| Title: | File Database Management for 'raadtools' |
|---|---|
| Description: | Tools for managing collections of files for the 'raad' family. |
| Authors: | Michael D. Sumner [aut, cre],
Ben Raymond [ctb],
Kimberlee Baldry [ctb] |
| Maintainer: | Michael D. Sumner <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.4.9012 |
| Built: | 2024-11-13 00:51:40 UTC |
| Source: | https://github.com/AustralianAntarcticDivision/raadfiles |
Sea Surface Height measured by Altimetry and derived variables. SSALTO/DUACS Near-Real-Time Level-4 sea surface height and derived variables measured by multi-satellite altimetry observations over Global Ocean.
altimetry_daily_files(all = FALSE)altimetry_daily_files(all = FALSE)
all |
return all files or only the final processing (NRT is included either way) |
In 2018/2019 the file servers migrated to 'my.cmems-du.au' and 'nrt.cmems-du.eu' (NRT) from 'ftp.sltac.cls.fr', but the files and file name scheme remained unchanged so no net effect (so far that we are aware of).
There are NRT (near-real-time) and final processed files, identifiable from the root domain and in the filename '^nrt_'. Both are returned by this function. The 'all' argument can be set to 'TRUE' to include all NRT files, which has overlapping processing dates that are ultimately consolidated into the daily sequence.
tibble data frame of file names, data 'date', and 'processing_date'
## Not run: altimetry_daily_files() ## End(Not run)## Not run: altimetry_daily_files() ## End(Not run)
Sea Level Anomaly measured by Altimetry and derived variables
altimetry_antarctica_files()altimetry_antarctica_files()
sla, formal_error, U, V
data frame of file paths
## Not run: aaf <- altimetry_antarctica_files() ## End(Not run)## Not run: aaf <- altimetry_antarctica_files() ## End(Not run)
A polar-transformed copy of the 'u' and 'v' components of surface currents from [altimetry_daily_files]. Only available for the southern hemisphere.
altimetry_currents_polar_files(hemisphere = "south")altimetry_currents_polar_files(hemisphere = "south")
hemisphere |
south only for now |
The code that creates these derived files is at [raad-deriv](https://github.com/AustralianAntarcticDivision/raad-deriv).
## Not run: altimetry_currents_polar_files() ## End(Not run)## Not run: altimetry_currents_polar_files() ## End(Not run)
Antarctic Mesoscale Prediction System GRIB files.
amps_files() amps_model_files(time.resolution = "4hourly", grid = "d1", ...) amps_d1files(time.resolution = "4hourly", ...) amps_d2files(time.resolution = "4hourly", ...)amps_files() amps_model_files(time.resolution = "4hourly", grid = "d1", ...) amps_d1files(time.resolution = "4hourly", ...) amps_d2files(time.resolution = "4hourly", ...)
time.resolution |
a placeholder, defaults to "4hourly" and remains unused |
grid |
one of 'd1' (30km resolution) or 'd2' (10km resolution) |
... |
reserved, unused |
'amps_files' returns all the files, 'amps_model_files' returns the files with date set from the file name, 'amps_d1files' and 'amps_d2files' return only the 30km and 10 km resolution grids respectively.
## Not run: amps_files() amps_model_files() amps_d1files() amps_d2files() ## End(Not run)## Not run: amps_files() amps_model_files() amps_d1files() amps_d2files() ## End(Not run)
Sea ice concentration files at 6.25 km resolution, southern hemisphere.
amsr2_3k_daily_files(type = c("tif", "hdf")) amsre_daily_files() amsr2_daily_files() amsr_daily_files()amsr2_3k_daily_files(type = c("tif", "hdf")) amsre_daily_files() amsr2_daily_files() amsr_daily_files()
type |
tif or hdf |
'amsre_daily_files()' returns HDF files
'amsr2_daily_files()' returns TIF files
'amsr2_3k_daily_files()' returns TIF files
'amsr_daily_files()' returns HDF files
The HDF files require flipping about the y-axis, and setting the polar extent and the crs (projection) metadata. The TIF files don't require this, they are completely oriented and metadata-complete.
