read.abares - Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources

adamhsparks commented 2 weeks ago

Submitting Author Name: Adam Sparks Submitting Author Github Handle: !--author1-->@adamhsparks<!--end-author1-- Repository: https://github.com/adamhsparks/read.abares Version submitted: 0.1.0 Submission type: Standard Editor: TBD Reviewers: TBD

Archive: TBD Version accepted: TBD Language: en

Paste the full DESCRIPTION file inside a code block below:

Type: Package
Package: read.abares
Title: Provides simple downloading, parsing and importing of Australian
    Bureau of Agricultural and Resource Economics and Sciences (ABARES)
    data sources
Version: 0.1.0
Authors@R: 
    person("Adam H.", "Sparks", , "adamhsparks@gmail.com", role = c("cre", "aut"),
           comment = c(ORCID = "0000-0002-0061-8359"))
Description: Download and import data from the Australian Bureau of
    Agricultural and Resource Economics and Sciences (ABARES)
    <https://www.agriculture.gov.au/abares>.
License: MIT + file LICENSE
URL: https://github.org/adamhsparks/read.abares,
    https://adamhsparks.github.io/read.abares/
BugReports: https://github.com/adamhsparks/read.abares/issues
Imports: 
    cli,
    curl,
    data.table,
    lubridate,
    openxlsx2,
    purrr,
    readtext,
    sf,
    stars,
    stringr,
    terra,
    tidync,
    withr
Suggests: 
    knitr,
    pander,
    rmarkdown,
    roxyglobals,
    spelling,
    testthat (>= 3.0.0)
VignetteBuilder: 
    knitr
Config/roxyglobals/filename: globals.R
Config/roxyglobals/unique: FALSE
Config/testthat/edition: 3
Encoding: UTF-8
Language: en-US
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c("collate", "namespace", "rd",
    "roxyglobals::global_roclet"))
RoxygenNote: 7.3.2

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.):
- [x] data retrieval
- [ ] data extraction
- [ ] data munging
- [ ] data deposition
- [ ] data validation and testing
- [ ] workflow automation
- [ ] version control
- [ ] citation management and bibliometrics
- [ ] scientific software wrappers
- [ ] field and lab reproducibility tools
- [ ] database software bindings
- [x] geospatial data
- [ ] text analysis
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?
- Ag economists
- Biometricians working in agriculture
- Anyone else who is interested in Australian agricultural and economic data
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.

(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

NA

If you made a pre-submission inquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.
Explain reasons for any pkgcheck items which your package is unable to pass.

Technical checks

Confirm each of the following by checking the box.

[x] I have read the rOpenSci packaging guide.
[x] I have read the author guide and I expect to maintain this package for at least 2 years or to find a replacement.

This package:

[x] does not violate the Terms of Service of any service it interacts with.
[x] has a CRAN and OSI accepted license.
[x] contains a README with instructions for installing the development version.
[x] includes documentation with examples for all functions, created with roxygen2.
[x] contains a vignette with examples of its essential functions and uses.
[x] has a test suite.
[x] has continuous integration, including reporting of test coverage.

Publication options

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[ ] Do you intend for this package to go on Bioconductor?
[ ] Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:

MEE Options

- [ ] The package is novel and will be of interest to the broad readership of the journal. - [ ] The manuscript describing the package is no longer than 3000 words. - [ ] You intend to archive the code for the package in a long-term repository which meets the requirements of the journal (see [MEE's Policy on Publishing Code](http://besjournals.onlinelibrary.wiley.com/hub/journal/10.1111/(ISSN)2041-210X/journal-resources/policy-on-publishing-code.html)) - (*Scope: Do consider MEE's [Aims and Scope](http://besjournals.onlinelibrary.wiley.com/hub/journal/10.1111/(ISSN)2041-210X/aims-and-scope/read-full-aims-and-scope.html) for your manuscript. We make no guarantee that your manuscript will be within MEE scope.*) - (*Although not required, we strongly recommend having a full manuscript prepared when you submit here.*) - (*Please do not submit your package separately to Methods in Ecology and Evolution*)

Code of conduct

[x] I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

ropensci-review-bot commented 2 weeks ago

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ropensci-review-bot commented 2 weeks ago

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ropensci-review-bot commented 2 weeks ago

