nc
- f2022.c2004.p2022.t2022.nc
- f2022.c2005.p2022.t2022.nc
- f2022.c2006.p2022.t2022.nc
- f2022.c2007.p2022.t2022.nc
- f2022.c2008.p2022.t2022.nc
- f2022.c2009.p2022.t2022.nc
- f2022.c2010.p2022.t2022.nc
- f2022.c2011.p2022.t2022.nc
- f2022.c2012.p2022.t2022.nc
- f2022.c2013.p2022.t2022.nc
- f2022.c2014.p2022.t2022.nc
- f2022.c2015.p2022.t2022.nc
- f2022.c2016.p2022.t2022.nc
- f2022.c2017.p2022.t2022.nc
- f2022.c2018.p2022.t2022.nc
- f2022.c2019.p2022.t2022.nc
- f2022.c2020.p2022.t2022.nc
- f2022.c2021.p2022.t2022.nc
- f2022.c2022.p2022.t2022.nc
- f2022.c2023.p2022.t2022.nc
-- Locally Available ABARES AGFD NetCDF Files ----------------------------------
- f2022.c1991.p2022.t2022.nc
- f2022.c1992.p2022.t2022.nc
- f2022.c1993.p2022.t2022.nc
- f2022.c1994.p2022.t2022.nc
- f2022.c1995.p2022.t2022.nc
- f2022.c1996.p2022.t2022.nc
- f2022.c1997.p2022.t2022.nc
- f2022.c1998.p2022.t2022.nc
- f2022.c1999.p2022.t2022.nc
- f2022.c2000.p2022.t2022.nc
- f2022.c2001.p2022.t2022.nc
- f2022.c2002.p2022.t2022.nc
- f2022.c2003.p2022.t2022.nc
- f2022.c2004.p2022.t2022.nc
- f2022.c2005.p2022.t2022.nc
- f2022.c2006.p2022.t2022.nc
- f2022.c2007.p2022.t2022.nc
- f2022.c2008.p2022.t2022.nc
- f2022.c2009.p2022.t2022.nc
- f2022.c2010.p2022.t2022.nc
- f2022.c2011.p2022.t2022.nc
- f2022.c2012.p2022.t2022.nc
- f2022.c2013.p2022.t2022.nc
- f2022.c2014.p2022.t2022.nc
- f2022.c2015.p2022.t2022.nc
- f2022.c2016.p2022.t2022.nc
- f2022.c2017.p2022.t2022.nc
- f2022.c2018.p2022.t2022.nc
- f2022.c2019.p2022.t2022.nc
- f2022.c2020.p2022.t2022.nc
- f2022.c2021.p2022.t2022.nc
- f2022.c2022.p2022.t2022.nc
- f2022.c2023.p2022.t2022.nc
-- Locally Available ABARES AGFD NetCDF Files ----------------------------------
- f2022.c1991.p2022.t2022.nc
- f2022.c1992.p2022.t2022.nc
- f2022.c1993.p2022.t2022.nc
- f2022.c1994.p2022.t2022.nc
- f2022.c1995.p2022.t2022.nc
- f2022.c1996.p2022.t2022.nc
- f2022.c1997.p2022.t2022.nc
- f2022.c1998.p2022.t2022.nc
- f2022.c1999.p2022.t2022.nc
- f2022.c2000.p2022.t2022.nc
- f2022.c2001.p2022.t2022.nc
- f2022.c2002.p2022.t2022.nc
- f2022.c2003.p2022.t2022.nc
- f2022.c2004.p2022.t2022.nc
- f2022.c2005.p2022.t2022.nc
- f2022.c2006.p2022.t2022.nc
- f2022.c2007.p2022.t2022.nc
- f2022.c2008.p2022.t2022.nc
- f2022.c2009.p2022.t2022.nc
- f2022.c2010.p2022.t2022.nc
- f2022.c2011.p2022.t2022.nc
- f2022.c2012.p2022.t2022.nc
- f2022.c2013.p2022.t2022.nc
- f2022.c2014.p2022.t2022.nc
- f2022.c2015.p2022.t2022.nc
- f2022.c2016.p2022.t2022.nc
- f2022.c2017.p2022.t2022.nc
- f2022.c2018.p2022.t2022.nc
- f2022.c2019.p2022.t2022.nc
- f2022.c2020.p2022.t2022.nc
- f2022.c2021.p2022.t2022.nc
- f2022.c2022.p2022.t2022.nc
- f2022.c2023.p2022.t2022.nc
Ran 1/1 deferred expressions
-- Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Ho
-- Dataset ANZLIC ID ANZCW1202000149 --
Feature attribute definition Predicted average Thickness (mm) of soil layer 1
in the 0.01 X 0.01 degree quadrat.
Custodian: CSIRO Land & Water
Jurisdiction Australia
Short Description The digital map data is provided in geographical coordinates
based on the World Geodetic System 1984 (WGS84) datum. This raster data set has
a grid resolution of 0.001 degrees (approximately equivalent to 1.1 km).
The data set is a product of the National Land and Water Resources Audit
(NLWRA) as a base dataset.
Data Type: Spatial representation type RASTER
Projection Map: projection GEOGRAPHIC
Datum: WGS84
Map Units: DECIMAL DEGREES
Scale: Scale/ resolution 1:1 000 000
Usage Purpose Estimates of soil depths are needed to calculate the amount of
any soil constituent in either volume or mass terms (bulk density is also
needed) - for example, the volume of water stored in the rooting zone
potentially available for plant use, to assess total stores of soil carbon for
greenhouse inventory or to assess total stores of nutrients.
Provide indications of probable thickness soil layer 1 in agricultural areas
where soil thickness testing has not been carried out.
Use Limitation: This dataset is bound by the requirements set down by the
National Land & Water Resources Audit
To see the full metadata, call print_soil_thickness_metadata() in your R
session.
-- Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Ho
-- Dataset ANZLIC ID ANZCW1202000149 --
Feature attribute definition Predicted average Thickness (mm) of soil layer 1
in the 0.01 X 0.01 degree quadrat.
Custodian: CSIRO Land & Water
Jurisdiction Australia
Short Description The digital map data is provided in geographical coordinates
based on the World Geodetic System 1984 (WGS84) datum. This raster data set has
a grid resolution of 0.001 degrees (approximately equivalent to 1.1 km).
The data set is a product of the National Land and Water Resources Audit
(NLWRA) as a base dataset.