tibble data frame of file names
## Not run: ## this combines amsr2 (2012-) and amsre (2002-2011) amsr_daily_files() ## End(Not run)## Not run: ## this combines amsr2 (2012-) and amsre (2002-2011) amsr_daily_files() ## End(Not run)
ARGO, by default we have 'profiles', alternatively 'meta' for type
argo_files(type = c("prof", "meta", "traj", "tech", "Mprof"), dac = NULL)argo_files(type = c("prof", "meta", "traj", "tech", "Mprof"), dac = NULL)
type |
file type, i.e. prof, meta, traj, tech |
dac |
data acqusition centre e.g.* "aoml", "bodc", "coriolis", "csio", "csiro", "incois", all returned if not specified |
(No traj, tech, or Mprof at this time of writing 2022-11-21)
'Cafe' MODIS files
cafe_monthly_files()cafe_monthly_files()
data frame of file names and date, 'date', 'fullname'
## Not run: cafe_monthly_files() ## End(Not run)## Not run: cafe_monthly_files() ## End(Not run)
RSS CCMP_RT V2.1 derived surface winds (Level 3.0), variables 'uwnd', 'vwnd', 'nobs' 'u-wind vector component at 10 meters', 'v-wind vector component at 10 meters', and 'number of observations used to derive wind vector components' from [Remote Sensing Systems](http://www.remss.com/).
ccmp_6hourly_files()ccmp_6hourly_files()
Each file contains four time steps at six hourly intervals aligned to the file base date.
tibble data frame of file names, with columns 'fullname' and 'date'
"Mears et al., Journal of Geophysical Research: Oceans,124, 6997-7010, 2019, Hoffman et al., Journal of Atmospheric and Oceanic Technology, 2013; Atlas et al., BAMS, 2011; Atlas et al., BAMS, 1996". "Available for public use with proper citation".
## Not run: ccmp_6hourly_files() ## End(Not run)## Not run: ccmp_6hourly_files() ## End(Not run)
Sea ice concentration files at 12.5 km resolution, southern hemisphere.
cersat_daily_files()cersat_daily_files()
tibble data frame of file names
## Not run: cersat_daily_files() ## End(Not run)## Not run: cersat_daily_files() ## End(Not run)
Data frame of all available fast ice files.
fasticefiles( product = c("circum_fast_ice", "binary_fast_ice"), mask = FALSE, ... )fasticefiles( product = c("circum_fast_ice", "binary_fast_ice"), mask = FALSE, ... )
product |
which product |
mask |
if TRUE return mask file name |
... |
reserved for future use, currently ignored |
A data frame with file, date, fullname
Note that this product changed from the legacy 2000-2008 initial East Antarctic product "binary_fast_ice" to the circumpolar update "circum_fast_ice" 2000-2018 in Feburary 2021.
If you want the old files, use ‘product = "binary_fast_ice"', but it’s safe to assume the default product supersedes the old one.
The initial product was in Cylindrical Equal Area projection, while the circumpolar product uses the NSIDC-compatible polar stereographic (but with an unspecified extent, though implicit in the longitude and latitude arrays of the NetCDF files).
Exists in 'public.services.aad.gov.au/datasets/science' (Feb 2021).
data frame
Fraser, A. D., Massom, R. A., Ohshima, K. I., Willmes, S., Kappes, P. J., Cartwright, J., and Porter-Smith, R.: High-resolution mapping of circum-Antarctic landfast sea ice distribution, 2000–2018, Earth Syst. Sci. Data, 12, 2987–2999, https://doi.org/10.5194/essd-12-2987-2020, 2020.