Checks for read.abares (v0.1.0)

git hash: cd3d61e5

:heavy_check_mark: Package name is available
:heavy_check_mark: has a 'codemeta.json' file.
:heavy_check_mark: has a 'contributing' file.
:heavy_check_mark: uses 'roxygen2'.
:heavy_check_mark: 'DESCRIPTION' has a URL field.
:heavy_check_mark: 'DESCRIPTION' has a BugReports field.
:heavy_check_mark: Package has at least one HTML vignette
:heavy_check_mark: All functions have examples.
:heavy_check_mark: Package has continuous integration checks.
:heavy_multiplication_x: Package coverage failed
:heavy_multiplication_x: R CMD check found 1 error.
:heavy_check_mark: R CMD check found no warnings.
:eyes: Function names are duplicated in other packages

Important: All failing checks above must be addressed prior to proceeding

(Checks marked with :eyes: may be optionally addressed.)

Package License: MIT + file LICENSE

1. Package Dependencies

Details 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. |type |package | ncalls| |:----------|:-----------|------:| |internal |base | 77| |internal |read.abares | 10| |internal |methods | 3| |internal |stats | 3| |internal |tools | 2| |internal |utils | 2| |imports |data.table | 21| |imports |purrr | 6| |imports |httr2 | 5| |imports |terra | 4| |imports |openxlsx2 | 3| |imports |sf | 3| |imports |stars | 3| |imports |tidync | 3| |imports |stringr | 2| |imports |readtext | 1| |imports |cli | NA| |imports |lubridate | NA| |imports |withr | NA| |suggests |knitr | NA| |suggests |pander | NA| |suggests |rmarkdown | NA| |suggests |roxyglobals | NA| |suggests |spelling | NA| |suggests |testthat | NA| |linking_to |NA | NA| 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()', and examining the 'external_calls' table.

base

file.path (32), tempdir (13), c (12), dirname (7), url (4), lapply (2), list (2), list.files (2), append (1), message (1), nchar (1)

data.table

fread (9), fifelse (7), as.data.table (3), rbindlist (1), setcolorder (1)

read.abares

clear_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)

purrr

map (4), modify_depth (1), quietly (1)

httr2

req_options (2), request (2), req_retry (1)

terra

rast (4)

methods

new (3)

openxlsx2

read_xlsx (3)

sf

read_sf (2), st_read (1)

stars

read_ncdf (2), read_stars (1)

stats

var (2), dt (1)

tidync

tidync (2), hyper_tibble (1)

stringr

str_locate (1), str_sub (1)

tools

R_user_dir (2)

utils

unzip (2)

readtext

readtext (1)

**NOTE:** Some imported packages appear to have no associated function calls; please ensure with author that these 'Imports' are listed appropriately.

2. Statistical Properties

This 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: - code in R (100% in 23 files) and - 1 authors - 1 vignette - no internal data file - 13 imported packages - 26 exported functions (median 9 lines of code) - 49 non-exported functions in R (median 15 lines of code) --- Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages The following terminology is used: - `loc` = "Lines of Code" - `fn` = "function" - `exp`/`not_exp` = exported / not exported All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by [the `checks_to_markdown()` function](https://docs.ropensci.org/pkgcheck/reference/checks_to_markdown.html) The final measure (`fn_call_network_size`) is the total number of calls between functions (in R), or more abstract relationships between code objects in other languages. Values are flagged as "noteworthy" when they lie in the upper or lower 5th percentile. |measure | value| percentile|noteworthy | |:------------------------|-----:|----------:|:----------| |files_R | 23| 83.4| | |files_vignettes | 2| 81.7| | |files_tests | 22| 95.6| | |loc_R | 607| 51.6| | |loc_vignettes | 265| 56.7| | |loc_tests | 853| 81.1| | |num_vignettes | 1| 58.8| | |n_fns_r | 75| 67.2| | |n_fns_r_exported | 26| 73.8| | |n_fns_r_not_exported | 49| 65.1| | |n_fns_per_file_r | 2| 34.9| | |num_params_per_fn | 1| 1.8|TRUE | |loc_per_fn_r | 11| 33.0| | |loc_per_fn_r_exp | 9| 19.8| | |loc_per_fn_r_not_exp | 15| 49.9| | |rel_whitespace_R | 21| 56.7| | |rel_whitespace_vignettes | 50| 70.4| | |rel_whitespace_tests | 10| 66.3| | |doclines_per_fn_exp | 25| 23.7| | |doclines_per_fn_not_exp | 0| 0.0|TRUE | |fn_call_network_size | 36| 57.9| | ---