Data Type: Spatial representation type RASTER
Projection Map: projection GEOGRAPHIC
Datum: WGS84
Map Units: DECIMAL DEGREES
Scale: Scale/ resolution 1:1 000 000
Usage Purpose Estimates of soil depths are needed to calculate the amount of
any soil constituent in either volume or mass terms (bulk density is also
needed) - for example, the volume of water stored in the rooting zone
potentially available for plant use, to assess total stores of soil carbon for
greenhouse inventory or to assess total stores of nutrients.
Provide indications of probable thickness soil layer 1 in agricultural areas
where soil thickness testing has not been carried out.
Use Limitation: This dataset is bound by the requirements set down by the
National Land & Water Resources Audit
To see the full metadata, call print_soil_thickness_metadata() in your R
session.
-- Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Ho
-- Dataset ANZLIC ID ANZCW1202000149 --
Dataset ANZLIC ID ANZCW1202000149
Title Soil Thickness for Australian areas of intensive agriculture of Layer 1
(A Horizon - top-soil) (derived from soil mapping)
Custodian CSIRO, Land & Water
Jurisdiction Australia
Description Abstract Surface of predicted Thickness of soil layer 1 (A Horizon
- top-soil) surface for the intensive agricultural areas of Australia. Data
modelled from area based observations made by soil agencies both State and
CSIRO and presented as .0.01 degree grid cells.
Topsoils (A horizons) are defined as the surface soil layers in which organic
matter accumulates, and may include dominantly organic surface layers (O and P
horizons).
The depth of topsoil is important because, with their higher organic matter
contents, topsoils (A horizon) generally have more suitable properties for
agriculture, including higher permeability and higher levels of soil nutrients.
Estimates of soil depths are needed to calculate the amount of any soil
constituent in either volume or mass terms (bulk density is also needed) - for
example, the volume of water stored in the rooting zone potentially available
for plant use, to assess total stores of soil carbon for Greenhouse inventory
or to assess total stores of nutrients.
The pattern of soil depth is strongly related to topography - the shape and
slope of the land. Deeper soils are typically found in the river valleys where
soils accumulate on floodplains and at the footslopes of ranges (zones of
deposition), while soils on hillslopes (zones of erosion) tend to be shallow.
Map of thickness of topsoil was derived from soil map data and interpreted
tables of soil properties for specific soil groups.
The quality of data on soil depth in existing soil profile datasets is
questionable and as the thickness of soil horizons varies locally with
topography, values for map units are general averages.
The final ASRIS polygon attributed surfaces are a mosaic of all of the data
obtained from various state and federal agencies. The surfaces have been
constructed with the best available soil survey information available at the
time. The surfaces also rely on a number of assumptions. One being that an area
weighted mean is a good estimate of the soil attributes for that polygon or
map-unit. Another assumption made is that the look-up tables provided by
McKenzie et al. (2000), state and territories accurately depict the soil
attribute values for each soil type.
The accuracy of the maps is most dependent on the scale of the original polygon
data sets and the level of soil survey that has taken place in each state. The
scale of the various soil maps used in deriving this map is available by
accessing the data-source grid, the scale is used as an assessment of the
likely accuracy of the modelling. The Atlas of Australian Soils is considered
to be the least accurate dataset and has therefore only been used where there
is no state based data. Of the state datasets Western Australian sub-systems,
South Australian land systems and NSW soil landscapes and reconnaissance
mapping would be the most reliable based on scale. NSW soil landscapes and
reconnaissance mapping use only one dominant soil type per polygon in the
estimation of attributes. South Australia and Western Australia use several
soil types per polygon or map-unit.
The digital map data is provided in geographical coordinates based on the World
Geodetic System 1984 (WGS84) datum. This raster data set has a grid resolution
of 0.001 degrees (approximately equivalent to 1.1 km).
The data set is a product of the National Land and Water Resources Audit
(NLWRA) as a base dataset.
Search Word(s) AGRICULTURE SOIL Physics Models
Geographic Extent Name(s) GEN Category
GEN Custodial Jurisdiction
GEN Name
Geographic Bounding Box North Bounding Latitude -10.707149 South Bounding
Latitude -43.516831 East Bounding Longitude 113.19673 West Bounding Longitude
153.990779
Geographic Extent Polygon(s) 115.0 -33.5,115.7 -33.3,115.7 -31.7,113.2
-26.2,113.5 -25.4,114.1 -26.4,114.3 -26.0,113.4 -24.3,114.1 -21.8,122.3
-18.2,122.2 -17.2,126.7 -13.6,129.1 -14.9,130.6 -12.3,132.6 -12.1,132.5
-11.6,131.9 -11.3,132.0 -11.1,137.0 -12.2,135.4 -14.7,140.0 -17.7,140.8
-17.4,141.7 -15.1,141.4 -13.7,142.2 -10.9,142.7 -10.7,143.9 -14.5,144.6
-14.1,145.3 -14.9,146.3 -18.8,148.9 -20.5,150.9 -22.6,153.2 -25.9,153.7
-28.8,153.0 -31.3,150.8 -34.8,150.0 -37.5,147.8 -37.9,146.3 -39.0,144.7
-38.4,143.5 -38.8,141.3 -38.4,139.7 -37.3,139.7 -36.9,139.9 -36.7,138.9
-35.5,138.1 -35.7,138.6 -34.7,138.1 -34.2,137.8 -35.1,136.9 -35.3,137.0
-34.9,137.5 -34.9,137.4 -34.0,137.9 -33.5,137.8 -32.6,137.3 -33.6,135.9
-34.7,136.1 -34.8,136.0 -35.0,135.1 -34.6,135.2 -34.5,135.4 -34.5,134.7
-33.3,134.0 -32.9,133.7 -32.1,133.3 -32.2,132.2 -32.0,131.3 -31.5,127.3
-32.3,126.0 -32.3,123.6 -33.9,123.2 -34.0,122.1 -34.0,121.9 -33.8,119.9
-34.0,119.6 -34.4,118.0 -35.1,116.0 -34.8,115.0 -34.3,115.0 -33.5
147.8 -42.9,147.9 -42.6,148.2 -42.1,148.3 -42.3,148.3 -41.3,148.3 -41.0,148.0
-40.7,147.4 -41.0,146.7 -41.1,146.6 -41.2,146.5 -41.1,146.4 -41.2,145.3
-40.8,145.3 -40.7,145.2 -40.8,145.2 -40.8,145.2 -40.8,145.0 -40.8,144.7
-40.7,144.7 -41.2,145.2 -42.2,145.4 -42.2,145.5 -42.4,145.5 -42.5,145.2
-42.3,145.5 -43.0,146.0 -43.3,146.0 -43.6,146.9 -43.6,146.9 -43.5,147.1
-43.3,147.0 -43.1,147.2 -43.3,147.3 -42.8,147.4 -42.9,147.6 -42.8,147.5
