FedData version 3.0 has been released to CRAN! There are several breaking changes in the FedData API from version 2.x. Please see NEWS.md for a list of changes.
FedData is an R package implementing functions to automate
downloading geospatial data available from several federated data
sources.
Currently, the package enables extraction from seven 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 4, available from the Oak Ridge National Laboratory’s Distributed Active Archive Center (DAAC)
- The International Tree Ring Data Bank (ITRDB), coordinated by National Climatic Data Center at NOAA
- The National Land Cover Database (NLCD)
- The NASS Cropland Data Layer from the National Agricultural Statistics Service
This package 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 sf and terra packages.
Development
- Kyle Bocinsky - Montana Climate Office, Missoula, MT
Contributors
- Dylan Beaudette - USDA-NRCS Soil Survey Office, Sonora, CA
- Jeffrey Hollister - US EPA Atlantic Ecology Division, Narragansett, RI
- Scott Chamberlain - ROpenSci and Museum of Paleontology at UC Berkeley
Install FedData
- From CRAN:
install.packages("FedData")- Development version from GitHub:
install.packages("devtools")
devtools::install_github("ropensci/FedData")- Linux: Follow instructions for installing
sfavailable at https://r-spatial.github.io/sf/.
Demonstration
This demonstration script is available as an R Markdown document in the GitHub repository: https://github.com/ropensci/FedData.
Load FedData and define a study area
# FedData Tester
library(FedData)
library(magrittr)
# FedData comes loaded with the boundary of Mesa Verde National Park, for testing
FedData::meveGet and plot the National Elevation Dataset for the study area
# Get the NED (USA ONLY)
# Returns a raster
NED <- get_ned(
template = FedData::meve,
label = "meve"
)
# 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 = FedData::meve,
label = "meve",
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 = FedData::meve,
label = "meve",
elements = c("prcp")
)
#> Warning: attribute variables are assumed to be spatially constant throughout
#> all geometries
#> Warning in CPL_write_ogr(obj, dsn, layer, driver,
#> as.character(dataset_options), : GDAL Error 1:
#> /private/var/folders/ys/7l0z3wlx7z14qxn9v0m9ckhw0000gq/T/RtmpGwfFJu/FedData/extractions/ghcn/meve/meve_GHCN_stations.shp
#> does not appear to be a file or directory.
# Plot the NED again
raster::plot(NED)
# Plot the spatial locations
sp::plot(GHCN.prcp$spatial,
pch = 1,
add = TRUE
)
#> Warning in plot.sf(GHCN.prcp$spatial, pch = 1, add = TRUE): ignoring all but
#> the first attribute
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 = FedData::meve,
label = "meve",
elements = c("tmin", "tmax"),