2a. Network visualisation

Click to see the interactive network visualisation of calls between objects in package

3. `goodpractice` and other checks

Details of goodpractice checks (click to open)

#### 3a. Continuous Integration Badges [![R-CMD-check.yaml](https://github.com/adamhsparks/read.abares/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/adamhsparks/read.abares/actions) **GitHub Workflow Results** | 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](https://r-lib.github.io/rcmdcheck/) R CMD check generated the following error: 1. 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: 1. > # 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: 1. rcmdcheck_tests_pass #### Test coverage with [covr](https://covr.r-lib.org/) ERROR: Test Coverage Failed #### Cyclocomplexity with [cyclocomp](https://github.com/MangoTheCat/cyclocomp) No functions have cyclocomplexity >= 15 #### Static code analyses with [lintr](https://github.com/jimhester/lintr) [lintr](https://github.com/jimhester/lintr) found no issues with this package!

4. Other Checks

Details of other checks (click to open)

:heavy_multiplication_x: The following function name is duplicated in other packages: - - `clear_cache` from 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 :heavy_multiplication_x: have been resolved.

adamhsparks commented 2 weeks ago

@ropensci-review-bot check package

ropensci-review-bot commented 2 weeks ago

Thanks, about to send the query.

ropensci-review-bot commented 2 weeks ago

:rocket:

Editor check started

:wave:

mpadge commented 2 weeks ago

@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?

adamhsparks commented 2 weeks ago

I’ve got nothing. It only takes a few minutes on GH now. 🤷

Screenshot 2024-11-07 at 06 44 47

adamhsparks commented 2 weeks ago

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.

adamhsparks commented 1 week ago

@ropensci-review-bot check package

ropensci-review-bot commented 1 week ago

Thanks, about to send the query.

ropensci-review-bot commented 1 week ago

:rocket:

Editor check started

:wave:

adamhsparks commented 3 days ago

@ropensci-review-bot check package

ropensci-review-bot commented 3 days ago

Thanks, about to send the query.

ropensci-review-bot commented 3 days ago

:rocket:

Editor check started

:wave:

mpadge commented 3 days ago

Sorry @adamhsparks, I'll try to find out the problem with the checks asap and get back to you ... :confused:

ropensci-review-bot commented 3 days ago

Checks for read.abares (v0.1.0)

git hash: a7a41aab

:heavy_check_mark: Package name is available
:heavy_check_mark: has a 'codemeta.json' file.
:heavy_check_mark: has a 'contributing' file.
:heavy_check_mark: uses 'roxygen2'.
:heavy_check_mark: 'DESCRIPTION' has a URL field.
:heavy_check_mark: 'DESCRIPTION' has a BugReports field.
:heavy_check_mark: Package has at least one HTML vignette
:heavy_check_mark: All functions have examples.
:heavy_check_mark: Package has continuous integration checks.
:heavy_multiplication_x: Package coverage is 5.3% (should be at least 75%).
:heavy_check_mark: R CMD check found no errors.
:heavy_check_mark: R CMD check found no warnings.
:eyes: Function names are duplicated in other packages

Important: All failing checks above must be addressed prior to proceeding

(Checks marked with :eyes: may be optionally addressed.)

Package License: MIT + file LICENSE

1. Package Dependencies

Details 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. |type |package | ncalls| |:----------|:-----------|------:| |internal |base | 76| |internal |read.abares | 9| |internal |methods | 3| |internal |stats | 3| |internal |tools | 2| |internal |utils | 2| |imports |data.table | 21| |imports |purrr | 6| |imports |httr2 | 5| |imports |terra | 4| |imports |openxlsx2 | 3| |imports |sf | 3| |imports |stars | 3| |imports |tidync | 3| |imports |stringr | 2| |imports |readtext | 1| |imports |cli | NA| |imports |lubridate | NA| |imports |withr | NA| |suggests |knitr | NA| |suggests |pander | NA| |suggests |rmarkdown | NA| |suggests |roxyglobals | NA| |suggests |spelling | NA| |suggests |testthat | NA| |linking_to |NA | NA| 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()', and examining the 'external_calls' table.