-42.8,147.8 -42.9,147.9 -43.0,147.7 -43.0,147.8 -43.2,147.9 -43.2,147.9
-43.2,148.0 -43.2,148.0 -43.1,148.0 -42.9,147.8 -42.9
136.7 -13.8,136.7 -13.7,136.6 -13.7,136.6 -13.8,136.4 -13.8,136.4 -14.1,136.3
-14.2,136.9 -14.3,137.0 -14.2,136.9 -14.2,136.7 -14.1,136.9 -13.8,136.7
-13.8,136.7 -13.8
139.5 -16.6,139.7 -16.5,139.4 -16.5,139.2 -16.7,139.3 -16.7,139.5 -16.6
153.0 -25.2,153.0 -25.7,153.1 -25.8,153.4 -25.0,153.2 -24.7,153.2 -25.0,153.0
-25.2
137.5 -36.1,137.7 -35.9,138.1 -35.9,137.9 -35.7,137.6 -35.7,137.6 -35.6,136.6
-35.8,136.7 -36.1,137.2 -36.0,137.5 -36.1
143.9 -39.7,144.0 -39.6,144.1 -39.8,143.9 -40.2,143.9 -40.0,143.9 -39.7
148.0 -39.7,147.7 -39.9,147.9 -39.9,148.0 -40.1,148.1 -40.3,148.3 -40.2,148.3
-40.0,148.0 -39.7
148.1 -40.4,148.0 -40.4,148.4 -40.3,148.4 -40.5,148.1 -40.4
130.4 -11.3,130.4 -11.2,130.6 -11.3,130.7 -11.4,130.9 -11.3,131.0 -11.4,131.1
-11.3,131.2 -11.4,131.3 -11.2,131.5 -11.4,131.5 -11.5,131.0 -11.9,130.8
-11.8,130.6 -11.7,130.0 -11.8,130.1 -11.7,130.3 -11.7,130.1 -11.5,130.4 -11.3
Data Currency Beginning date 1999-09-01
Ending date 2001-03-31
Dataset Status Progress COMPLETE
Maintenance and Update Frequency NOT PLANNED
Access Stored Data Format DIGITAL - ESRI Arc/Info integer GRID
Available Format Type DIGITAL - ESRI Arc/Info integer GRID
Access Constraint Subject to the terms & condition of the data access &
management agreement between the National Land & Water Audit and ANZLIC parties
Data Quality Lineage The soil attribute surface was created using the following
datasets 1. The digital polygon coverage of the Soil-Landforms of the Murray
Darling Basis (MDBSIS)(Bui et al. 1998), classified as principal profile forms
(PPF's) (Northcote 1979). 2. The digital Atlas of Australian Soils (Northcote
et al.1960-1968)(Leahy, 1993). 3. Western Australia land systems coverage
(Agriculture WA). 4. Western Australia sub-systems coverage (Agriculture WA).
5. Ord river catchment soils coverage (Agriculture WA). 6. Victoria soils
coverage (Victorian Department of Natural Resources and Environment - NRE). 7.
NSW Soil Landscapes and reconnaissance soil landscape mapping (NSW Department
of Land and Water Conservation - DLWC). 8. New South Wales Land systems west
(NSW Department of Land and Water Conservation - DLWC). 9. South Australia soil
land-systems (Primary Industries and Resources South Australia - PIRSA). 10.
Northern Territory soils coverage (Northern Territory Department of Lands,
Planning and Environment). 11. A mosaic of Queensland soils coverages
(Queensland Department of Natural Resources - QDNR). 12. A look-up table
linking PPF values from the Atlas of Australian Soils with interpreted soil
attributes (McKenzie et al. 2000). 13. Look_up tables provided by WA
Agriculture linking WA soil groups with interpreted soil attributes. 14.
Look_up tables provided by PIRSA linking SA soil groups with interpreted soil
attributes.
The continuous raster surface representing Thickness of soil layer 1 was
created by combining national and state level digitised land systems maps and
soil surveys linked to look-up tables listing soil type and corresponding
attribute values.
Because thickness is used sparingly in the Factual Key, estimations of
thickness in the look-up tables were made using empirical correlations for
particular soil types.
To estimate a soil attribute where more than one soil type was given for a
polygon or map-unit, the soil attribute values related to each soil type in the
look-up table were weighted according to the area occupied by that soil type
within the polygon or map-unit. The final soil attribute values are an area
weighted average for a polygon or map-unit. The polygon data was then
converted to a continuous raster surface using the soil attribute values
calculated for each polygon.
The ASRIS soil attribute surfaces created using polygon attribution relied on a
number of data sets from various state agencies. Each polygon data set was
turned into a continuous surface grid based on the calculated soil attribute
value for that polygon. The grids where then merged on the basis that, where
available, state data replaced the Atlas of Australian Soils and MDBSIS.
MDBSIS derived soil attribute values were restricted to areas where MDBSIS was
deemed to be more accurate that the Atlas of Australian Soils (see Carlile et
al (2001a).
In cases where a soil type was missing from the look-up table or layer 2 did
not exist for that soil type, the percent area of the soils remaining were
adjusted prior to calculating the final soil attribute value. The method used
to attribute polygons was dependent on the data supplied by individual State
agencies.
The modelled grid was resampled from 0.0025 degree cells to 0.01 degree cells
using bilinear interpolation
Positional Accuracy The predictive surface is a 0.01 X 0.01 degree grid and has
a locational accurate of about 1m.
The positional accuracy of the defining polygons have variable positional
accuracy most locations are expected to be within 100m of the recorded
location. The vertical accuracy is not relevant. The positional assessment has
been made by considering the tools used to generate the locational information
and contacting the data providers.
The other parameters used in the production of the led surface have a range of
positional accuracy ranging from + - 50 m to + - kilometres. This contribute
to the loss of attribute accuracy in the surface.
Attribute Accuracy Input attribute accuracy for the areas is highly variable.
The predictive has a variable and much lower attribute accuracy due to the
irregular distribution and the limited positional accuracy of the parameters
used for modelling.
There are several sources of error in estimating soil depth and thickness of
horizons for the look-up tables. Because thickness is used sparingly in the
Factual Key, estimations of thickness in the look-up tables were made using
empirical correlations for particular soil types. The quality of data on soil
depth in existing soil profile datasets is questionable, in soil mapping,
thickness of soil horizons varies locally with topography, so values for map
units are general averages. The definition of the depth of soil or regolith is
imprecise and it can be difficult to determine the lower limit of soil.
