FedData has gone through software review and is now part of rOpenSci.
FedData includes functions to automate downloading geospatial data available from several federated data sources (mainly sources maintained by the US Federal government).
Currently, the package enables extraction from six datasets:
- The National Elevation Dataset (NED) digital elevation models (1 and 1/3 arc-second; USGS)
- The National Hydrography Dataset (NHD) (USGS)
- The Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (NCSS), which is led by the Natural Resources Conservation Service (NRCS) under the USDA,
- The Global Historical Climatology Network (GHCN), coordinated by National Climatic Data Center at NOAA,
- The Daymet gridded estimates of daily weather parameters for North America, version 3, available from the Oak Ridge National Laboratory's Distributed Active Archive Center (DAAC), and
- The International Tree Ring Data Bank (ITRDB), coordinated by National Climatic Data Center at NOAA.
FedData is designed with the large-scale geographic information system (GIS) use-case in mind: cases where the use of dynamic web-services is impractical due to the scale (spatial and/or temporal) of analysis. It functions primarily as a means of downloading tiled or otherwise spatially-defined datasets; additionally, it can preprocess those datasets by extracting data within an area of interest (AoI), defined spatially. It relies heavily on the
The current CRAN version of
FedData, v2.4.6, will (hopefully) be the final CRAN release of
FedData 3 will be released in the coming months, but some code built on
FedData 2 will not be compatible with FedData 3.
FedData was initially developed prior to widespread use of modern web mapping services and RESTful APIs by many Federal data-holders. Future releases of
FedData will limit data transfer by utilizing server-side geospatial and data queries. We will also implement
dplyr verbs, tidy data structures, (
magrittr) piping, functional programming using
purrr, simple features for spatial data from
sf, and local data storage in OGC-compliant data formats (probably GeoJSON and NetCDF). I am also aiming for 100% testing coverage.
All that being said, much of the functionality of the
FedData package could be spun off into more domain-specific packages. For example, ITRDB download functions could be part of the
dplR dendrochronology package; concepts/functions having to do with the GHCN data integrated into
rnoaa; and Daymet concepts integrated into
daymetr. I welcome any and all suggestions about how to improve the utility of FedData; please submit an issue.
FedData and define a study area
# FedData Tester library(FedData) library(magrittr) # Extract data for the Village Ecodynamics Project "VEPIIN" study area: # http://veparchaeology.org vepPolygon <- polygon_from_extent(raster::extent(672800, 740000, 4102000, 4170000), proj4string = "+proj=utm +datum=NAD83 +zone=12")
Get and plot the National Elevation Dataset for the study area
# Get the NED (USA ONLY) # Returns a raster NED <- get_ned(template = vepPolygon, label = "VEPIIN") # Plot with raster::plot raster::plot(NED)
Get and plot the Daymet dataset for the study area
# Get the DAYMET (North America only) # Returns a raster DAYMET <- get_daymet(template = vepPolygon, label = "VEPIIN", elements = c("prcp","tmax"), years = 1980:1985) # Plot with raster::plot raster::plot(DAYMET$tmax$X1985.10.23)
Get and plot the daily GHCN precipitation data for the study area
# Get the daily GHCN data (GLOBAL) # Returns a list: the first element is the spatial locations of stations, # and the second is a list of the stations and their daily data GHCN.prcp <- get_ghcn_daily(template = vepPolygon, label = "VEPIIN", elements = c('prcp')) # Plot the NED again raster::plot(NED) # Plot the spatial locations sp::plot(GHCN.prcp$spatial, pch = 1, add = TRUE) legend('bottomleft', pch = 1, legend="GHCN Precipitation Records")
Get and plot the daily GHCN temperature data for the study area
# Elements for which you require the same data # (i.e., minimum and maximum temperature for the same days) # can be standardized using standardize==T GHCN.temp <- get_ghcn_daily(template = vepPolygon, label = "VEPIIN", elements = c('tmin','tmax'), years = 1980:1985, standardize = TRUE) # Plot the NED again raster::plot(NED) # Plot the spatial locations sp::plot(GHCN.temp$spatial, add = TRUE, pch = 1) legend('bottomleft', pch = 1, legend = "GHCN Temperature Records")
Get and plot the National Hydrography Dataset for the study area
# Get the NHD (USA ONLY) NHD <- get_nhd(template = vepPolygon, label = "VEPIIN") # Plot the NED again raster::plot(NED) # Plot the NHD data NHD %>% lapply(sp::plot, col = 'black', add = TRUE)
Get and plot the NRCS SSURGO data for the study area
# Get the NRCS SSURGO data (USA ONLY) SSURGO.VEPIIN <- get_ssurgo(template = vepPolygon, label = "VEPIIN") #> Warning: 1 parsing failure. #> row # A tibble: 1 x 5 col row col expected actual expected <int> <chr> <chr> <chr> actual 1 1276 slope.r no trailing characters .5 file # ... with 1 more variables: file <chr> # Plot the NED again raster::plot(NED) # Plot the SSURGO mapunit polygons plot(SSURGO.VEPIIN$spatial, lwd = 0.1, add = TRUE)
Get and plot the NRCS SSURGO data for particular soil survey areas
# Or, download by Soil Survey Area names SSURGO.areas <- get_ssurgo(template = c("CO670","CO075"), label = "CO_TEST") # Let's just look at spatial data for CO675 SSURGO.areas.CO675 <- SSURGO.areas$spatial[SSURGO.areas$spatial$AREASYMBOL=="CO075",] # And get the NED data under them for pretty plotting NED.CO675 <- get_ned(template = SSURGO.areas.CO675, label = "SSURGO_CO675") # Plot the SSURGO mapunit polygons, but only for CO675 plot(NED.CO675) plot(SSURGO.areas.CO675, lwd = 0.1, add = TRUE)
Get and plot the ITRDB chronology locations in the study area
# Get the ITRDB records ITRDB <- get_itrdb(template = vepPolygon, label = "VEPIIN", makeSpatial = TRUE) # Plot the NED again raster::plot(NED) # Map the locations of the tree ring chronologies plot(ITRDB$metadata, pch = 1, add = TRUE) legend('bottomleft', pch = 1, legend = "ITRDB chronologies")