years = 1980:1985,
standardize = TRUE
)
# Plot the NED again
raster::plot(NED)
# Plot the spatial locations
sp::plot(GHCN.temp$spatial,
add = TRUE,
pch = 1
)
#> Warning in plot.sf(GHCN.temp$spatial, add = TRUE, pch = 1): ignoring all but
#> the first attribute
legend("bottomleft",
pch = 1,
legend = "GHCN Temperature Records"
)Get and plot the National Hydrography Dataset for the study area
# Get the NHD (USA ONLY)
get_nhd(
template = FedData::meve,
label = "meve"
) %>%
plot_nhd(template = FedData::meve)Get and plot the NRCS SSURGO data for the study area
# Get the NRCS SSURGO data (USA ONLY)
SSURGO.MEVE <- get_ssurgo(
template = FedData::meve,
label = "meve"
)
# Plot the NED again
raster::plot(NED)
# Plot the SSURGO mapunit polygons
plot(SSURGO.MEVE$spatial$geom,
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$spatial %>%
dplyr::filter(AREASYMBOL == "CO075")
# And get the NED data under them for pretty plotting
NED.CO675 <- get_ned(
template = SSURGO.areas.CO675,
label = "SSURGO_CO675"
)
# Plot the SSURGO mapunit polygons, but only for CO675
raster::plot(NED.CO675)
plot(SSURGO.areas.CO675$geom,
lwd = 0.1,
add = TRUE
)Get and plot the ITRDB chronology locations in the study area
# Get the ITRDB records
# Buffer MEVE, because there aren't any chronologies in the Park
ITRDB <- get_itrdb(
template = FedData::meve %>%
sf::st_buffer(50000),
label = "meve",
measurement.type = "Ring Width",
chronology.type = "Standard"
)
#> Warning in eval(jsub, SDenv, parent.frame()): NAs introduced by coercion
#> Warning: attribute variables are assumed to be spatially constant throughout
#> all geometries
# Plot the MEVE buffer
plot(
FedData::meve %>%
sf::st_buffer(50000) %>%
sf::st_transform(4326)
)
# Map the locations of the tree ring chronologies
plot(ITRDB$metadata$geometry,
pch = 1,
add = TRUE
)
legend("bottomleft",
pch = 1,
legend = "ITRDB chronologies"
)Get and plot the National Land Cover Dataset for the study area
# Get the NLCD (USA ONLY)
# Returns a raster
NLCD <- get_nlcd(
template = FedData::meve,
year = 2011,
label = "meve"
)
# Plot with raster::plot
raster::plot(NLCD)Get and plot the NASS Cropland Data Layer for the study area
# Get the NASS (USA ONLY)
# Returns a raster
NASS_CDL <- get_nass_cdl(
template = FedData::meve,
year = 2016,
label = "meve"
)
# Plot with raster::plot
raster::plot(NASS_CDL)# Get the NASS CDL classification table
raster::levels(NASS_CDL)[[1]]
#> ID Land Cover
#> 1 0 Background
#> 2 1 Corn
#> 3 2 Cotton
#> 4 3 Rice
#> 5 4 Sorghum
#> 6 5 Soybeans
#> 7 6 Sunflower
#> 8 7 <NA>
#> 9 8 <NA>
#> 10 9 <NA>
#> 11 10 Peanuts
#> 12 11 Tobacco
#> 13 12 Sweet Corn
#> 14 13 Pop or Orn Corn
#> 15 14 Mint
#> 16 15 <NA>
#> 17 16 <NA>
#> 18 17 <NA>
#> 19 18 <NA>
#> 20 19 <NA>