The assumption made that an area weighted mean of soil attribute values based
on soil type is a good estimation of a soil property is debatable, in that it
does not supply the soil attribute value at any given location. Rather it is
designed to show national and regional patterns in soil properties. The use of
the surfaces at farm or catchment scale modelling may prove inaccurate. Also
the use of look-up tables to attribute soil types is only as accurate as the
number of observations used to estimate a attribute value for a soil type. Some
soil types in the look-up tables may have few observations, yet the average
attribute value is still taken as the attribute value for that soil type.
Different states are using different taxonomic schemes making a national soil
database difficult. Another downfall of the area weighted approach is that some
soil types may not be listed in look-up tables. If a soil type is a dominant
one within a polygon or map-unit, but is not listed within the look-up table or
is not attributed within the look-up table then the final soil attribute value
for that polygon will be biased towards the minor soil types that do exist.
This may also happen when a large area is occupied by a soil type which has no
B horizon. In this case the final soil attribute value will be area weighted on
the soils with a B horizon, ignoring a major soil type within that polygon or
map-unit. The layer 2 surfaces have large areas of no-data because all soils
listed for a particular map-unit or polygon had no B horizon.
Logical Consistency Surface is fully logically consistent as only one parameter
is shown, as predicted average Soil Thickness within each grid cell
Completeness Surface is nearly complete. There are some areas (about %1
missing) for which insufficient parameters were known to provide a useful
prediction and thus attributes are absent in these areas.
Contact Information Contact Organisation (s) CSIRO, Land & Water
Contact Position Project Leader
Mail Address ACLEP, GPO 1666
Locality Canberra
State ACT
Country AUSTRALIA
Postcode 2601
Telephone 02 6246 5922
Facsimile 02 6246 5965
Electronic Mail Address neil.mckenzie@cbr.clw.csiro.au
Metadata Date Metadata Date 2001-07-01
Additional Metadata Additional Metadata
Entity and Attributes Entity Name Soil Thickness Layer 1 (derived from mapping)
Entity description Estimated Soil Thickness (mm) of Layer 1 on a cell by cell
basis
Feature attribute name VALUE
Feature attribute definition Predicted average Thickness (mm) of soil layer 1
in the 0.01 X 0.01 degree quadrat
Data Type Spatial representation type RASTER
Projection Map projection GEOGRAPHIC
Datum WGS84
Map units DECIMAL DEGREES
Scale Scale/ resolution 1:1 000 000
Usage Purpose Estimates of soil depths are needed to calculate the amount of
any soil constituent in either volume or mass terms (bulk density is also
needed) - for example, the volume of water stored in the rooting zone
potentially available for plant use, to assess total stores of soil carbon for
Greenhouse inventory or to assess total stores of nutrients.
Provide indications of probable Thickness soil layer 1 in agricultural areas
where soil thickness testing has not been carried out
Use Use Limitation This dataset is bound by the requirements set down by the
National Land & Water Resources Audit
-- Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Ho
-- Dataset ANZLIC ID ANZCW1202000149 --
Dataset ANZLIC ID ANZCW1202000149
Title Soil Thickness for Australian areas of intensive agriculture of Layer 1
(A Horizon - top-soil) (derived from soil mapping)
Custodian CSIRO, Land & Water
Jurisdiction Australia
Description Abstract Surface of predicted Thickness of soil layer 1 (A Horizon
- top-soil) surface for the intensive agricultural areas of Australia. Data
modelled from area based observations made by soil agencies both State and
CSIRO and presented as .0.01 degree grid cells.
Topsoils (A horizons) are defined as the surface soil layers in which organic
matter accumulates, and may include dominantly organic surface layers (O and P
horizons).
The depth of topsoil is important because, with their higher organic matter
contents, topsoils (A horizon) generally have more suitable properties for
agriculture, including higher permeability and higher levels of soil nutrients.
Estimates of soil depths are needed to calculate the amount of any soil
constituent in either volume or mass terms (bulk density is also needed) - for
example, the volume of water stored in the rooting zone potentially available
for plant use, to assess total stores of soil carbon for Greenhouse inventory
or to assess total stores of nutrients.
The pattern of soil depth is strongly related to topography - the shape and
slope of the land. Deeper soils are typically found in the river valleys where
soils accumulate on floodplains and at the footslopes of ranges (zones of
deposition), while soils on hillslopes (zones of erosion) tend to be shallow.
Map of thickness of topsoil was derived from soil map data and interpreted
tables of soil properties for specific soil groups.
The quality of data on soil depth in existing soil profile datasets is
questionable and as the thickness of soil horizons varies locally with
topography, values for map units are general averages.
The final ASRIS polygon attributed surfaces are a mosaic of all of the data
obtained from various state and federal agencies. The surfaces have been
constructed with the best available soil survey information available at the
time. The surfaces also rely on a number of assumptions. One being that an area
weighted mean is a good estimate of the soil attributes for that polygon or
map-unit. Another assumption made is that the look-up tables provided by
McKenzie et al. (2000), state and territories accurately depict the soil
attribute values for each soil type.
The accuracy of the maps is most dependent on the scale of the original polygon
data sets and the level of soil survey that has taken place in each state. The
scale of the various soil maps used in deriving this map is available by
accessing the data-source grid, the scale is used as an assessment of the
likely accuracy of the modelling. The Atlas of Australian Soils is considered
to be the least accurate dataset and has therefore only been used where there
is no state based data. Of the state datasets Western Australian sub-systems,
South Australian land systems and NSW soil landscapes and reconnaissance
mapping would be the most reliable based on scale. NSW soil landscapes and
reconnaissance mapping use only one dominant soil type per polygon in the
estimation of attributes. South Australia and Western Australia use several
soil types per polygon or map-unit.
The digital map data is provided in geographical coordinates based on the World
Geodetic System 1984 (WGS84) datum. This raster data set has a grid resolution
of 0.001 degrees (approximately equivalent to 1.1 km).
The data set is a product of the National Land and Water Resources Audit
(NLWRA) as a base dataset.
