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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:

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

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")

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::meve

Get 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/Rtmprn5BVC/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
#> # … with 246 more rows

Acknowledgements

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.