#> 21 20 <NA>
#> 22 21 Barley
#> 23 22 Durum Wheat
#> 24 23 Spring Wheat
#> 25 24 Winter Wheat
#> 26 25 Other Small Grains
#> 27 26 Dbl Crop WinWht/Soybeans
#> 28 27 Rye
#> 29 28 Oats
#> 30 29 Millet
#> 31 30 Speltz
#> 32 31 Canola
#> 33 32 Flaxseed
#> 34 33 Safflower
#> 35 34 Rape Seed
#> 36 35 Mustard
#> 37 36 Alfalfa
#> 38 37 Other Hay/Non Alfalfa
#> 39 38 Camelina
#> 40 39 Buckwheat
#> 41 40 <NA>
#> 42 41 Sugarbeets
#> 43 42 Dry Beans
#> 44 43 Potatoes
#> 45 44 Other Crops
#> 46 45 Sugarcane
#> 47 46 Sweet Potatoes
#> 48 47 Misc Vegs & Fruits
#> 49 48 Watermelons
#> 50 49 Onions
#> 51 50 Cucumbers
#> 52 51 Chick Peas
#> 53 52 Lentils
#> 54 53 Peas
#> 55 54 Tomatoes
#> 56 55 Caneberries
#> 57 56 Hops
#> 58 57 Herbs
#> 59 58 Clover/Wildflowers
#> 60 59 Sod/Grass Seed
#> 61 60 Switchgrass
#> 62 61 Fallow/Idle Cropland
#> 63 62 <NA>
#> 64 63 Forest
#> 65 64 Shrubland
#> 66 65 Barren
#> 67 66 Cherries
#> 68 67 Peaches
#> 69 68 Apples
#> 70 69 Grapes
#> 71 70 Christmas Trees
#> 72 71 Other Tree Crops
#> 73 72 Citrus
#> 74 73 <NA>
#> 75 74 Pecans
#> 76 75 Almonds
#> 77 76 Walnuts
#> 78 77 Pears
#> 79 78 <NA>
#> 80 79 <NA>
#> 81 80 <NA>
#> 82 81 Clouds/No Data
#> 83 82 Developed
#> 84 83 Water
#> 85 84 <NA>
#> 86 85 <NA>
#> 87 86 <NA>
#> 88 87 Wetlands
#> 89 88 Nonag/Undefined
#> 90 89 <NA>
#> 91 90 <NA>
#> 92 91 <NA>
#> 93 92 Aquaculture
#> 94 93 <NA>
#> 95 94 <NA>
#> 96 95 <NA>
#> 97 96 <NA>
#> 98 97 <NA>
#> 99 98 <NA>
#> 100 99 <NA>
#> 101 100 <NA>
#> 102 101 <NA>
#> 103 102 <NA>
#> 104 103 <NA>
#> 105 104 <NA>
#> 106 105 <NA>
#> 107 106 <NA>
#> 108 107 <NA>
#> 109 108 <NA>
#> 110 109 <NA>
#> 111 110 <NA>
#> 112 111 Open Water
#> 113 112 Perennial Ice/Snow
#> 114 113 <NA>
#> 115 114 <NA>
#> 116 115 <NA>
#> 117 116 <NA>
#> 118 117 <NA>
#> 119 118 <NA>
#> 120 119 <NA>
#> 121 120 <NA>
#> 122 121 Developed/Open Space
#> 123 122 Developed/Low Intensity
#> 124 123 Developed/Med Intensity
#> 125 124 Developed/High Intensity
#> 126 125 <NA>
#> 127 126 <NA>
#> 128 127 <NA>
#> 129 128 <NA>
#> 130 129 <NA>
#> 131 130 <NA>
#> 132 131 Barren
#> 133 132 <NA>
#> 134 133 <NA>
#> 135 134 <NA>
#> 136 135 <NA>
#> 137 136 <NA>
#> 138 137 <NA>
#> 139 138 <NA>
#> 140 139 <NA>
#> 141 140 <NA>
#> 142 141 Deciduous Forest
#> 143 142 Evergreen Forest
#> 144 143 Mixed Forest
#> 145 144 <NA>
#> 146 145 <NA>
#> 147 146 <NA>
#> 148 147 <NA>
#> 149 148 <NA>
#> 150 149 <NA>
#> 151 150 <NA>
#> 152 151 <NA>
#> 153 152 Shrubland
#> 154 153 <NA>
#> 155 154 <NA>
#> 156 155 <NA>
#> 157 156 <NA>
#> 158 157 <NA>
#> 159 158 <NA>
#> 160 159 <NA>
#> 161 160 <NA>
#> 162 161 <NA>
#> 163 162 <NA>
#> 164 163 <NA>
#> 165 164 <NA>
#> 166 165 <NA>
#> 167 166 <NA>
#> 168 167 <NA>
#> 169 168 <NA>
#> 170 169 <NA>
#> 171 170 <NA>
#> 172 171 <NA>
#> 173 172 <NA>