Search Word(s) AGRICULTURE SOIL Physics Models
Geographic Extent Name(s) GEN Category
GEN Custodial Jurisdiction
GEN Name
Geographic Bounding Box North Bounding Latitude -10.707149 South Bounding
Latitude -43.516831 East Bounding Longitude 113.19673 West Bounding Longitude
153.990779
Geographic Extent Polygon(s) 115.0 -33.5,115.7 -33.3,115.7 -31.7,113.2
-26.2,113.5 -25.4,114.1 -26.4,114.3 -26.0,113.4 -24.3,114.1 -21.8,122.3
-18.2,122.2 -17.2,126.7 -13.6,129.1 -14.9,130.6 -12.3,132.6 -12.1,132.5
-11.6,131.9 -11.3,132.0 -11.1,137.0 -12.2,135.4 -14.7,140.0 -17.7,140.8
-17.4,141.7 -15.1,141.4 -13.7,142.2 -10.9,142.7 -10.7,143.9 -14.5,144.6
-14.1,145.3 -14.9,146.3 -18.8,148.9 -20.5,150.9 -22.6,153.2 -25.9,153.7
-28.8,153.0 -31.3,150.8 -34.8,150.0 -37.5,147.8 -37.9,146.3 -39.0,144.7
-38.4,143.5 -38.8,141.3 -38.4,139.7 -37.3,139.7 -36.9,139.9 -36.7,138.9
-35.5,138.1 -35.7,138.6 -34.7,138.1 -34.2,137.8 -35.1,136.9 -35.3,137.0
-34.9,137.5 -34.9,137.4 -34.0,137.9 -33.5,137.8 -32.6,137.3 -33.6,135.9
-34.7,136.1 -34.8,136.0 -35.0,135.1 -34.6,135.2 -34.5,135.4 -34.5,134.7
-33.3,134.0 -32.9,133.7 -32.1,133.3 -32.2,132.2 -32.0,131.3 -31.5,127.3
-32.3,126.0 -32.3,123.6 -33.9,123.2 -34.0,122.1 -34.0,121.9 -33.8,119.9
-34.0,119.6 -34.4,118.0 -35.1,116.0 -34.8,115.0 -34.3,115.0 -33.5
147.8 -42.9,147.9 -42.6,148.2 -42.1,148.3 -42.3,148.3 -41.3,148.3 -41.0,148.0
-40.7,147.4 -41.0,146.7 -41.1,146.6 -41.2,146.5 -41.1,146.4 -41.2,145.3
-40.8,145.3 -40.7,145.2 -40.8,145.2 -40.8,145.2 -40.8,145.0 -40.8,144.7
-40.7,144.7 -41.2,145.2 -42.2,145.4 -42.2,145.5 -42.4,145.5 -42.5,145.2
-42.3,145.5 -43.0,146.0 -43.3,146.0 -43.6,146.9 -43.6,146.9 -43.5,147.1
-43.3,147.0 -43.1,147.2 -43.3,147.3 -42.8,147.4 -42.9,147.6 -42.8,147.5
-42.8,147.8 -42.9,147.9 -43.0,147.7 -43.0,147.8 -43.2,147.9 -43.2,147.9
-43.2,148.0 -43.2,148.0 -43.1,148.0 -42.9,147.8 -42.9
136.7 -13.8,136.7 -13.7,136.6 -13.7,136.6 -13.8,136.4 -13.8,136.4 -14.1,136.3
-14.2,136.9 -14.3,137.0 -14.2,136.9 -14.2,136.7 -14.1,136.9 -13.8,136.7
-13.8,136.7 -13.8
139.5 -16.6,139.7 -16.5,139.4 -16.5,139.2 -16.7,139.3 -16.7,139.5 -16.6
153.0 -25.2,153.0 -25.7,153.1 -25.8,153.4 -25.0,153.2 -24.7,153.2 -25.0,153.0
-25.2
137.5 -36.1,137.7 -35.9,138.1 -35.9,137.9 -35.7,137.6 -35.7,137.6 -35.6,136.6
-35.8,136.7 -36.1,137.2 -36.0,137.5 -36.1
143.9 -39.7,144.0 -39.6,144.1 -39.8,143.9 -40.2,143.9 -40.0,143.9 -39.7
148.0 -39.7,147.7 -39.9,147.9 -39.9,148.0 -40.1,148.1 -40.3,148.3 -40.2,148.3
-40.0,148.0 -39.7
148.1 -40.4,148.0 -40.4,148.4 -40.3,148.4 -40.5,148.1 -40.4
130.4 -11.3,130.4 -11.2,130.6 -11.3,130.7 -11.4,130.9 -11.3,131.0 -11.4,131.1
-11.3,131.2 -11.4,131.3 -11.2,131.5 -11.4,131.5 -11.5,131.0 -11.9,130.8
-11.8,130.6 -11.7,130.0 -11.8,130.1 -11.7,130.3 -11.7,130.1 -11.5,130.4 -11.3
Data Currency Beginning date 1999-09-01
Ending date 2001-03-31
Dataset Status Progress COMPLETE
Maintenance and Update Frequency NOT PLANNED
Access Stored Data Format DIGITAL - ESRI Arc/Info integer GRID
Available Format Type DIGITAL - ESRI Arc/Info integer GRID
Access Constraint Subject to the terms & condition of the data access &
management agreement between the National Land & Water Audit and ANZLIC parties
Data Quality Lineage The soil attribute surface was created using the following
datasets 1. The digital polygon coverage of the Soil-Landforms of the Murray
Darling Basis (MDBSIS)(Bui et al. 1998), classified as principal profile forms
(PPF's) (Northcote 1979). 2. The digital Atlas of Australian Soils (Northcote
et al.1960-1968)(Leahy, 1993). 3. Western Australia land systems coverage
(Agriculture WA). 4. Western Australia sub-systems coverage (Agriculture WA).
5. Ord river catchment soils coverage (Agriculture WA). 6. Victoria soils
coverage (Victorian Department of Natural Resources and Environment - NRE). 7.
NSW Soil Landscapes and reconnaissance soil landscape mapping (NSW Department
of Land and Water Conservation - DLWC). 8. New South Wales Land systems west
(NSW Department of Land and Water Conservation - DLWC). 9. South Australia soil
land-systems (Primary Industries and Resources South Australia - PIRSA). 10.
Northern Territory soils coverage (Northern Territory Department of Lands,
Planning and Environment). 11. A mosaic of Queensland soils coverages
(Queensland Department of Natural Resources - QDNR). 12. A look-up table
linking PPF values from the Atlas of Australian Soils with interpreted soil
attributes (McKenzie et al. 2000). 13. Look_up tables provided by WA
Agriculture linking WA soil groups with interpreted soil attributes. 14.
Look_up tables provided by PIRSA linking SA soil groups with interpreted soil
attributes.
The continuous raster surface representing Thickness of soil layer 1 was
created by combining national and state level digitised land systems maps and
soil surveys linked to look-up tables listing soil type and corresponding
attribute values.