[Fraser et al. 2018](https://doi.org/10.5194/essd-12-2987-2020)
FSLE - MAPS OF FINITE SIZE LYAPUNOV EXPONENTS AND ORIENTATIONS OF THE ASSOCIATED EIGENVECTORS
fsle_files()fsle_files()
These are daily files.
[https://www.aviso.altimetry.fr/en/data/products/value-added-products/fsle-finite-size-lyapunov-exponents.html]( Finite-Size Lyapunov Exponents)
Global 2.5 Minute Geoid Undulations, a
geoid_files(all = FALSE, ...)geoid_files(all = FALSE, ...)
all |
return all files, or just the core grid files for GDAL? './w001001.adf' |
... |
: additional parameters, currently ignored |
Each file is an ESRI GRID raster data set of 2.5-minute geoid undulation values covering a 45 x 45 degree area. Each raster file has a 2.5-minute cell size and is a subset of the global 2.5 x 2.5-minute grid of pre-computed geoid undulation point values found on the EGM2008-WGS 84 Version web page. This ESRI GRID format represents a continuous surface of geoid undulation values where each 2.5-minute raster cell derives its value from the original pre-computed geoid undulation point value located at the SW corner of each cell.
data frame of file paths
## Not run: geoid_files() ## End(Not run)## Not run: geoid_files() ## End(Not run)
The Group for High Resolution Sea Surface Temperature (GHRSST) files.
ghrsst_daily_files() ghrsst_daily_files_netcdf()ghrsst_daily_files() ghrsst_daily_files_netcdf()
tibble data frame of file names
## Not run: ghrsst_daily_files() ## End(Not run)## Not run: ghrsst_daily_files() ## End(Not run)
Average Lead-Frequency for the polar oceans for winter months November-April 2002/03-2018/19 based on daily lead composites as derived from MOD/MYD-29 IST 5 min granules.
iceclim_south_leadsfiles(all = FALSE) iceclim_north_leadsfiles(all = FALSE)iceclim_south_leadsfiles(all = FALSE) iceclim_north_leadsfiles(all = FALSE)
all |
return all files, or just the core grid files (*.nc)? |
F. Reiser, S. Willmes, G. Heinemann (2020): A new algorithm for daily sea ice lead identification in the Arctic and Antarctic winter from thermal-infrared satellite imagery.
## Not run: iceclim_south_leadsfiles() iceclim_north_leadsfiles() ## End(Not run)## Not run: iceclim_south_leadsfiles() iceclim_north_leadsfiles() ## End(Not run)
NCEP2 six-hourly reanalysis2 gaussian grid
ncep2_uwnd_6hr_files() ncep2_vwnd_6hr_files()ncep2_uwnd_6hr_files() ncep2_vwnd_6hr_files()
tibble data frame of file names
## Not run: ncep2_uwnd_6hr_files() ncep2_vwnd_6hr_files() ## End(Not run)## Not run: ncep2_uwnd_6hr_files() ncep2_vwnd_6hr_files() ## End(Not run)
Sea ice concentration files.
nsidc_south_monthly_files() nsidc_north_monthly_files() nsidc_monthly_files() nsidc_south_daily_files() nsidc_north_daily_files() nsidc_daily_files() nsidc_daily_files_v2(extra_pattern = NULL) nsidc_monthly_files_v2(extra_pattern = NULL)nsidc_south_monthly_files() nsidc_north_monthly_files() nsidc_monthly_files() nsidc_south_daily_files() nsidc_north_daily_files() nsidc_daily_files() nsidc_daily_files_v2(extra_pattern = NULL) nsidc_monthly_files_v2(extra_pattern = NULL)
extra_pattern |
argument for restricted string matching |
tibble data frame of file names
## Not run: nsidc_south_monthly_files() nsidc_north_monthly_files() nsidc_monthly_files() nsidc_south_daily_files() nsidc_north_daily_files() nsidc_daily_files() ## End(Not run)## Not run: nsidc_south_monthly_files() nsidc_north_monthly_files() nsidc_monthly_files() nsidc_south_daily_files() nsidc_north_daily_files() nsidc_daily_files() ## End(Not run)
Optimally Interpolated Sea Surface Temperature, from https://www.ncei.noaa.gov/. These files contain four variables 'sst', 'anom', 'err' and 'ice' for sea surface temperature, sst anomaly, sst error and sea ice concentration on a regular global longitude latitude grid, with dimensions 1440x720 grid (0.25 degree spatial resolution).