#> 174 173 <NA>
#> 175 174 <NA>
#> 176 175 <NA>
#> 177 176 Grassland/Pasture
#> 178 177 <NA>
#> 179 178 <NA>
#> 180 179 <NA>
#> 181 180 <NA>
#> 182 181 <NA>
#> 183 182 <NA>
#> 184 183 <NA>
#> 185 184 <NA>
#> 186 185 <NA>
#> 187 186 <NA>
#> 188 187 <NA>
#> 189 188 <NA>
#> 190 189 <NA>
#> 191 190 Woody Wetlands
#> 192 191 <NA>
#> 193 192 <NA>
#> 194 193 <NA>
#> 195 194 <NA>
#> 196 195 Herbaceous Wetlands
#> 197 196 <NA>
#> 198 197 <NA>
#> 199 198 <NA>
#> 200 199 <NA>
#> 201 200 <NA>
#> 202 201 <NA>
#> 203 202 <NA>
#> 204 203 <NA>
#> 205 204 Pistachios
#> 206 205 Triticale
#> 207 206 Carrots
#> 208 207 Asparagus
#> 209 208 Garlic
#> 210 209 Cantaloupes
#> 211 210 Prunes
#> 212 211 Olives
#> 213 212 Oranges
#> 214 213 Honeydew Melons
#> 215 214 Broccoli
#> 216 215 Avocados
#> 217 216 Peppers
#> 218 217 Pomegranates
#> 219 218 Nectarines
#> 220 219 Greens
#> 221 220 Plums
#> 222 221 Strawberries
#> 223 222 Squash
#> 224 223 Apricots
#> 225 224 Vetch
#> 226 225 Dbl Crop WinWht/Corn
#> 227 226 Dbl Crop Oats/Corn
#> 228 227 Lettuce
#> 229 228 Dbl Crop Triticale/Corn
#> 230 229 Pumpkins
#> 231 230 Dbl Crop Lettuce/Durum Wht
#> 232 231 Dbl Crop Lettuce/Cantaloupe
#> 233 232 Dbl Crop Lettuce/Cotton
#> 234 233 Dbl Crop Lettuce/Barley
#> 235 234 Dbl Crop Durum Wht/Sorghum
#> 236 235 Dbl Crop Barley/Sorghum
#> 237 236 Dbl Crop WinWht/Sorghum
#> 238 237 Dbl Crop Barley/Corn
#> 239 238 Dbl Crop WinWht/Cotton
#> 240 239 Dbl Crop Soybeans/Cotton
#> 241 240 Dbl Crop Soybeans/Oats
#> 242 241 Dbl Crop Corn/Soybeans
#> 243 242 Blueberries
#> 244 243 Cabbage
#> 245 244 Cauliflower
#> 246 245 Celery
#> 247 246 Radishes
#> 248 247 Turnips
#> 249 248 Eggplants
#> 250 249 Gourds
#> 251 250 Cranberries
#> 252 251 <NA>
#> 253 252 <NA>
#> 254 253 <NA>
#> 255 254 Dbl Crop Barley/Soybeans
#> 256 255 <NA>
# Also, a convenience function loading the NASS CDL categories and hex colors
cdl_colors()
#> # A tibble: 256 × 3
#> ID `Land Cover` Color
#> <int> <fct> <chr>
#> 1 0 Background #00000000
#> 2 1 Corn #FFD300FF
#> 3 2 Cotton #FF2626FF
#> 4 3 Rice #00A8E4FF
#> 5 4 Sorghum #FF9E0BFF
#> 6 5 Soybeans #267000FF
#> 7 6 Sunflower #FFFF00FF
#> 8 7 <NA> #000000FF
#> 9 8 <NA> #000000FF
#> 10 9 <NA> #000000FF
#> # ℹ 246 more rowsAcknowledgements
This package is a product of SKOPE (Synthesizing Knowledge of Past Environments) and the Village Ecodynamics Project through grants awarded to the Crow Canyon Archaeological Center and Washington State University by the National Science Foundation. This software is licensed under the MIT license. Continuing development is supported by the Montana Climate Office.
FedData was reviewed for rOpenSci by @jooolia, and was greatly improved as a result. rOpenSci on-boarding was coordinated by @sckott.