Because thickness is used sparingly in the Factual Key, estimations of
thickness in the look-up tables were made using empirical correlations for
particular soil types.
To estimate a soil attribute where more than one soil type was given for a
polygon or map-unit, the soil attribute values related to each soil type in the
look-up table were weighted according to the area occupied by that soil type
within the polygon or map-unit. The final soil attribute values are an area
weighted average for a polygon or map-unit. The polygon data was then
converted to a continuous raster surface using the soil attribute values
calculated for each polygon.
The ASRIS soil attribute surfaces created using polygon attribution relied on a
number of data sets from various state agencies. Each polygon data set was
turned into a continuous surface grid based on the calculated soil attribute
value for that polygon. The grids where then merged on the basis that, where
available, state data replaced the Atlas of Australian Soils and MDBSIS.
MDBSIS derived soil attribute values were restricted to areas where MDBSIS was
deemed to be more accurate that the Atlas of Australian Soils (see Carlile et
al (2001a).
In cases where a soil type was missing from the look-up table or layer 2 did
not exist for that soil type, the percent area of the soils remaining were
adjusted prior to calculating the final soil attribute value. The method used
to attribute polygons was dependent on the data supplied by individual State
agencies.
The modelled grid was resampled from 0.0025 degree cells to 0.01 degree cells
using bilinear interpolation
Positional Accuracy The predictive surface is a 0.01 X 0.01 degree grid and has
a locational accurate of about 1m.
The positional accuracy of the defining polygons have variable positional
accuracy most locations are expected to be within 100m of the recorded
location. The vertical accuracy is not relevant. The positional assessment has
been made by considering the tools used to generate the locational information
and contacting the data providers.
The other parameters used in the production of the led surface have a range of
positional accuracy ranging from + - 50 m to + - kilometres. This contribute
to the loss of attribute accuracy in the surface.
Attribute Accuracy Input attribute accuracy for the areas is highly variable.
The predictive has a variable and much lower attribute accuracy due to the
irregular distribution and the limited positional accuracy of the parameters
used for modelling.
There are several sources of error in estimating soil depth and thickness of
horizons for the look-up tables. Because thickness is used sparingly in the
Factual Key, estimations of thickness in the look-up tables were made using
empirical correlations for particular soil types. The quality of data on soil
depth in existing soil profile datasets is questionable, in soil mapping,
thickness of soil horizons varies locally with topography, so values for map
units are general averages. The definition of the depth of soil or regolith is
imprecise and it can be difficult to determine the lower limit of soil.
The assumption made that an area weighted mean of soil attribute values based
on soil type is a good estimation of a soil property is debatable, in that it
does not supply the soil attribute value at any given location. Rather it is
designed to show national and regional patterns in soil properties. The use of
the surfaces at farm or catchment scale modelling may prove inaccurate. Also
the use of look-up tables to attribute soil types is only as accurate as the
number of observations used to estimate a attribute value for a soil type. Some
soil types in the look-up tables may have few observations, yet the average
attribute value is still taken as the attribute value for that soil type.
Different states are using different taxonomic schemes making a national soil
database difficult. Another downfall of the area weighted approach is that some
soil types may not be listed in look-up tables. If a soil type is a dominant
one within a polygon or map-unit, but is not listed within the look-up table or
is not attributed within the look-up table then the final soil attribute value
for that polygon will be biased towards the minor soil types that do exist.
This may also happen when a large area is occupied by a soil type which has no
B horizon. In this case the final soil attribute value will be area weighted on
the soils with a B horizon, ignoring a major soil type within that polygon or
map-unit. The layer 2 surfaces have large areas of no-data because all soils
listed for a particular map-unit or polygon had no B horizon.
Logical Consistency Surface is fully logically consistent as only one parameter
is shown, as predicted average Soil Thickness within each grid cell
Completeness Surface is nearly complete. There are some areas (about %1
missing) for which insufficient parameters were known to provide a useful
prediction and thus attributes are absent in these areas.
Contact Information Contact Organisation (s) CSIRO, Land & Water
Contact Position Project Leader
Mail Address ACLEP, GPO 1666
Locality Canberra
State ACT
Country AUSTRALIA
Postcode 2601
Telephone 02 6246 5922
Facsimile 02 6246 5965
Electronic Mail Address neil.mckenzie@cbr.clw.csiro.au
Metadata Date Metadata Date 2001-07-01
Additional Metadata Additional Metadata
Entity and Attributes Entity Name Soil Thickness Layer 1 (derived from mapping)
Entity description Estimated Soil Thickness (mm) of Layer 1 on a cell by cell
basis
Feature attribute name VALUE
Feature attribute definition Predicted average Thickness (mm) of soil layer 1
in the 0.01 X 0.01 degree quadrat
Data Type Spatial representation type RASTER
Projection Map projection GEOGRAPHIC
Datum WGS84
Map units DECIMAL DEGREES
Scale Scale/ resolution 1:1 000 000
Usage Purpose Estimates of soil depths are needed to calculate the amount of
any soil constituent in either volume or mass terms (bulk density is also
needed) - for example, the volume of water stored in the rooting zone
potentially available for plant use, to assess total stores of soil carbon for
Greenhouse inventory or to assess total stores of nutrients.
Provide indications of probable Thickness soil layer 1 in agricultural areas
where soil thickness testing has not been carried out
Use Use Limitation This dataset is bound by the requirements set down by the
National Land & Water Resources Audit
Ran 1/1 deferred expressions
There do not appear to be any files cached for {read.abares}.
There do not appear to be any files cached for {read.abares}.
-- Locally Available {read.abares} Cached Files --------------------------------
- test.R
-- Locally Available {read.abares} Cached Files --------------------------------
- test.R
Ran 1/1 deferred expressions
Will return stars object with 612226 cells.
No projection information found in nc file.
Coordinate variable units found to be degrees,
assuming WGS84 Lat/Lon.
Killed
R CMD check generated the following check_fail:
- rcmdcheck_tests_pass
Test coverage with covr
ERROR: Test Coverage Failed
Cyclocomplexity with cyclocomp
No functions have cyclocomplexity >= 15
Static code analyses with lintr
lintr found no issues with this package!