oisst_daily_files() oisst_monthly_files()oisst_daily_files() oisst_monthly_files()
At the time of writing (2021-01-18) the files are accessible at https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/. See the [blueant](https://github.com/AustralianAntarcticDivision/blueant) package for a convenient way to obtain this data set named "NOAA OI 1/4 Degree Daily SST AVHRR".
These files can be accessed individually 'raster' package function 'raster' or as multiple layers with 'brick' or 'raster::stack'. Use the 'varname' argument to choose one of the four variables.
To obtain full NetCDF header metadata use 'ncdf4::open.nc(file)' or 'RNetCDF::print.nc(RNetCDF::open.nc(file))' to see the equivalent of 'ncdump -h' output.
Optimally Interpolated version 2 SST moved from 'eclipse.ncdc.noaa.gov', to 'www.ncei.noaa.gov' at the end of 2017. Version 2 was superseded by version 2.1 during 2020.
tibble data frame of file names, with columns 'fullname' and 'date'
## Not run: oisst_daily_files() ## End(Not run)## Not run: oisst_daily_files() ## End(Not run)
Each file contains four time steps at six hourly intervals aligned to the file base date.
par_files(time.resolution = "8D")par_files(time.resolution = "8D")
time.resolution |
time resolution (only "8D" is available) |
tibble data frame of file names, with columns 'fullname' and 'date'
oceandata.sci.gsfc.nasa.gov/MODISA/Mapped/8Day/4km/par
par_files()par_files()
Imagery from www.polarview.aq
polarview_files(type = c("jpeg", "tarball"))polarview_files(type = c("jpeg", "tarball"))
type |
jpeg or tarball |
The JPEGs are simple images, the GeoTIFFs are 16-bit integers (haven't explored further)
tibble data frame of file names, with columns 'fullname' and 'date'
## Not run: files <- polarview_files() tiffiles <- polarview_files(type = "tarball") ## End(Not run)## Not run: files <- polarview_files() tiffiles <- polarview_files(type = "tarball") ## End(Not run)
Administration tools for managing a data library.
get_raad_data_roots() get_raad_filenames(all = FALSE) set_raad_data_roots( ..., replace_existing = TRUE, use_known_candidates = FALSE, verbose = TRUE ) raad_filedb_path(...) set_raad_filenames(clobber = FALSE) run_build_raad_cache()get_raad_data_roots() get_raad_filenames(all = FALSE) set_raad_data_roots( ..., replace_existing = TRUE, use_known_candidates = FALSE, verbose = TRUE ) raad_filedb_path(...) set_raad_filenames(clobber = FALSE) run_build_raad_cache()
all |
if ‘TRUE' include ’data_deprecated', expert-use only |
... |
input file paths to set |
replace_existing |
replace existing paths, defaults to TRUE |
use_known_candidates |
apply internal logic for known candidates (for internal use at raad-hq), defaults to FALSE |
verbose |
issue warnings? |
clobber |
by default do not ignore existing file cache, set to TRUE to ignore and set |
These management functions are aimed at raadtools users, but can be used for any file collection. The administration tools consist of **data roots** and control over the building, reading, and caching of the available file list. No interpretation of the underlying files is provided in the administration tools.
A typical user won't use these functions but may want to investigate the contents of the raw file list, with 'get_raad_filenames()'.
A user setting up a raadfiles collection will typically set the root directory/directories with 'set_raad_data_roots()', then run the file cache list builder with 'run_build_raad_cache()', and then 'set_raad_filenames()' to actually load the file cache into memory.