base

file.path (32), tempdir (13), c (12), dirname (7), url (4), lapply (2), list (2), list.files (2), append (1), nchar (1)

data.table

fread (9), fifelse (7), as.data.table (3), rbindlist (1), setcolorder (1)

read.abares

clear_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)

purrr

map (4), modify_depth (1), quietly (1)

httr2

req_options (2), request (2), req_retry (1)

terra

rast (4)

methods

new (3)

openxlsx2

read_xlsx (3)

sf

read_sf (2), st_read (1)

stars

read_ncdf (2), read_stars (1)

stats

var (2), dt (1)

tidync

tidync (2), hyper_tibble (1)

stringr

str_locate (1), str_sub (1)

tools

R_user_dir (2)

utils

unzip (2)

readtext

readtext (1)

**NOTE:** Some imported packages appear to have no associated function calls; please ensure with author that these 'Imports' are listed appropriately.

2. Statistical Properties

This 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: - code in R (100% in 23 files) and - 1 authors - 1 vignette - no internal data file - 13 imported packages - 26 exported functions (median 9 lines of code) - 49 non-exported functions in R (median 16 lines of code) --- Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages The following terminology is used: - `loc` = "Lines of Code" - `fn` = "function" - `exp`/`not_exp` = exported / not exported All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by [the `checks_to_markdown()` function](https://docs.ropensci.org/pkgcheck/reference/checks_to_markdown.html) The final measure (`fn_call_network_size`) is the total number of calls between functions (in R), or more abstract relationships between code objects in other languages. Values are flagged as "noteworthy" when they lie in the upper or lower 5th percentile. |measure | value| percentile|noteworthy | |:------------------------|-----:|----------:|:----------| |files_R | 23| 83.4| | |files_vignettes | 2| 81.7| | |files_tests | 22| 95.6| | |loc_R | 628| 52.5| | |loc_vignettes | 265| 56.8| | |loc_tests | 893| 81.8| | |num_vignettes | 1| 58.9| | |n_fns_r | 75| 67.2| | |n_fns_r_exported | 26| 73.8| | |n_fns_r_not_exported | 49| 65.1| | |n_fns_per_file_r | 2| 34.9| | |num_params_per_fn | 1| 1.8|TRUE | |loc_per_fn_r | 11| 32.9| | |loc_per_fn_r_exp | 9| 19.8| | |loc_per_fn_r_not_exp | 16| 53.0| | |rel_whitespace_R | 20| 56.4| | |rel_whitespace_vignettes | 50| 70.4| | |rel_whitespace_tests | 10| 66.2| | |doclines_per_fn_exp | 25| 23.7| | |doclines_per_fn_not_exp | 0| 0.0|TRUE | |fn_call_network_size | 36| 57.9| | ---

2a. Network visualisation

Click to see the interactive network visualisation of calls between objects in package

3. `goodpractice` and other checks

Details of goodpractice checks (click to open)

#### 3a. Continuous Integration Badges [![R-CMD-check.yaml](https://github.com/adamhsparks/read.abares/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/adamhsparks/read.abares/actions) **GitHub Workflow Results** | 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](https://r-lib.github.io/rcmdcheck/) rcmdcheck found no errors, warnings, or notes #### Test coverage with [covr](https://covr.r-lib.org/) 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](https://github.com/MangoTheCat/cyclocomp) No functions have cyclocomplexity >= 15 #### Static code analyses with [lintr](https://github.com/jimhester/lintr) [lintr](https://github.com/jimhester/lintr) found no issues with this package!

4. Other Checks

Details of other checks (click to open)

:heavy_multiplication_x: The following function name is duplicated in other packages: - - `clear_cache` from 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 :heavy_multiplication_x: have been resolved.

adamhsparks commented 3 days ago

@emilyriederer, the test coverage is >90%, see attached screenshot of local covr::report().

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 skip_on_ci() for any function that downloads data. But I'm open to suggestions as to how to improve this. It's just one reason I've submitted it for review. 😃

ropensci / software-review