climodr (version 1.0.0)

climplot: Create Maps using the `terra` package graphic parameters

Description

Plot results of climodr into maps. Right now maps are created using the terra package. The maps created are very basic. Will be updated to run with tidyterra in future.

Usage

climplot(
  envrmt = .GlobalEnv$envrmt,
  mnote,
  sensor,
  aoa = FALSE,
  mapcolors = rev(grDevices::terrain.colors(50)),
  scale_position = "bottomleft",
  north_position = "topright"
)

Value

Maps in PNG-Format to your harddrive.

Arguments

envrmt

variable name of your envrmt list created using climodr's `envi.create` function. Default = envrmt.

mnote

character. The modelnote you want to create maps of.

sensor

character. The sensor you want to create maps for.

aoa

logical. Do you want the area of applicability to be added to your map?

mapcolors

The color pallete you want to use for the map. Default is `rev(grDevices::terrain.colors(50))`

scale_position

character. Graphical parameter. The relative positiion of the Scale for the map. See `terra::plot` for more details.

north_position

character. Graphical parameter. The relative positiion of the Scale for the map. See `terra::plot` for more details.

See Also

`terra::plot`

Examples

# \donttest{
#create climodr environment and allow terra-functions to use 70% of RAM
envrmt <- envi.create(proj_path = tempdir(),
                      memfrac = 0.7)

# Load the climodr example data into the current climodr environment
clim.sample(envrmt = envrmt)

#prepare csv-files
prep.csv(envrmt = envrmt,
         method = "proc",
         save_output = TRUE)

#process csv-files
csv_data <- proc.csv(envrmt = envrmt,
                     method = "monthly",
                     rbind = TRUE,
                     save_output = TRUE)

# Crop all raster bands
crop.all(envrmt = envrmt,
         method = "MB_Timeseries",
         overwrite = TRUE)

# Calculate Indices from cropped raster bands
calc.indices(envrmt = envrmt,
             vi = "all",
             bands = c("blue", "green", "red",
                       "nir", "nirb",
                       "re1", "re2", "re3",
                       "swir1", "swir2"),
             overwrite = TRUE)

#extract station coordinates
csv_spat <- spat.csv(envrmt = envrmt,
                     method = "monthly",
                     des_file = "plot_description.csv",
                     save_output = TRUE)


#extract predictor values from raster files
csv_fin <- fin.csv(envrmt = envrmt,
                   method = "monthly",
                   save_output = TRUE)

# Test data for autocorrelation after running fin.csv
autocorr(envrmt = envrmt,
         method = "monthly",
         resp = 5,
         pred = c(8:23),
         plot.corrplot = FALSE)

# Create 36 different models (12 months x 3 classifiers) for every month in 2017
calc.model(envrmt = envrmt,
           method = "monthly",
           timespan = c(2017),
           climresp = 5,
           classifier = c("rf",
                          "pls",
                          "lm"),
           seed = 707,
           p = 0.8,
           folds = "LLO",
           mnote = "normal",
           predrows = c(8:23),
           tc_method = "cv",
           metric = "RMSE",
           autocorrelation = TRUE,
           doParallel = FALSE)

# Make predictions
climpred(envrmt = envrmt,
         method = "monthly",
         mnote = "normal",
         AOA = TRUE)

# Create a Temperature Map from the vignette model
climplot(envrmt = envrmt,
         mnote = "normal",
         sensor = "Ta_200",
         aoa = TRUE,
         mapcolors = rev(heat.colors(50)),
         scale_position = "bottomleft",
         north_position = "topright")
# }