README.md
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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](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)](https://ned.usgs.gov) digital
elevation models (1 and 1/3 arc-second; USGS)
- The [National Hydrography Dataset
(NHD)](https://www.usgs.gov/national-hydrography/national-hydrography-dataset)
(USGS)
- The [Soil Survey Geographic (SSURGO)
database](https://websoilsurvey.sc.egov.usda.gov/) 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)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily),
coordinated by National Climatic Data Center at NOAA
- The [Daymet](https://daymet.ornl.gov/) 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)](https://www.ncei.noaa.gov/products/paleoclimatology/tree-ring),
coordinated by National Climatic Data Center at NOAA
- The [National Land Cover Database (NLCD)](https://www.mrlc.gov/)
- The [NASS Cropland Data
Layer](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php)
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**](https://cran.r-project.org/package=sf) and
[**terra**](https://cran.r-project.org/package=terra) packages.
### Development
- [Kyle Bocinsky](https://www.bocinsky.io) - 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:
``` r
install.packages("FedData")
```
- Development version from GitHub:
``` r
install.packages("devtools")
devtools::install_github("ropensci/FedData")
```
- Linux: Follow instructions for installing `sf` available 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
``` r
# 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
``` r
# Get the NED (USA ONLY)
# Returns a raster
NED <- get_ned(
template = FedData::meve,
label = "meve"
)
# Plot with raster::plot
raster::plot(NED)
```
<img src="man/figures/README-NED-1.png" width="100%" />
#### Get and plot the Daymet dataset for the study area
``` r
# 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)
```
<img src="man/figures/README-DAYMET-1.png" width="100%" />
#### Get and plot the daily GHCN precipitation data for the study area
``` r
# 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/RtmpYJ5skf/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"
)
```
<img src="man/figures/README-GHCN-precipitation-1.png" width="100%" />
#### Get and plot the daily GHCN temperature data for the study area
``` r
# 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"
)
```
<img src="man/figures/README-GHCN-temperature-1.png" width="100%" />
#### Get and plot the National Hydrography Dataset for the study area
``` r
# Get the NHD (USA ONLY)
get_nhd(
template = FedData::meve,
label = "meve"
) %>%
plot_nhd(template = FedData::meve)
```
<img src="man/figures/README-NHD-1.png" width="100%" />
#### Get and plot the NRCS SSURGO data for the study area
``` r
# 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
)
```
<img src="man/figures/README-SSURGO-1.png" width="100%" />
#### Get and plot the NRCS SSURGO data for particular soil survey areas
``` r
# 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
)
```
<img src="man/figures/README-SSURGO-area-1.png" width="100%" />
#### Get and plot the ITRDB chronology locations in the study area
``` r
# 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"
)
```
<img src="man/figures/README-ITRDB-1.png" width="100%" />
#### Get and plot the National Land Cover Dataset for the study area
``` r
# 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)
```
<img src="man/figures/README-NLCD-1.png" width="100%" />
#### Get and plot the NASS Cropland Data Layer for the study area
``` r
# 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)
```
<img src="man/figures/README-NASS-CDL-1.png" width="100%" /><img src="man/figures/README-NASS-CDL-2.png" width="100%" />
``` r
# 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](https://www.openskope.org/)) and the [Village Ecodynamics
Project](https://crowcanyon.github.io/veparchaeology/) through grants
awarded to the [Crow Canyon Archaeological
Center](https://www.crowcanyon.org) and Washington State University by
the National Science Foundation. This software is licensed under the
[MIT license](https://opensource.org/license/mit/). Continuing
development is supported by the [Montana Climate
Office](https://climate.umt.edu).
FedData was reviewed for [rOpenSci](https://ropensci.org) by
[@jooolia](https://github.com/jooolia), and was greatly improved as a
result. [rOpenSci](https://ropensci.org) on-boarding was coordinated by
[@sckott](https://github.com/sckott).
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