In a new R session there is no need to run 'set_raad_filenames()' directly as this will be done as the package loads. To disable this automatic behaviour use 'options(raadfiles.file.cache.disable = TRUE)' *before* the package is used or loaded. This is typically done when calling 'run_build_raad_cache()' in a cron task.
Every raadfiles file collection function (e.g. 'oisst_daily_files') will run 'get_raad_filenames' to obtain the full raw list of available files from the global in-memory option 'getOption("raadfiles.env")$raadfiles.filename.database' and there is a low threshold probability that this will also trigger a re-read of the file listing from the root directories. To avoid this trigger either use that directly directly to get the in-memory file list, or set 'options(raadfiles.file.refresh.threshold = 0)' to prevent the trigger. (Set it to 1 to force it always to be read, also controlled by 'set_raad_filenames(clobber = TRUE)').
There is a family of functions and global options used for administration.
set_raad_data_roots |
set data root paths, for normal use only one data root is needed |
set_raad_filenames |
runs the system to update the file listing and refresh it |
get_raad_data_roots |
returns the current list of visible root directories |
get_raad_filenames |
returns the entire list of all files found in visible root directories |
run_build_raad_cache |
scan all root directories and update the file listing in each |
raadfiles.data.roots |
the list of paths to root directories |
raadfiles.file.cache.disable |
disable on-load setting of the in-memory file cache (never set automatically by the package) |
raadfiles.file.refresh.threshold |
threshold probability of how often to refresh in-memory file cache (0 = never, 1 = every time `get_raad_filenames()` is called) |
Options used internally, and subject to control by adminstrator options and the running of admin functions (they may not be set).
raadfiles.env |
an environment with the data frame of all file names from the data roots in a object named 'raadfiles.filename.database' |
raadfiles.database.status |
a status record of the in-memory filename database (timestamp) |
Return files for various products from REMA Release 1
rema_tile_files(all = FALSE, ...) rema_100m_files(...) rema_200m_files(filled = TRUE, ...) rema_1km_files(filled = TRUE, ...) rema_8m_files(...) rema_8m_tiles() rema_200m_dem_files() .rema_file_filter(x) rema_200m_dem_geoid_files() rema_200m_slope_files() rema_200m_aspect_files() rema_200m_rugosity_files() rema_200m_rock_files() rema_100m_dem_files() rema_100m_dem_geoid_files() rema_100m_slope_files() rema_100m_aspect_files() rema_100m_rugosity_files() rema_100m_rock_files() rema_8m_dem_files() rema_8m_dem_geoid_files() rema_8m_slope_files() rema_8m_aspect_files() rema_8m_rugosity_files() rema_8m_rock_files()rema_tile_files(all = FALSE, ...) rema_100m_files(...) rema_200m_files(filled = TRUE, ...) rema_1km_files(filled = TRUE, ...) rema_8m_files(...) rema_8m_tiles() rema_200m_dem_files() .rema_file_filter(x) rema_200m_dem_geoid_files() rema_200m_slope_files() rema_200m_aspect_files() rema_200m_rugosity_files() rema_200m_rock_files() rema_100m_dem_files() rema_100m_dem_geoid_files() rema_100m_slope_files() rema_100m_aspect_files() rema_100m_rugosity_files() rema_100m_rock_files() rema_8m_dem_files() rema_8m_dem_geoid_files() rema_8m_slope_files() rema_8m_aspect_files() rema_8m_rugosity_files() rema_8m_rock_files()
all |
for |
... |
additional parameters, currently ignored |
filled |
return 'filled' variant if available |
x |
pattern to detect |
'rema_8m_files' returns the base level 8 GeoTIFF files, there are 1516 files at 8m resolution.