4. Other Checks
Details of other checks (click to open)
✖️ The following function name is duplicated in other packages:
-
clear_cachefrom catalog, lintr, quincunx, rmonad
Package Versions
| package | version |
|---|---|
| pkgstats | 0.2.0.46 |
| pkgcheck | 0.1.2.63 |
Editor-in-Chief Instructions:
Processing may not proceed until the items marked with ✖️ have been resolved.
|
@ropensci-review-bot check package |
|
Thanks, about to send the query. |
|
🚀 Editor check started 👋 |
|
@adamhsparks The tests on our machines just keep running for hours and hours and days even. Can you check on your side to make sure there's not some flag that works on GitHub machines to prevent this, yet might not stop tests on our machines? |
|
I’ve got nothing. It only takes a few minutes on GH now. 🤷
|
|
I'll just close this for now at least. Unsure how to fix issues with package checks and don't have the mental bandwidth to deal with it right now. |
|
@ropensci-review-bot check package |
|
Thanks, about to send the query. |
|
🚀 Editor check started 👋 |
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@ropensci-review-bot check package |
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Thanks, about to send the query. |
|
🚀 Editor check started 👋 |
|
Sorry @adamhsparks, I'll try to find out the problem with the checks asap and get back to you ... 😕 |
Checks for read.abares (v0.1.0)git hash: a7a41aab
Important: All failing checks above must be addressed prior to proceeding (Checks marked with 👀 may be optionally addressed.) Package License: MIT + file LICENSE 1. Package DependenciesDetails of Package Dependency Usage (click to open)
The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.
Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table. basefile.path (32), tempdir (13), c (12), dirname (7), url (4), lapply (2), list (2), list.files (2), append (1), nchar (1) data.tablefread (9), fifelse (7), as.data.table (3), rbindlist (1), setcolorder (1) read.abaresclear_cache (1), get_aagis_regions (1), get_abares_trade (1), get_abares_trade_regions (1), get_agfd (1), get_historical_forecast_database (1), get_historical_national_estimates (1), get_soil_thickness (1), inspect_cache (1) purrrmap (4), modify_depth (1), quietly (1) httr2req_options (2), request (2), req_retry (1) terrarast (4) methodsnew (3) openxlsx2read_xlsx (3) sfread_sf (2), st_read (1) starsread_ncdf (2), read_stars (1) statsvar (2), dt (1) tidynctidync (2), hyper_tibble (1) stringrstr_locate (1), str_sub (1) toolsR_user_dir (2) utilsunzip (2) readtextreadtext (1) NOTE: Some imported packages appear to have no associated function calls; please ensure with author that these 'Imports' are listed appropriately. 2. Statistical PropertiesThis package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing. Details of statistical properties (click to open)
The package has:
Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages
All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the The final measure (
2a. Network visualisationClick to see the interactive network visualisation of calls between objects in package 3.
|
| id | name | conclusion | sha | run_number | date |
|---|---|---|---|---|---|
| 11905521413 | pages build and deployment | success | 55bec0 | 29 | 2024-11-19 |
| 11905498648 | pkgdown.yaml | success | a7a41a | 31 | 2024-11-19 |
| 11905498644 | R-CMD-check.yaml | success | a7a41a | 31 | 2024-11-19 |
| 11693994081 | test-coverage.yaml | failure | bc173b | 27 | 2024-11-05 |
3b. goodpractice results
R CMD check with rcmdcheck
rcmdcheck found no errors, warnings, or notes
Test coverage with covr
Package coverage: 5.35
The following files are not completely covered by tests:
| file | coverage |
|---|---|
| R/get_aagis_regions.R | 0% |
| R/get_abares_trade_regions.R | 0% |
| R/get_abares_trade.R | 0% |
| R/get_agfd.R | 0% |
| R/get_estimates_by_performance_category.R | 0% |
| R/get_estimates_by_size.R | 0% |
| R/get_historical_forecast_database.R | 0% |
| R/get_historical_national_estimates.R | 0% |
| R/get_historical_regional_estimates.R | 0% |
| R/get_historical_state_estimates.R | 0% |
| R/get_soil_thickness.R | 0% |
| R/internal_functions.R | 10.71% |
| R/read_agfd_dt.R | 0% |
| R/read_agfd_stars.R | 0% |
| R/read_agfd_terra.R | 0% |
| R/read_agfd_tidync.R | 0% |
| R/read_soil_thickness_stars.R | 0% |
| R/read_soil_thickness_terra.R | 0% |
Cyclocomplexity with cyclocomp
No functions have cyclocomplexity >= 15
Static code analyses with lintr
lintr found no issues with this package!
4. Other Checks
Details of other checks (click to open)
✖️ The following function name is duplicated in other packages:
-
clear_cachefrom catalog, lintr, quincunx, rmonad
Package Versions
| package | version |
|---|---|
| pkgstats | 0.2.0.48 |
| pkgcheck | 0.1.2.68 |
Editor-in-Chief Instructions:
Processing may not proceed until the items marked with ✖️ have been resolved.
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@emilyriederer, the test coverage is >90%, see attached screenshot of local However, there are issues with connectivity between GitHub CI and ABARES where GitHub just won't download some of the data. From what @mpadge and I have sussed out, it's a local issue (GitHub) and I can't really troubleshoot their setup. I'd note here that this also seems to happen on some of the smaller files that are just CSVs for some reason, so it doesn't appear to be confined to large files only. So I've opted to skip most of the tests on CI using 
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Thanks @adamhsparks ! It looks like I'm fairly comfortable moving forward either way; just one thought to flesh out best practices here. |
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Thanks, @emilyriederer, I'd prefer not to exclude these functions as using testing helped me squash a few bugs last week and there's no reason to exclude them in the package where I might normally consider excluding functions. This appears to be a technical limitation outside of the package that would hinder the package's quality if implemented. |
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@emilyriederer I'm happy to accept Adam's explanation there. We do require coverage reports, but have no policies against locally-generated reports like used here. I think this would represent the first submission to do so, and so would maybe only ask @adamhsparks if you could have a look over the relevant section of the dev guide to see whether anything might be clarified or improved there? |
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Thanks, @mpadge. I'll have a look at that. I was reflecting while walking home from work. I can probably include small samples of the data in the package for testing purposes and examples as they are CC By licensed. That might get me to 75% coverage with CI. I'll investigate further. |
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For non-binary files (your csvs and maybe more?), you can also use httptest2 redactor functions to find some early pattern in a response and |
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Thanks, @adamhsparks ! That sounds like a great suggestion. I'll hold if you want to investigate including sample data or using @mpadge 's idea. If those prove intractable, I'm also comfortable with moving checks offline / local, so please feel free to ping and we can move the ball forward here. |
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Yeah, let’s hold. I may be a while though with holidays approaching. |
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Sorry @obrl-soil, that glitch is because you were supposed to be invited to the GitHub editors team, but haven't been yet. @maelle Can you please invite @obrl-soil to the editors team? Your membership of that team is your key to the bot duties you'll then need. |
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Hi @obrl-soil, I've taken your comments on board and fixed the bugs you've highlighted. Thank you for this comprehensive list that's improved the package even before the reviews, this has greatly improved the package already. So much better now for the reviewers. 👍
The README has been edited to include information about ABARES and a justification for the package existing and further revised for flow as suggested.
