install.packages("FedData")

install.packages("devtools")
devtools::install_github("ropensci/FedData")

# FedData Tester
library(FedData)
library(magrittr)

# FedData comes loaded with the boundary of Mesa Verde National Park, for testing
FedData::meve

# Get the NED (USA ONLY)
# Returns a raster
NED <- get_ned(
  template = FedData::meve,
  label = "meve"
)
# Plot with raster::plot
raster::plot(NED)

# 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 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: `select_()` was deprecated in dplyr 0.7.0.
#> ℹ Please use `select()` instead.
#> ℹ The deprecated feature was likely used in the dplyr package.
#>   Please report the issue at <]8;;https://github.com/tidyverse/dplyr/issueshttps://github.com/tidyverse/dplyr/issues]8;;>.
#> Warning in CPL_write_ogr(obj, dsn, layer, driver,
#> as.character(dataset_options), : GDAL Error 1: /private/var/folders/ys/
#> 7l0z3wlx7z14qxn9v0m9ckhw0000gq/T/RtmpcJyagw/FedData/extractions/ghcn/meve/
#> meve_GHCN_stations.shp does not appear to be a file or directory.
#> Warning: `filter_()` was deprecated in dplyr 0.7.0.
#> ℹ Please use `filter()` instead.
#> ℹ See vignette('programming') for more help
#> ℹ The deprecated feature was likely used in the dplyr package.
#>   Please report the issue at <]8;;https://github.com/tidyverse/dplyr/issueshttps://github.com/tidyverse/dplyr/issues]8;;>.
#> Warning: `funs_()` was deprecated in dplyr 0.7.0.
#> ℹ Please use `funs()` instead.
#> ℹ See vignette('programming') for more help
#> ℹ The deprecated feature was likely used in the dplyr package.
#>   Please report the issue at <]8;;https://github.com/tidyverse/dplyr/issueshttps://github.com/tidyverse/dplyr/issues]8;;>.
#> Warning: `funs()` was deprecated in dplyr 0.8.0.
#> ℹ Please use a list of either functions or lambdas:
#> 
#> # Simple named list: list(mean = mean, median = median)
#> 
#> # Auto named with `tibble::lst()`: tibble::lst(mean, median)
#> 
#> # Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
#> ℹ The deprecated feature was likely used in the dplyr package.
#>   Please report the issue at <]8;;https://github.com/tidyverse/dplyr/issueshttps://github.com/tidyverse/dplyr/issues]8;;>.
# 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"
)

# 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
)
#> Warning: `arrange_()` was deprecated in dplyr 0.7.0.
#> ℹ Please use `arrange()` instead.
#> ℹ See vignette('programming') for more help
#> ℹ The deprecated feature was likely used in the dplyr package.
#>   Please report the issue at <]8;;https://github.com/tidyverse/dplyr/issueshttps://github.com/tidyverse/dplyr/issues]8;;>.
# 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 the NHD (USA ONLY)
get_nhd(
  template = FedData::meve,
  label = "meve"
) %>%
  plot_nhd(template = FedData::meve)

# 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
)

# 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 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 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 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]]
#> NULL

# 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