data frame of file names
https://www.pgc.umn.edu/tag/rema/
## Not run: rema_8m_files() rema_100m_files(filled = TRUE) ## End(Not run)## Not run: rema_8m_files() rema_100m_files(filled = TRUE) ## End(Not run)
Global ocean low and mid trophic levels biomass hindcast
seapodym_weekly_files()seapodym_weekly_files()
http://www.cls.fr, http://www.seapodym.eu
Remote Sensing Systems SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8day running
smap_8day_files()smap_8day_files()
tibble data frame of file names, with columns 'fullname' and 'date'
## Not run: smap_daily_files() ## End(Not run)## Not run: smap_daily_files() ## End(Not run)
Files from Ocean State Estimation at Scripps for the Southern Ocean 'SOSE'.
sose_monthly_files(varname = "", iteration = "")sose_monthly_files(varname = "", iteration = "")
varname |
default is ” which is the first available, set to 'all' to return all file names without date expansion |
iteration |
default is ” which finds latest available, see details |
Iteration provided is latest available, otherwise this argument will be used to match with file names.
Dates in the files are extracted and expanded out for every time step, it's assumed this will be used in raadtools along with a 'level' argument to return a time step or time series.
data frame of file names and date, 'date', 'fullname'
http://sose.ucsd.edu/
sose_monthly_files()sose_monthly_files()
SRTM 90m Digital Elevation Database v4.1
srtm_files()srtm_files()
DOI: 0.1080/13658810601169899
tibble data frame of file names, with columns 'fullname', 'x', 'y' (tiles)
data frame with 'fullname' file path, 'x', 'y' tile column and row indices, 'root' the data file root path
[https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/]
## Not run: srtm_files() ## End(Not run)## Not run: srtm_files() ## End(Not run)
Local authority spatial data in Tasmania.
thelist_files( format = c("gdb", "tab", "shp", "asc", "xml", "lyr", "dbf", "zip", "all"), pattern = NULL )thelist_files( format = c("gdb", "tab", "shp", "asc", "xml", "lyr", "dbf", "zip", "all"), pattern = NULL )
format |
is used to targe tspecific formats see Details |
pattern |
is used to string match generally, if this is not NULL then format is ignored |
TheList.tas.gov.au is the standard local mapping authority in Tasmania, it was recently upgraded and works very well, and most of the data including newish LiDAR is available within the opendata tree. Also check out tasmap.org for an alternative interface with access to the same services.
These files are broken into sub-regions, administrative areas within the state of Tasmania. At time of checking there were 19 sub-regions, and 544 or so layers (type within format) and 37,616 total files. GDB detection is different to the other more definite formats so the file sets won't be analogous atm. There are Climate Futures Australia (CFA) layer indexes in here as well, it's on the todo list to build a comprehensive index (or access one).
The scheme uses the Map Grid of Australia 1994 (MGA94) on the Geocentric Datum of Australia 1994 (GDA94), an implementation of UTM Zone 55. GDA94 was rolled out in Australia in the early 2000s, Tasmania kept the old UTM scheme (it was AMG66, AGD66) but around the same time Victoria used the opportunity to move to a single-projection for the entire state, to avoid having to switch between zones. NSW took much longer to modernize and standardize around GDA94 and they stumbled forward with their three UTM zones (54, 55, 56), and while Tasmania did it quickly we only have the one zone (no one thought much about Macquarie Island) and Victoria did it more cleverly. I'm not sure how Queensland went, they were adding properties and roads at a very scary rate so probably took much longer than us. The software back then could only just handle an entire city worth of vector roads and cadastre, so experience with higher abstractions was pretty rare. As a nation, we could probably never have agreed on a national LCC projection given that the states had so much mess to sort out themselves, but that's what you see in the USA with its Albers family, and the elided Hawaii and and Alaskan montsrosities . During the time GDA94 was being rolled out the addressing system was being standardized for GPS and modern communication systems, the P-NAF was the original project that took the data from Australia Post. State of the art for address parsing in 2002 was Perl, Google Earth was but a keyhole glimmering in the background in early West Wing episodes, and the idea of "micro-services" was catching on among the venture capital elite. Today the echoes of Oracle and ESRI and RP-Data and ENVI and are still with us.