I've renamed the title to reflect the contents better and tried DT but the data size was too large to work client-side so I didn't pursue further. But it's an option I'll consider further, later when the package is more refined. I have included more on using the spatial files with some of the tabular data, which also meant more work with the spatial data further improving it utility through this package vs just downloading it, so it now contains more advanced use-cases.
I think I've fixed or improved all of these, if you notice any I missed or misinterpreted, I'm happy to correct them.
Good catch! The resulting {terra} object now returns continuous values. Should I just store it as a tif, not a Grid file? Is there a space saving or other advantage here?
I'm unsure what the issue is here. The codecov.io page shows the coverage reported using
Thank you for catching this! I've fixed it. |
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@obrl-soil, I checked using [ins] r$> covr::package_coverage()
read.abares Coverage: 98.87%
R/get_agfd.R: 92.31%
R/read_abares_trade.R: 98.41%
R/clear_cache.R: 100.00%
R/get_topsoil_thickness.R: 100.00%
R/inspect_cache.R: 100.00%
R/internal_functions.R: 100.00%
R/read_aagis_regions.R: 100.00%
R/read_abares_trade_regions.R: 100.00%
R/read_agfd_dt.R: 100.00%
R/read_agfd_stars.R: 100.00%
R/read_agfd_terra.R: 100.00%
R/read_agfd_tidync.R: 100.00%
R/read_estimates_by_performance_category.R: 100.00%
R/read_estimates_by_size.R: 100.00%
R/read_historical_forecast_database.R: 100.00%
R/read_historical_national_estimates.R: 100.00%
R/read_historical_regional_estimates.R: 100.00%
R/read_historical_state_estimates.R: 100.00%
R/read_topsoil_thickness_stars.R: 100.00%
R/read_topsoil_thickness_terra.R: 100.00%Can you help me out with what you're seeing on your machine for coverage perhaps? |
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Hey sorry I've been away on fieldwork - here's what I get with the same call I don't think it matters if its getting checked properly on at least one machine, just a bit curious about what's going on with mine! To answer your question about the tif vs grid issue - its a more widely accessible format and you get to store a single file object with embedded projection info instead of a folder of smaller files. It'll be pretty much the same size overall. I experimented with LERC compression but didn't see any real advantage since its already a fairly small dataset, so standard TIF or a COG version would work fine. |
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@obrl-soil, thanks, TIF it is! |
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@ropensci-review-bot assign @potterzot as reviewer |
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I'm sorry @obrl-soil, I'm afraid I can't do that. That's something only editors are allowed to do. |
I think it's skipping all the tests marked with |
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Interestingly, I just had the same behaviour somehow. I don't know how, it determined I was "offline" and only ran the tests that you have seen run, @obrl-soil |
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Weird - maybe a covr bug then |
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@ropensci-review-bot assign @potterzot as reviewer |
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@potterzot added to the reviewers list. Review due date is 2025-05-05. Thanks @potterzot for accepting to review! Please refer to our reviewer guide. rOpenSci’s community is our best asset. We aim for reviews to be open, non-adversarial, and focused on improving software quality. Be respectful and kind! See our reviewers guide and code of conduct for more. |
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@potterzot: If you haven't done so, please fill this form for us to update our reviewers records. |
{testthat}, I think. I was just running tests alone |
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📆 @potterzot you have 2 days left before the due date for your review (2025-05-05). |
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@ropensci-review-bot assign @mpaulacaldas as reviewer |
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@mpaulacaldas added to the reviewers list. Review due date is 2025-05-24. Thanks @mpaulacaldas for accepting to review! Please refer to our reviewer guide. rOpenSci’s community is our best asset. We aim for reviews to be open, non-adversarial, and focused on improving software quality. Be respectful and kind! See our reviewers guide and code of conduct for more. |
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@mpaulacaldas: If you haven't done so, please fill this form for us to update our reviewers records. |
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@adamhsparks and @obrl-soil I'm wrapping up my EiC rotation today and before I hand this over to the next EiC I want to thank you for your work and persistance. |
Package ReviewPlease check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide
DocumentationThe package includes all the following forms of documentation:
Functionality
Estimated hours spent reviewing: 4
Review CommentsGeneral commentsGreat work on a package that provides access to a really neat dataset. Tests were very complete, documentation was extensive, and I learned some new things reading through the code. It doesn't seem like there is a way to query subsets. It also seems like because file names must be directly coded, any changes / updates to the file paths will be package breaking, which is unfortunate. But in both cases I don't think there's anything that can be done on the package end to change this. Specific comments
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@potterzot, just a quick note of many thanks. Your comment on {pak} is timely. I've spent yesterday and today wrestling with it for other packages and it's coming up short. I've resorted to favouring {remotes} first with {pak} as an optional method for one of these packages and will implement the same here. I'll have a detailed response with all the changes that follows. |
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📆 @mpaulacaldas you have 2 days left before the due date for your review (2025-05-24). |
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It doesn’t bother me if your review takes longer than the deadline, @mpaulacaldas. I’m on holidays right now and won’t be working until next month. |
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Thank you @adamhsparks! The week got away from me and I wasn't able to finish my review. I will finalise it by the end of the week (May 30 latest) |
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Submitting Author Name: Adam Sparks
Due date for @potterzot: 2025-05-05Submitting Author Github Handle: @adamhsparks
Repository: https://github.com/adamhsparks/read.abares
Version submitted: 0.1.0
Submission type: Standard
Editor: @obrl-soil
Reviewers: @potterzot, @mpaulacaldas
Due date for @mpaulacaldas: 2025-05-24
Archive: TBD
Version accepted: TBD
Language: en
Scope
Please indicate which category or categories from our package fit policies this package falls under: (Please check an appropriate box below. If you are unsure, we suggest you make a pre-submission inquiry.):
Explain how and why the package falls under these categories (briefly, 1-2 sentences):
Provides workflow assistance for fetching freely available data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), the science and economics research division of the Department of Agriculture, Fisheries and Forestry. Data include trade information, soil depth, agricultural survey data on production and model outputs of agricultural production useful for agricultural researchers.
Who is the target audience and what are scientific applications of this package?
Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?
None that I'm aware of.
NA
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