It was around this time that the large players made their mark in Australia (mid-1990s-early 2000s), MapInfo had a tight hold on many local government authorities (LGAs) because they had the best software, the best formats (TAB, MIF and georeferenced tile TAB for imagery), and somehow got integrated into many state departments. That's why these TAB and MIF formats are here still, shp was the poor interchange cousin, limited to DBF, short field names, no data above the 32-bit index limit, no mixed topologies in a single layer. Aerial imagery was just starting to make an impact and the future business and research interests being recognized. MrSID and ECW were used to integrate large imagery collections into single files and services, while their parent companies waged a furious legal battle around wavelet compression. LizardTech has mostly faded from the scene, but NearMap continues today with "reality as a service", they certainly had the long-game in mind this whole time.
Manifold was in version 5.0 in 2002, and it could read all of these formats as well as provide very accessible rendering, ability to create tiles with links betweeen drawings and images for creating tile sets. ECW was absolutely hot, and ERMapper (Nearmap) had a free viewer that is probably still the best one around until leaflet happened. The point of this long story was to explain that in the early 2000s these files were LARGE and no one had a hope of reading a road line, cadastral parcel, or even address point shapefile for an entire state. We read them in parts, and in pairs or more of parts while we slowly rendered our way around the country building map tile sets deprecated immediately when Google Earth arrived. These days it's a pain to get the file list into one object so you lapply it with the latest R GIS i/o package, but there's really no problem with memory.
This function is here to make it easy to get the file list for Tasmania.
tab, gdb, shp is sf/rgdal ready - gdb works with the directory name, it might work with some of the individual files - I don't know how GDAL otherwise resolves layer names in the same folder but you can give it the path name, this is probably why gdb/, though note that for raster /anything-next-to.adf does work
all will give every thelist file
tab is that glorious ancient format
gdb is a newcomer format, recently reverse engineered by Even
shp is the usual suspect
dbf is the usual suspect's data (ggplot2 calls this metadata)
asc is DEM e.g. list_dem_25m_break_o_day.asc (part of a statewide effort in the early 2000s to build a DEM for Tasmania, it was used to build a networked drainage and topography graph of the state's physical landscape, and this helped spur the development of a powerful imagery orthorectification system and led to some interesting commerical initiatives in general geospatial data)
csv is something else e.g. list_fmp_data.csv
xml,txt is probably just xml, probably only relevant to GDAL and ESRI list_fmp_data_statewide.txt.xml
lyr - style files?
zip - unpackage zips
Arguments are used to pattern match on different aspects of the file name so that anything can be pulled out.
tibble data frame of file names
## Not run: thelist_files() ## to get statewide sets, find the individual groups first and pick one grps <- raadfiles:::thelist_groups() print(grps) #read_all <- function(pattern) { #files <- thelist_files(format = "shp", pattern = pattern) #do.call(rbind, lapply(files$fullname, sf::read_sf)) #} #x <- read_all(sample(grps, 1)) ## End(Not run)## Not run: thelist_files() ## to get statewide sets, find the individual groups first and pick one grps <- raadfiles:::thelist_groups() print(grps) #read_all <- function(pattern) { #files <- thelist_files(format = "shp", pattern = pattern) #do.call(rbind, lapply(files$fullname, sf::read_sf)) #} #x <- read_all(sample(grps, 1)) ## End(Not run)
Obtain file names for various topographic data.
gebco23_files(all = FALSE, ...) gebco21_files(all = FALSE, ...) gebco19_files(all = FALSE, ...) gebco14_files(all = FALSE, ...) gebco08_files(all = FALSE, ...) ramp_files(all = FALSE, ...) ibcso_files(all = FALSE, ...) ibcso_background_files(all = FALSE, ...) ibcso_bed_files(all = FALSE, ...) ibcso_digital_chart_files(all = FALSE, ...) ibcso_rid_files(all = FALSE, ...) ibcso_tid_files(all = FALSE, ...) ibcso_sid_files(all = FALSE, ...) cryosat2_files(all = FALSE, ...) etopo1_files(all = FALSE, ...) etopo2_files(all = FALSE, ...) lakesuperior_files(all = FALSE, ...) kerguelen_files(all = FALSE, ...) george_v_terre_adelie_1000m_files(all = FALSE, ...) george_v_terre_adelie_500m_files(all = FALSE, ...) george_v_terre_adelie_250m_files(all = FALSE, ...) george_v_terre_adelie_100m_files(all = FALSE, ...) smith_sandwell_files(all = FALSE, ...) smith_sandwell_unpolished_files(all = FALSE, ...) smith_sandwell_lon180_files(all = FALSE, ...) smith_sandwell_unpolished_lon180_files(all = FALSE, ...) macquarie100m_57S_files(all = FALSE, ...) macquarie100m_58S_files(all = FALSE, ...)gebco23_files(all = FALSE, ...) gebco21_files(all = FALSE, ...) gebco19_files(all = FALSE, ...) gebco14_files(all = FALSE, ...) gebco08_files(all = FALSE, ...) ramp_files(all = FALSE, ...) ibcso_files(all = FALSE, ...) ibcso_background_files(all = FALSE, ...) ibcso_bed_files(all = FALSE, ...) ibcso_digital_chart_files(all = FALSE, ...) ibcso_rid_files(all = FALSE, ...) ibcso_tid_files(all = FALSE, ...) ibcso_sid_files(all = FALSE, ...) cryosat2_files(all = FALSE, ...) etopo1_files(all = FALSE, ...) etopo2_files(all = FALSE, ...) lakesuperior_files(all = FALSE, ...) kerguelen_files(all = FALSE, ...) george_v_terre_adelie_1000m_files(all = FALSE, ...) george_v_terre_adelie_500m_files(all = FALSE, ...) george_v_terre_adelie_250m_files(all = FALSE, ...) george_v_terre_adelie_100m_files(all = FALSE, ...) smith_sandwell_files(all = FALSE, ...) smith_sandwell_unpolished_files(all = FALSE, ...) smith_sandwell_lon180_files(all = FALSE, ...) smith_sandwell_unpolished_lon180_files(all = FALSE, ...) macquarie100m_57S_files(all = FALSE, ...) macquarie100m_58S_files(all = FALSE, ...)
all |
return a larger set of files (for exploratory use only) |
... |
reserved |
Each function exists to match a specific data set, but the optional 'all' argument may be used to easily discover a broader set of files that ship with the data, or that represent older versions, documentation and other metadata files.
There's no single format, there are GeoTIFFs, ArcInfo binary, ERStorage, NetCDF, NetCDF GMT, (Geo) PDF, and some VRT wrappers for handling raw binary files.
data frame of 'file' and 'fullname' columns
Versions 2008, 2014, 2019, 2021, 2023.
'is' ('is_PS71' tif, or grd), 'background_hq', 'bed' ('bed_PS71'), 'digital_chart', 'sid' ('sid_PS71')
Etopo1 and Etopo2, Lake Superior
Polished and unpolished, version 18.1 replaces 18.
Cryosat2
George V Terre Adelie, Kerguelen, Macquarie 100m
## Not run: gebco23_files() ## End(Not run)## Not run: gebco23_files() ## End(Not run)
WOA find files
woa13_files() woa09_files() woa09_daily_files()woa13_files() woa09_files() woa09_daily_files()
Current returns all NetCDF files, without any date information, there's a mix of variables month/year climatologies.
tibble data frame of file names
## Not run: woa13_files() ## End(Not run)## Not run: woa13_files() ## End(Not run)